The master production schedule (MPS) and final assembly schedule (FAS) are most closely linked in the assemble-to-order (ATO) production strategy. ATO is a production strategy that produces customized products or services by assembling standardized components or modules according to customer specifications. The MPS is a plan that specifies the quantity and timing of finished products to be produced in a given period. The FAS is a plan that specifies the quantity and timing of final assembly operations to be performed in a given period. In the ATO strategy, the MPS and FAS are closely linked because the MPS determines the demand for the finished products, and the FAS determines the demand for the components or modules. The MPS and FAS are synchronized to ensure that the components or modules are available when needed for the final assembly, and that the finished products are delivered on time to the customers.

The MPS and FAS are not closely linked in the other production strategies. Make-to-stock (MTS) is a production strategy that produces standardized products or services in advance of customer demand, and stores them in inventory until they are sold. The MPS is based on the forecasted demand, and the FAS is not relevant for this strategy, as there is no customization involved. Make-to-order (MTO) is a production strategy that produces customized products or services from raw materials or components after receiving customer orders. The MPS is based on the actual customer orders, and the FAS is not relevant for this strategy, as there is no assembly involved. Engineer-to-order (ETO) is a production strategy that produces customized products or services that require engineering design or modification after receiving customer orders. The MPS is based on the actual customer orders, and the FAS is not relevant for this strategy, as there is no standardization involved.Reference: CPIM Exam Content Manual Version 7.0, Domain 4: Plan and Manage Supply, Section 4.1: Supply Planning Concepts, p. 23; Master Production Schedule; Final Assembly Schedule; Assemble to order.

Resource planning is a planning module that considers the longest-range planning goals. Resource planning is a method of determining the long-term capacity and resource requirements for a manufacturing system, based on the aggregate production plan, the sales and operations plan, and the business plan. Resource planning helps to align the production capacity and resources with the strategic objectives and goals of the organization. Resource planning considers the longest-range planning goals, which are usually expressed in terms of years or quarters.

The other options are not planning modules that consider the longest-range planning goals. Capacity requirements planning (CRP) is a planning module that calculates the capacity and load for each work center in a manufacturing system, based on the material requirements plan, the routing file, and the open order file. CRP helps to identify and resolve the capacity constraints and bottlenecks in the production process. CRP considers the short-range planning goals, which are usually expressed in terms of days or weeks. Input/output analysis is a planning module that compares the actual input/output of each work center in a manufacturing system with the planned input/output, based on the capacity requirements plan and the shop floor data. Input/output analysis helps to monitor and control the performance and efficiency of each work center. Input/output analysis considers the short-range planning goals, which are usually expressed in terms of days or weeks. Rough-cut capacity planning (RCCP) is a planning module that estimates the feasibility and adequacy of the key resources or work centers in a manufacturing system, based on the master production schedule and the bill of resources. RCCP helps to validate and adjust the master production schedule according to the available capacity and resources. RCCP considers the medium-range planning goals, which are usually expressed in terms of months or weeks.Reference: CPIM Exam Content Manual Version 7.0, Domain 4: Plan and Manage Supply, Section 4.2: Supply Planning Methods, p. 26; Resource Planning; Capacity Requirements Planning.

An outlier is a data point that falls outside of the expected range of the data, i.e., it is an unusually large or small data point1.Outliers can have a significant adverse impact on the forecasts, as they can skew the data distribution and distort the statistical analysis2. Therefore, it is important to detect and remove outliers from the demand data before generating forecasts.

One of the techniques that can be used to detect outliers is to use the standard deviation of the data, or the equivalent z-score, to determine the outlier limit3. For example, one approach is to set the lower limit to three standard deviations below the mean, and the upper limit to three standard deviations above the mean. Any data point that falls outside this range is detected as an outlier.

However, detecting outliers is not enough. The most appropriate next step would be to screen the outlier for manual review.This means that the detected outlier should be examined by a human expert to determine whether it is a true outlier or not, and whether it should be corrected or not4. This is because not all outliers are erroneous or irrelevant. Some outliers may be valid observations that reflect real changes in demand, such as seasonal peaks, promotional effects, or market trends. In such cases, correcting or removing the outliers may lead to inaccurate or biased forecasts.

Therefore, screening the outlier for manual review can help verify the cause and validity of the outlier, and decide on the best course of action. Some of the possible actions are:

Correcting the outlier: replacing the outlier with a more typical value based on historical data or expert judgment. This can smooth out the data and reduce the noise.

Separating the demand streams: splitting the data into two or more series based on different factors that influence demand, such as product type, customer segment, or distribution channel. This can isolate the outliers and allow different forecasting methods to be applied to each series.

Adjusting the forecasting model: modifying the parameters or assumptions of the forecasting model to account for the outliers, such as using a different smoothing factor, trend component, or error term. This can improve the fit and accuracy of the model.

Finished goods inventory is a type of inventory that consists of the final products that are ready for sale to the customers. Finished goods inventory investment is the value of the finished goods inventory held by the company. Service level is a measure of customer satisfaction that indicates the percentage of customer orders that can be fulfilled from the available inventory. Service level typically will increase when finished goods inventory investment is increased, because more inventory means more ability to meet the customer demand. However, the relationship between service level and finished goods inventory investment is not linear, but rather asymptotic. This means that service level will increase at a decreasing rate as finished goods inventory investment increases. In other words, the marginal benefit of increasing finished goods inventory investment will diminish as the service level approaches 100%. This is because there is a limit to how much inventory can improve the service level, and beyond a certain point, the additional inventory will not have a significant impact on customer satisfaction.

The results from responding to uncertainty in the supply chain by exaggerating lead times and increasing lot sizes is called the bullwhip effect. The bullwhip effect is a phenomenon that occurs when small changes in demand at the downstream end of the supply chain (such as retailers or customers) cause larger and larger fluctuations in demand at the upstream end of the supply chain (such as wholesalers, distributors, or manufacturers). The bullwhip effect can create inefficiencies, waste, and costs in the supply chain, as well as reduce customer satisfaction and profitability.

One of the causes of the bullwhip effect is the response to uncertainty in the supply chain by exaggerating lead times and increasing lot sizes. Lead time is the time between placing an order and receiving it from a supplier. Lot size is the quantity of units ordered or produced at a time. When there is uncertainty or variability in demand or supply, such as due to seasonality, promotions, disruptions, or forecasting errors, some supply chain members may try to cope by exaggerating lead times and increasing lot sizes. For example, a retailer may increase its safety stock or reorder point to avoid stockouts or delays, or a manufacturer may produce more than needed to take advantage of economies of scale or discounts. However, these actions can have unintended consequences, as they can distort the demand information and amplify the demand variability along the supply chain. This can result in excess inventory, low inventory turnover, high holding costs, poor service levels, lost sales, obsolete products, or capacity issues.

To prevent or reduce the bullwhip effect caused by responding to uncertainty in the supply chain by exaggerating lead times and increasing lot sizes, some possible solutions are:

Improving communication and collaboration among supply chain members to share accurate and timely demand information and forecasts.

Reducing lead times and lot sizes by using lean production techniques, just-in-time inventory systems, or quick response methods.

Implementing vendor-managed inventory (VMI) systems, where suppliers are responsible for managing and replenishing the inventory of their customers based on their actual consumption data.

Adopting advanced technologies, such as radio-frequency identification (RFID), artificial intelligence (AI), or blockchain, to enhance visibility, traceability, and coordination in the supply chain.