Explanation: A purchase order is issued to the supplier for each delivery requirement is an activity that represents waste in a system. Waste is any activity or process that does not add value to the customer or the product, but consumes resources, time, or money. Waste can reduce the efficiency, productivity, and quality of the system, as well as increase the costs, defects, or delays. Waste can be classified into seven types: overproduction, inventory, transportation, motion, waiting, overprocessing, and defects1.

Issuing a purchase order to the supplier for each delivery requirement is an example of overprocessing waste. Overprocessing waste is any activity or process that is unnecessary or excessive for meeting the customer needs or specifications. Overprocessing waste can result from poor communication, unclear requirements, redundant tasks, or outdated procedures. Issuing a purchase order to the supplier for each delivery requirement is an overprocessing waste because it involves more paperwork, approvals, and transactions than needed. It can also create confusion, errors, or delays in the delivery process. A better way to eliminate this waste is to use a pull system, such as kanban2, that signals the supplier to deliver only when there is a demand from the customer.

The other options are not activities that represent waste in a system. More kanbans with smaller quantities are added to the supply chain is an activity that reduces waste in a system. Kanban is a pull system that uses visual signals, such as cards or containers, to indicate when and how much to produce or deliver. Kanban can help reduce waste by synchronizing the production and delivery processes with the customer demand, minimizing inventory levels, improving quality and efficiency, and preventing overproduction or underproduction3. Adding more kanbans with smaller quantities can help reduce inventory waste by lowering the holding costs, transportation costs, or obsolescence costs of inventory. It can also help reduce overproduction waste by producing or delivering only what is needed by the customer.

A kanban is eliminated from the system is an activity that reduces waste in a system.
Eliminating a kanban from the system means reducing the number of signals or containers used in the production or delivery process. Eliminating a kanban from the system can help reduce waste by increasing the throughput and velocity of the process, reducing cycle times and lead times, improving responsiveness and flexibility, and enhancing customer satisfaction4.

A production forecast is issued to the supplier is not an activity that represents waste in a system. A production forecast is an estimate of the future demand or sales of a product or service. A production forecast can help plan and manage the production and delivery processes by determining how much and when to produce or deliver. A production forecast can help reduce waste by optimizing the use of resources and capacity, minimizing inventory levels and costs, improving service levels and quality, and avoiding stockouts or shortages5. Issuing a production forecast to thesupplier can help align the production and delivery processes with the customer demand and expectations.

References := The 7 Wastes With Examples: How to Identify Them | Lean Manufacturing, What Is Overprocessing Waste? Definition And Examples, Kanban - Wikipedia, How To Reduce Inventory With Kanban | Lean Manufacturing, Production Forecasting - an overview | ScienceDirect Topics

Explanation: A move from acceptance sampling to 100% inspection would be caused by the circumstance of downstream operators encountering recurring defects. Acceptance sampling is a quality control technique that uses statistical sampling to determine whether to accept or reject a production lot of material. It is employed when one or several of the following hold: testing is destructive; the cost of 100% inspection is very high; and 100% inspection takes too long1. 100% inspection is a quality control technique that examines every item in a production lot for defects or nonconformities. It is employed when the cost of passing a defective item is very high; testing is nondestructive; and 100% inspection does not take too long2.

Downstream operators are the workers or machines that perform the subsequent operations or processes on the products after they have been inspected or tested.
Downstream operators encountering recurring defects means that the products that have passed the acceptance sampling or testing are still found to be defective or nonconforming by the downstream operators. This can indicate that the acceptance sampling or testing is not effective or reliable in detecting or preventing defects or nonconformities. This can also result in negative consequences, such as rework, waste, delays, customer complaints, or safety issues. Therefore, this circumstance would cause a move from acceptance sampling to 100% inspection, as it would require a more thorough and rigorous quality control technique to ensure that no defective or nonconforming products are passed to the downstream operators.

The other options are not circumstances that would cause a move from acceptance sampling to 100% inspection. History shows that the quality level has been stable from lot to lot is not a circumstance that would cause a move from acceptance sampling to 100% inspection, but rather a circumstance that would support the use of acceptance sampling. Quality level is the proportion of conforming items in a production lot. Quality level being stable from lot to lot means that there is little variation or fluctuation in the quality of the products over time. This can indicate that the production process is under control and consistent in meeting the quality standards or specifications. Therefore, this circumstance would support the use of acceptance sampling, as it would reduce the risk of accepting a defective lot or rejecting a conforming lot.

The company uses one of its qualified suppliers is not a circumstance that would cause a move from acceptance sampling to 100% inspection, but rather a circumstance that would support the use of acceptance sampling. A qualified supplier is a supplier that has met certain quality, delivery, and service standards and has been approved by the company to supply goods or services without inspection or testing. A qualified supplier is expected to maintain a high level of performance and reliability, as well as to report any issues or deviations that may affect the delivery process. Therefore, this circumstance would support the use of acceptance sampling, as it would reduce the need for 100% inspection by relying on the supplier’s quality assurance system.

The percent of defects is expected to be greater than 5% is not a circumstance that would cause a move from acceptance sampling to 100% inspection, but rather a circumstance that would require a change in the acceptance sampling plan. The percent of defects is the proportion of defective items in a production lot. The percent of defects being expected to be greater than 5% means that there is a high probability of finding defective items in the production lot. This can indicate that the production process is out of control or inconsistent in meeting the quality standards or specifications. Therefore, this circumstance would require a change in the acceptance sampling plan, such as reducing the acceptable quality limit (AQL), increasing the sample size, or decreasing the acceptance number, to increase the likelihood of rejecting a defective lot.

References := Acceptance Sampling - an overview | ScienceDirect Topics, What Is Acceptance Sampling? Definition And Examples

Explanation: Capable-to-promise (CTP) is a method of order promising that considers both material and capacity availability. CTP is more appropriate than available-to-promise (ATP), which only considers material availability, in environments where the production process is complex, customized, or resource-intensive, and where the demand is uncertain or variable. CTP can provide more accurate and realistic delivery dates, as well as optimize the use of resources and reduce inventory costs.

Among the options given, specialty chemicals packaged and shipped to order is the most suitable environment for CTP. This is because specialty chemicals are often produced in small batches or on demand, according to the specific requirements and preferences of each customer. Therefore, the production process requires high flexibility and customization, as well as careful coordination of materials and capacity. The demand for specialty chemicals may also vary depending on the market conditions and customer needs. CTP can help the company to promise delivery dates that take into account the availability of both materials and capacity, as well as the production lead time and transportation time.

The other options are less suitable for CTP, as they are more likely to use standard or mass production processes, where the products are made in large quantities or in advance, and where the demand is more stable or predictable. In these environments, ATP may be sufficient to promise delivery dates based on material availability alone, without considering capacity constraints.

References : What is a Capable-to-Promise System (CTP System … - Techopedia; Order promising - Supply Chain Management | Dynamics 365; Capable to Promise (CTP) (MRP and Supply Chain Planning Help) - Oracle; Calculate sales order delivery dates using CTP - Supply Chain ….

Explanation: Exponential smoothing is a forecasting technique that uses a weighted average of past and present data to predict future values. It is suitable for time series data that have a stable or slowly changing trend and no significant seasonal variations.
Exponential smoothing assigns more weight to the most recent data, giving it a higher influence on the forecast. This makes it more responsive to changes in demand patterns than other techniques, such as moving average or time series decomposition, which use fixed weights or historical data. The Delphi method is a qualitative technique that involves a panel of experts who provide their opinions and feedback on a topic through multiple rounds of surveys. It is not based on historical data or mathematical formulas, but rather on human judgment and consensus. Therefore, it is not appropriate for developing a forecast.

References: CPIM Part 2 Exam Content Manual, Version 7.0, Domain 3: Plan and Manage Demand, Section A: Demand Management, Subsection 2: Forecasting Techniques and Methods, p. 14-15.

Explanation: The horizon for forecasts that are input to the sales and operations planning (S&OP) process should be long enough that required resources can be properly planned. The S&OP process is a cross-functional process that aligns the demand and supply plans of an organization. The S&OP process consists of several steps, such as data gathering, demand planning, supply planning, pre-S&OP meeting, executive S&OP meeting, and S&OP implementation. The output of the S&OP process is the production plan, which is a statement of the resources needed to meet the aggregate demand plan over a mediumterm horizon. The production plan can be stated in different units of measure depending on the type of manufacturing environment, such as hours, units, tons, or dollars. The horizon for forecasts that are input to the S&OP process should be long enough that required resources can be properly planned, meaning that the organization can anticipate and allocate the necessary capacity, materials, labor, equipment, and facilities to meet the expected demand. The horizon for forecasts should also match the lead time for acquiring or changing the resources, as well as the planning cycle for updating the production plan.

References: CPIM Exam Content Manual Version 7.0, Domain 4: Plan and Manage Supply, Section 4.1: Develop Supply Plans, Subsection 4.1.2: Describe how to develop a production plan (page 36).

Explanation: A capacity-leading strategy is a proactive approach that adds or subtracts capacity in anticipation of future market demand. It is an aggressive strategy with the objective of improving the service level and decreasing lead time1. An advantage of adopting a capacity-leading strategy is that there issufficient capacity to meet demand, which means that the organization can satisfy customer needs and expectations, as well as capture new market opportunities. A capacity-leading strategy can also help the organization gain a competitive edge by being the first to offer new products or services, or by lowering prices due to economies of scale2.

The other options are not advantages of adopting a capacity-leading strategy. There is not necessarily sufficient demand to consume capacity, which means that the organization may face overcapacity problems, such as high inventory costs, low utilization rates, and reduced profitability3. All demand is not satisfied, and profit is not maximized, because there may be other factors that affect customer satisfaction and profitability, such as quality, price, or service4. Overcapacity problems are not minimized, but rather increased, by adopting a capacity-leading strategy, because the organization may have more capacity than needed if demand does not increase as expected3.

References: CPIM Part 2 Exam Content Manual, Domain 4: Plan and Manage Supply, Section 4.1: Supply Management Concepts and Tools, p. 33-34; Capacity Planning Strategies: Types, Examples, Pros And Cons - Toggl; Lead Capacity Strategy, Lead Demand Strategy - UniversalTeacher.com; Capacity Planning Strategies For End-to-End Supply Chain Profitability; Capacity Planning Strategies: Types, Examples, Pros And Cons - Toggl.

Explanation: Obsolescence is the loss of value or usefulness of an item due to changes in technology, fashion, customer preferences, or other factors. Obsolescence is considered a carrying cost, because it is an expense associated with holding inventory over a period of time1. Carrying costs are the various costs a business pays for holding inventory in stock, such as warehousing, insurance, taxes, depreciation, and opportunity costs2. Obsolescence can increase the carrying costs of inventory,because it can reduce the demand and sales potential of the item, and may require the item to be written off or sold at a lower price3.

The other options are not considered carrying costs, because they are not related to holding inventory in stock. Setup is the cost of preparing a machine or a process for production. Transportation is the cost of moving goods from one place to another. Scrap rate is the percentage of defective or unusable units produced in a process. These costs are more related to production or distribution activities than inventory holding activities.

Explanation: Manufacturing lead time is the time required to acquire, manufacture, or ship goods1. It includes the time required for preprocessing, processing, and postprocessing of a finished product2. The formula for manufacturing lead time is:

Manufacturing lead time = Preprocessing time + Processing time + Postprocessing time Preprocessing time is the time needed for handling the order, making sales order, and preparing supplies2. Processing time is the period when the product is manufactured or collected. Postprocessing time is the time of delivery2.

In this question, we are given the following information:
The product is Item A, which requires Operations 10, 20, and 30 in a work cell
The order quantity is 10 units
The operations require no set up time
To find the shortest manufacturing lead time, we need to assume that the preprocessing and postprocessing times are zero, and that the operations can be performed in parallel.
This means that the work cell can process 10 units of Item A simultaneously, without any waiting or transportation time.
Therefore, the shortest manufacturing lead time is equal to the longest processing time among the three operations. Since Operation 10 has the longest processing time of 1 hour per unit, the shortest manufacturing lead time is:

Manufacturing lead time = 1 hour x 10 units = 10 hours

However, this answer is not among the options given. Therefore, we need to consider another possibility: that the work cell can only process one unit of Item A at a time, and that the operations must be performed in sequence. This means that each unit of Item A must complete Operation 10 before moving to Operation 20, and then to Operation 30. In this case, the shortest manufacturing lead time is equal to the sum of the processing times for all three operations multiplied by the order quantity. Therefore, the shortest manufacturing lead time is:

Manufacturing lead time = (1 hour + 0.5 hour + 0.5 hour) x 10 units = 20 hours

However, this answer is also not among the options given. Therefore, we need to consider one more possibility: that the work cell can process one unit of Item A at a time, but that the operations can be performed in parallel with overlapping times. This means that as soon as one unit of Item A finishes Operation 10, it moves to Operation 20, while another unit of Item A starts Operation 10. Similarly, as soon as one unit of Item A finishes Operation 20, it moves to Operation 30, while another unit of Item A starts Operation 20. In this case, the shortest manufacturing lead time is equal to the sum of the processing times for all three operations plus the processing times for each operation multiplied by the order quantity minus one. Therefore, the shortest manufacturing lead time is:

Manufacturing lead time = (1 hour + 0.5 hour + 0.5 hour) + (1 hour + 0.5 hour + 0.5 hour) x (10 units - 1) = 12 hours
This answer is among the options given and it is the shortest possible manufacturing lead time under these assumptions. Therefore, the correct answer is B. 12 hours.
References : Manufacturing Lead Time; How to Calculate and Reduce Lead Time; How To Calculate Lead Time?; What Is Lead Time? How to Calculate Lead Time in Different Industries.

Explanation: 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.

Explanation: A statistical safety stock calculation is a method to determine the optimal amount of safety stock based on the demand variability, the lead time variability, and the desired service level. A statistical safety stock calculation would be appropriate for end items with stable demand, because these items have a predictable demand pattern and a low coefficient of variation. For items with unstable or unpredictable demand, such as components used in multiple end items, new products at time of introduction, or supplyconstrained raw materials, a statistical safety stock calculation may not be accurate or reliable, and other methods such as judgmental or simulation-based approaches may be preferred.
References: CPIM Part 2 Exam Content Manual, Domain 5: Plan and Manage Inventory, Section 5.4: Inventory Management Techniques, p. 29.

Explanation: In a make-to-stock (MTS) environment, improving estimates of customer demand would improve the trade-off between the cost of inventory and the level of customer service. MTS is a production strategy that manufactures products in anticipation of customer demand, based on forecasts. The main challenge of MTS is to balance the inventory costs and the customer service levels. Inventory costs include holding costs, ordering costs, and obsolescence costs. Customer service levels measure the ability to meet customer demand without delay or stockout. A trade-off exists between these two objectives, as higher inventory levels can increase customer service levels but also increase inventory costs, and vice versa.

Improving estimates of customer demand can help reduce the trade-off between inventory costs and customer service levels, as it can lead to more accurate production planning and inventory management. By forecasting demand more accurately, a company can avoid overproduction or underproduction, which can result in excess inventory or stockouts, respectively. By producing the right amount of products at the right time, a company can lower its inventory costs and increase its customer service levels.

Eliminating raw material stockouts would not improve the trade-off between inventory costs and customer service levels in a MTS environment, as it would not affect the finished goods inventory or the customer demand. Raw material stockouts are a supply issue that can disrupt the production process and cause delays or shortages in the finished goods. However, they do not directly impact the inventory costs or the customer service levels of the finished goods, which are determined by the demand forecasts and the production plans. Decreasing the frozen time zone would not improve the trade-off between inventory costs and customer service levels in a MTS environment, as it would increase the variability and uncertainty in the production process. The frozen time zone is the period of time in which no changes can be made to the production schedule, as it is considered fixed and final. Decreasing the frozen time zone would allow more flexibility and responsiveness to changes in demand or supply, but it would also increase the risk of errors, disruptions, or inefficiencies in the production process. This could resultin higher production costs, lower quality, or longer lead times, which could negatively affect the inventory costs and the customer service levels.

Reducing manufacturing overtime would not improve the trade-off between inventory costs and customer service levels in a MTS environment, as it would reduce the production capacity and output. Manufacturing overtime is a way of increasing the production capacity and output by extending the working hours of the production resources, such as labor or equipment. Reducing manufacturing overtime would lower the production costs, but it would also lower the production output. This could result in insufficient inventory to meet customer demand, which could lower the customer service levels.

References := Make-to- Stock (MTS) Definition, Make-to-Stock (MTS) vs Make-to-Order (MTO) | TradeGecko, Value Creation: Assessing the Cost-Service Trade-off