Explanation: Risk pooling is a strategy to reduce the total safety stock by aggregating the inventory of multiple items or locations. Risk pooling works best for items with high demand uncertainty and long lead times, because these items have higher variability and require more safety stock. By pooling the inventory, the variability of the total demand is reduced, and the safety stock can be lowered without increasing the risk of stockouts.

References:
CPIM Part 2 Exam Content Manual, Domain 5: Plan and Manage Inventory, Section 5.3: Inventory Management Policies and Objectives, p. 28.

Explanation: 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 humanexpert 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.

References: 1: Outlier Definition 1 2: How to Forecast Data Containing Outliers 2 3: How to Detect Outliers in Machine Learning – 4 Methods for Outlier Detection 1 4: How Outlier Detection and Correction Works 4 : How to Understand What is an Outlier in Forecasting 3

Explanation: 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.

References: Production Activity
Control - Tutorial; Production Control: Process, Types and Best Practices - ProjectManager; Production control - Wikipedia.

Explanation: The results from responding to uncertainty in the supply chain by exaggerating lead times and increasing lot sizes is called the bullwhip effect. The bullwhip effect is a phenomenon that occurs when small changes in demand at the downstream end of the supply chain (such as retailers or customers) cause larger and larger fluctuations in demand at the upstream end of the supply chain (such as wholesalers, distributors, or manufacturers). The bullwhip effect can create inefficiencies, waste, and costs in the supply chain, as well as reduce customer satisfaction and profitability.
One of the causes of the bullwhip effect is the response to uncertainty in the supply chain by exaggerating lead times and increasing lot sizes. Lead time is the time between placing an order and receiving it from a supplier. Lot size is the quantity of units ordered or produced at a time. When there is uncertainty or variability in demand or supply, such as due to seasonality, promotions, disruptions, or forecasting errors, some supply chain members may try to cope by exaggerating lead times and increasing lot sizes. For example, a retailer may increase its safety stock or reorder point to avoid stockouts or delays, or a manufacturer may produce more than needed to take advantage of economies of scale or discounts. However, these actions can have unintended consequences, as they can distort the demand information and amplify the demand variability along the supply chain. This can result in excess inventory, low inventory turnover, high holding costs, poor service levels, lost sales, obsolete products, or capacity issues.
To prevent or reduce the bullwhip effect caused by responding to uncertainty in the supply chain by exaggerating lead times and increasing lot sizes, some possible solutions are:

Improving communication and collaboration among supply chain members to share accurate and timely demand information and forecasts.
Reducing lead times and lot sizes by using lean production techniques, just-in-time inventory systems, or quick response methods.
Implementing vendor-managed inventory (VMI) systems, where suppliers are responsible for managing and replenishing the inventory of their customers based on their actual consumption data.
Adopting advanced technologies, such as radio-frequency identification (RFID), artificial intelligence (AI), or blockchain, to enhance visibility, traceability, and coordination in the supply chain.

References := Bullwhip Effect: Meaning, Example, Impact - Investopedia, Bullwhip Effect - What Is It, Causes, Supply Chain, Examples, Bullwhip Effect: Example, Causes, and Impact on Supply Chain

Explanation: A process capability study is a method of evaluating how well a process can produce outputs that meet the specifications or requirements. A process capability study involves collecting data from a sample of the process output, calculating the process mean and standard deviation, and comparing them with the specification limits1. A process capability study can help identify the sources and causes of variation, measure the performance and quality of the process, and determine the potential for improvement2. A process capability study would be necessary in a laboratory when frequent failures are occurring. Frequent failures indicate that the process is not capable of producing reliable and consistent results, and that there may be some problems or defects in the process. A process capability study can help diagnose the issues and suggest corrective actions to reduce or eliminate the failures. For example, a laboratory that performs blood tests may conduct a process capability study to find out why some of the test results are inaccurate or invalid, and what factors affect the accuracy and validity of the test results.
The other options are not situations that would require a process capability study, because they are either unrelated or irrelevant to the process performance or quality. A test results are consistently late (A) is a problem of timeliness, not capability. A new technician is hired © is a change of personnel, not process. Hours of operation are to be extended (D) is a change of schedule, not process.

References:
Process Capability Analysis Cp, Cpk, Pp, Ppk - A Guide - 1factory What is Process Capability? Capability Estimates & Studies | ASQ

Explanation: A push system is a production system that operates based on planned or forecasted demand, rather than actual or current demand. In a push system, work orders or tasks are released to the work centers according to a predetermined schedule, regardless of the availability of capacity or resources at the work centers. This means that work centers may receive work even if they are already overloaded or have no idle time, which can result in long lead times, high inventory levels, and poor customer service1.

The other options are more likely to occur when using a pull system, which is a production system that operates based on actual or current demand, rather than planned or forecasted demand. In a pull system, work orders or tasks are released to the work centers only when there is a need or a request from the downstream work centers or customers. This means that work centers are scheduled using finite capacity planning, which is a method of allocating capacity and resources based on the actual availability and constraints of the work centers2. Work centers also operate using decentralized control, which means that each work center has the autonomy and authority to make decisions based on the local conditions and signals from the environment3. Work centers also signal previous work centers when they are ready for more work, which is a way of synchronizing the flow of materials and information along the production process4.

References: Push System vs. Pull System: Adopting A Hybrid Approach To MRP; Push Systems vs. Pull System: Definitions and Differences; JUST-IN-TIME MANUFACTURING | SpringerLink; 9 Just-In-Time and Lean Systems - Seneca College.

Explanation: Mixed-model scheduling is a production technique that allows for the simultaneous production of different products or features on the same production line or system. Mixed-model scheduling can help reduce lead times, inventory levels, setup times, and material shortages by increasing the flexibility and responsiveness of the production process. One of the benefits of mixed-model scheduling is improved demand response, which means the ability to meet customer demand without delay or stockout. Improved demand response can enhance customer satisfaction and loyalty, as well as reduce the need for safety stock or buffer inventory. By using mixed-model scheduling, a company can produce products or features according to the actual or forecasted customer demand, rather than producing large batches of standardized products or features. This can help avoid overproduction or underproduction, which can result in excess inventory or lost sales.
Mixed-model scheduling can also help adjust the production output quickly and easily when there are changes or fluctuations in demand, by using flexible automation, lean production techniques, or quick response methods.

The other options are not benefits of mixed-model scheduling. Increased inventory is not a benefit of mixed-model scheduling, but rather a drawback. Increased inventory can increase inventory costs, such as holding costs, transportation costs, or obsolescence costs. It can also reduce inventory visibility and control, as well as increase the risk of quality issues or spoilage. Mixed-model scheduling can help reduce inventory by producing products or features in small batches or single units that match customer demand. Fewer setups are not a benefit of mixed-model scheduling, but rather a requirement. Fewer setups mean less time and resources spent on changing or adjusting the production system to produce different products or features. Fewer setups can increase the efficiency and productivity of the production process, as well as reduce the setup costs and waste. Mixed-model scheduling requires fewer setups to enable the simultaneous production of different products or features on the same production line or system. Fewer material shortages are not a benefit of mixed-model scheduling, but rather an outcome. Fewer material shortages mean less disruption or delay in the production process due to the lack of materials or components needed for production. Fewer material shortages can improve the quality and reliability of the production process, as well as reduce the material costs and waste. Mixed-model scheduling can result in fewermaterial shortages by reducing the lead times and inventory levels of materials or components, as well as by improving the communication and coordination with suppliers.

References := Mixed Model Scheduling - Mountain Home Academy, Reduce Lot Sizes, Mixed Model Scheduling - Academic library, Introduction To Mixed Model Production …{Strategos}

Explanation: Manufacturing flexibility can be measured by using changeover time. Changeover time is the time it takes to go from the last good part of one product run to the first good part of the next product run1. Manufacturing flexibility is the ability of a system to handle a range of products or variants with fast setups2. By using changeover time as a measure of manufacturing flexibility, we can assess how quickly and efficiently a system can switch from one product to another, and how well it can respond to changes in customer demand, product mix, quality standards, and delivery schedules3.
Some of the benefits of reducing changeover time and increasing manufacturing flexibility are4:
Lower manufacturing costs: More value-added capacity can be unlocked because the equipment is idle for less time.
Higher customer satisfaction: Customers can get their products faster and with more variety.
Greater competitive advantage: The system can adapt to market changes and offer more customized products or services.
Improved quality and productivity: The system can avoid defects, waste, and errors that may occur during long or complex changeovers.
Some of the methods or tools that can help reduce changeover time and increase manufacturing flexibility are5:
Single-minute exchange of die (SMED): A technique that aims to reduce changeover time to less than 10 minutes by converting internal setup activities (those that can only be done when the machine is stopped) to external setup activities (those that can be done while the machine is running), and streamlining both types of activities.
Total productive maintenance (TPM): A technique that involves maintaining and improving the equipment performance and reliability by involving all employees in preventive maintenance, autonomous maintenance, focused improvement, and quality management.
Quick response manufacturing (QRM): A technique that focuses on reducing lead times throughout the entire organization by applying the principles of time-based competition, cellular manufacturing, system dynamics, and enterprise-wide application.
Therefore, changeover time is a measure that can be used to evaluate the manufacturing flexibility of a system.

References: 1: What is Changeover? (Lean terminology) - Velaction 5 2: FLEXIBILITY IN MANUFACTURING | SpringerLink 3 3: How to Reduce Changeover Time - MachineMetrics 6 4: The Tradeoff Between Inventory Costs And Transportation Costs 5: Changeover [Manufacturing Definition] | Creative Safety Supply

Explanation: Compared to traditional supplier relationships, a more strategic view of supplier relationships would require maintaining communication based on trust. Trust is a key factor that enables effective collaboration, information sharing, problem solving, and innovation between supply chain partners12. Trust can also reduce transaction costs, conflicts, and opportunism, and increase commitment, loyalty, and performance34.
Therefore, maintaining communication based on trust is essential for developing and sustaining strategic supplier relationships that can create value and competitive advantage for both parties.
The other options are not necessarily required for a more strategic view of supplier relationships, because they are either insufficient or irrelevant. Offering the supplier more business may increase the volume or frequency of transactions, but it does not guarantee a more strategic or long-term relationship. Adopting electronic data interchange (EDI) may improve the efficiency or accuracy of information exchange, but it does not ensure a more collaborative or innovative relationship. Implementing concurrent engineering may enhance the product design or development process, but it does not address the other aspects of a strategic relationship, such as quality, delivery, or risk management.

Explanation: Providing a realistic basis for setting internal performance targets can be accomplished through benchmarking. Benchmarking is a process of comparing one’s own performance, processes, or practices with those of other organizations that are recognized as leaders or best in class in a specific area. Benchmarking can help identify gaps, strengths, weaknesses, opportunities, and threats in one’s own performance, as well as learn from the experiences and successes of others. Benchmarking can also help set realistic, achievable, and challenging goals and targets for improvement, based on external standards or benchmarks. Benchmarking can be done internally(within the same organization), externally (with other organizations in the same industry or sector), or functionally (with other organizations that perform similar functions or processes).
Beta testing is not a way of providing a realistic basis for setting internal performance targets. Beta testing is a stage of product development where a sample of potential users or customers test a product or service before it is released to the general public. Beta testing can help identify and fix any bugs, errors, or issues in the product or service, as well as collect feedback and suggestions for improvement. Beta testing can also help evaluate the usability, functionality, and quality of the product or service, as well as measure customer satisfaction and loyalty. Beta testing is not related to setting internal performance targets, as it is focused on the product or service, not the organization.
Breakthrough innovation is not a way of providing a realistic basis for setting internal performance targets. Breakthrough innovation is a type of innovation that creates significant value for customers and markets by introducing new products, services, or business models that are radically different from existing ones. Breakthrough innovation can help create competitive advantage, disrupt existing markets, or create new markets.
Breakthrough innovation is not related to setting internal performance targets, as it is focused on the outcome, not the process.
Best practices are not a way of providing a realistic basis for setting internal performance targets. Best practices are methods or techniques that have been proven to be effective and efficient in achieving desired results or outcomes. Best practices can be derived from one’s own experience, research, or benchmarking. Best practices can help improve performance, quality, or productivity by adopting proven solutions or standards. Best practices are not related to setting internal performance targets, as they are focused on the implementation, not the measurement.

References := Benchmarking - Wikipedia, Benchmarking: Definition & Process | Study.com, What Is Benchmarking? Definition And Examples, What Is Beta Testing? Definition And Examples, What Is Breakthrough Innovation? Definition And Examples, What Are Best Practices? Definition And Examples

Explanation: According to the CPIM Exam Content Manual, a make-or-buy decision is a strategic decision that involves choosing between manufacturing a product or service internally or purchasing it from an external supplier1. A make-or-buy decision is based on a cost-benefit analysis that considers various factors, such as quality, cost, capacity, lead time, technology, and competitive advantage2.

Some of the potential reasons to make instead of buy a product may include:
Maintain quality: Making a product internally may allow the firm to control and ensure the quality standards of the product, which may affect customer satisfaction and loyalty. Buyinga product from an external supplier may involve quality risks or uncertainties, especially if the supplier is located in a different country or has different quality systems3.
Reduce cost: Making a product internally may reduce the total cost of ownership of the product, which includes not only the purchase price, but also the costs of transportation, inventory, inspection, warranty, and maintenance. Buying a product from an external supplier may incur higher total costs due to these factors.
Keep confidential processes within the firm: Making a product internally may protect the firm’s proprietary or confidential processes that give it a competitive edge in the market. Buying a product from an external supplier may expose the firm’s processes to potential imitation or leakage.
Therefore, the correct answer is C. maintain quality, reduce cost, and keep confidential processes within the firm.

References:
CPIM Exam Content Manual
Make-or-Buy Decision Explained: How to Make Outsourcing Decisions
Make or Buy Decision - What Is It, Examples, Factors, Advantages
Make-or-Buy Decision - Overview, How It Works, Triggers
Make or Buy Decision - Definition & Examples | Marketing Tutor

Explanation: Producing to backorders means that the company only produces goods when there is a confirmed customer order. This strategy is most appropriate during times of highly fluctuating demand, as it allows the company to avoid holding excess inventory that may incur high carrying costs and become obsolete. Producing to backorders also enables the company to adjust its workforce according to the actual demand, which can be easily changed as the question states. This strategy can improve customer satisfaction, as the products are tailored to the specific needs and preferences of each customer. However, producing to backorders also has some drawbacks, such as longer lead times, higher production costs, and lower economies of scale.
The other strategies are less suitable for highly fluctuating demand. Producing at a constant level means that the company produces goods at a fixed rate regardless of the demand fluctuations. This strategy can result in either excess inventory or stockouts, depending on whether the demand is lower or higher than the production level. Producing to the sales forecast means that the company produces goods based on the projected demand for a certain period. This strategy can be effective if the forecast is accurate, but it can also lead to inventory imbalances if the forecast is inaccurate or if there are unexpected changes in demand. Producing to demand means that the company produces goods based on the current demand in the market. This strategy can be responsive and flexible, but it can also be challenging to implement, as it requires high visibility, coordination, and agility in the supply chain.

References : CPIM Part 2 Exam Content Manual, Domain 4: Plan and Manage Supply, Section B: Production Planning and Control, Subsection 1: Production Strategies and Techniques, Page 19.