CPIM-8.0 Exam Dumps - APICS CPIM Questions and Answers

 Mean time between failures (MTBF) is the average time that elapses from one unplanned breakdown to the next, under normal operating conditions. It is a useful measure of reliability because it indicates how long a repairable system typically operates before failing. Reliability is the absence of unplanned downtime, and MTBF measures how often a system stops performing as expected. The other statements are not true about MTBF. MTBF is not used for non-repairable products, as they cannot be fixed and put back into operation after a failure. For non-repairable products, mean time to failure (MTTF) is used as a measure of reliability. MTBF is not proportional to quality, as quality is a broader concept that encompasses not only reliability, but also performance, durability, and customer satisfaction. MTBF is not the same as operating life or service life, as they refer to the total time that a system can function before reaching the end of its useful life, while MTBF refers to the average time between failures within the operating life. References: Mean Time Between Failures (MTBF): How to Calculate & Increase, APICS CPIM 8 Planning and Inventory Management | ASCM

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 of service 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

 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

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, not inclusive. 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

One advantage of adopting a supply network perspective is that it enhances understanding of competitive and cooperative forces. A supply network perspective is a holistic view of the supply chain that considers the interdependencies and interactions among the various entities involved in the flow of materials, information, and money. A supply network perspective helps to identify the sources of value creation and capture, the opportunities and threats in the market, and the potential synergies and conflicts among the supply chain partners. A supply network perspective also helps to design and implement effective strategies and tactics to achieve competitive advantage and customer satisfaction. The other statements are not advantages of adopting a supply network perspective. Protecting global markets, defining market relationships and partnerships, and encouraging rivals to collaborate are possible outcomes or objectives of adopting a supply network perspective, but they are not the benefits of the perspective itself. References: Supply Network Perspective | APICS Dictionary Term of the Day, APICS CPIM 8 Planning and Inventory Management | ASCM

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