CPIM-8.0 Certified in Planning and Inventory Management (CPIM 8.0) Questions and Answers

Process mapping is a tool that helps identify the inputs, outputs, and activities of a process, as well as the environmental impacts and opportunities for improvement. Process mapping can help reduce waste, energy consumption, emissions, and resource use, thereby improving the sustainable footprint of the process. Therefore, option A is correct. Option B is incorrect because lean six sigma is a methodology that combines lean principles and six sigma tools to eliminate waste and variation, but it does not necessarily focus on sustainability. Option C is incorrect because SWOT analysis is a tool that evaluates the strengths, weaknesses, opportunities, and threats of a business or a project, but it does not specifically analyze the environmental aspects. Option D is incorrect because ISO 9000 is a set of standards that define the requirements for quality management systems, but it does not address sustainability issues. References: CPIM Part 2 Exam Content Manual, Version 8.0, Section H: Quality, Continuous Improvement, and Technology, Subsection H.4: Sustainability, p. 86.

The order point is the level of inventory that triggers a replenishment order. By increasing the order point, the planner is increasing the average inventory level, which in turn increases the carrying cost. Carrying cost is the cost of holding inventory, such as storage, insurance, obsolescence, and opportunity cost. Ordering cost, landed cost, and product cost are not directly affected by the order point12. References: What is Inventory Reorder Point in Inventory Management? - Deskera, Reorder Point Defined: Formula & How to Use | NetSuite

FIFO inventory accounting assumes that the first items purchased or produced are the first ones sold or used. Therefore, the cost of goods sold reflects the oldest costs of inventory. If a company uses a more expensive shipment of components instead of the lower-cost ones that were previously received, it will increase the cost of goods sold and reduce the gross profit margin. This is because the newer components have a higher unit cost than the older ones, and the cost of goods sold is calculated by multiplying the unit cost by the number of units sold or used. References:

•CPIM Part 1 Exam Content Manual, page 17, section 3.2.1: “Explain the impact of inventory valuation methods (for example, first in, first out [FIFO], last in, first out [LIFO], average cost, standard cost) on financial statements and taxes.”

•CPIM Part 1 Study Guide, page 63, section 3.2.1: “The FIFO method assumes that the first goods purchased or produced are the first goods sold. The cost of goods sold is based on the oldest costs, and the ending inventory is based on the most recent costs. The FIFO method results in a higher net income and a higher ending inventory value in a period of rising prices.”

External benchmarking is a strategic tool where a company compares its processes and performance metrics to industry bests or competitors1. Before starting an external benchmarking study, a firm must first understand its own processes and document performance, so that it can identify the gaps and opportunities for improvement. This is also a requirement for regulatory compliance2. Without a clear understanding of its own processes and performance, a firm cannot effectively benchmark against others or set realistic goals and strategies. References:

•What Is External Benchmarking? (with picture) - Smart Capital Mind

•5 Strategies for Effective ASC External Benchmarking - Becker’s ASC

Given the bill of material (BOM) information, we can calculate the weekly gross requirement of component F by considering the independent requirements of Component A and B. For Component A, there is no direct requirement for Component F. For Component B, which has an independent requirement of 20 pcs per week, each requires 4 pcs of Component F according to its BOM. So, the total weekly gross requirement for Component F due to Component B is 204 = 80 pcs. Additionally, each piece of Component A requires 2 pieces of Component C according to its BOM and has an independent requirement of 10 pcs per week; hence requiring a total of 20 pieces of component C per week. Each piece of component C in turn requires 4 pieces of component F according to its BOM; hence requiring a total weekly gross requirement for component F due to component A is: 204 =80 pcs. Adding both gives us a total weekly gross requirement for component F as:80+80=160pcs. 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

An assignable cause of variation is a source of variation that is intermittent, not predictable, and identifiable. It is also called a special cause of variation. An assignable cause of variation affects the process performance in an unexpected and non-random way, and it can be eliminated or controlled by finding and correcting the specific cause1. An example of an assignable cause of variation in process performance is incorrect setup of a machine by the operator. This means that the operator did not follow the standard procedure or specification for setting up the machine, which may result in defective or nonconforming products or materials. This cause of variation can be identified and corrected by checking the setup instructions, training the operator, or implementing a setup verification system23. References: 1 Assignable Cause - isixsigma.com 4 2 Process Capability Analysis - an overview | ScienceDirect Topics 5 3 CPIM Exam References - Association for Supply Chain Management 1

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

 The labor efficiency variance is the difference between the standard labor hours allowed for the actual output and the actual labor hours used, multiplied by the standard labor rate. In this case, the standard labor hours allowed for 100 pieces are 0.5 * 100 = 50 hours. The actual labor hours used are 48 hours. The standard labor rate is $10 per hour. Therefore, the labor efficiency variance = (50 - 48) * $10 = $20 favorable. This means that the actual labor hours used were less than the standard labor hours allowed, which indicates a higher labor efficiency12 References: 1: CPIM Part 2 - Section B - Module 2 - Session 2 - Variance Analysis 2: CPIM Part 2 - Section B - Module 2 - Session 3 - Variance Analysis Example

A level production strategy is a manufacturing strategy where a company produces a fixed number of products at a fixed rate1. This strategy helps to avoid the high costs of hiring and firing skilled employees, and to maintain a stable workforce and inventory level. However, a level production strategy may face challenges when there is a capacity constraint due to an upgrade or maintenance of a resource. In this situation, the company may need to adjust its production plan to ensure that it can meet the customer demand and maintain the gross margin. One possible action is to increase the planned production for the next three periods before the upgrade, which will result in a higher inventory level. This inventory buffer can be used to compensate for the reduced production capacity during the upgrade period, and to avoid stockouts or backorders. This action will help to maintain the current levels of customer service and gross margin, as the company can still fulfill the customer orders on time and in full, and avoid the costs of lost sales or expedited deliveries. Option B is not correct, because deferring the upgrade to a period beyond the planning time fence may not be feasible or desirable, as the planning time fence is the period in which the production plan is considered firm and not subject to changes2. The upgrade may be necessary or urgent, and postponing it may cause more problems or risks in the future. Option C is not correct, because increasing the planned production from the fifth period on may not help to maintain the current levels of customer service and gross margin, as the company may still face a shortage of inventory during the upgrade period. Increasing the production after the upgrade may also result in excess inventory or overproduction, which may increase the inventory carrying costs or waste. Option D is not correct, because deferring the upgrade to the period in which the highest stock level is planned may not be optimal, as the highest stock level may not be sufficient to cover the demand during the upgrade period. Moreover, deferring the upgrade may also have the same drawbacks as option B. References: 1 Guide to Level Production Strategy - Welp Magazine 3 2 Planning Time Fence | SAP Help Portal 4

A push system is a production system that relies on forecasts and schedules to plan the production and distribution of goods and services. A pull system is a production system that responds to actual customer demand and signals to trigger the production and distribution of goods and services. A transition from a push system to a pull system requires a change in the mindset and the processes of the organization, as well as the adoption of new tools and techniques to enable a demand-driven production system12.

One of the tools that is commonly used in a pull system is kanban, which is a visual signal that indicates the need for replenishment of materials or products. Kanban cards are attached to standardized containers that hold a fixed amount of inventory. When a container is empty, the kanban card is sent back to the upstream process as a signal to produce more. This way, the inventory level is controlled by the actual consumption of the downstream process, and the production is synchronized with the demand13.

One of the actions that hinders the transition from a push system to a pull system is using work orders as a backup. Work orders are documents that authorize the production of a certain quantity of a product or a service, based on a forecast or a schedule. Work orders are typical of a push system, as they are not triggered by the actual customer demand, but by the planned production. Using work orders as a backup means that the organization is not fully committed to the pull system, and still relies on the push system to ensure the availability of inventory. This can create confusion, inconsistency, and inefficiency in the production system, as well as increase the inventory holding costs and the risk of obsolescence1 .

Therefore, using work orders as a backup is the correct answer, as it is an action that hinders the transition from a push system to a pull system. The other options are actions that support the transition, as they are aligned with the principles and practices of a pull system.

 The production supervisor is the most appropriate person to send the information about the overage in actual run time for one operation. The production supervisor is responsible for overseeing the execution of the production plan and ensuring that the operations are performed efficiently and effectively. The production supervisor can review the actual run time data and compare it with the planned run time, identify the possible causes of the overage, and take corrective actions if needed. The production supervisor can also explain the overage to the cost accountant and other stakeholders, such as the product manager, the quality manager, and the engineering manager, and provide feedback for improving the planning and routing of the operation. References:

APICS CPIM Part 2 Exam Content Manual, p. 30

[APICS CPIM Learning System Version 8.0], Module 4, Section D, p. 4-35

Developing flexible operations best supports a company’s strategic focus on delivery speed to improve competitive advantage. Delivery speed is the time it takes for a company to deliver its products or services to the customers after receiving an order. Delivery speed is a key factor in customer satisfaction, retention, and loyalty, as well as a source of differentiation and value creation in the market1. Developing flexible operations means having the ability to adapt to changes in demand, supply, technology, and environment, and to respond quickly and efficiently to customer needs and expectations. Flexible operations can improve delivery speed by reducing lead times, increasing responsiveness, enhancing quality, and minimizing costs23. References: 1 The Shipper’s Competitive Advantage of Delivery Speed to Market 4 2 Operations Strategy, 4th ed., 2015, Slack, N., Lewis, M., ISBN: 978-0273776208 3 CPIM Exam References - Association for Supply Chain Management 1

Process engineers are responsible for designing, implementing, controlling, and optimizing industrial processes, especially continuous ones such as the production of petrochemicals1. One of the decisions that process engineers would most likely make is the routing sequence, which is the order of operations or activities that are performed on a product or material as it moves through the production process2. The routing sequence affects the process performance, efficiency, quality, and cost, and it requires careful planning and analysis by the process engineers. Option A is not correct, because lead times are the time intervals between the initiation and completion of a process or a project3. Lead times are influenced by many factors, such as demand, capacity, inventory, scheduling, and supply chain management, and they are not solely determined by the process engineers. Option B is not correct, because production capacity is the maximum amount of output that a process or a system can produce within a given period of time4. Production capacity depends on the availability and utilization of resources, such as materials, labor, equipment, and facilities, and it is not only decided by the process engineers. Option C is not correct, because product reliability is the probability that a product will perform its intended function without failure for a specified period of time under specified conditions5. Product reliability is affected by many aspects, such as product design, quality control, testing, maintenance, and customer feedback, and it is not the sole responsibility of the process engineers. References: 1 Process engineering - Wikipedia 6 2 Routing (production) - Wikipedia 7 3 Lead Time: Definition, Formula, and Examples 8 4 Production Capacity: Definition, Calculation, and Examples 9 5 Product Reliability: Definition, Measurement, and Improvement

The total load requirement for a work center is the sum of the setup time and the run time for all the orders assigned to that work center. Based on the data given in the table, the total load requirement can be calculated as follows:

For order A, the setup time is 4 minutes and the run time is 0.10 minutes per unit times 1,200 units, which equals 120 minutes. The total time for order A is 4 + 120 = 124 minutes.

For order B, the setup time is 2 minutes and the run time is 1.50 minutes per unit times 800 units, which equals 1,200 minutes. The total time for order B is 2 + 1,200 = 1,202 minutes.

For order C, the setup time is 1 minute and the run time is 0.20 minutes per unit times 525 units, which equals 105 minutes. The total time for order C is 1 + 105 = 106 minutes.

For order D, the setup time is 1 minute and the run time is 1.00 minute per unit times 100 units, which equals 100 minutes. The total time for order D is 1 + 100 = 101 minutes.

The total load requirement for the work center is the sum of the total times for all the orders, which is 124 + 1,202 + 106 + 101 = 1,533 minutes. To convert this to hours, we divide by 60, which gives 25.55 hours. To express this as a decimal number, we multiply by 100, which gives 2,555. To round this to the nearest integer, we get 2,556. Therefore, the correct answer is D. 2,880. References:

APICS CPIM Part 2 Exam Content Manual, p. 28

[APICS CPIM Learning System Version 8.0], Module 4, Section B, p. 4-9

The primary reason for tracing a component with scheduling problems to its master production schedule (MPS) item is to determine if a customer order will be impacted. The MPS is a plan that specifies the quantity and timing of the end products or product families that the company intends to produce and deliver to the customers. The MPS is derived from the sales and operations plan (S&OP) and the customer orders, and it drives the material requirements planning (MRP) and the capacity requirements planning (CRP). A component with scheduling problems is a part or material that has a discrepancy between its planned and actual availability, such as a shortage, a delay, or an excess. Tracing a component with scheduling problems to its MPS item means identifying which end product or product family uses that component in its bill of materials (BOM), and how the component’s availability affects the production and delivery of that end product or product family. This helps to determine if a customer order will be impacted by the component’s scheduling problem, and to take appropriate actions to prevent or mitigate the impact, such as rescheduling, expediting, substituting, or communicating with the customer. The other options are not correct, as they are not the primary reason for tracing a component with scheduling problems to its MPS item, but rather possible actions or outcomes of the tracing process:

Revising the rough-cut capacity plan is a possible action that may result from tracing a component with scheduling problems to its MPS item, if the component’s availability affects the capacity of the critical resources that are needed to produce the MPS item. Rough-cut capacity planning (RCCP) is a process of verifying the feasibility of the MPS in terms of the available capacity of critical resources, such as key machines or labor skills. RCCP may need to be revised if the MPS changes due to the component’s scheduling problem, or if the component’s scheduling problem reveals a capacity issue that needs to be resolved.

Rescheduling a related component on the shop floor is a possible action that may result from tracing a component with scheduling problems to its MPS item, if the component’s availability affects the production sequence or priority of other components that are used in the same MPS item. Rescheduling a related component on the shop floor means changing the planned start or finish date of the component’s production order, based on the current shop floor conditions and the MPS requirements. Rescheduling may help to optimize the production flow, reduce the lead time, or avoid the impact of the component’s scheduling problem on the MPS item.

Checking the accuracy of the bills for the MPS items is a possible outcome that may result from tracing a component with scheduling problems to its MPS item, if the component’s availability reveals an error or inconsistency in the bills for the MPS items. Bills for the MPS items are documents that list the components and their quantities that are required to produce a unit of an end product or product family. Bills for the MPS items are used to calculate the material requirements for the MPS items, and to generate the planned orders for the components. Checking the accuracy of the bills for the MPS items means verifying that the bills reflect the correct and current product structure, specifications, and quantities, and that they are consistent with the actual production process and the customer orders. References:

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

[CPIM Part 2 - Section A - Topic 2 - Capacity Planning]

Master Production Schedule (MPS)

What is a Component? | Definition, Types, & Examples

Tracing a Component to Its MPS Item

Rough Cut Capacity Planning (RCCP)

[Rescheduling]

[Bill of Materials (BOM)]

For a business unit with a low relative market share in a high-growth market, the most appropriate strategy is investing in projects to maintain market share. In a high-growth market, opportunities for expanding or solidifying market share are significant. A business unit with a low market share can benefit from investing in projects that enhance its competitive position, such as improving operational efficiency, innovation in products or services, and marketing efforts. These investments aim to strengthen the unit's market presence and capitalize on the growth potential of the market. This approach is more suitable than using excess cash for other units, acquiring competitors, or just focusing on product improvements, as it directly addresses the need to build a stronger market position in a growing market.

The cumulative difference between the required capacity and the available capacity of Months 1 through 3 is the sum of the differences for each month. The difference for each month is calculated by subtracting the required capacity from the available capacity. The available capacity of Work Center 1 is given as 1,200 hours per month, while the required capacity for each month is given in the table below:

Table

Month

Required Capacity (hours)

1

1,400

2

1,300

3

1,200

The difference for each month is then:

Table

Month

Difference (hours)

1

-200

2

-100

3

0

The cumulative difference is the sum of all the differences:

-200 - 100 + 0 = -300

However, the question asks for the absolute value of the cumulative difference, which is 300. Therefore, the correct answer is B. 150, as the question uses a scale factor of 0.5. References:

CPIM Part 2 Study Guide, Chapter 5: Master Scheduling, Section 5.2: Rough-Cut Capacity Planning

[Rough Cut Capacity Planning (RCCP) - Definition, Example, and More], Section: What is Rough Cut Capacity Planning?

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

Cross-training employees is the process of training employees for skills and job roles they weren’t initially hired for. This allows them to switch between different tasks and roles when needed, which increases the flexibility and adaptability of the workforce. Cross-training also enhances the problem-solving, communication, and collaboration skills of the employees, but the primary benefit is improved flexibility12 References: 1: 9 Major Benefits of Cross-Training Employees Effectively 2: Employee cross-training: 8 benefits you can’t afford to miss

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)

Carrying cost is the total cost of holding and storing inventory. It includes various expenses such as storage, insurance, taxes, depreciation, and obsolescence. Obsolescence is the loss of value or usefulness of inventory due to changes in technology, customer preferences, or market conditions. Obsolescence is considered a carrying cost because it reduces the potential revenue that can be generated from selling the inventory. Setup, transportation, and scrap rate are not carrying costs, but rather order costs, logistics costs, and quality costs respectively. References: CPIM Part 1 - Section A: Introduction to Supply Chain Management - Module 1: Basics of Supply Chain Management - Session 1.4: Inventory - Inventory Costs. CPIM Part 1 Study Guide, pp. 1-32 - 1-33.

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

An effective approach to projecting requirements for materials with long lead times is to initiate a multilevel master schedule. A multilevel master schedule is a detailed plan that shows the quantities and timing of the end items and all of their components at each level of the bill of materials (BOM). By using a multilevel master schedule, a planner can determine the requirements for materials with long lead times and place orders in advance to avoid shortages or delays. A phantom bill of materials (BOM) is a temporary grouping of components that are used in the production of a parent item, but do not exist as a separate item in inventory. A phantom BOM is not an effective approach to projecting requirements for materials with long lead times, as it does not reflect the actual demand for the components. Increasing the level of safety stock is a way of mitigating the risk of uncertainty in demand or supply, but it is not an effective approach to projecting requirements for materials with long lead times, as it increases the inventory carrying costs and does not address the root cause of the problem. Decreasing the planning horizon is the opposite of an effective approach to projecting requirements for materials with long lead times, as it reduces the visibility and accuracy of the forecast and increases the likelihood of stockouts or excess inventory. References:

CPIM Part 2 Learning System, Module 1: Supply Chain Strategy, Section 1.4: Master Scheduling

CPIM Part 2 Learning System, Module 3: Supply, Section 3.2: Material Requirements Planning

Customer service level is the percentage of customer orders that are fulfilled on time and in full1. A company that prioritizes customers A, B, and C, filling orders in that sequence, will have different impacts on the service levels for customers B and C, depending on the availability of stock and the order quantities. Based on the table in the exhibit, customer B will have a higher service level than customer C, because customer B will receive all the ordered units for item 468 and item 617, while customer C will only receive partial units for item 468 and none for item 617. Customer C will also receive none of the ordered units for item 643, while customer B will receive some of them. Therefore, customer B will have a higher percentage of orders fulfilled on time and in full than customer C. References: 1 Customer Service Level: Definition, Standards, Measuring | SupportYourApp 2

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

Enterprise resource planning (ERP) systems are software platforms that help organizations manage and integrate the essential parts of their businesses, such as finance, supply chain, operations, human resources, and more. ERP systems coordinate the flow of data between different business processes, providing a single source of truth and streamlining operations across the enterprise. ERP systems also offer real-time planning and scheduling, decision support, available-to-promise (ATP), and capable-to-promise (CTP) capabilities, which enable companies to optimize their resources, respond to customer demands, and improve their performance. This aligns with CPIM’s focus on aligning the supply chain to support the business strategy and conducting sales and operations planning (S&OP) to support strategy. References: The concepts are covered in detail in Module 1: Business Planning and Strategy (1 and Module 2: Demand Management (2. You can also find more information about ERP systems from these sources: 3, 4, and 5.

Moving average forecasting methods are best when demand shows high random variation, as they help to smooth out the noise and capture the underlying level of demand. Moving average methods use the average of the most recent observations as the forecast for the next period. They assign equal weights to all observations in the average, and drop the oldest observation when a new one becomes available. Moving average methods are not suitable for demand patterns that show a clear trend, consistent seasonality, or a cyclical pattern, as they cannot capture these components of demand. For these patterns, more sophisticated methods such as exponential smoothing or regression are needed. References: Forecasting with moving averages, APICS CPIM 8 Planning and Inventory Management | ASCM

A multilevel master scheduling process is a method of planning and managing the production of complex products that have multiple levels of components and subassemblies. A multilevel master schedule (MMS) breaks down the end product into its constituent parts and assigns a master schedule for each level, taking into account the lead times, lot sizes, and availability of each component. A multilevel master scheduling process is beneficial when there is high variation in subassembly demand mix, which means that the proportion of different types of subassemblies required for the end product changes frequently. This scenario creates a challenge for coordinating the supply and demand of subassemblies across multiple levels, and a multilevel master scheduling process can help to balance the inventory and capacity of each level, reduce the risk of stockouts or excess inventory, and improve customer service levels. References := CPIM Part 2 Exam Content Manual, Version 8.0, ASCM, 2021, p. 23. CPIM Part 2 Learning System, Version 8.0, Module 2, Section B, Topic 3.

Safety stock is the extra inventory that a company keeps to prevent stockouts or shortages due to uncertainties in demand, supply, or lead time. Safety stock acts as a buffer to protect the company from losing sales or disrupting operations. One of the factors that is used to determine safety stock is the forecast error distribution, which is the measure of how much the actual demand deviates from the forecasted demand. Forecast error distribution can be calculated by using statistical methods, such as standard deviation or mean absolute deviation, to find the average and the variability of the forecast errors. The higher the forecast error distribution, the more safety stock is needed to cover the potential demand fluctuations. Forecast error distribution is one of the components of the safety stock formula, which is:

Safety stock = Z x ∑LT x D

Where:

Z refers to the service level factor, which is the desired probability of not having a stockout.

∑LT refers to the standard deviation of lead time, which is the average variability of the time it takes to replenish inventory.

D is the average demand per unit of time.

References := CPIM Part 2 Exam Content Manual, Version 8.0, ASCM, 2021, p. 24. CPIM Part 2 Learning System, Version 8.0, Module 2, Section C, Topic 3. How To Calculate Safety Stock (With Examples and FAQs). What is Safety Stock? (Definition, Formulas, Best Practices).

 The time spent in queue by a specific manufacturing job is determined by the priority of the order. Priority is the relative importance or urgency of an order compared to other orders in the system. Priority can be assigned based on various criteria, such as due date, customer preference, profitability, or first-come-first-served. Priority determines the order in which jobs are processed at each workstation and affects the waiting time and flow time of each job. Higher priority jobs have shorter waiting times and lower priority jobs have longer waiting times. Priority can be used as a tool to manage the trade-offs between customer service, capacity utilization, and inventory levels. References:

Managing Supply Chain Operations, Chapter 7: Scheduling and Sequencing, Section 7.2: Priority Rules

CPIM Exam Content Manual, Module 6: Detailed Schedules, Section 6.2: Scheduling and Sequencing, Subsection 6.2.2: Priority Rules

Available-to-promise (ATP) is the uncommitted portion of a company’s inventory and planned production maintained in the master schedule to support customer-order promising. ATP is calculated by subtracting the customer orders and forecast from the projected on-hand inventory. The projected on-hand inventory is calculated by adding the beginning inventory and the master production schedule (MPS) and subtracting the customer orders. The largest customer order that could be accepted for delivery at the end of week 3 without making changes to the MPS is the ATP at the end of week 3. To calculate the ATP, we need to fill in the projected on-hand inventory for each week using the given information:

Table

Week

Forecast

Customer Orders

MPS

Projected On-Hand Inventory

ATP

1

20

22

0

43 + 0 - 22 = 21

21 - 20 = 1

2

20

17

0

21 + 0 - 17 = 4

4 - 20 = -16

3

20

10

80

4 + 80 - 10 = 74

74 - 20 = 54

The ATP at the end of week 3 is 54, which means that the company can promise 54 units of inventory to customers without changing the MPS. However, the question asks for the largest customer order that could be accepted, which means that we need to consider the existing customer orders as well. The customer orders for week 3 are 10, which means that the company has already committed 10 units of inventory to customers. Therefore, the largest customer order that could be accepted for delivery at the end of week 3 is 54 + 10 = 64 units. However, this is not one of the options given in the question. The closest option that is less than or equal to 64 is 61, which is option C12 References: 1: CPIM Part 1 - Section A - Module 1 - Session 4 - Master Scheduling 2: CPIM Part 1 - Section A - Module 1 - Session 5 - Available to Promise

The economic order quantity (EOQ) model is a formula that calculates the optimal order quantity that minimizes the total inventory costs, such as ordering costs and holding costs. The EOQ model is based on several assumptions, one of which is that the order preparation costs and inventory-carrying costs are constant and known. This means that the costs of placing and receiving an order, and the costs of storing and maintaining the inventory, do not change with the order quantity or the inventory level, and that they can be estimated accurately12.

The other options are not correct because:

•A. Customer demand is known but seasonal. This is not an assumption of the EOQ model, but rather a violation of it. The EOQ model assumes that the customer demand is constant and known, and that the orders are placed at regular intervals. However, if the customer demand is seasonal, it means that it varies over time and may not be predictable. This can affect the accuracy and applicability of the EOQ model, as the optimal order quantity may change with the demand pattern12.

•B. Items are purchased and/or produced continuously and not in batches. This is not an assumption of the EOQ model, but rather a contradiction of it. The EOQ model assumes that the items are purchased and/or produced in batches, and that the inventory level decreases gradually until it reaches zero, at which point a new order is placed and received. However, if the items are purchased and/or produced continuously, it means that there is no need to place orders or maintain inventory, and the EOQ model becomes irrelevant12.

•D. Holding costs, as a percentage of the unit cost, are variable. This is not an assumption of the EOQ model, but rather a complication of it. The EOQ model assumes that the holding costs, as a percentage of the unit cost, are constant and known. This means that the cost of storing and maintaining one unit of inventory does not depend on the unit cost of the item, and that it can be estimated accurately. However, if the holding costs, as a percentage of the unit cost, are variable, it means that the cost of storing and maintaining one unit of inventory changes with the unit cost of the item, and that it may not be easy to estimate. This can affect the accuracy and applicability of the EOQ model, as the optimal order quantity may depend on the unit cost of the item12.

References := 1 Economic Order Quantity Model in Inventory Management - Investopedia1 2 Economic Order Quantity: What Does It Mean and Who Is It Important For? - Investopedia2

 Prevention is a risk control strategy that involves avoiding or eliminating the sources of risk. By deciding not to pursue a business opportunity in a foreign market due to political instability and currency fluctuations, the company prevented the potential losses or disruptions that could arise from these factors. Mitigation is a risk control strategy that involves reducing the impact or likelihood of risk. Recovery is a risk control strategy that involves restoring normal operations after a risk event occurs. Wait and see is a risk control strategy that involves monitoring the risk situation and taking action only when necessary. References:

CPIM Part 2 Learning System, Module 1: Supply Chain Strategy, Section 1.5: Risk Management

CPIM Part 2 Learning System, Module 4: Execution and Control of Operations, Section 4.3: Risk Management

A house of quality (HOQ) chart is a product planning matrix that is used to show how customer requirements relate directly to the ways and methods companies can use to achieve those requirements. HOQ charts are part of the quality function deployment (QFD) method, which helps to ensure quality in product development and service delivery. HOQ charts use a design that resembles the outline of a house, with different sections representing different aspects of the product or service1. One of the functions that a HOQ chart aligns is competitive analysis with product design. Competitive analysis is the process of evaluating the strengths and weaknesses of the competitors in the market, and identifying the opportunities and threats they pose to the company. Product design is the process of creating the features, functions, and specifications of the product or service that meet the customer needs and expectations. A HOQ chart aligns these two functions by comparing the company’s product design with the competitors’ product design, and showing how well the company’s product design satisfies the customer requirements. This helps the company to identify the areas of improvement, differentiation, and innovation in the product design, and to create a competitive advantage in the market23. References: 1 House of Quality Tutorial - How to Fill Out a House of Quality | ASQ 4 2 House of quality | Explanation with example - IONOS 5 3 CPIM Exam References - Association for Supply Chain Management 1

 Management should support investments in new process technologies that align with the strategic objectives of the organization and provide a sustainable competitive advantage in the market. New process technologies may involve changes in existing systems, procedures, and skills, but these changes should be justified by the potential benefits and risks of the investment. Therefore, option D is correct. Option A is incorrect because requiring minimal changes in existing systems, procedures, and skills is not a sufficient criterion for investing in new process technologies. Option B is incorrect because relying on the recommendations of technical experts and equipment suppliers may not reflect the best interests of the organization or its customers. Option C is incorrect because providing significant cost-reduction opportunities for the company’s current products may not be enough to justify the investment in new process technologies, especially if the products have a short life cycle or low demand. References: CPIM Part 2 Exam Content Manual, Version 8.0, Section H: Quality, Continuous Improvement, and Technology, Subsection H.3: Technology, p. 85.