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Excess capacity is the amount of capacity that is available beyond the normal or expected demand. Safety stock is the inventory that is held to protect against uncertainties in demand, supply, or lead time. Excess capacity can be a good substitute for safety stock when the cost of excess capacity is less than the cost of an additional unit of safety stock in the same period. This means that the opportunity cost of having idle resources is lower than the carrying cost of holding extra inventory. In this case, excess capacity can be used to produce more units in response to demand fluctuations, rather than relying on safety stock to meet customer orders. References:

•[CPIM Part 1 Learning System, Module 4: Inventory Management, Section 4.2: Inventory Management Policies and Objectives]

•[CPIM Part 2 Learning System, Module 1: Supply Chain Strategy, Section 1.3: Capacity Management]

A bill of distribution is the planned channels of inventory disbursement from one or more sources to field warehouses and ultimately to the customer. There may be one or more levels in the disbursement system. It is used to allocate inventory among different distribution centers based on demand, capacity, and costs. A bill of distribution is similar to a bill of materials, but for distribution planning instead of production planning. The other options are not correct, as they refer to different concepts in distribution management:

•A supply chain community is a network of organizations that collaborate to achieve common goals and objectives in the supply chain.

•Interplant demand is the demand for a product or component from one plant to another within the same company.

•Logistics data interchange (LDI) is the electronic exchange of information between logistics partners, such as suppliers, carriers, and customers. References:

•[CPIM Part 2 - Section A - Topic 4 - Distribution Planning]

•Distribution Channel Design

•APICS Flashcards

Reducing the inventory value by 10% would contribute most to shortening the cash-to-cash cycle time. The cash-to-cash cycle time is calculated as the days of inventory outstanding plus days of sales outstanding minus days of payables outstanding. By reducing the inventory value, the company can decrease the days of inventory outstanding, leading to a shorter cash-to-cash cycle time. This aligns with CPIM’s focus on efficient inventory management to optimize the supply chain. References: The concepts are covered in detail in Module 4: Inventory Management

Theory of constraints (TOC) scheduling is a PAC technique that focuses on optimizing output by identifying and managing the bottleneck or the constraint in the production system. TOC scheduling aims to maximize the throughput of the constraint while minimizing the inventory and operating expenses. Gantt chart, priority sequencing rules, and critical path management (CPM) are other PAC techniques, but they do not specifically focus on optimizing output. Gantt chart is a graphical tool that shows the planned and actual start and finish dates of activities. Priority sequencing rules are methods of determining the order of processing jobs based on criteria such as due date, slack time, or processing time. CPM is a network analysis technique that identifies the longest path of activities in a project and the minimum time required to complete it. References:

APICS CPIM Part 2 Exam Content Manual, p. 29

[APICS CPIM Learning System Version 8.0], Module 4, Section C, p. 4-25

Exponential smoothing is a forecasting method that uses weighted averages of past observations to predict future values. The weights decrease exponentially as the observations get older, giving more importance to recent data. Exponential smoothing can be applied to data with different patterns, such as level, trend, or seasonality. Depending on the pattern, different exponential smoothing models and parameters are used. Two common parameters are alpha (α) and beta (β):

Alpha is the smoothing parameter for the level component of the forecast. The level component is the average or typical value of the data. Alpha can range from 0 to 1, not inclusive. A low alpha value gives more weight to older observations and produces a smoother forecast. A high alpha value gives more weight to recent observations and produces a more responsive forecast.

Beta is the smoothing parameter for the trend component of the forecast. The trend component is the direction and rate of change of the data over time. Beta can also range from 0 to 1, notinclusive. A low beta value gives more weight to older trends and produces a smoother forecast. A high beta value gives more weight to recent trends and produces a more responsive forecast.

A company with stable demand that uses exponential smoothing to forecast demand would typically use a low alpha value. Stable demand means that the data do not have significant variations, fluctuations, or patterns over time. In this case, a simple exponential smoothing model that estimates only the level component is sufficient. A low alpha value would produce a smooth and stable forecast that reflects the average demand level and does not react to random noise or outliers. The other options are not correct, as they either refer to a different parameter (beta) or a different scenario (high alpha value):

A low beta value would be used for data with a trend component, but a stable demand does not have a trend component. A low beta value would produce a smooth and stable trend forecast that does not react to random noise or outliers.

A high beta value would also be used for data with a trend component, but a stable demand does not have a trend component. A high beta value would produce a responsive and dynamic trend forecast that reflects the recent changes in the data.

A high alpha value would be used for data with a high variability or uncertainty, but a stable demand does not have these characteristics. A high alpha value would produce a responsive and dynamic level forecast that reflects the recent changes in the data. References:

[CPIM Part 2 - Section A - Topic 3 - Demand Management]

Exponential Smoothing for Time Series Forecasting

What is alpha and beta in exponential smoothing?

Value of alpha and beta in Holt’s exponential smoothing method