Summary
# Introduction to a digital economy
The introduction to the digital economy chapter lays the groundwork for understanding the pervasive influence of digital technologies on business and society.
## 1. The rise of a digital economy
An increasing number of technology-driven business opportunities are significantly impacting how organizations operate and how individuals live, thereby affecting the economy and society at large. While the concept of a digital economy is rapidly evolving, its roots can be traced to the information society, an outcome of the third industrial revolution following the introduction of computers and the internet. Today's emerging digital technologies are considered to be driving a fourth industrial revolution, sometimes referred to as Industry 4.0, which is more disruptive than the widespread adoption of social media in the 2010s [21](#page=21) [22](#page=22).
Industry 4.0, in its original and more restrictive sense, refers to the integration of specific digital technologies to revolutionize manufacturing, creating smart factories through the adoption of technologies like the Internet of Things (IoT), cloud computing, and big data. However, this book adopts a broader scope for Industry 4.0, equating it with the general meaning of a potential fourth industrial revolution that impacts various industries. A key characteristic is the integration of emerging digital technologies, which often reinforce each other; for instance, IoT devices can collect big data analyzed by artificial intelligence (AI). The concept of Industry 5.0 is also emerging, aiming to complement Industry 4.0 with considerations for sustainability, human-centricity, and resilience, particularly in response to global challenges like climate change and resource preservation [22](#page=22).
The core of a digital economy is defined by digital products (goods, services, information/data) produced by the IT sector, along with social media and digital platform services. It encompasses "that part of economic output derived solely or primarily from digital technologies with a business model based on digital goods and services". However, a broader interpretation, often termed a "digitalized economy," includes the use of technologies across all economic fields, encompassing e-commerce and Industry 4.0 manufacturing technologies [22](#page=22) [23](#page=23).
Some authors describe the digital economy with the acronym "ExConomy," highlighting four key characteristics:
* **Experience:** Delivering exceptional customer experiences and value is central to digital innovations and transformations [23](#page=23).
* **Experimentation:** A trial-and-error approach is necessary to compare solutions and improve understanding of customer experience and value [23](#page=23).
* **Cocreation:** Involving end customers in reshaping value chains ensures alignment with their expectations and needs [23](#page=23).
* **Collaboration:** Working within ecosystems with partners, or even competitors, can consolidate efforts or allow focus on core activities [23](#page=23).
The value of digital technologies is not always measured by monetary value alone but rather by what end users are willing to pay or forgo. Measuring a digital economy is complex due to varied customer valuations of benefits. Organizations often interpret value as a proxy for customer well-being, including life satisfaction, health, and environmental impact. This broader concept of value helps explain high valuations of digital offerings, such as Facebook's acquisition of WhatsApp for twenty-two billion dollars or Elon Musk's purchase of Twitter for forty-four billion dollars, due to their innovative nature and distinct customer experience. Governments worldwide monitor digital skills due to the importance and impact of the digital economy, with initiatives like the European Union's Digital Economy and Society Index (DESI) tracking progress and informing policy [23](#page=23).
### 1.1 The rise of a digital economy
### 1.2 Defining the digi-related concepts in a digital economy
Before delving into digital technologies, it is essential to define key concepts related to a digital economy, starting with digital technology itself [24](#page=24).
#### 1.2.1 Differentiating IT from a digital technology
Information technology (IT), or Information and Communications Technology (ICT), traditionally functions as a collector, storage, processor, and transmitter of information, existing in both digital and physical forms (software or hardware). IT has become integral to products, services, processes, and daily life. A digital technology, in contrast, emphasizes the underlying digital characteristics and is "technology that is embedded in products and services and can hardly be disentangled from the underlying IT infrastructure" [24](#page=24).
#### 1.2.2 Differentiating digital innovations
Several key terms are frequently used in the context of digital economies and technologies: digital innovation, digital transformation, digitalization, and digitization [24](#page=24).
* **Digital innovation** is the overarching term that includes the others. It is defined as "the creation of (and consequent change in) market offerings, business processes, or models that result from the use of digital technology. Stated differently, in digital innovation, digital technologies and associated digitizing processes form an innate part of the new idea and/or its development, diffusion, or assimilation". Digital innovation is an ongoing process for organizations, involving the use of one or more digital technologies to innovate products, services, business processes, or business models. It exists in various gradations, with digital transformation being the most disruptive [24](#page=24).
* **Digital transformation** is "a process where digital technologies play a central role in the creation as well as the reinforcement of disruptions taking place at the society and industry levels. These disruptions trigger strategic responses from the part of organizations. [...] Organizations use digital technologies to alter the value creation paths they have previously relied upon to remain competitive. To that end, they must implement structural changes and overcome barriers that hinder their transformation effort". It involves strategic changes and organizational restructuring through digitalization projects, aiming for significant business improvements. Digital transformation typically entails fundamental, long-term, and large-scale changes affecting multiple organizational facets like processes, IT systems, operating models, and culture, with the goal of stimulating growth and enhancing competitive advantage [24](#page=24) [25](#page=25).
* **Digitization** is the simple conversion of analogue information to a digital format without further changes, such as converting a paper book to a digital version [25](#page=25).
* **Digitalization** "represents a wide sociotechnical process and implies the integration of multiple technologies into daily social life," encompassing applications like smart homes, smart cities, e-healthcare, and smart mobility [25](#page=25).
These concepts are often used interchangeably, but digital transformation represents a more profound and strategic shift than digitization or digitalization, which are typically on a smaller and more immediate scale [25](#page=25).
### 1.3 Central role of a digital business model
The digi-related concepts drive the need for new business contexts and necessitate changes in technical and managerial skills, demanding digital entrepreneurship to experiment with business models. Digital entrepreneurship aims to establish new economic activities enabled by digital technologies. These ideas are initially conceptualized in a business model, which then requires iterative testing through experimentation and an agile approach [25](#page=25).
Business model innovation (BMI) is crucial in a digital economy for sustaining competitive advantage and long-term performance by capitalizing on business opportunities and avoiding disruption to the core business. Business model thinking aids organizations in designing, iteratively testing, and rapidly scaling novel business models. BMI is described as "the art of enhancing advantage and value creation by making simultaneous—and mutually supportive—changes, both to an organization’s value proposition to customers and to its underlying operating model" [25](#page=25) [26](#page=26).
Thinking in terms of a business model provides a structured approach to finding market fit and making strategic decisions regarding business objectives, such as launching new products or services. A business model "describes the rationale of how an organization creates, delivers, and captures value". It outlines how an organization generates profit (value capture) while creating value for customers and society (value creation) and identifies the necessary strategic partnerships and channels for realization (value delivery). A business model is more developed than a business idea, requiring proof of viability and business logic [26](#page=26).
Each idea or innovation desire translates into value propositions that hypothesize a market fit. These value propositions must be tested iteratively using criteria of desirability, feasibility, and viability (DFV) [26](#page=26).
* **Desirability:** Do end users find the innovated products or services desirable? [27](#page=27).
* **Feasibility:** Are the innovations technically possible with current digital technologies and IT knowledge? [27](#page=27).
* **Viability:** Are the costs lower than the benefits, resulting in a positive return on investment (ROI)? [27](#page=27).
These DFV criteria represent the essential characteristics of any innovation or transformation, forming its "sweet spot". Missing any of these characteristics increases the risk and cost of realization [27](#page=27).
Value creation is central to a business model, with four types of value existing, organized in a value pyramid from fundamental to advanced needs [27](#page=27):
* **Functional value:** Relates to operational advantages like saving time, simplifying tasks, earning money, reducing costs/risks/efforts, offering quality/variety, integration, information provision, organization, or remote connectivity [27](#page=27).
* **Emotional value:** Pertains to personal feelings, such as reducing anxiety, increasing entertainment, or providing personal rewards [27](#page=27).
* **Life-changing value:** Addresses needs related to increasing self-actualization, hope, motivation, and a sense of belonging [27](#page=27).
* **Social value:** Encompasses needs related to self-transcendence and broader societal needs [27](#page=27).
Fulfilling more value types is expected to enhance customer loyalty and sustained revenue growth. This also highlights a broader interpretation of value beyond "value in exchange" (monetary gain) to include "value in use" [28](#page=28).
Various modeling languages exist to represent and communicate business model knowledge, providing a shared, formal design for conceptualizing new or documenting existing business models and considering strategic alternatives. The Business Model Canvas is a popular tool for this purpose [28](#page=28).
The Business Model Canvas is a strategic management and entrepreneurial tool that allows for the description, design, challenging, invention, and pivoting of a business model. It comprises nine building blocks:
* **Customer segments:** The groups of people or organizations the business aims to reach and serve [28](#page=28).
* **Value propositions:** The bundle of products and services that create value for a specific customer segment, explaining why customers would choose the organization [28](#page=28).
* **Customer channels:** How the business communicates with and reaches customers to deliver value propositions, including communication, distribution, and sales channels [28](#page=28).
* **Customer relationships:** The type of relationship established with each customer segment for customer acquisition, retention, or boosting sales [28](#page=28).
* **Revenue streams:** The cash generated from each customer segment, indicating what value they are willing to pay for [29](#page=29).
* **Key resources:** The most important assets required to realize value propositions (physical, financial, intellectual, human) [29](#page=29).
* **Key activities:** The core actions within related business processes [29](#page=29).
* **Key partnerships:** The network of suppliers and partners needed to optimize the business model [29](#page=29).
* **Cost structure:** The operational costs incurred to realize the business model, considering whether it is cost-driven or value-driven [29](#page=29).
Ultimately, revenues are compared to costs to determine the potential profit of a business model. Iterative testing of a business model allows for comparison of alternatives and avoidance of significant financial losses from unconsidered investments [29](#page=29).
### 1.4 Agility as a new way of working
Testing a business model is often linked to an agile way of working and experimentation. Agility refers to embracing unexpected change, both reactively and proactively, and involves "ways of planning and doing work in which it is understood that making changes as they are needed is an important part of the job". In the context of digital business models and breakthrough innovations, agility is viewed from the perspective of agile software engineering and new product development, enabling iterative testing and adjustment of value propositions. Agility allows for responding to fast-changing markets and business needs [29](#page=29).
#### 1.4.1 Agile manifesto
The concept of agility has a long history, but its formal introduction in software engineering dates back to 2011 with the Agile Manifesto. Seventeen software developers proposed a manifesto for lightweight engineering methodologies, emphasizing short build-test iterations for early feedback. This agile software development paradigm is more flexible, efficient, and team-oriented than traditional methods like waterfall development. The Agile Manifesto is guided by four core values and twelve principles [29](#page=29) [30](#page=30).
The four agile values are:
1. **Agility values individuals and interactions** over processes and tools [30](#page=30).
2. **Agility values working software** over comprehensive documentation [30](#page=30).
3. **Agility values customer collaboration** over contract negotiation [30](#page=30).
4. **Agility values responding to change** over following a plan [30](#page=30).
The twelve agile principles provide further guidance:
1. Satisfy the customer through early and continuous delivery of valuable software [30](#page=30).
2. Welcome changing requirements, even late in development, to harness change for customer advantage [30](#page=30).
3. Deliver working software frequently, from a few weeks to a few months, with a preference for shorter timescales [30](#page=30).
4. Business people and developers must work together daily throughout the project [30](#page=30).
5. Build projects around motivated individuals, providing them with the necessary environment and support, and trusting them [30](#page=30).
6. Face-to-face conversation is the most efficient method for conveying information within a development team [30](#page=30).
7. Working software is the primary measure of progress [30](#page=30).
8. Agile processes promote sustainable development, maintaining a constant pace for sponsors, developers, and users [30](#page=30).
9. Continuous attention to technical excellence and good design enhances agility [30](#page=30).
10. Simplicity, defined as maximizing the amount of work not done, is essential [30](#page=30).
11. The best architectures, requirements, and designs emerge from self-organizing teams [31](#page=31).
12. Teams regularly reflect on how to become more effective and adjust their behavior accordingly [31](#page=31).
Agile software development has gained significant popularity due to its higher success rates compared to traditional approaches, as evidenced by IT project reports. The notion of agility has also expanded into managerial fields, with terms like organizational agility or business agility referring to a flexible way of working and openness to change [31](#page=31).
#### 1.4.2 Scrum
Agile software engineering is a paradigm that encompasses various methods and practices, including collaboration techniques like Scrum. Scrum, inspired by Takeuchi and Nonaka's mid-1980s work on innovation success through multidisciplinary team collaboration, is a team-oriented term. It originates from rugby football, symbolizing the need for regular project plan changes and reactions to ongoing events. In software engineering, each "sprint" typically lasts 2–4 weeks, delivering a small, operational piece of software to the customer, followed by replanning for the next sprint [31](#page=31).
While appearing chaotic, Scrum uses key practices as a framework to structure this and achieve collective ownership. Projects often involve multiple Scrum teams that are self-organized and self-directed, without a defined team leader. Each team sets its own goals for a sprint, working with a fixed set of requirements called a sprint backlog. Daily Scrum meetings (15-minute stand-ups) allow team members to report on past activities, current plans, and impediments. Scrum-of-Scrums meetings may occur for inter-team coordination [32](#page=32).
A typical Scrum team consists of 5–10 members:
* **Product owner:** Represents the customer's voice, clarifies and prioritizes stakeholder needs, and possesses a clear vision [32](#page=32).
* **Scrum master:** A Scrum expert acting as a coach and mentor to ensure adherence to agile principles [32](#page=32).
* **Interdisciplinary or cross-functional development team:** Composed of various specialists like testers, analysts, designers, programmers, and planners [32](#page=32).
The product owner defines and prioritizes product requirements in a product backlog. Teams select requirements for a sprint from this backlog, forming a sprint backlog. Work is divided into smaller functional user stories, which describe what needs to be done for whom, and typically include a title, role, action, and benefit [32](#page=32).
The INVEST acronym helps clarify user story scope: independent, negotiable, valuable, estimable, sized appropriately (small), and testable. Each user story should contribute to the overall product value, regardless of implementation order. For example, CRUD functionalities can be split into basic operations across different sprints [33](#page=33).
Workload per sprint is estimated using "story points," assigned in whole numbers reflecting "relative size" and "complexity". Consensus on story points is reached among development teams, often through techniques like planning poker. A team's output per sprint is measured in total completed story points, known as the team's velocity. Average velocity over multiple sprints can be used for project time forecasting [33](#page=33).
#### 1.4.3 Lean start-ups
Agility also relates to new product development and new business creation, such as Lean start-ups. Lean principles, originating from manufacturing, focus on reducing waste and creating customer value. This aligns with agile philosophies of efficient meetings and planning with minimal waste [33](#page=33).
Kanban, another concept from continuous process improvement, is used in an agile context to visualize planning and measure lead times, limiting work in progress (WIP). Unlike a simple task board, a Kanban board visualizes project phases and focuses on lead time measurement [34](#page=34).
Lean is commonly applied to creating new organizations as "Lean start-ups," a popular business creation approach. Lean start-ups test their business models through trial-and-error experiments, following build–measure–learn cycles. They aim to acquire early customer feedback and develop a minimum viable product (MVP) [34](#page=34).
An MVP is an early, operational version of a product that maximizes learning about customer requirements with minimal effort. It can be as simple as a landing page or a service with manual back-office processes [34](#page=34).
A start-up is typically less than five years old and is searching for a sustainable business model. A scale-up is a start-up experiencing exponential financial growth and market development, defined by an average annual growth rate of at least 20% over three consecutive financial years with a minimum of ten employees. A "unicorn" is a start-up valued at one billion dollars or more on paper due to hypergrowth [34](#page=34).
### 1.5 Takeaways
This chapter provides a foundational understanding of the digital economy, the significance of digital technologies, and how organizations can leverage them. It positions the book's subject within the broader digital economy, emphasizing digital innovation and transformation. The distinctions between digitization, digitalization, and digital transformation are clarified, highlighting their different gradations within digital innovation. The chapter details the elements and purpose of a digital business model, underscoring the need for managers to act as digital entrepreneurs before investing in digital projects. It stresses that innovation desires must be tested for desirability, feasibility, and viability, creating value that extends beyond business metrics to encompass "value in use" for end customers. The Business Model Canvas is presented as an illustration of business modeling, emphasizing the importance of testing for a potential return on investment (ROI). The chapter then introduces agility as a crucial way of working, detailing the Agile Manifesto's values and principles, and explaining Scrum as an agile software engineering approach. Finally, agility is explored from the perspective of new product development through Lean start-ups and their use of minimum viable products (MVPs) for testing digital business models, concluding that only promising digital business models should lead to large-scale digital technology investments.
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# Introduction to selected digital technologies
This chapter provides an overview of seven selected digital technologies, discusses theories of adoption and acceptance, and introduces the experts and case studies featured in the book [39](#page=39).
### 2.1 Adopting a digital technology
Adopting new technologies involves change management to overcome resistance and prepare end-users. Several theories explain this process from different perspectives [39](#page=39).
#### 2.1.1 Theories of technology acceptance and adoption
* **Individual End-User Perspective:**
* **Technology Acceptance Model (TAM):** Focuses on perceived usefulness and perceived ease of use to predict technology adoption [39](#page=39).
* **Unified Theory of Acceptance and Use of Technology (UTAUT):** Considers perceived performance outcomes, effort, social influence, and facilitating conditions (e.g., training, help desk) as key drivers of behavioral intention and use [40](#page=40).
* **Organizational Perspective:**
* **Technology–Organization–Environment (TOE) Framework:** Differentiates between technology-related characteristics (availability, price), the environmental context (industry regulation), and organizational success factors (top management support, communication) for technology adoption [40](#page=40).
* **Macro-Level Perspective:**
* **Diffusion of Innovations Theory (Rogers, 2003):** Suggests innovations are adopted gradually by different target groups: innovators, early adopters, early majority, late majority, and laggards [40](#page=40).
* **Gartner Hype Cycle:** An alternative evolutionary graph that illustrates the typical phases an emerging technology goes through, from trigger to plateau of productivity, helping to predict a technology's future and distinguish between hype and mainstream potential [40](#page=40).
> **Tip:** Understanding these adoption theories helps organizations strategize their technology implementation to avoid adopting too early or giving up too soon, while also recognizing market risks and opportunities [40](#page=40).
#### 2.1.2 Hype cycle phases
The Gartner Hype Cycle describes five phases of technology adoption [40](#page=40):
* **Phase 1: Technology trigger:** Early market entrance with low expectations, primarily involving innovators [41](#page=41).
* **Phase 2: Peak of inflated expectations:** High expectations due to excitement, potentially leading to unrealistically high expectations; involves innovators and early adopters [41](#page=41).
* **Phase 3: Trough of disillusionment:** Low expectations stemming from a reality check or poor usage, often after losing confidence and money on poorly implemented projects; involves the early majority [40](#page=40) [41](#page=41).
* **Phase 4: Slope of enlightenment:** Growing understanding and more realistic expectations lead to renewed excitement; involves the late majority [40](#page=40) [41](#page=41).
* **Phase 5: Plateau of productivity:** A mature technology with steady productivity, positive business benefits, and mainstream adoption; involves laggards [41](#page=41).
There is also a potential outcome where a digital technology disappears or is replaced by something better, with nihil to low expectations [41](#page=41).
> **Example:** Technologies like VR/AR were near Phase 3 in 2018, while IoT and blockchain were moving from Phase 2 to 3, and biochips, digital twins, and AI variants were close to Phase 2 [42](#page=42).
### 2.2 Selecting digital technologies for this book
This book focuses on seven selected digital technologies based on Gartner's hype cycle, chosen for their varied development stages and emerging trends. These technologies are [41](#page=41):
* **Artificial Intelligence (AI):** For leading Industry 4.0 and facilitating other digital technologies [43](#page=43).
* **Internet of Things (IoT):** For introducing a digital platform with increased interconnectivity [43](#page=43).
* **Virtual Reality (VR) and Augmented Reality (AR):** For offering immersive experiences that bridge the real and digital worlds [43](#page=43).
* **Digital Twin Technology:** For combining AI, IoT, and VR/AR in a digitalized ecosystem [43](#page=43).
* **Blockchain Technology:** For enabling trusted collaboration and data transparency in digital ecosystems [43](#page=43).
* **3D Printing:** For challenging conventional manufacturing with complex and customized designs [43](#page=43).
* **Biochips:** For biohacking and interdisciplinary entrepreneurial applications [43](#page=43).
> **Tip:** The selection of these seven technologies is not exhaustive but provides a valuable overview. The increasing importance of CIOs/CTOs in strategic decisions and the growing demand for digital skills highlight the critical need for understanding these technologies [42](#page=42).
### 2.3 Overview of the subsequent book chapters
The following chapters will consolidate inspirational interviews with academic and business experts, supplemented by real-life success stories, rather than solely relying on theoretical viewpoints [44](#page=44).
#### 2.3.1 Interview structure for academic experts
Each academic interview is structured into four parts:
1. **Description and Terminology:** Defining the technology in layman's terms, identifying key terms, highlighting aspects the expert likes, and outlining current applications across sectors [45](#page=45).
2. **Research and Future Outlook:** Discussing the current state of research, personal research interests, promising research avenues, and future advancements within 10 years [45](#page=45).
3. **Sustainable Automation Concerns:** Reflecting on economic, social, and environmental sustainability, including new skills required, educational system roles, security concerns, ethical considerations, and inclusion issues related to Industry 5.0 [45](#page=45).
4. **Extra Hints:** Providing recommendations for books or articles for further learning [46](#page=46).
#### 2.3.2 Interview structure for business experts and their organization’s success story
Business interviews are divided into four groups:
1. **General Case Information:** Understanding how the organization uses the technology, the respondent's role, sector positioning, departmental involvement, and the number of affected employees [46](#page=46).
2. **Planning Phase:** Investigating the trigger for the initiative, the driving force, strategic goals, and the criteria for defining the project scope [46](#page=46).
3. **Adoption and Evaluation:** Examining the realization of the initial plan, changes to organizational structure, other facilitating changes, reactions from stakeholders, overcoming resistance, impact on training programs, security issues, customer impact, performance monitoring, and future organizational evolution [46](#page=46) [47](#page=47).
4. **Best Practice Advice:** Eliciting insights on the biggest challenges, critical success factors, and practical tips for other organizations [47](#page=47).
#### 2.3.3 Experts and case organizations
The book features a wide range of academic and business experts, along with diverse case companies. Academic experts hold PhDs and have published peer-reviewed articles. Business experts are in managerial or executive positions and have been directly involved in technology adoption. Case companies vary in region, size, sector, age (start-up to established), and customer groups (B2C, B2B) [47](#page=47).
> **Example:** Table 2.3 lists academic experts, Table 2.4 lists business experts, and Table 2.5 lists case companies for each technology chapter [48](#page=48) [49](#page=49).
### 2.4 Takeaways
This chapter introduced seven key digital technologies: AI, IoT, VR–AR, digital twin technology, blockchain, 3D printing, and biochips, which will be explored in detail in subsequent chapters. The selection was guided by Gartner's hype cycle, illustrating technology adoption phases and helping to distinguish between fleeting trends and mainstream technologies. Theories like TAM, UTAUT, and TOE provide frameworks for understanding technology acceptance by end-users and adoption by organizations. The remaining chapters will delve into the technical backgrounds and success stories of these technologies, considering sustainability concerns for Industry 5.0, including performance, skills, education, security, ethics, and inclusion. The chapter also acknowledged the subject matter experts and highlighted the diverse profiles of the featured case companies [49](#page=49) [50](#page=50).
---
# Artificial intelligence (AI)
Artificial intelligence (AI) is a rapidly evolving digital technology that aims to build computer systems capable of tasks traditionally requiring human intelligence, enabling numerous other digital technologies and driving significant advancements across various sectors [52](#page=52) [76](#page=76).
### 3.1 Introduction to artificial intelligence
AI is a foundational technology for Industry 4.0, empowering other digital innovations and transforming work processes across all business functions. It fuels a productivity revolution through applications like robotics, autonomous vehicles, and virtual assistants, necessitating strategic organizational adoption to harness its potential. The icon for AI symbolizes the human brain, reflecting its goal to mirror human intelligence for learning and action, thereby transcending human limitations. The chapter will explore AI's theoretical background, research, sustainability concerns, and a case study from CNH Industrial [52](#page=52) [53](#page=53).
### 3.2 Background of artificial intelligence
The background of AI is detailed through an interview with Professor Zachary Lipton, an expert in machine learning from Carnegie Mellon University and Chief Scientific Officer at Abridge [54](#page=54).
#### 3.2.1 Terminology and explanations
AI, originally coined around 1955 to distinguish from cybernetics, is now a broad term for technologies related to machine intelligence. A common definition states that AI is "the science and technology of building computer systems that perform tasks or exhibit behaviors that until recently required human intelligence". This definition highlights AI as both a fundamental research area and an engineering discipline, being goal-oriented rather than tied to specific methodologies. Over the last two decades, progress in AI has been largely driven by breakthroughs in machine learning (ML) [54](#page=54) [55](#page=55).
AI's key terms can be categorized along two axes: approaches to learning and application areas [55](#page=55).
* **Approaches to learning:**
* **Machine Learning (ML):** Systems that improve with experience.
* **Supervised learning:** ML with annotated data.
* **Unsupervised learning:** ML with unannotated data.
* **Reinforcement learning:** Learning through trial and error with an interactive system aiming to achieve a goal.
* **Artificial Neural Networks (ANNs):** Powerful methods, especially in their modern incarnation, deep learning [55](#page=55).
* **Deep Learning:** Refers to very large neural networks with many layers of computation, characterized by massive models, training sets, and computation [56](#page=56).
* **Application areas:**
* **Computer Vision:** Focuses on image generation and processing [56](#page=56).
* **Natural Language Processing (NLP):** Deals with language, including systems like ChatGPT and machine translation [56](#page=56).
* **Automatic Speech Recognition (ASR):** Encompasses text-to-speech and speech-to-text synthesis [56](#page=56).
* Other areas include machine learning for healthcare, involving data structures, decision-making guidance, and risk estimation [56](#page=56).
Professor Lipton expresses excitement about AI's fast-paced evolution and its potential impact, particularly in healthcare. He also notes AI's versatility, allowing application across diverse fields like finance, robotics, and drug discovery [56](#page=56) [57](#page=57).
AI is effective where abundant, representative data or a reward signal is available, especially for pattern recognition tasks. Examples include self-driving cars, which leverage vast amounts of camera data for training computer vision models, and automatic translation, benefiting from extensive translated text datasets. However, AI application requires caution due to risks, particularly in contexts with limited data, high risk, unstable patterns, or biased data. Applying AI in areas like the criminal justice system is problematic due to disagreements on system operation and potential for automating existing biases [57](#page=57).
> **Tip:** AI is best suited for tasks where risks are relatively low, there's ample opportunity to learn, and the available data accurately represents the desired outcomes [57](#page=57).
#### 3.2.2 Current and future research
AI research has evolved from simple statistical methods to deep learning and, more recently, to "foundation models" trained on web-scale data. Foundation models are trained for generic tasks (e.g., next word prediction) and can be specialized for specific applications post-training. This shift underpins technologies like large language models (LLMs) and text-to-image synthesis [58](#page=58).
Professor Lipton's research focuses on two areas:
1. **Problem Formulation:** Investigating when models will perform well in new environments and under what assumptions robustness can be expected, including developing adaptive algorithms [58](#page=58).
2. **Societal Concerns:** Formalizing how AI models should be governed and aligned with societal values [58](#page=58).
His work emphasizes guiding decisions rather than just making predictions, particularly in healthcare, aiming for reliable technologies that improve patient outcomes. He is also involved with Abridge, a company using AI for medical note automation to reduce physician burnout. This involves speech recognition, summarization, and responsible AI practices [59](#page=59).
The future of AI research is characterized by the increasing importance of generalized foundation models, which present challenges for traditional NLP tasks and raise questions about future research directions. Decision-making research, moving from prediction to meaningful machine work, remains critical. Predicting AI's landscape in 10-15 years is highly uncertain due to its rapid evolution [59](#page=59) [60](#page=60).
#### 3.2.3 Sustainable automation concerns
Skills required for AI development and usage are diverging. While core ML skills remain vital, working with large-scale AI models increasingly demands engineering skills for high-performance computing and distributed systems. New roles like "prompt engineering" are emerging, requiring intuition for creative technology application and problem-domain understanding rather than deep technical knowledge [60](#page=60).
The educational system faces challenges in maintaining integrity with tools like ChatGPT, necessitating a reevaluation of student assessment methods [61](#page=61).
Security concerns encompass computer security, privacy, safety, and misinformation. AI tools can empower hackers, and models may inadvertently memorize sensitive personal information. There are also concerns about AI generating instructions for harmful activities or producing misinformation, impacting elections [61](#page=61).
Ethical concerns arise with automated decision-making systems, inheriting existing ethical biases. AI systems can exacerbate disparities and amplify existing biases by codifying them. Automating decisions can reduce accountability and contestability. Ensuring AI systems perform equitably across different subpopulations is crucial [61](#page=61) [62](#page=62).
AI can promote inclusion through technologies like Google Translate, improving participation for individuals in diverse socio-economic contexts. It can also benefit people with sensory disabilities, for example, through image captioning for the visually impaired. However, concerns remain about potential irresponsible use and biases, such as the Anglocentric focus in NLP datasets [62](#page=62).
#### 3.2.4 Extra hints
While direct book recommendations were not provided, suggested resources include:
* "Understanding Machine Learning: From Theory to Algorithms" by Shalev-Shwartz and Ben-David [63](#page=63).
* An interactive book on deep learning by Zhang et al. [63](#page=63).
* Lil'Log blog by Lilian Weng for AI-related learning notes [63](#page=63).
### 3.3 Success story about artificial intelligence
Paul Snauwaert shares CNH Industrial's experience with AI implementation, focusing on agricultural and construction equipment. CNH Industrial designs, produces, and sells agricultural and construction equipment through brands like Case IH and New Holland Agriculture [63](#page=63).
#### 3.3.1 General case information
CNH Industrial uses AI as a key technology for automating agricultural machinery, developing the "IntelliSense system". This system aims to gradually automate functions controlled by machine operators by analyzing images (e.g., camera, laser) to extract parameters for control algorithms. For grain harvesters, AI analyzes grain samples to detect anomalies like impurities or breakage, adjusting machine settings to maximize performance and operating point. This feature enhances machine output, even for less experienced drivers, and supports multiple crops [64](#page=64).
CNH Industrial began exploring AI in 2019, building on existing camera-based grain quality monitoring. A pilot project with an AI start-up demonstrated rapid algorithm development, leading to internal expertise development. Current AI applications under development include optimizing straw residue chopping, crop monitoring, precision spraying, and autonomous driving. The general approach involves collecting images, analyzing them with AI, and using the extracted information to control and automate sub-processes [64](#page=64) [65](#page=65).
Paul Snauwaert leads innovation studies, technology scouting, and talent development, focusing on open innovation with universities and research centers. His role involves guiding teams in identifying research topics and applying research results within the company, often through on-the-job training with external partners. Innovations are pursued if they can be integrated into machines and provide customer value [65](#page=65).
CNH Industrial's competitors are also developing AI solutions, often providing drivers with information. However, the IntelliSense system's differentiator is its automatic processing and closed-loop control for machine configuration, leading to a 10% to 15% increase in daily machine capacity compared to human operators. CNH Industrial holds several patents to protect its innovations. Competitors are also working on AI applications like selective spraying to reduce chemical usage [66](#page=66) [67](#page=67).
While AI offers immense potential, significant questions remain regarding legislation for safety-critical systems. CNH Industrial currently avoids using AI for functions directly impacting machine safety, such as steering and braking, due to the uncertainty of AI system behavior [67](#page=67).
AI development is managed by a central digital team and co-located AI teams within agricultural machine development due to the need for detailed machine function knowledge. AI is expanding beyond computer vision to big data and data analytics, impacting the entire company. CNH Industrial has also acquired start-up companies to accelerate AI adoption and has an offshore tech center in India [67](#page=67).
A growing number of key specialists are involved in AI algorithm development, supported by larger engineering teams in data collection, system development, and validation. The India tech center is training employees for image labeling to create a global competence center [68](#page=68).
#### 3.3.2 Planning
The initial trigger for AI adoption was the desire to improve an existing camera-based system for grain monitoring, which had limitations in processing various crops due to long development times. A pilot project in 2019 with a start-up revealed AI's potential, leading to internal expertise development and research collaborations [68](#page=68).
CNH Industrial's corporate strategy integrates AI for machine automation and autonomous operation, aiming to enhance machine efficiency and address challenges in image processing for automation. The success of an initial pilot project led to incorporating AI capabilities into roadmaps, adjusting the original plans. AI's integration was enabled by advancements in embedded computing capacity and memory on machines [68](#page=68) [69](#page=69).
The project scope has gradually expanded across numerous automation use cases, driven by the AI application making previously unfeasible ideas usable. The availability and affordability of technology, similar to the introduction of GPS, accelerate integration and the emergence of new applications. AI is seen as a standard functionality that will rapidly evolve and become pervasive in machines [69](#page=69).
#### 3.3.3 Adoption and case evaluation
Initial results exceeded expectations, leading to rapid AI expansion. A limitation was the original camera's insufficient computing power and memory, necessitating simplified algorithms. The adoption of a new AI camera with enhanced capabilities has enabled more applications. The speed of AI adoption is currently limited by the availability of AI-skilled employees [69](#page=69) [70](#page=70).
CNH Industrial R&D, traditionally organized by product lines, has established competence centers for electronics, hydraulics, and other areas to improve efficiency. New technologies like AI are being managed through transversal competence centers to promote reuse and knowledge sharing across products [70](#page=70).
Significant changes include an increased R&D budget and the acquisition of companies like Raven, which supplied electronic components and software development. This shifted the company from primarily mechanical design to a dominant mechatronics focus, requiring new internal knowledge and skills [71](#page=71).
The launch of the combine IntelliSense system was well-received. Financial analysts and investors expect clear strategies for accelerating sustainable technology deployment [71](#page=71).
Customer resistance was overcome through intensive demonstrations allowing customers to experience the AI system's benefits firsthand, showing the machine's superior performance and reduced reliance on experienced drivers. Customers value the increased capacity and efficiency, not the underlying AI technology. A major concern remains the use of AI in safety-critical systems, with ongoing discussions about certification and the company's responsibility for machine safety [71](#page=71) [72](#page=72).
There isn't a specific AI training program; expertise grows through hands-on projects and collaboration. Best practices are shared internally through competence centers, digital platforms (MS Teams, SharePoint), and dedicated knowledge-sharing sessions. Collaboration with universities and research consortia involves intellectual property agreements, granting CNH Industrial exclusivity in its core business while allowing broader use in other sectors [72](#page=72) [73](#page=73).
Cybersecurity is a growing challenge due to increased machine connectivity. Ensuring machines are not manipulated externally through software is crucial, aligning with emerging EU directives on cybersecurity [73](#page=73).
AI-based machines enhance customer efficiency by increasing operational capacity and reducing grain loss, leading to economic advantages. For investment goods like agricultural equipment, value is determined by efficiency, influencing pricing strategies. CNH Industrial is also exploring AI for food chain traceability and data provision [73](#page=73).
The company experiences a positive ROI, evidenced by a high take rate for the automated IntelliSense system. Performance is measured at the system level rather than isolated AI applications, focusing on the innovation project's market positioning and profitability [73](#page=73).
CNH Industrial anticipates potential shifts towards smaller robots for certain applications and continued exploration of robotics and drones. Climate change considerations are driving efforts to reduce CO2 emissions and the use of sprays and fertilizers, balancing food production with environmental targets [74](#page=74).
#### 3.3.4 Best-practice advice
The biggest challenge lies in finding valuable use cases and developing robust functional AI solutions, rather than computational or data-analytical aspects. Ensuring robustness requires sufficient data to cover all conditions and extensive validation. The use of AI in safety-critical systems remains a significant challenge due to certification and proving safety assurance [74](#page=74).
Critical success factors include pilot projects with start-ups to develop efficient algorithms compatible with hardware limitations and the gradual application of AI, starting with successful use cases and expanding. Continuous exploration of AI as an enabler for new applications is crucial [75](#page=75).
Practical advice includes joining and sponsoring AI research initiatives and communities to exchange use cases and connect with companies and universities. Collaborating with AI start-ups helps in understanding AI possibilities and determining internal development or partnership strategies. Informing oneself about regional AI initiatives and focusing on application rather than invention is recommended. While initial development may involve subcontractors, the goal is to build internal expertise for implementation [75](#page=75).
### 3.4 Takeaways
AI is a versatile research discipline and a collection of methods enabling other digital technologies, aiming to replicate human intelligence. Different learning approaches exist (supervised, unsupervised, reinforcement), often leveraging big data. Major breakthroughs in ML, ANNs, and deep learning drive AI's rapid evolution across applications like computer vision and NLP. AI applications are found in healthcare, manufacturing, agriculture, and construction, requiring mathematical, statistical, engineering, and creative skills. Concerns include educational challenges, security risks, privacy, safety, ethics, and potential biases, but AI's potential for a growing digital economy is significant [76](#page=76).
### 3.5 Self-test
* Explain the basic ideas that underly AI.
* Explain the different approaches for AI-related learning.
* Explain the importance of Web-related or cloud-related data for AI.
* Explain how AI can enable other digital technologies covered in this book.
* Conduct a SWOT analysis related to the adoption of AI by examining the current strengths and weaknesses as well as the opportunities and threats for the future.
* Explain how AI can help organizations become more sustainable (namely, in terms of financial, environmental, and social sustainability).
* Explain which considerations are important for organizations before and after implementing AI.
* Search for an academic article related to AI and try to position it in the research streams mentioned in this chapter.
* Look for mass media news or read Web sites about the latest trends related to AI and reflect on the implications related to organizations’ technology adoption.
---
# Internet of Things (IoT)
The Internet of Things (IoT) is a digital technology characterized by sensors and interconnected devices within an online ecosystem, enabling intensive data sharing and transforming business models toward a service-centric approach, particularly in manufacturing for applications like predictive maintenance and servitization [78](#page=78).
### 4.1 Introduction to Internet of Things
IoT represents a promising digital technology that establishes a platform for increased interconnection between devices and significant data sharing across various business and private settings. This interconnectivity, often referred to as "digitalized ecosystems," facilitates applications like digital twins and contributes to a more profound understanding of a "smart world" through connected devices. The icon used to represent IoT (Fig. 4.1) primarily signifies online connections and data sharing, often facilitated through the cloud. The chapter includes interviews with an academic expert on theoretical background and sustainability concerns, and a business expert sharing a success story on IoT implementation and adoption [78](#page=78) [79](#page=79).
### 4.2 Background of Internet of Things
#### 4.2.1 Terminology and Explanations
The Internet of Things (IoT) can be described as a vast network of smart, computer-enabled objects capable of communicating over a network, such as the Internet. These "things" range from everyday items like smartphones and home appliances to complex industrial machinery. Essentially, IoT connects the physical and digital worlds, making objects more intelligent and responsive, leading to a more connected and efficient world [80](#page=80).
Key terms relevant to the digital economy include:
* **Industrial IoT (IIoT):** The application of IoT technology within industries like manufacturing, logistics, and energy management to improve efficiency and productivity through smart devices and real-time data analysis [80](#page=80).
* **Cyber-physical systems (CPS):** Systems that fuse computation, networking, and physical processes, involving tight integration between software and physical components for advanced monitoring, automation, and decision-making [80](#page=80).
Other related concepts include cloud computing, edge computing, and various "smart X" applications such as smart home, smart city, smart factory, and smart maintenance [80](#page=80).
IoT facilitates global analysis and decision-making by leveraging extensive sensor data, which, when processed by powerful analytics, can inform both central and decentralized strategies. Its applications span multiple sectors, including manufacturing, logistics, smart homes, wearable devices, agriculture, and healthcare. An example is smart waste management in cities like Barcelona, using sensor-equipped bins to optimize collection routes [81](#page=81).
#### 4.2.2 Current and Future Research
Current IoT research focuses on technology development, industry applications (like CPS and IIoT), and consumer-oriented smart service systems. A significant trend is the integration of Artificial Intelligence (AI) with IoT to enable advanced data analysis, improved decision-making, and adaptive systems [81](#page=81) [82](#page=82).
Research into IoT often involves investigating Cyber-Physical Systems (CPS) or smart service systems, viewing IoT as a technological baseline. Specific research areas include using machine learning to improve responses to complex sensor events, particularly within business process management (BPM) contexts. For instance, research has explored how explainable AI affects users interacting with IoT sensor networks, addressing issues of reliability and user acceptance in predicting machine failures and improving data cleaning and automated labeling [82](#page=82).
Promising research avenues include:
* **AI/Machine Learning:** Processing heterogeneous input data from multiple sensors to enhance IoT system efficiency and responsiveness [83](#page=83).
* **BPM and IoT Intersection:** Investigating whether a process-oriented perspective can be applied to IoT systems or if dynamic multi-agent systems will continue to dominate, aiming to integrate IoT better with existing business processes [83](#page=83).
Future expectations for IoT in the next 10 years include:
* Further miniaturization of smart devices and sensors [83](#page=83).
* Enhanced network range for more effective communication [83](#page=83).
* Development of more comprehensive sensing, sensemaking, and reactive/proactive behavior capabilities, leading to more intelligent systems (e.g., traffic management, healthcare monitoring) [83](#page=83).
* Ubiquitous integration of IoT into daily lives and industries, becoming the "new normal" [83](#page=83).
#### 4.2.3 Sustainable Automation Concerns
Using IoT devices is expected to become user-friendly and accessible without specialized skills. However, implementing IoT technology requires diverse skills [83](#page=83):
* **Hardware Engineering:** Expertise in embedded systems, chip design, and network adapters for creating and optimizing IoT devices [83](#page=83).
* **System Engineering:** A sociotechnical understanding of IoT applications, considering human aspects and ensuring technology enhances human experiences, particularly for hybrid human-device interactions [83](#page=83).
**Educational System's Role:**
Higher education offers majors like computer science and engineering with IoT-related courses. It's crucial to impart technology skills to all students. Potential solutions include school-university collaborations (workshops, guest lectures) and integrating IoT concepts into existing subjects like science or computer classes [84](#page=84).
**Security Concerns:**
Security and privacy are concerns due to the potential for remote physical access to networks connected to the Internet [84](#page=84).
**Ethical Concerns:**
* **Privacy:** IoT devices collect vast amounts of personal data, increasing the risk of unauthorized access and misuse [85](#page=85).
* **Data Security:** The sheer volume of data poses challenges for secure storage and transmission, potentially exposing users to cyber threats [85](#page=85).
* **Digital Divide:** IoT technology may exacerbate social inequalities by benefiting those with access while leaving behind underprivileged communities [85](#page=85).
Addressing these concerns involves robust security protocols, data encryption, transparent data handling, user consent, data minimization, and ensuring equitable access to technology [85](#page=85).
**Inclusion Issues:**
IoT can alleviate inclusion issues by assisting individuals with disabilities or enabling remote healthcare. However, it can also create issues if systems are not designed with accessibility in mind (e.g., for the elderly or disabled) or if the digital divide widens. Inclusive design principles and promoting digital literacy are crucial to mitigate these concerns [85](#page=85).
#### 4.2.4 Extra Hints
Recommended reading for IoT:
1. **Basics:** ITU's report on vision, technology, potentials, challenges, and opportunities [86](#page=86).
2. **Current Developments:** Springer book series on the IoT for understanding current trends and extensions to the original vision [86](#page=86).
3. **Relevant Application:** A position paper on the synergies between Business Process Management (BPM) and IoT [86](#page=86).
### 4.3 Success Story About Internet of Things
Atlas Copco, a Swedish industrial concern, leverages IoT in two domains: internally for transforming factories into "smart factories" and externally by connecting their machines to the back office via the cloud, a domain that connects 230,000 machines. This internal transformation serves as a testing ground for new technologies [87](#page=87).
#### 4.3.1 General Case Information
Atlas Copco's IoT strategy is built on three pillars:
* **Data Pillar:** Gathers customer information, generates sales leads, and enables prediction of machine problems for proactive maintenance [87](#page=87).
* **Operational Excellence Pillar:** Focuses on "machine as a service" by offering long-term contracts for machine maintenance at a fixed price, ensuring machines remain in good condition and optimizing maintenance costs based on actual usage data. This leads to higher uptime and machine availability compared to customer-maintained machines [88](#page=88).
* **Engineering Pillar:** Uses machine behavior data from customer sites to inform the design of future machine generations, creating a closed-loop design system. For example, they observe if customers buy machines larger than needed and operate them at lower pressures, allowing engineers to adapt future designs [88](#page=88).
Wouter Ceulemans, President of Atlas Copco’s Airtec Division, was involved in IoT investment decisions and operationalizing the first IoT pillar and the platform setup. Atlas Copco was an early adopter in its sector, investing in IoT in 2010 when connectivity was considered peripheral, leading to a significant catch-up movement among competitors [88](#page=88).
Departments involved include sales, service, operations, engineers, software development, data scientists, and a diagnostic center in India. The IoT platform is managed centrally, but operationalization occurs at customer centers in 88 industrial countries [89](#page=89).
#### 4.3.2 Planning
The trigger for IoT adoption came from the service organization's need for better compressor maintenance support. Compressors often run 24/7, necessitating intensive professional maintenance to guarantee a lifespan of 15 to 20 years. IoT allows precise measurement of machine running hours, usage, and maintenance needs. Connectivity was made a standard feature on larger machines in 2010 [89](#page=89) [90](#page=90).
The initiative was driven by the management team, with CEO approval and high-level sponsorship due to the considerable investment [90](#page=90).
Organizational goals for IoT included:
* **Technological Advancements:** Falling hardware and memory chip costs, increasing Internet importance and decreasing costs, globalization (abolished roaming costs), and the rise of cloud computing drove the investment [90](#page=90).
* **Customer Value:** Convincing customers about improved service and building long-term relationships through a "win-win" business creation strategy [90](#page=90).
The scope of the project evolved. The first pillar, focused on customer information for sales and service, was the easiest and paid for itself quickly. Subsequent pillars, like preventive and predictive maintenance and using data for engineering, were more complex and took longer to implement [90](#page=90) [91](#page=91).
* The second pillar required a culture shift towards proactive maintenance, impacting logistics, competencies, and scheduling [91](#page=91).
* The third pillar involved convincing engineers to use IoT data as an engineering starting point, fostering a "data mindset" [91](#page=91).
#### 4.3.3 Adoption and Case Evaluation
Initial challenges included customer apprehension about connected machines, though this fear proved unfounded. Setting up connectivity could be difficult, especially at customer sites with poor internet access [91](#page=91).
Organizational structure changes were necessary for IoT governance, aligning budgetary and technological decisions across divisions. No new divisions were created for the initial pillars, but the shift towards servitization is prompting further organizational changes [92](#page=92).
Other changes facilitated IoT adoption:
* **Emergence of Software Component:** A new way of reasoning and thinking, with an increasing role for data scientists [92](#page=92).
* **Data Management:** Learning to organize vast amounts of data from connected machines [92](#page=92).
* **Mental Shift:** Engineers needed to recognize the availability and utility of IoT data, developing a "data reflex" to consult data before considering solutions [92](#page=92).
Initial reactions were generally positive. Board members reacted positively due to top management initiative. Data scientists and salespeople saw opportunities, while employees recognized the added value for their jobs [92](#page=92).
Limited resistance was encountered. Acceptance in the sales organization took longer due to the need for clear communication about IoT's added value and ensuring high-quality data for sales leads. Slowing down initial enthusiasm was sometimes necessary to ensure quality information was gathered [93](#page=93).
Training programs have been extensive, focusing on those who set up the platform, data analysis, and ultimately, end-users across sales, services, operations, and engineering. Upskilling covers IoT functionality and data analysis, with ongoing efforts for new employees and evolving areas like AI integration and cybersecurity [93](#page=93).
Security is a high priority, with significant investment in cybersecurity and regular platform updates. While they started with security by design (one-way communication), they are moving towards two-way communication and employ external consultants for security assessments [93](#page=93).
IoT has made Atlas Copco more customer-centric by enabling better service and providing statistical proof of improved machine condition through connected and maintained machines. This leads to higher customer satisfaction and new services like uptime contracts and servitization ("air plan" where customers pay for compressed air as a service) [93](#page=93).
Performance outcomes are monitored, and a positive Return on Investment (ROI) is experienced, with a strong correlation between IoT data and customer satisfaction measurements [93](#page=93).
Atlas Copco is developing a "green pillar" focused on sustainability, energy efficiency, and CO2 emissions, likely involving a separate organization but relying on existing customer centers [94](#page=94).
#### 4.3.4 Best Practice Advice
**Biggest Challenge:**
Treating IoT as an organizational project, not just a technology project. It requires mobilizing the entire organization, adapting strategies, and ensuring everyone learns to use it [94](#page=94).
**Critical Success Factors:**
1. **Off-the-shelf solution:** Avoid getting too involved in non-core business; leverage external partners with IoT maturity [95](#page=95).
2. **Small-scaled, realistic experiments:** Demonstrate technology works, is robust, and scalable to build confidence [95](#page=95).
3. **Well-thought strategy:** Ensure the organization knows what to achieve, viewing IoT as a value creator, not just an IT product [95](#page=95).
4. **Right people:** Involving the correct individuals is crucial and challenging [95](#page=95).
**Practical Advice:**
1. **Technology vs. Machine Lifecycle:** Recognize that connectivity and internet technologies evolve much faster than machines. Plan for technology upgrades to machines designed before current internet standards (e.g., 5G) [95](#page=95).
2. **Hardware vs. Software Thinking:** Understand the distinct ways hardware and software people think and reason. Develop both capabilities and foster collaboration. Testing, for instance, is critical in software but works differently than in hardware [95](#page=95).
3. **Decentralization Challenges:** While central strategies and tools are important, ensure they are adopted and understood locally. Significant effort is needed to prepare technology for local use by all relevant employees (e.g., technicians, salespeople) [96](#page=96).
### 4.4 Takeaways
The Internet of Things (IoT) is an interconnected network of sensor-based objects for data collection, processing, and sharing, impacting both smart home applications and industrial settings within the digital economy. IoT is closely linked to Cyber-Physical Systems and relies heavily on AI-supported data analysis for improved decision-making. Effective IoT implementation can significantly improve organizational business processes and performance. While applications span various sectors, this chapter focused on manufacturing, illustrating predictive maintenance and servitization as a novel digital business model. Sustainable IoT applications are seen in smart cities and homes for energy, waste, and traffic management. Sociotechnical skills are crucial for IoT developers due to the importance of user-centered design and human interaction, while digital literacy is essential for end-users, especially considering the collection and processing of private or sensitive data [96](#page=96).
---
# Virtual Reality and Augmented Reality (VR–AR)
This chapter explores Virtual Reality (VR) and Augmented Reality (AR) as interconnected technologies that enhance human-computer interaction through immersive user experiences, detailing their applications, theoretical underpinnings, and future potential [98](#page=98).
### 5.1 Introduction to virtual reality and augmented reality
Virtual Reality (VR) and Augmented Reality (AR) are discussed together due to their shared aim of extending reality and creating immersive experiences. These technologies bridge the gap between the real and digital worlds, offering applications in education, entertainment, and contributing to the development of smart environments and digital twins. The chapter uses an icon of glasses to represent VR/AR, symbolizing the need for a viewing apparatus like headsets or smart glasses to perceive digital content. AR superimposes digital elements onto the real world, enhancing both, while VR creates a completely simulated reality. The chapter includes interviews with an academic expert on the theoretical background and a business expert on a success story [98](#page=98) [99](#page=99).
### 5.2 Background of virtual reality and augmented reality
Dr. Selen Türkay, a senior lecturer in human-computer interaction, provides the theoretical background for VR and AR. Her research focuses on immersive systems and their impact on social-emotional and cognitive outcomes [100](#page=100) [99](#page=99).
#### 5.2.1 Terminology and explanations
**Virtual Reality (VR)** creates a computer-generated environment that users can explore and interact with, typically using a head-mounted display (HMD) for visuals and audio, and input devices like controllers for manipulation [100](#page=100).
**Augmented Reality (AR)** enhances the physical environment by overlaying digital objects, images, or information onto the real world, often using a camera and screen (like a smartphone). An example is using AR to visualize furniture in a room before purchasing [100](#page=100).
Key terms relevant to the digital economy include:
* **Spatial computing**: The ability of technology to understand and map physical space and convincingly overlay virtual objects onto it, using sensors and cameras for real-time tracking [100](#page=100).
* **Immersion**: The feeling of being fully enveloped in a virtual environment through multi-sensory engagement (audio, visuals) [100](#page=100).
* **Six Degrees of Freedom (6DOF)**: Refers to movement in a 3D space in six directions: yawing, pitching, rolling, left-right, forward-backward, and upward-downward. This contrasts with **Three Degrees of Freedom (3DOF)**, which only allows the first three rotational movements [100](#page=100).
* **Presence**: The subjective feeling of "being there" in a virtual or augmented environment [100](#page=100).
* **Social presence**: The feeling of being in the same digital space with other people [100](#page=100).
* **Interactivity**: The human ability to interact with virtual objects naturally and intuitively through physical movements, touch, or voice commands .
* **Haptic feedback**: The use of touch and tactile sensations to enhance user experiences in AR and VR environments .
Dr. Türkay highlights VR's ability to transport users to different places and enable impossible real-life experiences, making it powerful for education, therapy, and entertainment. She also appreciates AR's accessibility via mobile devices and available authoring tools, seeing its potential for everyday experiences, education, and entertainment. Both technologies enhance human-computer interaction (HCI), particularly through natural gesture mapping, which can improve the "virtual body ownership illusion" and facilitate suspension of disbelief .
**Applications of VR-AR:**
* **Education and Training**: Simulating complex or dangerous scenarios for training (e.g., construction, medical procedures), providing immersive learning experiences (virtual field trips, visualizing complex systems), and training for high-stress situations (defense, police forces) .
> **Tip:** VR and AR offer safe environments for learning and practicing skills without real-world consequences .
#### 5.2.2 Current and future research
Research in VR-AR dates back to the 1960s, but has seen rapid evolution and increased interest in the last decade due to consumer technology availability .
**Current Research Areas:**
* **Immersive Technology Innovation**: Advancements in HMD resolution, frame rates, and new technologies for augmenting senses via haptics and hand-and-body tracking .
* **Social Emotional and User Experience (UX) Aspects**: Understanding the impact of realistic VR-AR on users' emotions, attitudes, and social interactions, including empathy, social presence, and emotional regulation. This also includes studies on VR-AR in therapeutic settings like exposure therapy for anxiety and PTSD. UX research focuses on designing intuitive interfaces and interactions, studying user preferences, behaviors, and the impact of user diversity on usability and accessibility .
Dr. Türkay's research focuses on designing interactive technologies for remote learning, training, and work to overcome physical and psychological distances, emphasizing the need for more immersive solutions to support social and emotional connections. Her projects include developing interactive virtual fieldwork environments for scientific discovery, enabling remote collaboration and data interrogation .
**Promising Research Avenues:**
* **Augmenting Senses**: Further research into creating more immersive and engaging experiences through haptic and other sensory augmentations for higher-fidelity training and education .
* **Remote Learning and Work**: Utilizing VR-AR for interactive 3D environments with intelligent agents to enhance student motivation, hands-on learning, and sense of agency .
* **Generative AI Integration**: Exploring how generative AI can maximize the potential of immersive environments in education, training, and entertainment .
**Future Expectations (10 Years):**
* **Cost Reduction and Accessibility**: Cheaper, more commercially available, and accessible VR-AR technologies for everyday users .
* **Enhanced Immersion**: More intuitive interactions and advanced sensors (haptics, full-body tracking) .
* **Integration with Emerging Technologies**: Further integration with AI, machine learning, and the Internet of Things (IoT) .
* **Mainstream Adoption**: Increased use in healthcare, engineering, and entertainment .
* **Improved Accessibility**: Greater accessibility for individuals with disabilities, requiring further research and innovation .
#### 5.2.3 Sustainable automation concerns
**New Skills Needed:** Software development, interaction design, user experience (UX) design, programming (e.g., Unity 3D using C#), teamwork, and communication are essential for implementing and using VR-AR .
**Educational System Facilitation:**
* **Educator Training**: Training educators on how to use VR-AR effectively through online courses and in-person workshops .
* **VR-AR Labs**: Establishing labs to provide students and educators with access to necessary technology and resources .
* **Curriculum Development**: Creating lesson plans and specific courses that incorporate VR-AR technologies .
**Best Practices in Education:**
* **Primary Schools**: Using AR for interactive experiences and bringing textbook illustrations to life .
* **Higher Education**: Simulating job training (e.g., medical procedures), job interviews, public speaking skills, visualizing 3D designs, and improving 3D thinking in science .
**Security Concerns:**
* **Privacy**: AR collects environmental and user data. VR systems collect extensive personal data (face geometry, voice, eye tracking) that can be used for accurate identification and create "deepfakes." .
* **Addressing Privacy**: Developers must be transparent about data collection, obtain user consent, and ensure data is securely stored and encrypted .
* **Physical Safety**: Users are blocked from the physical world in VR, requiring awareness of play areas. In AR, users need to be aware of their surroundings to avoid injuries, as seen with Pokémon Go .
**Ethical Concerns:**
* **Digital Divide**: The cost of VR-AR technologies creates access barriers for lower-income individuals .
* **Addressing Digital Divide**: Developers can partner with schools, libraries, and community organizations; funding programs exist to upskill individuals .
* **Unknown Long-Term Effects**: Lack of knowledge on the short- and long-term effects of VR-AR on users' social-emotional, physiological, and cognitive well-being .
* **Addressing Long-Term Effects**: Developers have moral obligations to disclose potential problems and consider solutions to reduce risk and harm .
* **Desensitization**: Repeated exposure to emotional stimuli in VR-AR may lead to reduced responsiveness, potentially affecting emotional well-being and real-world behavior .
* **Proteus Effect**: A user's behavior in a virtual world is influenced by their avatar's characteristics, which can lead to increased confidence but also perpetuate stereotypes if avatars embody negative biases .
* **Addressing Desensitization and Proteus Effect**: Developers must be aware of risks, take proactive steps to mitigate them, conduct more research, be transparent, and design avatars promoting positive values .
* **Inclusion Issues**:
* **Positive Impact**: VR/AR can provide scenarios for experiencing the world from minority perspectives and are used in diversity and inclusion training .
* **Concerns**: The VR user base is often male-dominated. Women may experience more cybersickness due to visual-vestibular conflicts or motion sickness sensitivity. VR content and hardware can be designed with male preferences in mind (e.g., headset fit, lens spacing, content appeal) .
* **Addressing Inclusion**: Prioritize user-centered design considering diverse needs, conduct user testing with diverse groups, and incorporate feedback to create inclusive content .
#### 5.2.4 Extra hints
Recommended resources for further reading on VR-AR:
* **Conferences**: ACM Proceedings related to HCI .
* **Online Magazines**: Wired for up-to-date news, ScienceDaily for recent research .
* **Journals**: Springer's Virtual Reality journal for state-of-the-art papers .
* **Books**: Neil Stephenson's "Snow Crash" for a glimpse of the metaverse .
### 5.3 Success story about virtual reality and augmented reality
Daniel Surya Wirjatmo from WIR Group shares their experience with VR and AR. WIR Group is an immersive technology and Web3 company focusing on VR, AR, and AI, holding five global patents for AR .
#### 5.3.1 General case information
WIR Group is a pioneer in introducing immersive technology in Southeast Asia, believing VR-AR combined with AI will be crucial for the future. They integrate VR, AR, and AI to create immersive and automated experiences and are advancing Web3 technologies. Their applications span various industries, including consumer goods, education, and property, collaborating with companies like Meta and governments for events like G20 .
**Use Cases:**
* **Immersive Shopping Experience**: Visualizing products in a "metaspace." .
* **Property Development**: Creating AR brochures for real estate salespeople to showcase house models remotely, increasing customer reach .
* **Sales and Marketing**: Assisting sales executives, engaging consumers through gamification, and training employees .
WIR Group emphasizes VR-AR's role in bridging Web2 and Web3. They explain that Web2, while user-generated and usable, lacks user ownership of content (e.g., Instagram posts belonging to Instagram). Web3, utilizing blockchain, aims for a decentralized, user-centric internet where information is stored, shared, and owned by users .
Daniel Surya Wirjatmo's role involves bridging brands and technology, ensuring their VR-AR solutions are market-relevant. WIR Group was an early mover in the VR-AR space, starting in 2009, and initially served international clients due to higher market receptivity outside Indonesia .
**Organizational Structure:** WIR Group has evolved into specialized units offering solutions like commerce platforms, AR-based geolocation games, and IoT-based smart screen kiosks in high-traffic locations. Their business unit DA V provides IoT kiosk solutions that distribute AR content, enabling purchases and customer engagement. These kiosks use AI to gather customer insights (gender, age, purchase history) for brands to refine marketing strategies .
**Employee Involvement:** All employees are encouraged to be ambassadors of the technology, fostering innovation and passion .
#### 5.3.2 Planning
The co-founders' belief in the future coexistence of the digital and actual realities spurred the establishment of WIR Group. Their manifesto, the "frameless future," reflects their view that technology will increasingly blur the lines between physical and digital content consumption, moving from traditional media to smartphones and ultimately to immersive environments like the metaverse .
**Corporate Strategy Changes:** WIR Group aims to make technology accessible and impactful on a large scale. Inspired by Industry 5.0, they contribute to Indonesia's smart society development, helping individuals and companies adapt to technological changes, improve business processes, and create differentiation .
**Project Scope Determination:** Project scope is customized based on deep dives into client objectives, pain points, business goals, and current business. WIR Group educates clients on the potential of VR-AR and collaborates to find relevant and immediate implementation strategies .
#### 5.3.3 Adoption and case evaluation
**Adaptation to Client Needs:** WIR Group's technology solutions are highly customized, varying significantly by industry, business goals, and target market. Brainstorming phases are crucial for defining objectives and evolving solutions .
**Organizational Structure Impact:** WIR Group's expansion led to specialized units (commerce, IoT, Web3) allowing focus and encouraging integrated solutions through collaboration. For clients, VR-AR facilitates technology adoption and adaptation to market changes .
**Facilitating Adoption:** A dedicated R&D department develops new concepts, prototypes, and patents .
**Stakeholder Reactions:** Internally, co-founders were aligned from the start. Externally, stakeholders are generally receptive but often unsure where to begin, making WIR Group's role in realizing these aspirations vital .
**Overcoming Resistance:** WIR Group helps users understand the need for continuous adaptation to changing technologies to maintain relevance without sacrificing authenticity, highlighting benefits .
**Example Case Study - Alfamind:** To help Alfamart (a large convenience store chain) expand, WIR Group created Alfamind, a virtual store concept. This allowed individuals in less accessible cities to own a virtual franchise with minimal investment, earn commissions, and increase market access to Alfamart products .
**Training Programs:** WIR Group selects employees based on innovation mentality and passion. Client challenges drive new solution creation, leading to acquired skills that are passed on. They also invite industry practitioners to share trends .
**Security Issues:** Security is paramount. A dedicated security team tests development stages thoroughly. WIR Group is ISO-certified .
**Customer Impact:** VR/AR provides customers with more immersive and interactive experiences with brands and products, creating lasting impressions and helping businesses achieve goals .
**Example Case Study - Multivitamin Company:** A campaign involved collecting character cards upon purchase, unlocking AR experiences. Combining cards led to various outcomes, motivating customers to buy more vitamins .
**Performance Monitoring:** WIR Group monitors expenses for each project and clients may request monitoring dashboards. They have experienced significant growth and have evolved from B2B to B2B2C models, preparing clients for Web3 .
**Future Evolution:** WIR Group is developing the metaverse platform ecosystem "Nusameta," providing tools for creators and facilitating user experiences. Nusameta aims to support national economic development and sustainable digital transformation while preserving national values and culture, enabling content creators to promote cultural heritage .
#### 5.3.4 Best practice advice
**Biggest Challenge:** Synchronizing with the rapid pace of technological evolution, especially with new AR glasses, affordable wearables, mobile devices, and enhanced connectivity (Internet, device-to-device), along with generative AI, IoT, and blockchain. A further challenge is getting the masses to adapt to technology, especially in regions like Indonesia where internet penetration needs improvement .
**Critical Success Factors:**
* **Education:** Educating stakeholders on VR-AR's value beyond a gimmick .
* **Demonstration:** Providing product demos to show clients how the technology can be implemented in their business .
**Practical Advice:**
* **Embrace Change:** Be part of technological evolution .
* **Start Small:** Identify a specific business process that can benefit and begin there .
* **Iterate:** One successful use case can lead to others .
### 5.4 Takeaways
VR technology creates a virtual world, while AR overlays digital elements onto the real world, forming a spectrum of immersion. VR-AR applications are prominent in online education, therapy, and entertainment, with growing potential in other fields. These technologies, alongside AI and digital twins, facilitate simulations, remote work, and online training. The transition from Web2 to Web3, a more user-centric internet, highlights the importance of blockchain for decentralization, ownership, and secure online interaction. Concerns include deepfakes, physical safety, cybersickness, virtual body ownership illusion, desensitization, and the Proteus effect. It is crucial to consider VR-AR's effects on user experience, social-emotional, physiological, and cognitive well-being .
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# Digital twin technology
Digital twin technology integrates physical and virtual entities for simulation and monitoring, playing a crucial role in Industry 4.0 and offering a path towards more intelligent cyber-physical systems .
### 6.1 Introduction to digital twin technology
Digital twin technology is characterized by the convergence of various digital technologies, creating "digitalized ecosystems" that bridge the physical and digital worlds through proxies. It offers a new perspective on data-intensive objects and environments, influencing how interactions are managed. The core concept involves a dual representation: a real-world entity or environment and its corresponding digital counterpart or proxy. This virtual model aids in simulating, testing, monitoring, and maintaining real situations by collecting sensor data and predicting outcomes. The chapter further explores this technology through interviews with an academic expert and a business expert, delving into theoretical foundations, research streams, sustainability concerns, and a practical success story .
### 6.2 Background of digital twin technology
#### 6.2.1 Terminology and explanations
A digital twin connects physical entities with nearly identical virtual representations. This is achieved by measuring the state of the physical entity and its environment and recording this information in the virtual entity. Sensors, actuators, and servers, often part of the Internet of Things (IoT), facilitate this connection between physical and virtual environments. For instance, a moisture sensor and a water pump in an irrigation system can be managed by a virtual model using AI to determine optimal watering schedules. Extended Reality (XR), including Virtual Reality (VR) and Augmented Reality (AR), is frequently associated with digital twins, enabling immersive experiences (VR) or overlaying digital twin data onto the real world (AR). VR can be used for training on virtual production lines, while AR can provide real-time assembly instructions .
Key terms relevant to digital twins include:
* **Models and Simulations:** Unlike traditional static models, digital twin models are high-fidelity and adapt in real-time to changing real-world states, enabling more accurate simulations .
* **Digital Twin Instances and Aggregates:** A digital twin instance represents a single entity (e.g., a specific car), while an aggregate contains accumulated data from multiple instances, allowing for improved learning and knowledge sharing .
* **Digital Thread:** This concept posits that every product generates a body of data across its lifecycle, making all product data available in a product-centric manner, beneficial for supply chain traceability and product disassembly/recycling .
* **Entities and Environments:** A physical entity/twin exists in a physical environment, while a virtual entity/twin exists in a virtual environment .
* **State:** Refers to the measured values of all parameters corresponding to an entity/twin and its environment .
* **Realization:** The act of changing the state of a physical or virtual entity/twin .
* **Metrology:** The act of measuring the state of a physical or virtual entity/twin .
* **Twinning:** The act of synchronizing the states of the physical and virtual entity/twin .
* **Twinning Rate:** The frequency with which twinning occurs .
* **Process Types:** Physical processes involve the physical entity, while virtual processes involve the virtual entity .
The integration of physical and virtual entities is a key area of interest, maximizing the benefits of both. Digital twins also offer an embodied approach to AI, potentially mimicking biological systems and enabling a new generation of AI capable of functional imagination .
Main application areas for digital twins are currently in manufacturing, seen as a driver for Industry 4.0 and smart factories. While many applications may be a rebranding of existing technologies, the sector benefits from sensor integration and performance improvement. Growth is also seen in other sectors, but adapting technology stacks to unconstrained, dynamic environments is challenging compared to the controlled nature of factories .
#### 6.2.2 Current and future research
Current research largely focuses on *how* to build digital twins, shifting from theoretical work to implementation attempts. There is a need for thorough long-term evaluations. Research also explores connecting existing components and demonstrating improvements in niche applications .
Specific research interests include the next generation of virtual processes delivered by digital twin technology, moving towards biologically inspired artificial cognitive systems for AI rather than deep learning .
Promising research avenues involve applying digital twin technology to specific domains and applications. Key questions revolve around what needs to be measured, the required fidelity, measurement frequency, and data utilization. Challenges include the cost and size of storing high-fidelity data and the feasibility of metrology techniques. There is a need to maximize learning from minimal data and processing time. For example, the fidelity of a road's digital twin depends on its purpose, whether for tire-road surface simulation or navigation. Adjusting the twinning rate to account for likely changes, like road damage during winter, is more efficient than constant real-time measurement .
Future expectations for digital twins within 10 years include niche implementations in sectors where the technology works well. Addressing current technological challenges is crucial for widespread adoption. The risk of the field disappearing due to hype and failure to deliver benefits exists. The term "cyber-physical system" (CPS) is suggested as a more credible alternative, acknowledging that digital twin is a subset of a wider research space. Demonstrating benefits without overselling is a key challenge .
#### 6.2.3 Sustainable automation concerns
New skills required for digital twins combine computer science expertise with domain-specific knowledge. Understanding *what* data to collect and *why* is crucial. Educational systems should prioritize STEM programs and domain expertise. Early exposure to programming languages like Python and interaction with the real environment through tools like Raspberry Pi prepares children for future innovations .
Security concerns are similar to existing cybersecurity risks but potentially amplified. Hackers could take control of connected systems, such as cars. Proper risk assessment is needed, and many systems may not require 24/7 direct internet control .
Ethical concerns largely mirror those of underlying technologies like big data. Bias in data, whether related to race or gender, can manifest in digital twins, as can models based on average rather than diverse human bodies. Capture and storage of user activities within digital twin systems raise ethical questions similar to those encountered with operating systems .
Digital twins can potentially help solve inclusion issues by allowing individuals to experience environments remotely, like virtual journeys or familiarizing with lecture rooms. However, they might also create new inclusion concerns, and addressing these requires careful consideration .
#### 6.2.4 Extra hints
Returning to the source of the concept, such as Grieves's foundational papers, is recommended for a theoretical perspective prior to the hype. Jones et al.'s systematic literature review provides a framework for developing digital twins. Research on aligning digital twins with artificial cognitive systems, which are more biologically inspired AI, is promising for handling dynamic and complex environments .
### 6.3 Success story about digital twin technology
#### 6.3.1 General case information
Siemens AG utilizes digital twin technology as a core element in its business, both as a tool for its hardware products and as a software business avenue. The company has invested significantly in software firms, becoming a dominant player in industrial R&D and production software. Digital twin technology, with roots tracing back to NASA publications in 2012, is now a central focus for Siemens, integrated into its core technologies. The concept builds upon decades of simulation in R&D, now extending to operational uses like training and monitoring. Examples include simulating rotor temperature for decision-making when physical sensors are difficult to place and providing digital twins for entire factories, buildings, campuses, and city parts. While operational applications are emerging, its role in R&D is established .
The expert at Siemens has been instrumental in developing the company's vision for digital twins, evolving from using virtual models in design to integrating these insights throughout a product's lifecycle, especially its operational phase. This involves identifying novel domains and applications and overcoming technical challenges, such as developing ultrafast simulation solutions for real-time feedback .
Siemens distinguishes itself by providing authoring tools for digital twins and utilizing the technology in its own hardware development. The introduction of the "executable digital twin" concept has gained traction .
Digital twin technology at Siemens spans all divisions and businesses, requiring a coordinated approach involving technology departments, software business units, and hardware production teams to create a cohesive ecosystem .
#### 6.3.2 Planning
Siemens' initial steps toward digital twins began with the acquisition of UGS Unigraphics in 2007. The formal adoption as a core strategic element occurred in 2015, following NASA's popularization of the concept. The corporate strategy of fusing the digital and physical world paved the way for digital twin technology, rather than the technology shaping the strategy .
Criteria for digital twin projects are based on the value they bring to customers and the organization. A specific project aimed to reduce downtime for electric large drives used in oil and gas compressors by enabling earlier machine starts. Direct temperature measurement was challenging, leading operators to be cautious. The digital twin, leveraging engineering knowledge, bypassed the lack of real-world data and machine failure risks associated with early data gathering .
#### 6.3.3 Adoption and case evaluation
Early challenges in using digital twins outside R&D involved making the technology user-friendly, scalable, and widespread for operational staff who were not as familiar with complex digital models. The "executable digital twin" solution, accessible on various devices and requiring less specialized skills, addresses this .
Digital twin technology promotes an interconnected organizational structure, fostering collaboration across departments. Initiatives are approached on a project-by-project basis within a horizontal ecosystem .
Successful digital twin adoption requires collaboration across departments spanning all lifecycle phases to avoid disjointed models and enable knowledge sharing. This can involve departments taking on additional roles, such as R&D developing both physical products and their digital counterparts. A horizontal structure facilitates this collaboration .
The response to digital twins has been largely positive, with employees familiar with its value and external recognition of Siemens as a leader .
Initial resistance was minimal, as adoption began at the R&D level where researchers are more experimental. Skepticism regarding implementation and accuracy was overcome by the evident added value at various product lifecycle stages .
Continuous training, from awareness-raising to advanced technical sessions and online learning courses, keeps employees updated. Efforts are ongoing to make digital twin technology more user-friendly to reduce expertise requirements, as current creation often demands specialized knowledge .
Security concerns primarily revolve around stakeholders' reluctance to share digital information, a mindset issue rather than a technical one, given secure methods exist. Siemens' long-standing focus on cybersecurity and secure data handling has been paramount. Digital twins can enhance security by countering cyberattacks that manipulate sensors, by comparing real and virtual sensor readings for consistency .
Digital twins provide customers with a preview of product performance, allowing informed decisions, such as testing how inverters handle heat before purchase .
Initially, performance outcomes focused on showcasing new possibilities like reducing physical prototype tests. The ability to evaluate a significantly higher number of designs virtually, faster and more effectively than with classic prototyping, leads to better products. Financial benefits, such as savings from using fewer sensors, were assessed in a second phase .
Siemens' evolution regarding digital twins involves the industrial metaverse, focusing on user-centric solutions for real-time collaboration, photorealistic visualization, and a closed loop between the digital twin and the real asset during operation .
#### 6.3.4 Best practice advice
The biggest challenges in scaling digital twin adoption are the complexity of creation due to expertise needs and the requirement for failsafe operation in critical systems. Simplifying both creation and use, as highlighted by the focus on executable twins, is crucial .
Critical success factors include a cross-departmental approach, ensuring digital twin projects involve the entire organization, and Siemens' unique blend of software tools and hardware products .
Practical advice includes adopting a structure that brings together teams from every stage of a product's lifecycle. Starting with valuable use cases and avoiding overcomplication is recommended. Companies should focus on one high-value use case and explore potential process changes or cost/revenue projections only after achieving initial success. Exploring executable twins is vital for accessible and scalable adoption .
### 6.4 Takeaways
Digital twin technology is best understood as a cyber-physical system requiring coordination across multiple organizational departments and support from other digital technologies like IoT, AI, and VR/AR. It is primarily applied in manufacturing and supply chain settings for smart factories, covering the entire product lifecycle from R&D to operational use. Specific terms such as digital twin instances, aggregates, states, and twinning are important. Benefits and issues often carry over from the underlying digital technologies. The concept of an executable digital twin aims to make the technology more user-friendly, scalable, and widespread. Future applications may extend to sectors like agriculture .
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# Blockchain technology
Blockchain technology enables trusted collaboration through data transparency and cryptography, forming the backbone for various applications beyond cryptocurrencies .
### 7.1 Introduction to blockchain technology
Blockchain technology facilitates trust among parties by leveraging data transparency and cryptography, thereby enabling collaboration. It represents a chain of records or transactions cryptographically linked together. Each node in a distributed, peer-to-peer network holds a copy of all transactions, making blockchains highly resistant to manipulation and hacking due to the cryptographic linkage of records. While well-known for digital money like Bitcoin, blockchain has diverse applications, including in the fashion industry with a focus on sustainability .
> **Tip:** The icon for blockchain technology visually represents linked cubes or blocks, symbolizing the chain of cryptographically linked transactions, and a network, highlighting transparent data sharing within a distributed network .
This chapter includes interviews with an academic expert, Prof. dr. Ingo Weber, who discusses the theoretical background, research, and sustainability concerns, and a business expert, Jolanda Kooi, who shares a success story of blockchain implementation in the fashion industry at tex.tracer .
### 7.2 Background of blockchain technology
#### 7.2.1 Terminology and explanations
Blockchain technology allows actors to collaborate even without mutual trust by replacing the need for trust with data transparency and cryptographic security. Data transparency is illustrated by the ability to verify transactions on a blockchain; for example, when purchasing an item with cryptocurrency, anyone can see the transaction and verify the digital signature using the sender's public key. This eliminates the need for traditional trusted authorities like banks .
Cryptocurrency, like Bitcoin, uses blockchain as its foundation to create digital money that cannot be easily stolen, copied, or double-spent, thus maintaining its value. While effective for digital money, this transparency doesn't inherently verify the real-world accuracy of off-chain data, such as the location of a shipping container, which still requires trust in the source of the information .
Blockchain can manage authoritative records for assets like real estate or vehicles, track rental agreements, and handle virtual assets such as software licenses or in-game items, where the digital world is the primary source of truth. It can also automate cross-organizational business processes and create permanent, tamper-proof records for compliance and certification .
**Key Terminology:**
* **Blocks:** Sets of transactions, such as transferring cryptocurrency .
* **Cryptographic hashing:** A function that takes input of any length and produces a fixed-size output (a "digest" or hash value). Changing even a tiny part of the input drastically alters the output, making it valuable for verifying data integrity and linking blocks. If a block's content is altered, its hash changes, breaking the chain of hashes linking it to subsequent blocks and alerting network nodes to manipulation. This ensures historical transaction data remains permanently recorded, with any corrections or reversals implemented as new transactions .
* **Public key:** Information accessible to everyone on the blockchain, associated with an account and used in conjunction with a private key for cryptography .
* **Private key:** Information that grants control over a blockchain account and its assets; losing it means losing control and ownership .
* **Distributed Ledger Technology (DLT):** A broader term for a shared digital ledger data structure where all participating computers have identical copies .
* **Blockchain:** A DLT structured as a chain of blocks linked by cryptographic hashes .
* **Hyperledger Fabric:** An open-source DLT example, often considered a blockchain, supporting controlled membership and suitable for large organizations .
* **Bitcoin:** The oldest blockchain and a specific cryptocurrency, also referring to its underlying technology and ledger .
* **Cryptocurrency:** Digital currency secured by cryptography, typically transacted and stored using blockchain technology. Cryptocurrencies are fungible tokens, meaning each unit is interchangeable .
* **Non-fungible token (NFT):** A token that tracks ownership of unique, non-interchangeable assets, analogous to owning property where each unit has distinct characteristics and value. NFTs are used for tracking ownership of digital assets like unique digital art .
* **Smart contract:** A computer program executed on a blockchain, often referred to as a "blockchain-based program." .
* **Public blockchain:** A blockchain where anyone can join, leave, transact, and read data without permission .
* **Permissioned blockchain:** A blockchain where access to join, leave, and perform operations requires explicit permission from registered participants .
#### 7.2.2 Consensus algorithms and types
A consensus algorithm is essential for nodes in a blockchain network to agree on the state of the ledger and the order of transactions .
* **Proof-of-Work (PoW):** A consensus algorithm, famously used by Bitcoin, that requires significant computational power and energy consumption .
* **Proof-of-Stake (PoS):** An alternative consensus algorithm that is far more energy-efficient. Ethereum transitioned from PoW to PoS in 2022, drastically reducing its energy consumption .
> **Tip:** The shift from PoW to PoS is driven by sustainability concerns, as PoW is extremely computationally intensive and consumes vast amounts of electricity .
#### 7.2.3 Current and future research
Current research focuses on improving blockchain speed, cost-effectiveness, and features, as well as exploring applications like smart legal contracts, cross-organizational business processes, data analysis from blockchain, and handling digital or physical assets as tokens. Research also addresses the trade-off between transparency and confidentiality in business processes, data extraction for auditing, and identifying obfuscation techniques used by malicious applications .
Promising research areas include blockchain scalability, confidentiality, improving temporal constraints (blockchain's current lack of a robust concept of time), and developing quantum-safe computing algorithms to counter future threats from quantum computers .
The future of blockchain technology is uncertain, with possibilities ranging from niche applications to widespread impact, particularly in countries with less trustworthy authorities. The focus is shifting towards value-adding use cases, moving beyond the initial hype .
#### 7.2.4 Sustainable automation concerns
Implementing and using blockchain technology requires a blend of technical skills in distributed computing and cryptography, alongside knowledge of game theory and economics. While basic understanding might suffice for end-users of blockchain applications, specialized courses are available for those involved in implementation .
**Security Concerns:**
* **Quantum computing:** Future quantum computers could potentially crack current cryptographic hashing functions and private keys, jeopardizing blockchain security. The industry is moving towards **quantum-safe computing** algorithms to mitigate this threat .
* **Private key management:** Users must diligently protect their private keys, as there is no central authority to help recover lost assets if keys are compromised .
**Ethical Concerns:**
* **Environmental impact:** The high energy consumption of PoW consensus algorithms, particularly in Bitcoin, is a significant ethical concern given the climate crisis. The adoption of PoS and other energy-efficient algorithms is crucial for sustainability .
* **Illegal activities:** Anonymous or pseudo-anonymous blockchains can be used for illegal purposes. While anonymous payments are seen by some as a building block of liberal democracy, regulatory measures may be needed .
**Inclusion Concerns:**
Blockchain technology can benefit those with the skills to use it, potentially exacerbating inclusion issues. However, features like censorship resistance in public blockchains can promote democratization and equitable access .
### 7.3 Success story about blockchain technology: tex.tracer
tex.tracer is a transparency platform for the fashion industry that uses blockchain technology to capture and verify supply chain and product information. Their goal is to create a more reliable and sustainable fashion industry, not just transparency for its own sake .
#### 7.3.1 General case information
tex.tracer operates as a B2B software-as-a-service (SaaS) platform. They prioritize data reliability by obtaining information directly from primary sources and authenticating it with time and geolocation stamps, fostering peer-to-peer interaction within the supply chain. All captured data are directly recorded on the blockchain to ensure immutability, forming the foundation of their platform .
As a startup, tex.tracer opted for a private or permissioned enterprise blockchain solution, deeming it more suitable than a public blockchain due to concerns around data security, platform speed, and energy emissions. They are one of the pioneers in applying blockchain technology in the fashion industry .
Their team of around 20 employees is involved in developing the platform, with customers and their supply chain partners being the primary users. The platform extends to suppliers' suppliers, tracing back to raw materials, involving a multiplier effect of numerous partners .
#### 7.3.2 Planning
The initiative for tex.tracer began with the need to collect verified and unalterable data in the fashion industry. After exploring concrete blockchain options in 2019, they consulted third parties to determine the best blockchain type, ultimately choosing a private/permissioned solution. The company's strategy has always been centered around being a blockchain platform .
Their software product is fully designed around blockchain technology from inception, with cryptographic hashes stored on the blockchain, not necessarily all detailed data. They use Hyperledger Fabric as their chosen DLT .
#### 7.3.3 Adoption and case evaluation
A significant deviation from the initial plan was the realization that a public blockchain, while offering benefits, was not suitable for their specific needs at that stage, leading to a pivot towards private/permissioned alternatives .
tex.tracer collaborates with Fujitsu's blockchain innovation lab for expertise in blockchain, security, and privacy, opting for external support rather than building all capabilities in-house. This strategic partnership has been validated by a successful technical due diligence during a funding round .
Convincing customers and their suppliers, who are often manufacturers from diverse backgrounds, to adopt the blockchain platform has been key. For smaller companies, the concept of blockchain can be intimidating, so tex.tracer focuses on explaining the benefits of immutable and reliable data storage rather than technical blockchain details .
The fashion industry, from an IT perspective, is often considered old-fashioned, with MS Excel still being a common tool for data capture. tex.tracer simplifies their "blockchain story" to highlight reliable data and a single source of truth, framing blockchain as a future-proofing measure, potentially enabling future applications like NFT gamification .
Blockchain developers are primarily based in India, with regular visits to the Amsterdam office for roadmap reviews and team education. Fujitsu also provides updates and training on blockchain developments .
Security is a paramount concern, addressed through their partnership with Fujitsu and a dedicated product owner focused on security and data privacy .
The platform's speed can be a challenge, as blockchain operations are inherently slower than traditional computing systems. tex.tracer is developing an additional read-only layer to improve data retrieval speed for end-users while maintaining data integrity on the blockchain. Performance is monitored by a DevOps team to ensure optimal functioning .
#### 7.3.4 Best practice advice
* **Explore Blockchain Types:** Investigate both public and private/permissioned blockchain alternatives, as permissioned blockchains often offer better governance and data security for internal platforms .
* **Partner for Expertise:** Given the evolving blockchain landscape, collaborate with specialized third-party solutions or experts, especially if internal CTO expertise is uncertain .
* **Focus on Primary Source Data:** Emphasize collecting verified data directly from the primary source to ensure authenticity and immutability, creating a competitive advantage .
* **Simplify the Blockchain Narrative:** For customers and partners, focus on the benefits of reliable and immutable data rather than complex technical blockchain details .
* **Consider Future Developments:** Stay abreast of emerging technologies and blockchain evolutions, such as NFTs and gamification, to leverage the platform's full potential .
* **Address Speed Challenges:** Proactively develop solutions, like additional read-only layers, to mitigate speed limitations inherent in blockchain technology .
### 7.4 Takeaways
Blockchain technology utilizes cryptographic hashing, public/private keys, and distributed ledger principles to create secure, transparent, and immutable records. It underpins applications beyond cryptocurrencies, including NFTs, and has applications in sectors like fashion for supply chain transparency. Key considerations include choosing between public and permissioned blockchains, understanding consensus algorithms (like PoW and PoS) and their sustainability impact, and preparing for future challenges like quantum-safe computing. Benefits include data transparency and trusted collaboration, but potential issues like illegal activities and censorship resistance must be considered .
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# 3D printing
3D printing, more accurately termed additive manufacturing (AM), is a digital technology enabling the creation of highly complex and customized three-dimensional objects by adding material layer by layer .
### 8.1 Introduction to 3D printing
3D printing fundamentally challenges traditional manufacturing methods by allowing for intricate designs and personalization. It offers new avenues for prototyping and experimentation and contributes to more immersive experiences beyond virtual and augmented reality. While a relatively mature technology, its applications are continually expanding, especially when integrated with Artificial Intelligence (AI) and its evolution towards 4D printing. The core concept of 3D printing involves printers adding materials layer by layer to construct objects. 4D printing builds upon this by incorporating a time dimension, utilizing smart materials that can change shape over time in response to stimuli .
### 8.2 Background of 3D printing
#### 8.2.1 Terminology and explanations
Additive manufacturing (AM), commonly known as 3D printing, is a manufacturing technique that emerged commercially in the late 1980s and constructs three-dimensional parts layer by layer. This contrasts with subtractive manufacturing processes like milling or turning, where material is removed from a larger piece. AM has advanced significantly, evolving from producing plastic prototypes to creating functional components from plastics, metals, ceramics, and multi-materials for various industries. Examples include personalized biomedical implants and intricate heat exchangers .
Numerous AM processes exist, ranging from inexpensive fused filament fabrication (FFF) printers to advanced powder and laser-based machines with integrated monitoring and post-processing capabilities. All AM processes begin with designing the part using computer-aided design (CAD) software. Additional software translates CAD data into instructions for the AM machine. Many printed parts require post-processing, such as heat treatments or surface finishing .
Key terms relevant to the digital economy include:
* **Digital Model:** AM relies on a digital design of the part to be produced .
* **First-time-right approach:** This digital nature facilitates simulations of the part's geometry and functional behavior before printing, allowing for adjustments to account for potential deformations or imperfections inherent in the layer-by-layer process .
* **Localized Manufacturing and Spare-Part Management:** Large databases of digital designs enable on-site printing when needed, reducing physical inventories and enhancing supply-chain resilience .
* **In-process monitoring:** This is a critical area of focus, where sensor data collected during manufacturing creates a digital signature for each part, potentially including details on the process, starting material, intended lifespan, and recyclability .
Engineers are particularly drawn to AM for its design freedom, which enables previously impossible optimized designs, and for the intricate process of powder and laser interactions in certain AM technologies .
Current applications for 3D printing are widespread across industries:
* **Medical Sector:** Personalized biomedical implants, surgical tools, anatomical models, hearing aids, and dental parts .
* **Aerospace Industry:** Lightweight, topologically optimized parts using high-performance polymers or metals to reduce fuel consumption .
* **Energy Sector:** High-efficiency heat transfer devices and filtering/mixing components .
* **General Manufacturing and Machine Building:** Tools like injection and extrusion molds with conformal cooling channels for increased lifespan and reduced product manufacturing cycles .
#### 8.2.2 Current and future research
Current research in AM focuses on several areas due to its ongoing maturation compared to conventional manufacturing. Significant variability in geometrical characteristics and mechanical properties exists between different machines and vendors, driving research into :
* Novel AM processes .
* Multi-scale simulations and testing of design-process-material-functionality relationships .
* New materials and multi-material printing .
* Improving the reliability of the entire manufacturing chain and enabling part certification through online and offline quality control .
Research at KU Leuven's Mechanical Engineering Department specifically targets powder-bed, laser-based AM of polymers, metals, and technical ceramics, focusing on challenging materials like pure copper, tungsten, and fiber-reinforced polymers. Their work also includes developing strategies to enhance fatigue life and surface quality, exploring multi-material and multi-laser printing, and creating lattice structures for lightweight components. An example of this research involves a dual-laser strategy for metal parts that uses conventional laser powder bed fusion (LPBF) followed by selectively removing powder with a pulsed laser to remelt inclined surfaces, reducing roughness and enabling laser marking. KU Leuven has also developed its own AM equipment since 1990, allowing fundamental investigation of manufacturing physics and integration of new hardware and software. This has led to spin-offs like Materialise and LayerWise (now 3DSystems) .
Promising research avenues emphasize multidisciplinary collaboration to create added value through AM. This involves mechanical engineers, material scientists, chemists, computer scientists, and biomedical researchers working closely with end-users. The Leuven.AM institute at KU Leuven fosters these collaborations, involving over 45 professors from various faculties. Beyond maturing AM machines and materials, research aims to improve as-printed surface quality to reduce post-processing, increase productivity via smart printing strategies (e.g., multiple lasers), and advance multi-material AM technologies, though challenges remain in material interfaces and design philosophies .
**4D Printing:** This emerging field adds a time dimension to AM. It involves printing shape memory materials that can revert to their original shape after deformation, triggered by external stimuli like heat. An example is a custom stent designed to be small for insertion and then expand to its original size at body temperature .
**AI and ML Integration:** Machine learning (ML) and AI are increasingly applied to AM processes and materials. AM's digital nature, sensor fusion during printing, and complex multi-physics models generate vast amounts of data requiring significant computational power. The synergy between AM, ML, and AI is crucial for machine, process, and part certification. Capturing process data and linking it to measured quality via non-destructive and destructive testing allows AI/ML to identify patterns, trigger smart actions during printing (e.g., stopping defective jobs or performing repairs), reduce scrap rates, and create digital fingerprints. This can reduce the need for costly post-process quality control, making AM more affordable and reliable .
**Future Expectations (Next 10 Years):** AM is expected to mature into a solid technology, coexisting with other manufacturing methods. Advancements in AI, ML, laser technology, multi-scale simulations, and process monitoring will enhance productivity and reliability. A broader material palette and improved part properties will enable more applications. Hybrid approaches combining AM with existing technologies like milling will reduce costs and increase benefits. However, intrinsic disadvantages such as long production times for medium to large series and limited surface quality will likely persist .
#### 8.2.3 Sustainable automation concerns
To achieve smart AM machines capable of automatic parameter optimization and in-process defect detection, extensive AM expertise and R&D are required. A crucial, less automatable skill is designing for AM to leverage its geometrical complexity and added value, which must outweigh AM's disadvantages like slow processing and high material costs. Engineers are trained in AM at KU Leuven to contribute to smart AM equipment and new design philosophies .
Educational systems can facilitate AM skills from an early age. Even young children can grasp 3D printing basics and experiment with CAD software and FFF printers. Affordable FFF printers are used in research labs for prototyping. FabLab Leuven offers summer camps and provides schools with software and printer packages to integrate 3D printing into lectures. Raising awareness among young people about AM's potential is vital to inspire interest in science and technology careers .
Security concerns in AM include:
* **Confidentiality of Medical Data:** Protecting sensitive patient data for personalized implants .
* **Cybersecurity Threads:** The digital nature of AM processing chains makes machines and PCs vulnerable to breaches, potentially exposing confidential data or allowing unauthorized control of printers. Up-to-date cybersecurity measures and trained users are essential .
Ethical concerns include:
* **Unauthorized Weapon Manufacturing:** The use of AM by unauthorized individuals to produce weapons .
* **Medical Advancements:** Regulating access to technologies like personalized medicine, 3D-printed organs, and exoskeletons that could enhance human performance. Addressing these requires multidisciplinary committees, not just engineers .
AM can address inclusion issues by acting as a catalyst for science and technology engagement, especially for young people. The technology's appeal can encourage more diverse participation in STEM fields. The initiative with Princess Elisabeth of Belgium as an ambassador for the AM lab has successfully fostered a heterogeneous group of researchers and students .
#### 8.2.4 Extra hints
Due to the rapid pace of innovation in AM, books quickly become outdated. Recommended sources for up-to-date information include :
* **Scientific Journals:** *Additive Manufacturing Journal* .
* **University LinkedIn Pages:** Leuven.AM LinkedIn page .
* **Specific Publications:** Gibson et al. Van Hooreweder and Wohlers et al. .
### 8.3 Success story about 3D printing
#### 8.3.1 General case information
HP Inc. utilizes 3D printing across its core businesses: personal systems (computers, accessories), traditional printing, and its strategy and incubations organization, which includes personalization and 3D printing. HP uses 3D printing not only for producing its own 3D printers but also for manufacturing components within computers and printers, thereby improving its product line .
HP's 3D printing solutions support customers in sustainably manufacturing plastic or stainless-steel applications across four key industries:
* **Healthcare:** Personalized medical devices such as braces for injuries, insoles for flat feet, baby helmets for shape correction, and custom orthotics like HP's Arize to alleviate foot pain .
* **Industrial:** Assisting other manufacturing companies by incorporating 3D-printed components into their products and for industrial tooling .
* **Mobility (Automotive/Drones):** Enabling lightweight parts for electric or hybrid vehicles to improve engine performance and reduce CO2 emissions .
* **Consumer Goods:** Producing personalized furniture and fashion items, such as customized eyewear. SMITH Optics' personalized ski goggles were recognized as a top invention .
Yves Jamers, as the Benelux sales leader for HP's personalization and 3D printing business, supports B2B customers in implementing and scaling HP's 3D printing technology across the EMEA region. HP began exploring 3D printing in the early 2010s, officially launching its technology in 2016 and going to market in 2017 .
HP's competitive approach involves exploring AM for production purposes and focusing on helping customers scale their operations. HP aims to digitalize a market that has been traditional for nearly a century, replacing conventional products with more flexible and sustainable polymer or metal components .
HP's decision to develop its own 3D printing technology, HP Multi Jet Fusion, stemmed from the early market in 2012-2013, where available solutions were primarily for prototyping and lacked scalability, speed, and cost-effectiveness for production. HP's technology addresses these customer requirements for speed, cost, and mass production .
3D printing facilitates faster and lower-cost production due to its local manufacturing capabilities. It allows for rapid iteration and agile development, enabling products to be designed and produced on the same day. This also reduces the need for large warehouses and extensive spare-part inventories, as digital files can be used to print components on demand. This localized production has significant environmental benefits and enhances supply-chain resilience, as seen during the COVID-19 pandemic when production from China was disrupted. AM also enables the production of flexible components with shapes difficult or impossible to achieve with conventional techniques. HP complements, rather than replaces, conventional techniques by offering a digital production method for plastic components where alternatives previously didn't exist .
The global 3D printing headquarters for HP is in Barcelona, Spain, housing the HP 3D printing Center of Excellence (CoE). They also have a manufacturing plant in Singapore and R&D facilities in Barcelona and the USA. Local sales teams cater to specific market needs. Employees across various functions, including R&D, sales, marketing, and HR, are involved globally .
#### 8.3.2 Planning
The introduction of 3D printing at HP was a top-down corporate decision, reflecting HP's forward-looking strategy to incorporate innovative technologies into its core business. The development leveraged existing print technologies. HP aimed to enter new markets and become industry leaders, driving innovation with solutions like HP Multi Jet Fusion and HP Metal Jet .
HP's strategy involves three main business areas: personal computers, conventional printers, and 3D printers, each with distinct goals. The 3D printing business aligns with HP's mission to change how people design and manufacture. Sustainability is a key driver, focusing on reducing HP's carbon footprint and that of its markets and customers through local production and sustainable packaging .
The initial scope for 3D printing projects focused on identifying the best-fit markets: consumer products, healthcare, mobility, and industrial goods, which align well with HP's polymer and metal technologies .
#### 8.3.3 Adoption and case evaluation
HP's extensive infrastructure meant logistical challenges were minimal, leveraging the transportation knowledge of other business units. Deviations from the initial plan were primarily driven by technological evolution and changing customer requirements. After market launch, customer feedback led to continuous adaptation and evolution of the technology. HP expanded its offerings from a polymer solution to include metals, a wider range of materials, and automation and software solutions to support scaling .
Introducing 3D printing necessitated establishing a dedicated sales organization, R&D, and market presence, with a strong emphasis on workforce collaboration. HP's structure shifted from an engineering-driven R&D focus to a customer-centric approach aimed at accelerating adoption. The Personalization & 3D Printing business unit now includes teams for polymers, metals, go-to-market, software, operations, marketing, and incubations .
HP initially focused internally on market execution and ensuring operational effectiveness. They quickly recognized the need to educate the market about 3D printing's benefits, design considerations for AM, and its potential impact on businesses, moving beyond simply selling a product. This included offering sessions on designing for AM. HP also developed a holistic solution offering, encompassing hardware, software, and services throughout an application's lifecycle, with application engineers collaborating with customers to identify value-add opportunities .
The introduction of 3D printing initially generated widespread interest from employees and customers, perceived as a significant technological advancement. HP engaged in extensive customer interactions to help them strategically understand and adopt 3D printing for maximum value .
Resistance to change was overcome through comprehensive education across design, engineering, and management levels, ensuring organizational support. Training programs are continuously updated with product and technological developments through weekly sessions covering technical and sales aspects. HP also trains its customers on maintenance, enabling them to keep machines operational and use them more optimally .
Security is paramount, with HP working closely with customers on privacy, secure data transfer, and adhering to legal guidelines regarding security, confidentiality, and intellectual property for network-connected products .
3D printing has made HP more customer-centric, as the technology is core to its customers' businesses. Their success is directly tied to their customers' success .
Performance outcomes are monitored, with the industry's rapid growth evident at trade shows like Formnext. HP's Multi Jet Fusion technology alone produced 100 million components by 2021, reaching 170 million within nine months, indicating a significant shift from prototyping to final part production. HP is extremely data-driven due to the constant need for monitoring and technology growth .
HP aims to accelerate 3D printing adoption by supporting large projects and customers scaling production. They are focused on driving sustainable manufacturing through new materials, recycling opportunities, and sustainable packaging solutions .
#### 8.3.4 Best practice advice
The biggest challenge has been facilitating customer acceptance and encouraging them to integrate 3D printing technology. Many early customers who started with one machine now operate multiple, highlighting the importance of frequent usage to realize benefits. A knowledge gap acts as a significant inhibitor to adoption, although universities and companies are working to address this. The "3D-printing triangle" in Europe (Leuven, Eindhoven, Aachen) represents a hub of knowledge. Adoption pace varies by region, with Scandinavian regions being technologically advanced but sales cycles longer in regions like the Benelux .
Critical success factors include the scaling-up phase, where customers transition from single machines to larger fleets. This transition was accelerated by the COVID-19 pandemic and supply chain disruptions, shifting the focus from a technology discussion to a supply chain solution. The development of dual strategies, producing components for both conventional and additive techniques, allows companies to switch to local production during supply chain issues .
Practical advice includes:
* **Be open, explore, and be bold:** Embrace out-of-the-box thinking as AM enables novel possibilities .
* **Attend trade shows and seminars:** Engage with dedicated discussion groups and organizations in the 3D printing field .
* **Talk to users:** Learn from their adoption journeys, struggles, and successes .
* **Embrace change:** Failing to adopt change will hinder business progress .
### 8.4 Takeaways
3D printing, or additive manufacturing (AM), involves layer-by-layer addition of dedicated materials (polymers, metals, ceramics) to create complex, customized shapes. Digital designs enable simulations for a "first-time-right" approach, and in-process monitoring creates digital signatures for parts, with AI assisting in data handling and quality control. AM applications range from prototyping to reducing inventory, improving resilience, responding to market fluctuations, and creating personalized medical devices. Interdisciplinary collaboration is key to maximizing AM's value across sectors. The chapter also touches on recycling and 4D printing, which introduces a time dimension for shape-shifting materials. The full potential of AM is yet to be realized .
---
# Biochips
Biochips are miniaturized devices that operate within biological environments, leveraging interdisciplinary expertise to analyze biological and chemical signals, with applications ranging from diagnostics to research and environmental monitoring.
### 9.1 Introduction to biochips
Biochips represent an evolution of traditional electronic chips, adapted for use in biological settings. They are miniaturized, integrated laboratory systems designed to collect and analyze data from biological sources like cells and bodily fluids. This technology is inherently interdisciplinary, requiring collaboration across fields such as biology, chemistry, engineering, and computer science. The visual representation of a biochip in this chapter is an electronic chip enhanced for biological interaction .
### 9.2 Background of biochips
#### 9.2.1 Terminology and explanations
Biochips can be described as miniaturized devices that process electronic signals originating from living cells, contrasting with traditional electronics that deal with inanimate sources. They aim to replicate the functions of a large physical laboratory, including instrumentation and analysis, on a small chip .
**Key Components and Concepts:**
* **Substrate:** The base material for biochips, which can be plastic, glass, or silicon, often coated to functionalize the surface for specific interactions .
* **Sensors:** Integrated components that detect electrical or chemical signals and facilitate molecular binding .
* **Electronic Readout:** Systems for interpreting the signals from the sensors .
* **Microfluidics:** The intricate control of tiny liquid volumes (microliters or nanoliters) flowing on the chip's substrate, essential for sample manipulation. This control can be mechanical (pumps, valves) or electrical (electro-osmotic, electrokinetic) .
**Types of Biochips:**
* **DNA Microarray Chip (DNA Chip):** Features DNA probes attached to the chip to detect complementary sequences in a sample. It can contain multiple miniaturized test sites for parallel analysis .
* **Protein Microarray Biochip:** Similar to DNA chips but uses protein probes instead of DNA probes .
* **Microfluidic Chip:** Designed for fluidic manipulation of biological substances, incorporating infrastructure like electrodes, channels, and reservoirs for sample preparation and waste management .
* **Lab-on-a-Chip (LOC):** A more holistic biochip that integrates a complete laboratory environment, including sample preparation and concentration, beyond the capabilities of a single biosensor .
**Applications and Evolution:**
* **Early Biochips (Early 1990s):** Primarily passive microarrays that required extensive pre-laboratory sample preparation. They used optical readouts .
* **Microfluidics Evolution:** Developed in the early 1990s, initially using micropumps and microchannels. Later evolved to two-dimensional chips with valve systems controlled by air pressure, and then to droplet-based biochips (electrowetting) using patterned electrodes .
* **Feedback Systems:** Later biochips incorporated sensors (e.g., cameras, capacitance sensors) to provide real-time feedback on analysis status, enabling cyber-physical systems and runtime adaptations .
* **Machine Learning (ML) Integration:** Recent advancements involve incorporating ML to analyze sensor feedback for complex pattern detection and prediction .
* **In Vivo Applications:** Implantable biochips have evolved from RFID to NFC implants since the 2000s, requiring ultralow power and compact packaging .
* **In Vitro Applications:** Portable chips used outside the body, prioritizing low cost and portability over extreme compactness .
**Key Terminology:**
* **Biomarker:** A measurable metric that provides insight into a broader biological or health condition .
* **Biosensor:** A sensor that detects changes in concentration or presence/absence of substances by interacting with biological signals or systems .
* **Lysis:** The process of breaking open cells to extract intracellular components like nucleic acids (DNA, RNA) .
* **Polymerase Chain Reaction (PCR) Tests:** Biochips can perform PCR tests, which amplify DNA. These can be labeled (using fluorophores for optical detection) or label-free (using electrical methods, but with potential for lower specificity) .
* **Point-of-Care Diagnostics:** Tests performed at the location where samples are collected, avoiding the need to send samples to distant laboratories .
#### 9.2.2 Current and future research
Current research focuses on improving the precision of biochemical analysis and biomolecular identification on biochips. A significant research gap lies in achieving precise quantitative measurements beyond simple presence/absence detection; physical laboratories are still often required for exact concentration determination .
**Research Gaps and Challenges:**
* **Precise Quantification:** Developing biochips that can accurately measure substance concentrations is a primary research challenge .
* **Decision Tree Architectures:** Implementing complex branching logic (if-then-else conditions) on biochips is difficult due to the need for numerous embedded, reliable, and well-calibrated sensors .
* **Low Costs:** Biochips are often single-use, necessitating significant cost reduction for widespread adoption .
**Specific Research Areas:**
* **Design Automation:** Creating multifunctional, software-programmable biochips that can perform multiple complex analyses in parallel and utilize embedded sensors for real-time feedback and adaptation .
* **Trust and Security:** Ensuring the trustworthiness of biochips by validating sensor integrity, sample authenticity, and result accuracy, potentially through measures like barcodes and primers .
**Promising Research Avenues:**
* **Protocol Miniaturization:** Adapting complex laboratory protocols for miniaturization onto biochips, addressing reaction sequences, volume combinations, and reaction conditions .
* **Robustness and Reproducibility:** Developing biochips that are less susceptible to environmental variations and consistently yield the same results, providing tight control over factors like temperature and humidity .
**Future Expectations (10 Years):**
* Broader application areas, including implantable biochips for purposes like payment or health monitoring .
* Addressing ethical and regulatory hurdles for in vivo applications .
* Developing smaller, more efficient batteries for implantable devices .
#### 9.2.3 Sustainable automation concerns
Implementing biochip technology requires multidisciplinary expertise, with engineers needing knowledge in electrical engineering, material science, biology, and chemistry. For end-users, the focus is on developing user-friendly interfaces to minimize the need for specialized technical knowledge, though understanding the protocols and the implications of working with minute liquid volumes remains important .
**Educational System's Role:**
* **Higher Education:** Emphasis should shift from traditional specialization to interdisciplinary studies, encouraging students to gain knowledge across fields like biology, physics, chemistry, and engineering .
**Security Concerns:**
* **Privacy:** Ensuring the integrity and trustworthiness of real-life biological samples and reagents .
* **Software Tampering:** Preventing modifications to built-in sensors that could alter calibration and lead to incorrect readings .
**Ethical Concerns:**
* **Transparency:** The importance of reporting all research results, including unexpected or unfavorable ones .
* **Privacy:** Managing personal data from patient samples, especially in point-of-care diagnostics involving large populations .
* **Decision Making:** Cautioning against making major financial or policy decisions solely based on biochip data, which should be validated with confirmatory tests .
**Inclusion:**
* **Democratization of Healthcare:** Biochips can improve access to diagnostics for people in remote areas, those with lower socioeconomic status, and older individuals by enabling point-of-care testing .
* **Gender Inclusivity:** Offering greater privacy for individuals who may feel uncomfortable with traditional healthcare settings, potentially allowing for home-based testing .
#### 9.2.4 Extra hints
Recommended reading for a broader audience includes tutorials and reviews such as Azizipour et al. and higher-level papers like Chakrabarty et al. Ibrahim et al. and Zhong et al. .
### 9.3 Success story about biochips
This section details the collaboration between Qurin Diagnostics and Surfix Diagnostics in developing biochips for early cancer detection, focusing on their journey from idea to implementation.
#### 9.3.1 General case information
Qurin Diagnostics, founded in 2017, is developing a diagnostic tool for early cancer detection using urine as a liquid biopsy. Their approach involves biomarker detection, sample concentration, and optical biosensors. Surfix Diagnostics, a spin-off from Wageningen University, specializes in nanocoating for biosensors and microfluidic devices, and provides a photonic diagnostics platform focused on early cancer detection .
**Organizational Use of Biochips:**
* Qurin Diagnostics uses biochips for cancer detection, specifically targeting certain cancer types like bladder cancer using urine samples .
* Surfix Diagnostics acts as the technology partner, focusing on developing a biochip for broad cancer research using photonics. Their biochip is a compact 3 by 3.5 millimeters with 12 sensing areas .
* The technology aims for sensitive measurements usable outside traditional hospital laboratories .
**Use Case - Early Cancer Detection:**
* The goal is to detect cancer early, using non-invasive urine samples, which are more accessible than blood .
* The vision is for GPs to perform diagnostics using biochips on-site, providing rapid results without needing lab technicians, similar to a pregnancy test but for cancer detection .
**Personal Roles:**
* Mark Verheijden at Qurin is involved in the technical development of transforming regular chips into biocompatible biochips and biosensors .
* Hans Dijk at Surfix Diagnostics manages commercial activities and business development, drawing on extensive experience in the in vitro diagnostics sector .
**Competitive Landscape:**
* There is significant interest in photonics due to AI, quantum computing, and biosensing advancements .
* Small companies are driving innovation, while larger companies are observing and potentially seeking acquisitions or collaborations .
* The market for early cancer diagnostics is large and competitive, with various technologies (optical, electronic) being developed .
**Involved Departments:**
* Qurin has a lab for PCR development and manufactures biochip technology .
* Surfix Diagnostics has teams in electronics, chemistry, and biology, supported by business development, marketing, and communication functions .
**Employee Impact:**
* Qurin has about 10-12 employees, with others in advisory roles, focusing on biochip and PCR development, and biomarker identification .
* Surfix Diagnostics has approximately 23 full-time equivalents, with part-time and advisory staff, organized around its biotechnology product .
#### 9.3.2 Planning
#### 9.3.3 Adoption and case evaluation
The initial plan for Qurin was to use an optical, silicon-based chip for cost-effectiveness due to large-scale production capabilities. However, a technical challenge was transforming general chips into highly sensitive biosensors capable of detecting low concentrations of molecules. This led to the development of a "sandwich" approach for signal amplification, which was crucial for detecting extremely low concentrations. The detection focuses on DNA sequences and epigenetic modifications, which serve as early cancer biomarkers .
**Strategy Evolution:**
* Qurin's strategy initially focused on standard PCR technology for rapid development and market awareness of urine biomarkers, followed by the implementation of a point-of-care system using biochips .
* Surfix Diagnostics aimed to create a versatile platform adaptable to various applications by simply exchanging the molecule on the sensor, with a current major focus on oncology .
**Scope and Criteria:**
* Qurin's scope is early cancer detection, specifically colon and bladder cancer, linked to urine biomarkers. Future expansion to other cancers (e.g., lung cancer) is considered .
* Surfix Diagnostics prioritizes oncology but has demonstrated platform adaptability for COVID-19 and sepsis detection, indicating potential for other diagnostic applications .
* A key criterion for expanding scope is collaborating with clinicians to understand their needs and identifying fields receptive to novel technology .
**Implementation Challenges and Changes:**
* The need for signal amplification for detecting low concentrations led to a significant technical adaptation .
* Developing a precise method to detect epigenetic modifications on DNA required extensive trial and error .
* For Surfix Diagnostics, the strategy shifted to targeting oncology departments in hospitals and GP offices rather than home use, based on market feedback and the complexity of interpreting results .
**Organizational Impact:**
* Surfix Diagnostics maintains a relatively flat structure with dedicated technology teams, supported by functions like sourcing, quality control, and legal compliance .
* Qurin operates as a scale-up with employees distributed geographically, emphasizing collaboration with external partners. It has distinct departments for PCR development, biochips, and AI for biomarker identification .
* Both organizations structure their operations around their biotechnology products and R&D efforts .
**Facilitating Adoption:**
* Qurin intensified collaboration with Surfix Diagnostics to overcome the technical difficulties of realizing their high-potential technology .
**Stakeholder Reactions:**
* While Qurin focused on biochips from the start, convincing doctors of the technology's validity and benefits remains a key task .
* GPs and specialists acknowledge point-of-care testing as the future but require the right applications and support to overcome adoption hurdles .
**Overcoming Resistance:**
* Demonstrating tangible results and a finished product is crucial to overcome resistance from users and stakeholders .
* Comparing biochip results with established methods (e.g., PCR) and showing superior or comparable performance is essential for market adoption .
**Training Programs:**
* Hiring skilled personnel and providing specific hands-on training for bio-sensor measurements are key .
* Multidisciplinary teams facilitate knowledge cross-pollination through regular meetings and information sharing .
* Continuous learning occurs through conferences, engagement with key opinion leaders, and academic partnerships .
**Security-Related Issues:**
* Currently, the biochip is not connected to the internet, operating offline and thus minimizing immediate cyber security risks .
* Future internet connectivity would require considering secure alternatives like Bluetooth, with security becoming part of broader institutional security systems in medical settings .
* Strict regulations govern the handling of clinical samples to ensure privacy .
**Customer Impact:**
* The development of flexible biochips that can detect multiple biomarkers aims to enable personalized medicine and easier adaptation for specific patient needs .
* For diagnostics in general, customer centricity involves ensuring human medical support for interpreting results of serious conditions like HIV or cancer, while home monitoring might be viable for patients already under treatment (e.g., diabetes) .
**Performance Monitoring and ROI:**
* Performance is evaluated by comparing biochip results against existing market products and through extensive clinical trials .
* Qurin uses PCR as a benchmark for DNA detection, establishing an internal standard for biochip advancement .
* The primary technical challenge is increasing measurement sensitivity .
* Prototypes are shared with key opinion leaders for evaluation and comparison with their current workflows .
* Successful testing of easier biomarkers (e.g., COVID-19, sepsis) and ongoing clinical trials provide confidence in the technology's potential .
* Consideration for large-scale production facilities and partnerships are being explored to ensure cost-effectiveness .
**Future Evolution:**
* Qurin anticipates expanding to detect more cancer types and potentially offer multi-cancer screening on a single device .
* Surfix Diagnostics aims to find more biomarkers and explore applications beyond oncology .
* Future developments could include disposable, handheld devices for precise measurements, even in resource-limited settings .
#### 9.3.4 Best practice advice
**Biggest Challenges:**
* Raising the sensitivity of biochips to match or exceed current gold standards like PCR .
* Navigating the complex regulatory landscape and market adoption in the human medical field, which is more challenging than other sectors like veterinary diagnostics .
* Determining the correct market strategy for a niche technology .
**Critical Success Factors:**
* **Proving Measurement Sensitivity:** Demonstrating high sensitivity early in the development process garners significant interest .
* **Real-World Sample Handling:** Successfully handling and analyzing real, uncleaned samples, not just laboratory-prepared ones, is critical and a point where many products fail .
* **Scalable Production:** Ensuring the technology can be produced in large quantities at reduced costs .
**Practical Advice:**
* **Long-Term Vision:** Understand that developing biochips is a difficult, long-term endeavor; focus not only on technology but also on the entire ecosystem and future steps .
* **Collaboration:** Engage with peers and the broader ecosystem to foster collaboration, as future companies will rely on partnerships to survive and innovate .
* **Embrace New Technologies:** Be open to implementing new technologies like robotics and AI .
* **Select Right Partners:** Choose partners who can complement internal capabilities, especially for functions like HR and IT .
* **Combine Strengths:** Leverage existing technologies and partners rather than reinventing solutions .
### 9.4 Takeaways
Biochips are miniaturized laboratories designed for biological environments, with common types including DNA microarrays, protein microarrays, and microfluidic chips. They analyze biological entities like DNA, RNA, and proteins, with AI enhancing analysis and biomarker detection. Key applications span clinical diagnostics, drug discovery, research, and environmental monitoring. Future research will focus on increasing measurement sensitivity and battery lifespan for implantable devices, alongside addressing trustworthiness and ethical concerns. The adoption of biochips is complex due to regional regulatory differences, and user-friendly interfaces are crucial for broader acceptance .
---
## Common mistakes to avoid
- Review all topics thoroughly before exams
- Pay attention to formulas and key definitions
- Practice with examples provided in each section
- Don't memorize without understanding the underlying concepts
Glossary
| Term | Definition |
|---|---|
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Digital innovation | The creation of new or significantly improved market offerings, business processes, or business models that result from the adoption and integration of digital technologies. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitization | The process of converting information from an analog format into a digital format, such as scanning a paper document to create a digital file, without necessarily altering the underlying process or business model. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber-physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber-physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital thread | A concept that describes the data generated throughout a product's lifecycle, from design and manufacturing to use and disposal, enabling traceability and informed decision-making. |
| Proof-of-work (PoW) | A consensus algorithm used in some blockchains (like Bitcoin) where network participants solve computationally intensive mathematical problems to validate transactions and create new blocks, requiring significant energy consumption. |
| Proof-of-stake (PoS) | A consensus algorithm where participants validate transactions and create new blocks based on the amount of cryptocurrency they "stake" or hold, generally consuming less energy than PoW. |
| Quantum-safe computing | The development of cryptographic algorithms and systems that are resistant to attacks from quantum computers, which have the potential to break current encryption methods. |
| Cybersickness | A form of motion sickness experienced by some individuals when using virtual reality or augmented reality devices, caused by a mismatch between visual and vestibular sensory input. |
| Proteus effect | The phenomenon where a user's behavior and self-perception are influenced by the characteristics of their avatar in a virtual environment. |
| Servitization | A business strategy where companies shift from selling physical products to offering product-related services, often as a bundle or subscription, to create ongoing customer value and recurring revenue. |
| Computer-aided design (CAD) | Software tools used for designing and documenting products and processes in a digital format, which is essential for many advanced manufacturing technologies like 3D printing. |
| First-time-right | A manufacturing approach aiming to produce a part correctly on the first attempt, minimizing defects and rework, often achieved through precise digital design and process simulation. |
| In-process monitoring | The continuous tracking and analysis of data generated during a manufacturing process, such as 3D printing, to ensure quality and identify potential issues in real-time. |
| Public key | In cryptography, a key that can be shared openly and is used to encrypt messages or verify digital signatures. |
| Private key | In cryptography, a secret key that is kept confidential and is used to decrypt messages or create digital signatures, providing control over digital assets. |
| Digest | The output of a cryptographic hash function, also known as a hash value, which is a unique fixed-size representation of input data. |
| Hash | The output of a hash function, used to create a unique identifier or fingerprint for a piece of data. |
| Digital signature | A cryptographic mechanism used to verify the authenticity and integrity of a digital document or message, ensuring it has not been tampered with and originates from the claimed sender. |
| Computer vision | A field of AI that enables computers to "see" and interpret images or videos, allowing them to identify objects, scenes, and activities. |
| Natural language processing (NLP) | A field of AI focused on enabling computers to understand, interpret, and generate human language, both written and spoken. |
| Automatic speech recognition (ASR) | A technology that converts spoken language into text, enabling voice commands and dictation for computer systems. |
| Foundation model | Large-scale AI models trained on vast amounts of data that can be adapted to a wide range of downstream tasks, such as large language models (LLMs). |
| Large language model (LLM) | A type of AI model specifically trained on massive text datasets to understand, generate, and process human language, powering applications like chatbots and translation tools. |
| Generative AI | A type of AI that can create new content, such as text, images, music, or code, based on patterns learned from existing data. |
| Hyperledger Fabric | A permissioned, modular blockchain framework hosted by the Linux Foundation, designed for enterprise use, allowing for controlled access and data confidentiality. |
| Fused filament fabrication (FFF) | A 3D printing process that builds objects by extruding thermoplastic filament layer by layer, commonly used in desktop 3D printers. |
| Laser powder bed fusion (LPBF) | An additive manufacturing process that uses a laser to fuse powdered material layer by layer to create solid objects, often used for metals and high-performance polymers. |
| Remelting | The process of melting and solidifying material again, often used in 3D printing to improve the quality or properties of printed parts. |
| Marking | The process of creating visible indicators or identifiers on a product or component, which can be done using lasers or other methods. |
| Agility | The ability of an organization or system to respond quickly and effectively to changes in its environment or requirements. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Entity | A distinct and independent being or thing; in the context of digital twins, it refers to the physical object, process, or system being modeled. |
| Twinning | The process of synchronizing the states and data between a physical entity and its virtual counterpart (digital twin). |
| Twinning rate | The frequency at which synchronization occurs between a physical entity and its digital twin. |
| Metrology | The scientific study of measurement and the application of such measurements; in AM, it refers to the precise measurement of dimensions and properties of printed parts. |
| Digital thread | A communication framework that connects all data generated throughout a product's lifecycle, ensuring traceability and data integrity. |
| Blockchain | A distributed ledger technology where transactions are recorded in blocks that are cryptographically linked in a chain, ensuring transparency and immutability. |
| Distributed ledger technology (DLT) | A type of database that is shared, replicated, and synchronized among members of a distributed network, where each participant has access to the same ledger. |
| Peer-to-peer | A network architecture where participants share resources and communicate directly with each other without a central server or intermediary. |
| Cryptographic hashing | A process that uses a mathematical algorithm to convert data of any size into a fixed-size string of characters (a hash), used for data integrity and security. |
| Public key | In cryptography, a key that can be shared openly and is used to encrypt messages or verify digital signatures. |
| Private key | In cryptography, a secret key that is kept confidential and is used to decrypt messages or create digital signatures, providing control over digital assets. |
| Digest | The output of a cryptographic hash function, also known as a hash value, which is a unique fixed-size representation of input data. |
| Hash | The output of a hash function, used to create a unique identifier or fingerprint for a piece of data. |
| Digital signature | A cryptographic mechanism used to verify the authenticity and integrity of a digital document or message, ensuring it has not been tampered with and originates from the claimed sender. |
| Cryptocurrency | A digital or virtual currency secured by cryptography, typically decentralized and based on blockchain technology, allowing for secure peer-to-peer transactions. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Smart contract | A self-executing contract with the terms of the agreement directly written into code, automatically executing when predefined conditions are met and recorded on a blockchain. |
| Public blockchain | A blockchain network where anyone can join, participate in consensus, and view transactions without requiring permission. |
| Permissioned blockchain | A blockchain network where access and participation are restricted and require authorization from a governing authority or network participants. |
| Consensus algorithm | A set of rules and protocols that a distributed network uses to agree on the validity of transactions and the state of the ledger, ensuring consistency across all nodes. |
| Quantum computing | A type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform computations, potentially capable of breaking current encryption methods. |
| Quantum-safe computing | The development of cryptographic algorithms and systems that are resistant to attacks from quantum computers, ensuring the security of data and communications in the quantum era. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Microarray | A biochip containing a collection of miniaturized test sites or spots, each with a specific probe, used for performing multiple biochemical assays simultaneously. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Miniaturization | The process of making devices or components significantly smaller, enabling portability, reduced cost, and enhanced functionality, particularly in biochips and electronics. |
| In vivo | Occurring or performed within a living organism, such as implantable biochips used for monitoring or medical purposes inside the body. |
| In vitro | Occurring or performed outside a living organism, typically in a controlled laboratory environment, such as diagnostic tests conducted on samples like blood or urine using biochips. |
| Implant | A device or material surgically placed within the body, such as an implantable biochip for medical monitoring or identification. |
| DNA | Deoxyribonucleic acid, the molecule that carries genetic instructions for the development, functioning, growth, and reproduction of all known organisms. |
| RNA | Ribonucleic acid, a nucleic acid present in all living cells that plays a role in coding, decoding, regulation, and expression of genes. |
| Protein | Large biomolecules consisting of one or more long chains of amino acid residues, performing a vast array of functions within organisms. |
| Radio-frequency identification (RFID) | A technology that uses radio waves to identify and track tags attached to objects, commonly used for inventory management, access control, and tracking. |
| Near-field communication (NFC) | A short-range wireless communication technology that enables two electronic devices to exchange data when they are brought close together, often used in contactless payments and data transfer. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Biosensor | A sensor that utilizes biological components or principles to detect and measure specific biological or chemical substances. |
| Lysis | The breakdown or destruction of a cell or its membrane, often performed to release intracellular contents for analysis. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| PCR | Polymerase Chain Reaction, a laboratory technique used to amplify and detect specific sequences of DNA. |
| Label-free | A type of diagnostic test or sensor that can detect and quantify molecules without the need for attaching a label (e.g., a fluorescent dye), relying on direct measurement of physical properties. |
| Point-of-care diagnostics | Medical diagnostic testing performed at or near the site of patient care, allowing for rapid results without the need to send samples to a central laboratory. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber–physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber–physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber–physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber–physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber–physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber–physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber–physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber–physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |
| Digital transformation | A profound and ongoing process where digital technologies play a central role in reshaping an organization's core strategies, operations, culture, and value creation paths to achieve significant business improvements and competitive advantage. |
| Digitalization | The process of integrating digital technologies into various aspects of daily life and business operations, often involving the adaptation and improvement of existing processes and models to leverage digital capabilities. |
| Digital economy | An economic system where digital technologies are central to the production, distribution, and consumption of goods and services, often characterized by data-driven business models and increased interconnectivity. |
| Agile | A methodology or mindset that emphasizes flexibility, responsiveness to change, collaboration, and iterative development to adapt quickly to evolving requirements and market conditions. |
| Scrum | An agile framework for managing complex projects, particularly in software development, that emphasizes teamwork, iterative progress through sprints, and regular feedback loops to deliver value incrementally. |
| Lean start-up | A business creation approach focused on rapid experimentation, validated learning, and iterative product development to minimize waste and efficiently discover a sustainable business model. |
| Minimum viable product (MVP) | The most basic functional version of a new product or service that allows a team to gather maximum validated learning about customers with the least effort, often used to test initial hypotheses. |
| Artificial intelligence (AI) | A field of computer science and engineering dedicated to creating systems that can perform tasks typically requiring human intelligence, such as learning, problem-solving, perception, and decision-making. |
| Machine learning (ML) | A subset of AI that enables computer systems to learn from data and improve their performance on specific tasks without being explicitly programmed, often by identifying patterns and making predictions. |
| Deep learning | A subset of machine learning that utilizes artificial neural networks with multiple layers (deep architectures) to learn complex patterns and representations from large datasets, driving advancements in areas like computer vision and natural language processing. |
| Internet of Things (IoT) | A network of physical objects or devices embedded with sensors, software, and connectivity, enabling them to collect, exchange, and act on data, thereby creating interconnected ecosystems. |
| Cyber–physical system (CPS) | A system that tightly integrates computation, networking, and physical processes, where physical components are controlled or monitored by algorithms executed on embedded computers and networks. |
| Digital twin | A virtual representation or model of a physical entity, process, or system that is continuously updated with real-time data, allowing for simulation, analysis, monitoring, and prediction of its performance and behavior. |
| Blockchain technology | A distributed, immutable ledger system that records transactions across a network of computers in a secure and transparent manner, using cryptographic hashing to link blocks of data and ensure data integrity. |
| Additive manufacturing (AM) | A manufacturing process that builds objects layer by layer from digital models, often referred to as 3D printing, enabling complex geometries and customization. |
| Biochip | A miniaturized laboratory device, often integrated onto a chip, designed to perform complex biochemical analyses or detect biological molecules and substances in biological samples. |
| Extended reality (XR) | An umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), referring to technologies that blend the physical and digital worlds or create immersive virtual environments. |
| Virtual reality (VR) | A technology that creates an immersive, computer-generated simulated environment that users can interact with, typically experienced through a head-mounted display (HMD). |
| Augmented reality (AR) | A technology that overlays computer-generated information or virtual objects onto the real-world environment, enhancing the user's perception of reality, often viewed through mobile devices or smart glasses. |
| Non-fungible token (NFT) | A unique digital asset recorded on a blockchain that represents ownership of a specific item, such as digital art, music, or collectibles, and is not interchangeable with other tokens. |
| Cyber–physical system (CPS) | A system that integrates computation, networking, and physical processes, where embedded computers and networks monitor and control physical entities. |
| Biomarker | A measurable indicator of a biological state or condition, used in diagnostics to detect diseases or monitor health. |
| Microfluidics | The science and technology of manipulating very small volumes of fluids, typically in the microliter or nanoliter range, on a miniaturized chip for various analytical and diagnostic applications. |
| Lab-on-a-chip (LOC) | A miniaturized device that integrates multiple laboratory functions, such as sample preparation, analysis, and detection, onto a single chip, enabling portable and efficient testing. |
| Spatial computing | A paradigm that involves computing in three-dimensional physical space, enabling devices to understand and interact with their surroundings and overlay digital information onto the real world. |
| Immersion | The subjective experience of being fully present and engaged within a virtual or simulated environment, often achieved by stimulating multiple senses. |