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Option overplanning is a technique used in an assemble-to-order (ATO) environment to address uncertainty in the product mix. An ATO environment is a production strategy where products are assembled from components or subassemblies after receiving customer orders1. Option overplanning is the practice of planning and stocking more components or subassemblies than the expected demand, based on historical data or forecasts2. The purpose of option overplanning is to increase the flexibility and responsiveness of the production system, by allowing the manufacturer to meet a variety of customer orders with different options or features. Option overplanning can help reduce the risk of stockouts, improve customer service, and capture new market opportunities.

Option overplanning is not used to verify appropriate inventory levels, schedule detailed production, or compensate for forecast bias. Verifying appropriate inventory levels is a function of inventory management, which involves monitoring and controlling the quantity and quality of materials and products in stock. Scheduling detailed production is a function of detailed scheduling, which involves allocating resources and setting priorities for specific tasks or orders in the production process. Compensating for forecast bias is a function of demand management, which involves adjusting the forecasts based on the difference between the actual and predicted demand.

References: Assemble-to-Order (ATO): Overview, Examples, Pros and Cons - Investopedia; Assemble-to-Order - Overview, How It Works, Advantages; [Assemble-to-Order (ATO) Definition | Operations & Supply Chain Dictionary]; [Assemble-to-Order (ATO) - MBA Skool-Study.Learn.Share.]; [InventoryManagement - Definition, Types, Objectives and Examples]; [Detailed Scheduling - an overview | ScienceDirect Topics]; [Forecast Bias - an overview | ScienceDirect Topics].

 Safety capacity in lean environments is where take time is greater than cycle time. Take time is the average time between the start of production of one unit and the start of production of the next unit1. Cycle time is the average time it takes to complete one unit of a product or service2. Safety capacity is the amount of capacity that is reserved to deal with unexpected events or fluctuations in demand or supply3.

In lean environments, the goal is to minimize waste and maximize value by producing only what the customer wants, when the customer wants it, and in the exact amount4. This means that the production system should be synchronized with the customer demand, and the take time should match the cycle time. However, in reality, there may be variations or uncertainties in the demand or supply, such as changes in customer preferences, seasonal patterns, quality issues, equipment breakdowns, or supplier delays. These variations or uncertainties can cause disruptions or imbalances in the production system, leading to stockouts, overproduction, waiting, defects, or rework5.

To cope with these variations or uncertainties, lean environments may use safety capacity as a buffer or contingency plan. Safety capacity is where take time is greater than cycle time, meaning that the production system has some extra capacity to produce more than what the customer currently demands. This extra capacity can be used to absorb the variations or uncertainties and maintain a smooth and stable production flow6. However, safety capacity should not be confused with excess capacity, which is where take time is much greater than cycle time, meaning that the production system has a lot of idle or underutilized resources. Excess capacity is a waste that should be eliminated or reduced in lean environments7.

Therefore, safety capacity in lean environments is where take time is greater than cycle time.

References: 1: Take Time Definition 1 2: Cycle Time Definition 2 3: Safety Capacity Definition 3 4: Lean Manufacturing Definition 4 5: The Seven Wastes of Lean 5 6: Capacity Planning Tools 6 7: Excess Capacity

 Failure Mode and Effects Analysis (FMEA) is a systematic, proactive method for identifying and evaluating the potential causes and impacts of failures in a process, product, or service1. It aims to anticipate and prevent failures by assessing the relative effect and risk of different failure modes1.

The use of FMEA would be most appropriate prior to a new product introduction (NPI). During the NPI phase, FMEA can be used to identify potential failure modes in the design of the product and assess their potential effects on the product’s performance and reliability. This allows for proactive measures to be taken to mitigate or eliminate these risks before the product is launched. FMEA is particularly useful in the early stages of design, as it helps in making informed decisions that can improve the quality and safety of the product1.

In contrast, using FMEA after a one-time quality incident investigation (A) or during evaluation of a new market opportunity © may not be as effective, as these situations do not involve the design or development of a product or process. While FMEA can be used during the define phase of a Six Sigma project (B), its most impactful application is during the design phase of a new product, where it can significantly influence the final outcome.

 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 humanexpert 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.

References: 1: Outlier Definition 1 2: How to Forecast Data Containing Outliers 2 3: How to Detect Outliers in Machine Learning – 4 Methods for Outlier Detection 1 4: How Outlier Detection and Correction Works 4 : How to Understand What is an Outlier in Forecasting 3

Sales and operations planning (S&OP) in a make-to-stock (MTS) environment is concerned with projecting inventory. S&OP is an integrated planning process that aligns demand, supply, and financial planning and is managed as part of a company’s master planning1. MTS is a traditional production strategy that is used by businesses to match inventory with anticipated consumer demand2. Inventory is the quantity and value of materials and products that are available in stock or in transit3.

S&OP in an MTS environment is concerned with projecting inventory because inventory is the key link between demand and supply. Inventory can be classified into three types: raw materials, work-in-process, and finished goods3. S&OP aims to balance the inventory levels of these types with the expected demand and supply plans, as well as the financial objectives of the company. S&OP can help optimize inventory management by:

Reducing inventory costs, such as holding, ordering, and shortage costs3.

Improving inventory turnover, which is the ratio of sales to average inventory3.

Increasing inventory availability, which is the percentage of orders that can be fulfilled from stock3.

Enhancing inventory quality, which is the degree of conformance to specifications and standards3.

The other options are not as relevant for S&OP in an MTS environment as inventory. Item forecasts are estimates of future demand for specific products or services based on historical data, market trends, or customer inputs4. Item forecasts are an input to S&OP, not an output. S&OP uses item forecasts to generate aggregate demand plans for product families or categories, which are then matched with aggregate supply plans for production capacity or resources1. Backlog is the quantity of customer orders that have been received but not yet fulfilled3. Backlog is not applicable for S&OP in an MTS environment, because MTS products are produced before customer orders are received. MTS products are delivered from stock, not from backlog. Bookings are the quantity of customer orders that have been received and confirmed3. Bookings are also not applicable for S&OP in an MTS environment, because MTS products are not dependent on customer orders. MTS products are based on forecasted demand, not actual demand.

References: Make To Stock (MTS): Definition, Example, and How It Works - Investopedia; Forecasting - Definition & Examples - ASQ; What is Sales and Operations Planning (S&OP) | Oracle; Inventory Management - Definition, Types, Objectives and Examples.

Measurements are quantitative or qualitative data that indicate the magnitude, frequency, or impact of the problem. Measurements help to define the problem clearly, objectively, and specifically, as well as to establish a baseline for improvement. Therefore, what should be added to the problem statement to make it more useful is measurements that help describe the problem. For example, a more useful problem statement could be “We have observed that the inventory system causes 15% of plastic parts to be damaged or misplaced every month, resulting in $10,000 of additional costs and 5% of customer complaints.”

 Exponential smoothing is a forecasting method that assigns weights to past observations, with more recent observations having higher weights. The alpha value is the smoothing constant that determines how much weight is given to the most recent observation. A low alpha value means that the forecast is based more on the historical average, while a high alpha value means that the forecast is more responsive to the latest changes in demand. A company with stable demand would typically use a low alpha value to smooth out random fluctuations and obtain a more accurate forecast. A beta value is another smoothing constant that is used for trend-adjusted exponential smoothing, which accounts for the presence of a linear trend in the data. A low beta value means that the trend component is based more on the historical average, while a high beta value means that the trend component is more responsive to the latest changes in demand. A company with stable demand would not need to use trend-adjusted exponential smoothing, since there is no significant trend in the data. References := CPIM Part 2 Exam Content Manual, Domain 3: Plan and Manage Demand, Section C: Forecast Demand, Subsection 2: Select appropriate forecasting technique(s) (p. 16)

Scrap allowance is a percentage or quantity of material that is expected to be lost or wasted during the production process. Scrap allowance is usually applied to the component items in a bill of materials (BOM), which is a document that lists the materials, quantities, and relationships required to produce an end item. An increase in the scrap allowance in an assembled item will result in replanning of the component items in material requirements planning (MRP), which is a system that calculates the timing and quantity of materials and resources needed to meet the production plan. Replanning of the component items in MRP means that the system will adjust the planned order releases, order quantities, and due dates of the component items to account for the increased scrap allowance. Replanning of the component items in MRP will ensure that enough material is available to meet the demand for the assembled item, and to avoid shortages or excess inventory.

References: CPIM Exam Content Manual Version 7.0, Domain 4: Plan and Manage Supply, Section 4.2: Implement Supply Plans, Subsection 4.2.1: Describe how to implement material requirements planning (MRP) (page 38).

 A work cell layout is a type of process layout that arranges equipment and workers according to the sequence of operations performed on a product or service. A work cell layout can improve space utilization by reducing the amount of floor space needed for production, eliminating unnecessary material handling and storage, and increasing the flexibility of the layout. A work cell layout can also reduce cycle time, improve quality, and enhance worker motivation. References: CPIM Exam Content Manual Version 7.0, Domain 6: Plan, Manage, and Execute Detailed Schedules, Section 6.2: Implement Detailed Schedules, Subsection 6.2.3: Describe the principles of work center design and layout (page 58).