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 A queue is the number of hours that a work order sits in front of a work center before it starts. It also includes the move time required to physically move partially completed items from the previous operation to the next work center1. A queue is used to provide a buffer between the time that a component arrives at a machine or work center and when the operation actually starts2. A queue can help to neutralize delays in previous operations and to reduce the waiting time and flow time of the work orders3.

An order that moves into a work center on schedule following completion of a previous operation will move into the queue in priority sequence. This means that the order will be placed in the queue according to its priority level, which is determined by the priority control technique used for the production system. A priority control technique is a method of determining the sequence and timing of production orders in a manufacturing system4. Some examples of priority control techniques are first-come, first-served (FCFS), shortest processing time (SPT), earliest due date (EDD), and critical ratio (CR). Depending on the priority control technique, an order may move to the beginning, end, or middle of the queue.

An order will not necessarily move to the end or the beginning of the queue, as these positions depend on the priority level of the order and the other orders in the queue. An order will not move into the queue when there is capacity available, as this implies that there is no queue at all. Capacity is the amount of time or resources available for production at a work center5. Capacity can be affected by factors such as machine availability, labor skills, setup time, and maintenance. Capacity planning is a function of determining and adjusting the optimal level of resources needed to meet the demand5.

 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

Available capacity is the difference between the required capacity and planned operating capacity1. It refers to how capable the resources in an organization are in formulating and implementing strategy1. To calculate available capacity, factors such as the number of machines or workers, the number of shifts, utilization, and efficiency are considered1. Efficiency, in particular, is a crucial factor as it measures how effectively resources are used to produce output. It is calculated as the ratio of actual output to standard output within a specific time period1. Therefore, efficiency directly impacts available capacity by determining how much output can be produced with the available resources and time.

The other options, while important in production and operations management, are not directly used to calculate available capacity:

Productivity measures the output per unit of input and is more about overall performance rather than available capacity.

Load refers to the amount of work assigned to a resource or facility but does not directly indicate available capacity.

Yield measures the percentage of products that meet quality standards out of total units produced but does not directly calculate available capacity.

References: Capacity planning - Wikipedia.

The measurement that indicates there may be bias in the forecast model is the tracking signal. The tracking signal is a ratio of the cumulative forecast error to the mean absolute deviation (MAD). The cumulative forecast error is the sum of the differences between the forecasted and actual values over a period of time. The MAD is the average of the absolute values of the forecast errors. The tracking signal can help detect and measure the bias of a forecast model by comparing the magnitude and direction of the forecast errors. A positive tracking signal indicates that the forecast model is consistently over-forecasting, while a negative tracking signal indicates that the forecast model is consistently under-forecasting. A zero tracking signal indicates that there is no bias in the forecast model. A rule of thumb is that if the tracking signal exceeds a certain threshold, such as ±4, then there is a significant bias in the forecast model that needs to be corrected.

The other measurements do not indicate bias in the forecast model, but rather other aspects of the forecast accuracy or variability. The MAD is a measure of the average error or deviation of the forecast model from the actual values. The MAD does not indicate bias, as it does not consider thedirection or sign of the errors. A low MAD indicates a high accuracy of the forecast model, while a high MAD indicates a low accuracy of the forecast model.

The standard deviation is a measure of the dispersion or variation of the forecast errors around their mean. The standard deviation does not indicate bias, as it does not consider the direction or sign of the errors. A low standard deviation indicates a low variability or uncertainty of the forecast model, while a high standard deviation indicates a high variability or uncertainty of the forecast model.

The variance is a measure of the squared deviation or dispersion of the forecast errors around their mean. The variance does not indicate bias, as it does not consider the direction or sign of the errors. The variance is related to the standard deviation, as it is equal to the square of the standard deviation. A low variance indicates a low variability or uncertainty of the forecast model, while a high variance indicates a high variability or uncertainty of the forecast model.

References := Forecast KPI: RMSE, MAE, MAPE & Bias | Towards Data Science, A Critical Look at Measuring and Calculating Forecast Bias – Demand Planning, Forecast bias - Wikipedia

Customer relationship management (CRM) is a program that uses data and technology to manage the interactions and relationships with customers. CRM helps to understand the needs, preferences, and behaviors of customers, and to provide them with better products, services, and experiences. In a rapidly changing business environment, a primary advantage of an effective CRM program is earlier identification of shifts in customer preferences. This means that CRM can help to detect and anticipate the changes in customer demand, tastes, or expectations, and to respond accordingly. This can help to improve customer satisfaction, loyalty, and retention, as well as to gain a competitive edge in the market. CRM does not necessarily reduce forecast variability, which is the degree of difference between the actual demand and the forecasted demand. CRM does not necessarily reduce customer order changes, which are the modifications or cancellations of orders by customers. CRM does not necessarily reduce customer defections, which are the losses of customers to competitors or other alternatives. References: CPIM Exam Content Manual Version 7.0, Domain 3: Plan and Manage Demand, Section 3.1: Demand Management Concepts, p. 16; Customer relationship management; Customer Relationship Management (CRM) Definition.