Free APICS CPIM-8.0 Practice Exam with Questions & Answers | Set: 4

 Forecast error is the difference between the actual demand and the forecasted demand for a given period. Forecast error can be caused by various factors, such as changes in customer preferences, market conditions, competitor actions, or random variation. Forecast error can be measured using different methods, such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), or tracking signal. Forecast error typically triggers forecast revision when it is continually increasing, which indicates that the forecast model is not capturing the underlying demand pattern or trend. A continually increasing forecast error can lead to poor customer service, excess or obsolete inventory, or lost sales opportunities. Therefore, it is important to monitor the forecast error and revise the forecast when necessary to improve the forecast accuracy and reliability. Forecast error does not trigger forecast revision when it is used in computing the tracking signal, associated with the introduction stage of the product life cycle, or caused by random variation. These are not valid reasons for revising the forecast, as they do not indicate a systematic or persistent deviation from the actual demand. References:

CPIM Part 1 Study Guide, Chapter 4: Demand Management, Section 4.2: Forecasting Techniques and Performance Measurement

CPIM Part 2 Study Guide, Chapter 3: Demand Management, Section 3.1: Demand Planning

A Critical Look at Measuring and Calculating Forecast Bias, Section: What Is Forecast Bias?

How Can Forecast Error be Calculated?, Section: Introduction

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 within the 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

Pareto analysis is a technique for separating input factors with the greatest impact on an outcome and prioritizing them based on their scores. It is based on the 80-20 rule, which states that 80% of a project’s benefit or problems can be achieved by doing 20% of the work or fixing 20% of the causes. Pareto analysis helps to identify the top portion of causes that need to be addressed to resolve the majority of problems. Pareto Analysis - Overview, Limitations, Pareto Diagram References: Pareto analysis - Wikipedia, What Is Pareto Analysis? How to Create a Pareto Chart and Example, APICS CPIM Part 1 Exam Content Manual (page 14)

The master scheduler has the authority to change the product mix in the master scheduling process. The product mix is the combination and proportion of different products or product families that the company offers to its customers. The master scheduler can adjust the product mix based on the customer demand, the production capacity, the inventory levels, and the strategic objectives of the company. The master scheduler can also use the product mix to balance the demand and supply, to optimize the resource utilization, and to maximize the profitability. The other options are not correct, as they are items that the master scheduler does not have the authority to change in the master scheduling process, but rather inputs or constraints that the master scheduler has to follow or consider:

Aggregate volume is the total quantity of products or product families that the company plans to produce and deliver in a given period. Aggregate volume is determined by the sales and operations planning (S&OP) process, which involves the senior management and the functional managers of the company. The master scheduler has to align the master production schedule (MPS) with the aggregate volume, and cannot change it without the approval of the S&OP team.

Engineering change effectivity date is the date when a change in the design or specification of a product or a component becomes effective. Engineering change effectivity date is determined by the engineering department, which is responsible for the product development and innovation. The master scheduler has to incorporate the engineering change effectivity date into the MPS, and cannot change it without the approval of the engineering department.

Customer order quantities are the amounts of products or product families that the customers order from the company. Customer order quantities are determined by the market demand and the customer preferences. The master scheduler has to satisfy the customer order quantities as much as possible, and cannot change them without the approval of the customers or the sales and marketing department. References:

[CPIM Part 2 - Section A - Topic 1 - Sales and Operations Planning]

Master Production Schedule (MPS)

Product Mix

Aggregate Planning

Engineering Change Management

Customer Order Management

The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation1.

The formula for the mean absolute deviation is the following:

MAD = (Σ|X – X̄|) / N

Where:

•X = the value of a data point

•X̄ = the mean of the data points

•|X – X̄| = the absolute deviation of a data point from the mean

•N = the number of data points

•Σ = the summation symbol

Based on the table, we can calculate the MAD as follows:

•X̄ = (80 + 50 + 50 + 75) / 4 = 63.75

•|X – X̄| = |80 - 63.75|, |50 - 63.75|, |50 - 63.75|, |75 - 63.75| = 16.25, 13.75, 13.75, 11.25

•MAD = (16.25 + 13.75 + 13.75 + 11.25) / 4 = 6.25

Therefore, the correct answer is B.

References := 1 CPIM Part 2 Exam Content Manual, Domain 3: Plan and Manage Demand, Task 3.1: Develop, validate, and review demand plans, p. 23.