APICS CPIM-Part-2 - Certified in Planning and Inventory Management(Part 2)

Input/output control (I/O) is a type of tool that is used for monitoring a capacity plan. A capacity plan is a statement of the resources needed to meet the production plan over a medium-term horizon. A capacity plan can be stated in different units of measure depending on the type of manufacturing environment, such as hours, units, tons, or dollars. Input/output control (I/O) is a method of measuring and comparing the actual input and output of a work center or a production line against the planned input and output. Input is the amount of work that is released to the work center or the production line, and output is the amount of work that is completed by the work center or the production line. Input/output control (I/O) helps to monitor the performance and efficiency of the work center or the production line, and to identify any deviations or problems that may affect the capacity plan. Input/output control (I/O) also helps to adjust the input or output levels as necessary to maintain the balance between demand and supply, and to achieve the desired throughput.

References: CPIM Exam Content Manual Version 7.0, Domain 6: Plan, Manage, and Execute Detailed Schedules, Section 6.3: Monitor Detailed Schedules, Subsection 6.3.2: Describe how to monitor input/output control (I/O) (page 60).

Visual control boards are tools that display the key performance indicators (KPIs) and metrics of a production system in a graphical and easy-to-understand format. Visual control boards are usually located at several locations within the production facility, such as the work centers, the shop floor, or the management office. Visual control boards help to communicate operational performance by providing real-time and relevant information, enabling quick feedback and corrective actions, and promoting transparency and accountability.

The other options are not the most effective approaches in communicating operational performance. Quality performance measures are indicators that evaluate the degree to which the products or services meet or exceed the specifications and standards. Quality performance measures are important for communicating operational performance, but they are not sufficient, as they do not cover other aspects of performance, such as cost, time, or customer satisfaction. Reviewing conformance to schedule is a method of comparing the actual production output with the planned production output, based on the master production schedule or the material requirements plan. Reviewing conformance to schedule is useful for communicating operational performance, but it is not timely, as it is usually done after the production is completed, and it does not provide enough details or explanations for the deviations or variances. Monthly meetings with employees are events that involve discussing and reviewing the operational performance with the staff members who are involved in the production process. Monthly meetings with employees are beneficial for communicating operational performance, but they are not frequent, as they are only held once a month, and they may not be effective, as they may lack participation or engagement from the employees.

A quantitative model is a mathematical representation of a real-world situation that involves numbers, variables, equations, and logic. A quantitative model can be used to support sales and operations planning (S&OP), which is a process of aligning the demand and supply plans of an organization at an aggregate level. To develop a quantitative model for S&OP, the following statements are most true:

It is not necessary to capture all of the detail in order to create a useful model. In fact, too much detail can make the model complex, unrealistic, and difficult to solve. A useful model should capture the essential features of the situation and simplify the irrelevant or insignificant aspects1.

Aggregation will be necessary to develop an appropriate model. Aggregation is the process of combining data or information into higher-level categories or groups. For example, products can be aggregated into product families, customers can be aggregated into market segments, and time periods can be aggregated into months or quarters. Aggregation can help reduce the size and complexity of the model, as well as improve its accuracy and reliability2.

Clear objectives are necessary to begin the modeling process. Objectives are the desired outcomes or goals that the model aims to achieve or optimize. For example, an objective of S&OP could be to maximize profit, minimize cost, or balance inventory. Clear objectives can help define the scope, structure, and criteria of the model3.

A significant level of effort is required to develop a model. Developing a model involves several steps, such as defining the problem, collecting and analyzing data, formulating and testing the model, implementing and validating the solution, and evaluating and improving the results. Each step requires careful planning, execution, and evaluation4.

References: CPIM Part 2 Exam Content Manual, Domain 3: Plan and Manage Demand, Section 3.1: Demand Management Concepts and Tools, p. 27-28; Quantitative Techniques Used in Sales & Operations Planning; Sales and Operations Planning (S&OP) 101| Smartsheet; Chapter 13 – Aggregate Planning - KSU; What is Sales and Operations Planning (S&OP) | Oracle; Aggregation and Disaggregation | SAP Help Portal.

A capacity-leading strategy is a proactive approach that adds or subtracts capacity in anticipation of future market demand. It is an aggressive strategy with the objective of improving the service level and decreasing lead time1. An advantage of adopting a capacity-leading strategy is that there issufficient capacity to meet demand, which means that the organization can satisfy customer needs and expectations, as well as capture new market opportunities. A capacity-leading strategy can also help the organization gain a competitive edge by being the first to offer new products or services, or by lowering prices due to economies of scale2.

The other options are not advantages of adopting a capacity-leading strategy. There is not necessarily sufficient demand to consume capacity, which means that the organization may face overcapacity problems, such as high inventory costs, low utilization rates, and reduced profitability3. All demand is not satisfied, and profit is not maximized, because there may be other factors that affect customer satisfaction and profitability, such as quality, price, or service4. Overcapacity problems are not minimized, but rather increased, by adopting a capacity-leading strategy, because the organization may have more capacity than needed if demand does not increase as expected3.

References: CPIM Part 2 Exam Content Manual, Domain 4: Plan and Manage Supply, Section 4.1: Supply Management Concepts and Tools, p. 33-34; Capacity Planning Strategies: Types, Examples, Pros And Cons - Toggl; Lead Capacity Strategy, Lead Demand Strategy - UniversalTeacher.com; Capacity Planning Strategies For End-to-End Supply Chain Profitability; Capacity Planning Strategies: Types, Examples, Pros And Cons - Toggl.

Product price is most effective in influencing demand in the introduction phase of the product life cycle. The product life cycle is a concept that describes the stages that a product goes through fromits development to its decline. The introduction phase is the first stage, when the product is launched into the market and consumers are made aware of its existence and benefits. In this phase, product price can have a significant impact on the demand for the product, depending on the following factors:

The degree of product innovation: If the product is highly innovative and offers a unique value proposition to customers, it may have a high price elasticity of demand, meaning that customers are willing to pay a high price for it regardless of the availability of substitutes or competitors1. This is often the case for products that create a new market or category, such as the iPhone or the Kindle2. On the other hand, if the product is not very innovative and offers a similar value proposition to existing products, it may have a low price elasticity of demand, meaning that customers are sensitive to price changes and will switch to cheaper alternatives or competitors if the price is too high1. This is often the case for products that enter an existing market or category, such as generic drugs or copycat products3.

The degree of market competition: If the product faces little or no competition in the market, it may have more pricing power and flexibility, meaning that it can charge a high price and still generate high demand4. This is often the case for products that have a strong brand image, a loyal customer base, or a patent protection5. On the other hand, if the product faces high competition in the market, it may have less pricing power and flexibility, meaning that it has to charge a low price or offer discounts and promotions to attract and retain customers4. This is often the case for products that have a weak brand image, a low customer loyalty, or a short product life cycle.

Therefore, product price can be an effective tool to influence demand in the introduction phase of the product life cycle, depending on how innovative and competitive the product is. A high price can signal quality, exclusivity, and differentiation, while a low price can signal affordability, accessibility, and penetration.

References: Price Elasticity of Demand - Definition & Formula - Corporate Finance Institute; Product Life Cycle Explained: Stage and Examples - Investopedia; Generic Drugs - Overview - Mayo Clinic; Pricing Strategies For The Product Life Cycle (Made Simple) - Tyonote; Brand Image - Definition & Examples - Marketing Tutor; [Customer Loyalty - Definition & Examples - Marketing Tutor].

Mean time between failures (MTBF) is the predicted elapsed time between inherent failures of a mechanical or electronic system during normal system operation1. MTBF can be calculated as the arithmetic mean (average) time between failures of a system1. MTBF is a useful measure of reliability, because it indicates how long a system is likely to work before failing. The higher the MTBF, the more reliable the system2. Reliability is the probability that a system will perform its intended function without failure for a specified period of time under specified conditions3.

The other statements about MTBF are false. MTBF is not used for non-repairable products, but for repairable systems. For non-repairable products, mean time to failure (MTTF) is used instead4. MTTF is the expected time to failure for a non-repairable system1. An increase in MTBF is not proportional to an increase in quality, because quality is not only determined by reliability, but also by other factors such as performance, functionality, durability, and customer satisfaction5. MTBF is not the same as operating life or service life, because operating life or service life is the total time that a system can operate before it reaches the end of its useful life, while MTBF is the average time between failures during the operating life6.

Point-of-use storage is a stock location system that places inventory close to where it is needed in the production process, reducing transportation and handling costs and improving efficiency. It is often used in repetitive manufacturing, lean environment, where the demand is stable and predictable, and the inventory is replenished frequently. Fixed location and central storage are stock location systems that store inventory in a designated area, which may require more space and movement. Floating location is a stock location system that assigns inventory to any available space, which may cause confusion and inefficiency. References: CPIM Exam Content Manual Version 7.0, Domain 5: Plan and Manage Inventory, Section 5.2: Inventory Management Methods, p. 32.

 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

Hedge is a risk management strategy that assumes that losses in one part of the supply chain will be offset by gains in another. Hedge is a method of reducing the exposure to price fluctuations, currency fluctuations, or other uncertainties by taking a position in a related market or asset that moves in the opposite direction. Hedge helps to protect the profitability and cash flow of the supply chain by locking in the prices or rates at a certain level. For example, a company that imports raw materials from another country may hedge against the exchange rate risk by buying a forward contract or an option that guarantees a fixed rate for the currency conversion.

The other options are not risk management strategies that assume that losses in one part of the supply chain will be offset by gains in another. Flexible is a risk management strategy that allows the supply chain to adapt to changing conditions and customer preferences by using multiple sources, modes, or routes. Fluctuation is not a risk management strategy, but a term that describes the variation or volatility of a market or asset over time. Speculative is not a risk management strategy, but a term that describes an activity or investment that involves a high degree of uncertainty or risk, with the expectation of earning a high return. References: CPIM Exam Content Manual Version 7.0, Domain 7: Plan and Manage Distribution, Section 7.1: Distribution Planning Concepts, p. 40; Hedging; Hedging Definition.

 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