Y2 Ops&SC - 2526 - Week 3 Slide Deck - Capacity Management.pdf

# Defining and understanding capacity management Capacity management involves understanding the nature of product or service demand and effectively planning and controlling capacity [3](#page=3). ### 1.1 The capacity management framework The capacity management framework outlines the structured approach to managing an organization's capacity. It encompasses various components and considerations to ensure that capacity aligns with demand and business objectives. While specific levels of the framework are mentioned, the provided content focuses on the fundamental definition and the existence of a framework without detailing its specific hierarchical levels or components [4](#page=4) [5](#page=5). > **Tip:** Understanding capacity management is crucial for ensuring that services can meet user demand without over-provisioning or under-provisioning resources. The core idea of capacity management is to match available capacity with the demand for products or services in an efficient and effective manner. This requires a thorough understanding of the patterns and volume of demand [3](#page=3). --- # Forecasting approaches and calculation methods Forecasting involves predicting future values based on historical data and other relevant information. There are two primary categories of forecasting approaches: qualitative and quantitative [6](#page=6) [7](#page=7). ### 2.1 Qualitative forecasting approaches Qualitative methods rely on subjective opinions and judgment when historical data is scarce or not directly applicable to future predictions. These methods are often used for new products or market entries [8](#page=8). #### 2.1.1 Panel approach This method involves a group of experts who convene to discuss and arrive at a consensus forecast [7](#page=7). #### 2.1.2 Delphi method The Delphi method is a structured technique that involves a panel of experts who are surveyed iteratively. Responses are anonymized, and feedback is provided to the experts to encourage convergence towards a more accurate forecast [7](#page=7). #### 2.1.3 Scenario planning Scenario planning involves developing multiple plausible future scenarios and forecasting outcomes for each. This approach is useful for understanding potential future states and their implications [7](#page=7). ### 2.2 Quantitative forecasting approaches Quantitative methods use historical data and mathematical models to generate forecasts. These methods assume that past patterns will continue into the future. They are broadly divided into time-series analysis and causal methods [8](#page=8). #### 2.2.1 Time-series analysis Time-series analysis uses historical data points ordered by time to forecast future values. Components of a time series can include trend, seasonality, and random fluctuations [12](#page=12) [8](#page=8). ##### 2.2.1.1 Simple moving average The simple moving average (SMA) calculates a forecast by averaging the actual values from a specified number of the most recent periods. Each period considered is given equal weight [9](#page=9). The formula for a simple moving average is: $$ \text{Forecast} = \frac{\sum_{i=1}^{n} \text{Actual Sales in Period } i}{n} $$ Where: - $n$ is the number of periods to average [9](#page=9). **Example:** If actual sales for the last three months were 32, 26, and 20 units, the simple moving average forecast for the next month would be: $$ \text{Forecast} = \frac{32 + 26 + 20}{3} = 26 \text{ units} [11](#page=11). $$ ##### 2.2.1.2 Weighted moving average In a weighted moving average, different weights are assigned to different periods, with more recent periods typically receiving higher weights. The sum of the weights must equal one [10](#page=10) [11](#page=11). The formula for a weighted moving average is: $$ \text{Forecast} = \sum_{i=1}^{n} (\text{Actual Sales in Period } i \times \text{Weight for Period } i) $$ The weights are often determined subjectively or through optimization [11](#page=11). **Example:** Using the same sales data (32, 26, 20) and assigning weights (for example, if $w_1=1/6$, $w_2=2/6$, $w_3=3/6$ for periods 1, 2, and 3 respectively, where period 3 is most recent), the forecast would be calculated as: $$ \text{Forecast} = (32 \times \frac{1}{6}) + (26 \times \frac{2}{6}) + (20 \times \frac{3}{6}) = \frac{32 + 52 + 60}{6} = \frac{144}{6} = 24 \text{ units} [11](#page=11). $$ ##### 2.2.1.3 Simple exponential smoothing Simple exponential smoothing is a time-series forecasting method that assigns exponentially decreasing weights to past observations. It uses a smoothing constant, denoted by alpha ($\alpha$), which ranges between 0 and 1. The most recent forecast and the most recent forecast error are used to calculate the new forecast, giving more weight to recent data [10](#page=10). The formula for simple exponential smoothing is: $$ \text{Forecast}_{t+1} = \alpha \times \text{Actual}_t + (1 - \alpha) \times \text{Forecast}_t $$ Alternatively, it can be expressed in terms of the forecast error: $$ \text{Forecast}_{t+1} = \text{Forecast}_t + \alpha \times (\text{Actual}_t - \text{Forecast}_t) $$ Where: - $\text{Forecast}_{t+1}$ is the forecast for the next period. - $\alpha$ is the smoothing constant (0 $\le \alpha \le$ 1) [10](#page=10). - $\text{Actual}_t$ is the actual demand in the current period ($t$). - $\text{Forecast}_t$ is the forecast for the current period ($t$). **Example:** Using the data where the actual sales in the previous month were 20 units, and the forecast for that month was 23 units, with a smoothing constant ($\alpha$) of 0.3. The forecast for the next month would be: $$ \text{Forecast}_{\text{Apr}} = 0.3 \times 20 + (1 - 0.3) \times 23 = 0.3 \times 20 + 0.7 \times 23 = 6 + 16.1 = 22.1 \text{ units} [11](#page=11) [22](#page=22). $$ > **Tip:** The choice of $\alpha$ is crucial. A higher $\alpha$ gives more weight to recent data, making the forecast more responsive to changes but also more susceptible to random fluctuations. A lower $\alpha$ gives more weight to historical data, resulting in a smoother forecast but one that may lag behind actual trends [10](#page=10). #### 2.2.2 Causal methods Causal methods, such as regression analysis, assume a relationship between the variable being forecasted and other independent variables. These methods are more complex than time-series analysis and are used when underlying causes for demand can be identified [8](#page=8). ### 2.3 Calculating forecast error It is essential to measure the accuracy of a forecast to understand its reliability and to make necessary adjustments. Forecast error is the difference between the actual demand and the forecasted demand. A high deviation indicates a less reliable forecast, while a low deviation suggests a more reliable one [13](#page=13). Common measures of forecast error include: * **Mean Absolute Deviation (MAD):** This measures the average magnitude of the errors in a set of forecasts, without considering their direction. $$ \text{MAD} = \frac{\sum_{t=1}^{n} |\text{Actual}_t - \text{Forecast}_t|}{n} $$ * **Mean Absolute Percentage Error (MAPE):** This expresses the forecast error as a percentage of the actual demand, providing a relative measure of accuracy. $$ \text{MAPE} = \frac{\sum_{t=1}^{n} \frac{|\text{Actual}_t - \text{Forecast}_t|}{\text{Actual}_t}}{n} \times 100\% $$ Where: - $n$ is the number of periods [13](#page=13). > **Tip:** While MAD provides an absolute measure of error, MAPE is useful for comparing forecast accuracy across different products or services with varying demand levels [13](#page=13). --- # Measuring operational capacity and effectiveness This section focuses on calculating key operational metrics, including capacity, utilization, efficiency, and Overall Equipment Effectiveness (OEE), detailing their components and significance. ### 3.1 Understanding operational capacity Capacity is defined as the maximum level of value-added activity that an operation can achieve over a specific period [15](#page=15). ### 3.2 Calculating utilization and efficiency * **Utilization** is a measure that compares the actual output of an operation to its design capacity. The formula is [17](#page=17): $$ \text{Utilization} = \frac{\text{Actual output}}{\text{Design capacity}} $$ * **Efficiency** compares the actual output to the effective capacity of an operation. The formula is [17](#page=17): $$ \text{Efficiency} = \frac{\text{Actual output}}{\text{Effective capacity}} $$ > **Tip:** While utilization indicates how much of the potential capacity is being used, efficiency highlights how well the available capacity is being leveraged. A high utilization rate might not always translate to high efficiency if the effective capacity is also low. ### 3.3 Calculating Overall Equipment Effectiveness (OEE) Overall Equipment Effectiveness (OEE) is a composite metric that measures how well manufacturing equipment is utilized. It is calculated by multiplying three components: availability, performance, and quality [18](#page=18). * **Availability Loss:** This accounts for factors that prevent the equipment from running as scheduled. Examples include staff absence, administrative tasks, and compliance training [18](#page=18). * **Performance Loss:** This considers instances where the equipment runs slower than its theoretical maximum speed. Causes can include learning losses, under-performing staff, and technology-related delays [18](#page=18). * **Quality Loss:** This addresses defects and rework. It includes losses from rework, complaint handling, and inspection activities [18](#page=18). The OEE is calculated as: $$ \text{OEE} = \text{Availability} \times \text{Performance} \times \text{Quality} $$ #### 3.3.1 Example OEE calculation Consider an example where: * Total available hours = 400 hours * Actual operating hours (after availability losses) = 365 hours * Actual output in terms of good parts (after performance losses) = 310 units * Good parts produced (after quality losses) = 285 units The components are calculated as follows: * Availability = $365 \text{ hours} / 400 \text{ hours} = 0.91$ [18](#page=18). * Performance = $310 \text{ units} / 365 \text{ hours} = 0.85$ (This likely represents the proportion of theoretical speed achieved during operating hours) [18](#page=18). * Quality = $285 \text{ good parts} / 310 \text{ total parts} = 0.92$ (This represents the proportion of good parts produced from the output achieved) [18](#page=18). Therefore, the OEE is: $$ \text{OEE} = 0.91 \times 0.85 \times 0.92 = 0.71 $$ This means there is a 29% "capacity leakage," indicating significant potential for improvement in the operational processes [18](#page=18). --- # Strategies for matching capacity and demand Aligning operational capacity with fluctuating customer demand is a critical challenge that organizations must address to optimize efficiency, profitability, and customer satisfaction. This involves employing strategies that either influence demand to better fit available capacity or adjust capacity to meet predicted demand levels [19](#page=19). ### 4.1 Influencing demand One primary approach to managing the capacity-demand imbalance is to actively influence the demand side. This can be achieved through several tactics [20](#page=20): * **Price differentiation:** Varying prices based on demand levels or customer segments can encourage or discourage consumption at specific times [20](#page=20). * **Scheduling promotions:** Offering discounts or special deals during periods of low demand can help stimulate business and smooth out demand peaks and troughs [20](#page=20). * **Constraining customer access:** Implementing reservation systems, appointment scheduling, or limiting entry can help regulate the flow of customers and prevent overwhelming capacity [20](#page=20). * **Service differentiation:** Offering different service levels or product variations can cater to diverse customer needs and preferences, potentially shifting demand towards times when capacity is more readily available [20](#page=20). * **Creating alternative products or services:** Developing complementary offerings that can be produced or delivered during off-peak times can help balance workload and utilize resources more effectively [20](#page=20). #### 4.1.1 Yield management A specialized strategy for influencing demand, particularly relevant for services with inflexible capacities and perishable outputs, is **Yield Management**. This approach is common in industries like hotels and airlines, where the objective is to maximize revenue and resource utilization. Yield management relies heavily on statistical analysis and historical data to forecast demand and adjust pricing dynamically. It allows for variable pricing for the same service across different times or markets, aiming to capture the maximum willingness to pay from customers [21](#page=21). ### 4.2 Matching capacity to demand Alternatively, organizations can focus on directly matching their capacity to the expected demand. Two primary strategies exist for this [28](#page=28): #### 4.2.1 Level capacity plan A **level capacity plan** aims to maintain a stable output rate regardless of demand fluctuations. Under this strategy [28](#page=28): * **Inventory Management:** In times of low demand, the organization produces goods or services and stores any unsold output as inventory. During periods of high demand, they fulfill customer needs by drawing from this accumulated inventory [28](#page=28). * **Stable Operations:** Staff schedules are kept fixed, and the inflow of materials remains constant [28](#page=28). * **Asset Utilization:** This approach typically leads to higher asset utilization due to consistent production levels [28](#page=28). **Example:** Paper mills often utilize a level capacity plan, as paper can be stored in inventory when demand is low [29](#page=29). #### 4.2.2 Chase capacity plan A **chase capacity plan**, conversely, involves adjusting capacity to closely follow the predicted levels of demand. This strategy is best suited for situations where storing the product or service is difficult or impossible. Key characteristics include [28](#page=28): * **Flexibility:** It requires flexibility in staffing and production levels to respond to demand changes [28](#page=28). * **Active Management:** This approach necessitates active management to handle the adjustments in capacity [28](#page=28). * **Potential for Lower Asset Utilization:** Since capacity is scaled to demand, there might be periods of underutilization when demand is low [28](#page=28). **Example:** Beach clubs are a classic example of a business employing a chase capacity plan, as their capacity (e.g., number of loungers, staff) directly fluctuates with daily or seasonal demand [29](#page=29). --- # Factors influencing capacity decisions This section details the critical factors influencing the setting of base capacity levels by examining operational performance objectives, output perishability, and demand/supply variability, using call center examples. ### 5.1 Factors influencing the supply side and setting base capacity levels The supply side of capacity decisions is influenced by several key factors: * **Operational performance objectives:** These are the desired levels of service or output quality that an operation aims to achieve. A common objective is to maintain a certain service level [24](#page=24) [26](#page=26). * **Perishability of the operation’s outputs:** The extent to which an operation's output cannot be stored for future use significantly impacts capacity planning [24](#page=24). * **Degree of variability in demand or supply:** The unpredictability of customer demand or the availability of resources introduces complexity into capacity decisions [24](#page=24). ### 5.2 Factors influencing the demand side and setting base capacity levels Capacity decisions are also heavily influenced by demand-side considerations, particularly performance objectives and the inherent nature of the services provided. #### 5.2.1 Performance objectives and base capacity Performance objectives directly inform the required base capacity. For instance, if a call center's objective is to provide "High Quality Service," this translates into a need for higher base capacity to minimize customer wait times [26](#page=26). > **Example:** A call center aiming for an average customer wait time of 2 minutes will need to implement a higher base capacity compared to one targeting an average wait time of 5 minutes, assuming similar demand patterns [26](#page=26). #### 5.2.2 Cost and flexibility in call centers The trade-offs between fixed costs, flexibility, and operational performance are crucial in capacity decisions, especially in service industries like call centers. * **Traditional Call Center Model:** This model typically involves high fixed costs due to office rent and long-term lease contracts. Staff also often work fixed base shift rosters, making it difficult to upscale capacity quickly [27](#page=27). * **Operators Working From Home Model:** This alternative model can reduce fixed costs by minimizing or eliminating the need for a central workspace. It allows for a smaller minimum shift size and rapid upscaling to meet required capacity without the constraints of travel time. This model can also incorporate small variable compensation for home office use and reserve availability [27](#page=27). > **Result:** The work-from-home model generally leads to lower fixed costs, a lower base staff capacity, higher flexibility, and potentially a higher service level. It can also contribute to sustainability objectives by requiring fewer buildings and reducing travel [27](#page=27). --- ## 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

| Term | Definition | |------|------------| | Capacity Management | The activity of understanding the nature of product or service demand, and effectively planning and controlling the available capacity to meet that demand. | | Capacity Management Framework | A structured approach that outlines the various components and processes involved in managing an operation's capacity effectively. | | Qualitative Forecasting | Forecasting methods that rely on subjective judgment, opinions, and non-numerical data, such as expert opinions, market research, and scenario planning. | | Quantitative Forecasting | Forecasting methods that use historical numerical data and statistical techniques to predict future demand, such as time-series analysis and causal models. | | Moving Average Forecasting | A forecasting technique that calculates the average of demand from a specified number of the most recent periods to predict future demand, assuming each period has equal weight. | | Simple Exponential Smoothing | A forecasting method that assigns exponentially decreasing weights to past observations, giving more weight to recent data points and less weight to older data. It uses a smoothing factor (alpha) to adjust the forecast. | | Forecast Error | The difference between the actual demand and the forecasted demand, which is crucial for evaluating the accuracy and reliability of a forecasting method. | | Mean Absolute Deviation (MAD) | A measure of forecast accuracy calculated by averaging the absolute differences between actual demand and forecasted demand over a period. | | Mean Absolute Percent Error (MAPE) | A measure of forecast accuracy calculated by averaging the absolute percentage differences between actual demand and forecasted demand over a period. | | Capacity | The maximum level of value-added activity that an operation can achieve over a specific period. | | Utilization | A measure of how much of the available capacity is actually being used, calculated as actual output divided by design capacity. | | Efficiency | A measure of how well an operation is performing relative to its effective capacity, calculated as actual output divided by effective capacity. | | Overall Equipment Effectiveness (OEE) | A key performance indicator that measures the percentage of planned production time that is truly productive, considering availability, performance, and quality losses. | | Availability Loss | Reductions in operating time due to equipment breakdowns, setup times, or material shortages, impacting the availability of the equipment. | | Performance Loss | Reductions in output speed or throughput due to minor stops, slowdowns, or machine inefficiencies, impacting the performance of the equipment. | | Quality Loss | Reductions in good output due to defects, rework, or scrap, impacting the quality of the products produced by the equipment. | | Level Capacity Plan | A capacity management strategy where output and staffing levels are kept constant, with excess production stored as inventory during low demand periods and used to meet demand during high periods. | | Chase Capacity Plan | A capacity management strategy that adjusts output and staffing levels to match forecasted demand fluctuations, often involving flexible workforces and scheduling. | | Yield Management | A strategy used in industries with inflexible capacities and high fixed costs to optimize revenue by varying prices for the same service or product based on demand and time, relying heavily on statistical analysis. |