Exactprep CPIM-8.0 Questions

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

 Improvements in an input/output control (I/O control) system will most likely lead to a reduction in queue size and queue time. An I/O control system is a method of managing the flow of work orders in a production system by matching the input rate to the output rate. The input rate is the number of work orders that are released to the shop floor in a given period. The output rate is the number of work orders that are completed and shipped to the customers in a given period. An I/O control system aims to keep the input rate equal to the output rate, or slightly lower, to avoid overloading the system and creating excess inventory. By improving the I/O control system, the production system can achieve a smoother and more balanced flow of work orders, which reduces the queue size and queue time at each work center. Queue size is the number of work orders that are waiting to be processed at a work center. Queue time is the amount of time that a work order spends in the queue before being processed. A reduction in queue size and queue time can improve the production efficiency, quality, and flexibility, as well as the customer service and satisfaction. The other options are not correct, as they are not the most likely outcomes of improvements in an I/O control system, but rather possible effects of other factors or methods:

• Flattened bills of material (BOMs) are the result of simplifying the product structure and reducing the number of components or levels in a BOM. Flattened BOMs can reduce the complexity and lead time of the production process, but they are not directly related to the I/O control system.
• A change in operation sequencing is the result of altering the order or priority of the work orders or operations in a production system. A change in operation sequencing can affect the production flow and capacity, but it is not necessarily caused by the I/O control system.
• Fewer engineering change notifications are the result of minimizing the changes in the product design or specification during the production process. Fewer engineering change notifications can reduce the disruption and cost of the production process, but they are not directly related to the I/O control system. References:
• [CPIM Part 2 - Section A - Topic 2 - Capacity Planning]
• Input/Output Control | SpringerLink
• Input/Output Control - an overview | ScienceDirect Topics
• Input/Output Control - InventoryOps.com

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

A control chart is a tool that shows process changes and random variation over time. It is a type of statistical process control (SPC) that monitors the performance of a process and detects whether it is in or out of control. A control chart consists of a center line, an upper control limit, and a lower control limit. The center line represents the average or target value of the process. The control limits represent the acceptable range of variation withinthe process. If the data points fall within the control limits, the process is considered stable and in control. If the data points fall outside the control limits, or show a non-random pattern, the process is considered unstable and out of control, indicating the presence of special causes of variation that need to be investigated and eliminated. References:

• Managing Supply Chain Operations, Chapter 9: Quality Management, Section 9.2: Statistical Process Control
• CPIM Exam Content Manual, Module 8: Quality, Technology and Continuous Improvement, Section 8.1: Quality Management, Subsection 8.1.2: Statistical Process Control