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Customer service performance is the degree to which a company meets or exceeds the expectations of its customers in terms of the quality, timeliness, and satisfaction of the service provided. The most relevant measure of customer service performance is the service perceived by the customer against the service expected by the customer, also known as the service quality gap. This measure captures the difference between what customers expect from a service and what they actually receive, and reflects the level of customer satisfaction or dissatisfaction. A positive service quality gap indicates that the service exceeded the expectations, while a negative service quality gap indicates that the service fell short of the expectations. The other options are not as relevant as the service quality gap because they do not account for the customer’s perspective or perception of the service. Service promised to the customer against service measured by the supplier is an internal measure of service performance, but it does not reflect how the customer perceives the service. Customer complaints received as a percentage of orders shipped is a measure ofservice failure, but it does not capture the positive feedback or the silent dissatisfied customers. Positive customer feedback as a percentage of customer feedback is a measure of service satisfaction, but it does not account for the customer’s expectations or the service quality dimensions. References:

CPIM Part 2 Exam Content Manual, p. 67

Customer Service Metrics: Top 10 to Measure

20 Customer Service KPIs You Need To Know

Concurrent engineering is a product design approach that involves the simultaneous and collaborative involvement of different functional areas, such as engineering, marketing, manufacturing, and suppliers, in the product development process. Concurrent engineering aims to reduce time to market, improve quality, lower costs, and enhance customer satisfaction by integrating and coordinating the inputs and feedback of all the stakeholders from the early stages of design. Concurrent engineering can also facilitate the standardization and modularization of product components, which can improve the flexibility and responsiveness of a global supply chain. References:

Managing Supply Chain Operations, Chapter 3: Product Design and Development, Section 3.2: Concurrent Engineering

CPIM Exam Content Manual, Module 1: Supply Chains and Strategy, Section 1.3: Product and Process Design, Subsection 1.3.1: Product and Process Design Concepts

Point-of-use storage is a stock location system that places inventory close to where it is needed or consumed in the production process. This reduces waste, handling, and transportation costs, and improves material flow and visibility. Point-of-use storage is a key element of a lean environment, where inventory is minimized and replenished frequently based on demand signals. References: EXAM CONTENT MANUAL PREVIEW, page 15, section 7.1.2. Manufacturing Planning and Control for Supply Chain Management: The CPIM Reference, Second Edition, page 462, section 13.3.

Concurrent engineering is a method of designing and developing products in which the different stages run simultaneously, rather than consecutively. It decreases product development time and also the number of errors and rework. By involving all the relevant stakeholders, such as engineering, manufacturing, marketing, and purchasing, in the design process from the beginning, concurrent engineering reduces the need for product design changes later in the product life cycle. References:

•APICS CPIM Part 2 Exam Content Manual, p. 15

•[APICS CPIM Learning System Version 8.0], Module 2, Section B, p. 2-17

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]

Sharing of demand data with a supplier is an example of collaboration between suppliers and operations which would give more lead time visibility. This is because it allows the supplier to plan and adjust their production and delivery schedules according to the customer’s needs, reducing the risk of stockouts or excess inventory. It also enables the supplier to provide feedback and suggestions to improve the demand planning process and the accuracy of the forecasts. Sharing of demand data is a key component of supplier relationship management (SRM) and collaborative planning, forecasting, and replenishment (CPFR). References :=

CPIM Exam Content Manual, Module 4: Supply, Section 4.2: Supplier Relationship Management, p. 15

Essentials of Supply Chain Management, Chapter 6: Demand Planning and Forecasting, Section 6.4: Collaborative Planning, Forecasting, and Replenishment, pp. 101-103

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

Cycle count is an inventory management technique that involves counting a subset of inventory items on a regular basis, usually based on some sampling criteria1. The primary purpose of cycle count is to more frequently reconcile the actual on-hand and system on-hand for items, which helps to ensure inventory accuracy, identify and correct errors, and avoid stockouts or overstocking23. Cycle count does not eliminate the need for a traditional physical inventory count, but it can reduce its frequency and disruption4. Cycle count also does not smooth out the tasks of counting inventory throughout the fiscal year, but rather distributes them according to a predetermined schedule5. Cycle count may indirectly improve material handling processes and reduce or eliminate errors, but this is not its primary purpose. References:

•What is cycle count in inventory management?

•Inventory Cycle Counting 101: Best Practices & Benefits

•Understanding The Cycle Count In Inventory Management

•What is Inventory Cycle Counting?: A 2023 Guide

•Cycle Count: Everything A Warehouse Manager Should Know

•[CPIM Part 2 Exam Content Manual], p. 40

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 life cycle assessment (LCA) is a method of evaluating the environmental impacts of a product or service throughout its life cycle, from raw material extraction to disposal or recycling1. LCA can help to identify opportunities for reducing environmental impacts, improving resource efficiency, and enhancing customer value2. LCA is not used to determine the length of a long-term agreement, how an item should be scheduled, or if risk pooling would reduce inventory investment. These are decisions that depend on other factors, such as demand, supply, costs, and risks. References:

•CPIM Part 2 Study Guide, Chapter 2: Supply Chain Strategy, Section 2.3: Sustainability and Corporate Social Responsibility

•ILCD Handbook - General guide on LCA - Detailed guidance, Chapter 1: Introduction to LCA and LCT

Pyramid forecasting is a method of forecasting that uses a hierarchical structure of data to improve the accuracy and consistency of the forecasts. The lowest level of the pyramid represents the most detailed data, such as individual product items, while the higher levels represent more aggregated data, such as product families or total business. The “roll up” process is the process of aggregating the forecasts from the lower level to the higher level, starting with the most detailed level. This process helps to align the forecasts across different levels and reduce the forecast error123 References: 1: Pyramid Forecasting Process 2: Rolling Forecast Model | FP&A Tutorial + Excel Template 3: ROLL-UP FORECASTS

 Absorption costing is a method of allocating all manufacturing costs to the units produced. It includes both fixed and variable costs in the calculation of the unit cost. In this example, the fixed cost per unit is §10, which is obtained by dividing the total fixed cost of §200,000 by the number of units produced (20,000). This fixed cost per unit is then added to the variable cost per unit to get the total unit cost under absorption costing. Variable costing, on the other hand, only assigns variable costs to the units produced and treats fixed costs as period costs. Activity-based costing (ABC) is a method of allocating overhead costs to products or services based on the activities they consume. Overhead costing is a general term that refers to any method of assigning overhead costs to products or services. References:

CPIM Part 2 Learning System, Module 2: Demand Management, Section 2.4: Costing Methods and Cost Behavior

CPIM Part 2 Learning System, Module 3: Supply, Section 3.5: Cost Management

Rough-cut capacity planning (RCCP) is a type of capacity planning method that uses the master production schedule (MPS) as its primary input. RCCP is a technique for checking the feasibility of the MPS by comparing the available capacity of critical resources (such as machines, labor, or materials) with the capacity required by the MPS. RCCP helps to identify and resolve any potential capacity problems or bottlenecks at an aggregate level, before committing to the MPS. RCCP can also be used to evaluate alternative MPS scenarios and to support the sales and operations planning (S&OP) process12. References: 1 Rough Cut Capacity Planning (RCCP) - Definition, Example, and … 3 2 CPIM Exam References - Association for Supply Chain Management

A cellular production system is a type of lean manufacturing system that reduces waste and improves efficiency by grouping machines and workers into cells that can produce a complete product or a product family. A pull production activity control (PAC) technique is most appropriate for a cellular production system because it allows the cells to produce only what is needed by the downstream processes or customers, thus minimizing inventory and overproduction. A pull PAC technique also enables quick response to changes in demand and feedback from quality control. A push PAC technique, on the other hand, is based on predetermined schedules and forecasts, which may not match the actual demand and may result in excess inventory and waste. The shortest processing time (SPT) rule and the distribution requirements planning (DRP) are not specific to cellular production systems and do not take into account the customer demand or the cell capacity. References:

•CPIM Part 2 Exam Content Manual, p. 49

•Cellular Manufacturing: A Comprehensive Guide

•Cellular manufacturing - Wikipedia

Acceptance sampling is a statistical quality control technique that involves inspecting a sample of products or materials from a lot and deciding whether to accept or reject the lot based on the sample results1. Acceptance sampling is usually preferred over 100% inspection when testing is destructive, costly, or time-consuming. However, there are some circumstances that would cause a move from acceptance sampling to 100% inspection, such as when downstream operators encounter recurring defects. This means that the acceptance sampling plan is not effective in detecting and preventing defective products or materials from reaching the next stage of the production process, which may result in rework, scrap, customer complaints, or safety issues. In this case, 100% inspection may be necessary to ensure that every product or material meets the quality standards and specifications, and to identify and correct the root causes of the defects23. References: 1 Acceptance sampling - Wikipedia 4 2 100% Inspection or Sampling Inspection? Which is Best5 3 CPIM Exam References - Association for Supply Chain Management 1