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Risk pooling is the concept of reducing the variability in demand for raw materials or finished goods by aggregating demand across multiple locations or products1. By doing so, the demand fluctuations are more likely to cancel out each other, resulting in a lower safety stock and inventory cost. Risk pooling works best for items with high demand uncertainty and long lead times, because these items have the highest risk of stockouts and the highest inventory holding cost. If the demand uncertainty is low, there is less need for risk pooling, as the demand can be easily forecasted and met. If the lead time is short, the replenishment orders can be placed more frequently and adjusted to the actual demand, reducing the need for safety stock and risk pooling2. References: 1 Inventory risk pooling definition — AccountingTools 3 2 Supply Chain Management: Risk pooling - UNB 4

The demonstrated capacity last week is the total number of models completed by both modelers in 40 hours. One modeler finished 5 models a day, which means 25 models in a week. The other modeler finished 1 model a day, which means 5 models in a week. Therefore, the demonstrated capacity last week is 25 + 5 = 30 models. However, this is not one of the options given. The reason is that the question does not account for the setup time of each model, which can vary depending on the complexity. If we assume that the average setup time of 3 hours is applicable to all models, then we need to subtract the total setup time from the total working hours to get the actual capacity. The total setup time for 30 models is 30 x 3 = 90 hours. The total working hours for both modelers is 2 x 40 = 80 hours. Since the setup time exceeds the working hours, the actual capacity is less than 30 models. To find the actual capacity, we need to solve the following equation:

80 = x * 3 + x * 1

where x is the number of models completed. Simplifying the equation, we get:

x = 10

Therefore, the actual capacity is 10 models, which is option C. References:

Managing Supply Chain Operations, Chapter 6: Capacity Management, Section 6.1: Capacity Concepts, Subsection 6.1.1: Capacity Definitions

CPIM Exam Content Manual, Module 4: Supply, Section 4.2: Capacity Management, Subsection 4.2.1: Capacity Concepts

Time series decomposition is a method that breaks down a time series of historical demand data into its components: trend, seasonality, cyclical, and random. It is appropriate for forecasting the demand for a product family when there is a significant trend and seasonality in the demand history, as it can isolate and estimate these components and project them into the future. Time series decomposition can also handle cyclical and random variations in demand, and it can be applied to different time intervals (such as monthly, quarterly, or yearly). The other methods are not suitable for this scenario. Econometric models are complex mathematical models that use regression analysis to relate demand to various explanatory variables, such as price, income, or advertising. They are not designed to capture trend and seasonality in demand. Computer simulation is a technique that uses a computer program to mimic the behavior of a real system under different scenarios and assumptions. It is not a forecasting method per se, but rather a tool for testing and evaluating different forecasting methods or policies. Weighted moving average is a simple method that uses the average of the most recent observations as the forecast for the next period, with more weight given to the recent observations than the older ones. It is not able to capture trend and seasonality in demand, as it assumes that demand is stable and does not change over time. References: Time Series Decomposition | APICS Dictionary Term of the Day, APICS CPIM 8 Planning and Inventory Management | ASCM