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A fishbone diagram, also known as a cause-and-effect diagram or an Ishikawa diagram, is a tool for identifying and analyzing the possible causes of a problem or an effect. It is often used in quality management to find the root causes of defects or errors. A fishbone diagram has a main branch that represents the problem or effect, and several sub-branches that represent the categories of causes, such as people, processes, equipment, materials, environment, etc. Each category can have further sub-branches that represent more specific causes. A fishbone diagram would help a service organization determine the source of a quality-of-service issue by allowing them to visualize and organize the potential factors that contribute to the problem and identify the most likely cause. References: CPIM Part 2 Exam Content Manual, Domain 8: Manage Quality, Continuous Improvement, and Technology, Section 8.1: Quality Management Concepts and Tools, p. 59-60.

The percentage of defective parts is the proportion of units that do not meet the specification limits. The process capability index (Cpk) is a measure of how well the process can produce within the specification limits. Both the percentage of defective parts and the Cpk depend on the specification range and the process variation1.

If the supplier changes the tolerance range from + 0.3 mm to + 0.5 mm, the specification range becomes wider, which means that more units will fall within the specification limits and fewer units will be defective. Therefore, the percentage of defective parts decreases.

However, if the process variation remains unchanged, the Cpk will decrease, because Cpk is inversely proportional to the specification range2. A wider specification range means a lower Cpk, which indicates a lower process capability. A lower Cpk also implies a higher percentage of defective parts in relation to the process variation3.

Therefore, the correct answer is D. The percentage of defective parts decreases, and the process capability index decreases.

References:

Understanding Process Capability Index (Cpk) [With Calculator]

[Process Capability Index - an overview | ScienceDirect Topics]

Converting A Capability Index to PPM Defective - Accendo Reliability

 Rivalry among competing sellers is the degree of competition between firms in the same industry. It can affect the profitability and market share of the firms, and influence their strategies and decisions. Rivalry tends to be stronger when the demand is slow, the products are similar, the switching costs are low, and the capacity is high. Rivalry can also lead to innovation, differentiation, and customer satisfaction.

Rivalry among competing sellers is generally weaker when buyer demand is growing rapidly. This is because a fast-growing market offers more opportunities for expansion and growth for all the firms, without having to compete aggressively for a limited number of customers. A fast-growing market also reduces the pressure to cut prices or increase advertising, as the demand exceeds the supply. A fast-growing market can also attract new entrants, which can increase the rivalry in the long run, but in the short run, it can create more diversity and segmentation in the market.

References: Competitive Rivalry: What Is It and Why Is It Important? | Indeed.com; Industry Rivalry & Competition | Porter’s Five Forces.

 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

The processing times for each operation are:

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.

A priority control technique is a method of determining the sequence and timing of production orders in a manufacturing system. A priority control technique can be either push or pull, depending on whether the production orders are initiated by the upstream or downstream processes. A cellular production system is a process of manufacturing that organizes the machines and workers into self-contained cells that can produce different products efficiently and flexibly. A cellular production system is usually based on the principles of lean manufacturing and group technology, which aim to eliminate waste and improve quality.

A pull production activity control (PAC) is a priority control technique that is most appropriate for a firm using a cellular production system. A pull PAC is a method of controlling the flow of materials and work-in-progress in a manufacturing system by using signals from the downstream processes to trigger the release of production orders from the upstream processes. A pull PAC helps to reduce inventory, lead time, and overproduction, as well as to synchronize the production with the customer demand. A pull PAC can be implemented using various techniques, such as kanban cards, containers, or electronic signals.

A shortest processing time (SPT) rule is a priority control technique that assigns the highest priority to the production order that has the shortest processing time at each workstation. An SPT rule helps to minimize the average waiting time and flow time of the production orders, as well as to increase the utilization of the machines and workers. However, an SPT rule does not consider the due dates or the customer demand of the production orders, and may result in poor customer service or low delivery performance.

A distribution requirements planning (DRP) is a priority control technique that determines the quantity and timing of finished goods to be delivered to various distribution centers or customers. A DRP is based on the forecasted demand, the inventory status, and the transportation lead time of the finished goods. A DRP helps to optimize the inventory level, reduce stockouts, and improve customer service. However, a DRP is not suitable for a cellular production system, as it does not control the flow of materials and work-in-progress within the manufacturing system.

A push production activity control (PAC) is a priority control technique that initiates the production orders based on the master production schedule or the forecasted demand from the upstream processes. A push PAC releases the production orders in batches or lots, regardless of the capacity or status of the downstream processes. A push PAC may result in high inventory, long lead time, and overproduction, as well as low flexibility and responsiveness to customer demand. A push PAC is not compatible with a cellular production system, as it contradicts the principles of lean manufacturing and group technology. References: CPIM Exam Content Manual Version 7.0, Domain 6: Plan, Manage, and Execute Detailed Schedules, Section 6.1: Detailed Scheduling Concepts, p. 36; Cellular manufacturing; [Production Activity Control].

 One of the trade-offs that should be evaluated when determining where to place inventory in a multi-echelon supply chain network is the transportation cost and delivery time. A multi-echelon supply chain network is a system of interconnected stages or echelons that perform different functions, such as production, distribution, and retailing, to deliver products or services to the end customers1. Inventory placement is the decision of how much and where to hold inventory in the supply chain network to balance the costs and service levels2.

Transportation cost is the expense of moving products or materials from one echelon to another in the supply chain network. Transportation cost depends on factors such as distance, mode, volume, weight, fuel, and tariffs3. Delivery time is the duration of moving products or materials from one echelon to another in the supply chain network. Delivery time depends on factors such as speed, reliability, frequency, and congestion3.

There is a trade-off between transportation cost and delivery time when determining where to place inventory in a multi-echelon supply chain network. Generally, holding more inventory closer to the customers can reduce the delivery time and increase the service level, but it can also increase the transportation cost and the inventory holding cost4. On the other hand, holding less inventory farther from the customers can reduce the transportation cost and the inventory holding cost, but it can also increase the delivery time and decrease the service level4. Therefore, finding the optimal inventory placement requires balancing the transportation cost and delivery time trade-off.

Some of the methods or tools that can help evaluate the transportation cost and delivery time trade-off are:

Network optimization: a technique that uses mathematical models to optimize the design and configuration of a supply chain network by minimizing the total costs (including transportation and inventory costs) while satisfying the service level requirements.

Multi-echelon inventory optimization: a technique that uses mathematical models to optimize the allocation and sizing of safety stocks across multiple echelons of a supply chain network by minimizing the total costs (including transportation and inventory costs) while satisfying the service level requirements.

Simulation: a technique that uses computer software to mimic the behavior and performance of a supply chain network under different scenarios and assumptions. Simulation can help evaluate the impact of different inventory placement strategies on the transportation cost and delivery time.

Therefore, transportation cost and delivery time is one of the trade-offs that should be evaluated when determining where to place inventory in a multi-echelon supply chain network.

References: 1: Multi-Echelon Inventory Optimization 2 2: Inventory Placement Definition 3 3: Transportation Cost Definition 4 4: The Tradeoff Between Inventory Costs And Transportation Costs : Supply Chain Network Design: Applying Optimization and Analytics to … 1

Return on investment (ROI) is a financial ratio that measures the profitability of an investment relative to its cost. ROI is calculated by dividing the net income (or profit) generated by the investment by the total cost of the investment. ROI is decreased by any activity that reduces the net income or increases the cost of the investment. Increasing cost of sales is an activity that decreases ROI because it reduces the net income generated by the sales revenue. Cost of sales (or cost of goods sold) is the direct cost of producing or purchasing the goods or services sold by an organization. Cost of sales includes materials, labor, and overhead costs. Increasing cost of sales means that the organization spends more money to produce or acquire the same amount of goods or services, which lowers its profit margin and ROI.

References: CPIM Exam Content Manual Version 7.0, Domain 8: Manage Quality, Continuous Improvement, and Technology, Section 8.1: Develop Quality and Continuous Improvement Plans, Subsection 8.1.2: Describe how to develop a business case for quality and continuous improvement initiatives (page 74).

A buffer in the theory of constraints (TOC) is a level of inventory that is placed before the governing constraint or the bottleneck to prevent it from being starved or idle. Buffers are used to immunize the system’s performance from variability in demand or production. Buffers are part of the drum buffer rope method of scheduling and managing operations that have constraints. The purpose of a buffer in TOC is to prevent unplanned idleness of the resource, which is the most important factor that determines the throughput of the system. Throughput is the rate at which the system generates money through sales. If the resource is idle, then the system loses potential throughput and profit. Therefore, buffers are designed to ensure that there is always enough work available for theresource to process, regardless of any fluctuations or disruptions in the upstream or downstream processes.

References: Theory of constraints - Wikipedia; Drum Buffer Rope and Theory of Constraints - opexlearning.com.

Fill rate is the percentage of customer orders that are fulfilled without running out of inventory or placing backorders1. Fill rate is an important measure of customer service and inventory management efficiency. A high fill rate indicates that the company can meet customer demand in a timely and accurate manner, while a low fill rate suggests that the company is struggling to satisfy customer expectations.

Operating costs are the expenses associated with running a business, such as rent, utilities, wages, transportation, etc2. Operating costs are influenced by various factors, such as production volume, inventory level, technology, and quality. A high operating cost means that the company spends more money to produce and deliver its products or services, while a low operating cost means that the company spends less money to do so.

Service level is the measure of how well a company delivers its products or services to its customers, based on criteria such as availability, timeliness, quality, and satisfaction3. Service level is affected by various factors, such as demand variability, supply reliability, capacity utilization, and customer feedback. A high service level means that the company meets or exceeds customer expectations, while a low service level means that the company fails or falls short of customer expectations.

As the company continues its initiative to increase the fill rate on these parts to 100%, it is most likely that operating costs will increase faster than service level. This is because increasing the fill rate requires increasing the inventory level, which in turn increases the carrying costs, such as warehousing, insurance, taxes, and obsolescence4. Moreover, increasing the fill rate also requires reducing the variability and uncertainty in demand and supply, which may involve investing in more advanced technology, improving quality control, enhancing supplier relationships, or implementing demand management techniques5. These actions can also increase the operating costs of the company.

However, increasing the fill rate does not necessarily increase the service level at the same rate. This is because service level depends not only on fill rate, but also on other factors, such as delivery speed, order accuracy, product quality, and customer satisfaction6. Therefore, increasing the fill rate may not be enough to improve the service level significantly. In fact, there may be a point of diminishing returns, where increasing the fill rate beyond a certain level does not result in a proportional increase in service level. For example, increasing the fill rate from 95% to 99% may have a noticeable impact on service level, but increasing it from 99% to 100% may have a negligible impact on service level.

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

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

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

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

References := Priority Rules - Tripod, Dispatching rules - Oxford Reference, Sequencing Rules and Due-Date Assignments in a Job Shop - JSTOR