Most Recent APICS CPIM-Part-2 Exam Questions & Answers

Mean time between failures (MTBF) is the predicted elapsed time between inherent failures of a mechanical or electronic system during normal system operation1.MTBF can be calculated as the arithmetic mean (average) time between failures of a system1. MTBF is a useful measure of reliability, because it indicates how long a system is likely to work before failing.The higher the MTBF, the more reliable the system2.Reliability is the probability that a system will perform its intended function without failure for a specified period of time under specified conditions3.

The other statements about MTBF are false. MTBF is not used for non-repairable products, but for repairable systems.For non-repairable products, mean time to failure (MTTF) is used instead4.MTTF is the expected time to failure for a non-repairable system1.An increase in MTBF is not proportional to an increase in quality, because quality is not only determined by reliability, but also by other factors such as performance, functionality, durability, and customer satisfaction5.MTBF is not the same as operating life or service life, because operating life or service life is the total time that a system can operate before it reaches the end of its useful life, while MTBF is the average time between failures during the operating life6.

Production activity control (PAC) is the function of managing the flow of materials and work-in-progress in a manufacturing system. PAC is responsible for executing the master production schedule and the material requirements plan, as well as for planning, implementing, and monitoring the production activities. PAC aims to ensure that the required resources are available, that the production orders are released and completed on time, and that the quality and quantity standards are met. A result of effective PAC is that the actual input/output matches the planned input/output. This means that the actual amount and timing of materials, labor, and machines used for production are consistent with the planned amount and timing. This indicates that the production process is efficient, reliable, and synchronized with the demand. This also helps to reduce inventory, lead time, and waste.

The other options are not necessarily results of effective PAC. Less scrap and rework on the shop floor may be a result of effective quality control, which is a separate function from PAC. Quality control is concerned with inspecting and testing the products or services to ensure that they meet the specifications and standards. Fewer machine hours are required for production may be a result of effective process improvement, which is a separate function from PAC. Process improvement is concerned with analyzing and enhancing the production methods and techniques to increase productivity and performance. Available capacity is increased may be a result of effective capacity planning, which is a separate function from PAC. Capacity planning is concerned with determining and adjusting the optimal level of resources needed to meet the demand.Reference:Production Activity Control - Tutorial;Production Control: Process, Types and Best Practices - ProjectManager;Production control - Wikipedia.

Process improvement is a method of analyzing and enhancing the production methods and techniques to increase productivity and performance. Process improvement aims to reduce costs, waste, defects, and errors, as well as to improve quality, efficiency, and customer satisfaction. When considering process improvement, the first approach that should be considered is making better use of existing resources. This means that the production system should optimize the utilization and allocation of the available resources, such as materials, labor, machines, and space. This can be achieved by implementing various techniques, such as lean manufacturing, six sigma, kaizen, or 5S. Making better use of existing resources can help to improve the process without requiring additional investment or expenditure.

The other options are not the first approaches that should be considered as part of process improvement. Hiring more skilled people to perform the job is not the first approach, as it may increase the labor cost and require more training and supervision. Hiring more skilled people may not necessarily improve the process if the existing methods and techniques are inefficient or ineffective. Buying better and faster equipment is not the first approach, as it may involve a large capital outlay and a long payback period. Buying better and faster equipment may not necessarily improve the process if the existing resources are underutilized or misallocated. Applying stricter quality control is not the first approach, as it may increase the inspection and testing cost and time. Applying stricter quality control may not necessarily improve the process if the existing methods and techniques are prone to errors or defects.Reference: CPIM Exam Content Manual Version 7.0, Domain 8: Manage Quality, Continuous Improvement, and Technology, Section 8.2: Continuous Improvement Concepts, p. 46; Process Improvement; Process Improvement Definition.

The priority rule that is most consistent with the objective of meeting due dates is slack time per operation. Slack time per operation is a priority rule that assigns a priority index to each job based on the ratio of the remaining slack time to the remaining number of operations. Slack time is the difference between the due date and the expected completion time of a job. A lower ratio means a higher priority, as it indicates that the job has less slack time per operation and is more likely to be late. Slack time per operation is a dynamic priority rule, as it updates the priority index after each operation is completed. Slack time per operation can help minimize the number of tardy jobs and the average tardiness of jobs, as it gives preference to the jobs that are closer to their due dates and have more operations left.

First-come-first-served (FCFS) is not a priority rule that is consistent with the objective of meeting due dates. FCFS is a priority rule that processes jobs in the order of their arrival or release times. FCFS is a simple and fair rule, but it ignores the processing times and due dates of jobs. FCFS can result in poor due date performance, as it can delay urgent or short jobs behind long or non-urgent jobs.

Shortest processing time (SPT) is not a priority rule that is consistent with the objective of meeting due dates. SPT is a priority rule that processes jobs in ascending order of their processing times. SPT is an effective rule for minimizing the average flow time and work-in-process inventory of jobs, as it clears out small jobs quickly and reduces congestion in the system. However, SPT does not consider the due dates of jobs, and it can make long or urgent jobs late.

Fewest operations remaining is not a priority rule that is consistent with the objective of meeting due dates. Fewest operations remaining is a priority rule that processes jobs in ascending order of their remaining number of operations. Fewest operations remaining is a rule that can reduce the variability and complexity of jobs, as it tends to complete jobs faster and reduce their flow times. However, fewest operations remaining does not take into account the slack times or due dates of jobs, and it can make urgent or short jobs late.

An outlier is a data point that falls outside of the expected range of the data, i.e., it is an unusually large or small data point1.Outliers can have a significant adverse impact on the forecasts, as they can skew the data distribution and distort the statistical analysis2. Therefore, it is important to detect and remove outliers from the demand data before generating forecasts.

One of the techniques that can be used to detect outliers is to use the standard deviation of the data, or the equivalent z-score, to determine the outlier limit3. For example, one approach is to set the lower limit to three standard deviations below the mean, and the upper limit to three standard deviations above the mean. Any data point that falls outside this range is detected as an outlier.

However, detecting outliers is not enough. The most appropriate next step would be to screen the outlier for manual review.This means that the detected outlier should be examined by a human expert to determine whether it is a true outlier or not, and whether it should be corrected or not4. This is because not all outliers are erroneous or irrelevant. Some outliers may be valid observations that reflect real changes in demand, such as seasonal peaks, promotional effects, or market trends. In such cases, correcting or removing the outliers may lead to inaccurate or biased forecasts.

Therefore, screening the outlier for manual review can help verify the cause and validity of the outlier, and decide on the best course of action. Some of the possible actions are:

Correcting the outlier: replacing the outlier with a more typical value based on historical data or expert judgment. This can smooth out the data and reduce the noise.

Separating the demand streams: splitting the data into two or more series based on different factors that influence demand, such as product type, customer segment, or distribution channel. This can isolate the outliers and allow different forecasting methods to be applied to each series.

Adjusting the forecasting model: modifying the parameters or assumptions of the forecasting model to account for the outliers, such as using a different smoothing factor, trend component, or error term. This can improve the fit and accuracy of the model.