Pass Using CPIM-Part-2 Exam Dumps

Finite loading is a capacity planning approach that considers adjustments to plans based on planned capacity utilization. It does not allow overloading of resources and schedules operations only when there is enough capacity available. Finite loading creates a more realistic schedule for the production processes than other approaches, such as infinite loading, that ignore the capacityconstraints and assume that the due dates of orders are absolute. Finite loading is not highly dependent on advanced computer software, although it can benefit from it. It is not only managed by shop floor supervisors, but also by planners and schedulers. It can use historical information, but it is not the only approach that can do so. Therefore, the fundamental difference between finite loading and other capacity planning approaches is that it considers adjustments to plans based on planned capacity utilization. References := CPIM Part 2 Exam Content Manual, Domain 6: Plan, Manage, and Execute Detailed Schedules, Section B: Schedule Production Activities, Subsection 1: Develop a detailed production schedule (p. 28)

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

The S&OP process in an ATO production environment focuses on control of key intermediate part inventory, which are the components or subassemblies that are produced in advance and assembledto order when the customer order is received. By controlling the key intermediate part inventory, the S&OP process can balance the demand and supply of the final products, while reducing the lead time and inventory costs. The key intermediate part inventory is also known as the decoupling point, where the production process switches from MTS to MTO mode. The S&OP process can determine the optimal level of key intermediate part inventory based on the forecast and backlog of customer orders, as well as the production capacity and costs.

The other options are less relevant for the S&OP process in an ATO production environment. End product backlog refers to the customer orders that have not been fulfilled yet. Finished goods inventory refers to the final products that are ready for sale. Raw material inventory refers to the basic materials that are used to produce the components or subassemblies. These types of inventory are more applicable for MTS or MTO production environments, where the production process is either entirely based on forecast or entirely based on sales order. In an ATO production environment, the S&OP process does not need to control these types of inventory, as they are either minimal or nonexistent. References : CPIM Part 2 Exam Content Manual, Domain 4: Plan and Manage Supply, Section B: Production Planning and Control, Subsection 1: Production Strategies and Techniques, Page 19; Demand management process in assemble to order (ATO) environment; Assemble-to-Order (ATO): Overview, Examples, Pros and Cons.

 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 ….