Free Practice Questions for APICS CPIM-8.0 Exam

Plan-do-check-action (PDCA) is a technique that would best be used to return a process under control when it is outside its upper control limit (UCL). PDCA is a four-step cycle of continuous improvement that involves planning a change, implementing the change, checking the results, and acting on the findings. PDCA can help identify and eliminate the root causes of variation, improve the process performance, and prevent the recurrence of problems. PDCA is also known as the Deming cycle or the Shewhart cycle. References:

Managing Supply Chain Operations, Chapter 9: Quality Management, Section 9.3: Quality Improvement, Subsection 9.3.1: Plan-Do-Check-Act Cycle

CPIM Exam Content Manual, Module 8: Quality, Technology and Continuous Improvement, Section 8.2: Continuous Improvement, Subsection 8.2.1: Continuous Improvement Concepts, Subsubsection 8.2.1.1: Plan-Do-Check-Act Cycle

Exponential smoothing is a forecasting method that uses weighted averages of past observations to predict future values. The weights decrease exponentially as the observations get older, giving more importance to recent data. Exponential smoothing can be applied to data with different patterns, such as level, trend, or seasonality. Depending on the pattern, different exponential smoothing models and parameters are used. Two common parameters are alpha (α) and beta (β):

Alpha is the smoothing parameter for the level component of the forecast. The level component is the average or typical value of the data. Alpha can range from 0 to 1, not inclusive. A low alpha value gives more weight to older observations and produces a smoother forecast. A high alpha value gives more weight to recent observations and produces a more responsive forecast.

Beta is the smoothing parameter for the trend component of the forecast. The trend component is the direction and rate of change of the data over time. Beta can also range from 0 to 1, not inclusive. A low beta value gives more weight to older trends and produces a smoother forecast. A high beta value gives more weight to recent trends and produces a more responsive forecast.

A company with stable demand that uses exponential smoothing to forecast demand would typically use a low alpha value. Stable demand means that the data do not have significant variations, fluctuations, or patterns over time. In this case, a simple exponential smoothing model that estimates only the level component is sufficient. A low alpha value would produce a smooth and stable forecast that reflects the average demand level and does not react to random noise or outliers. The other options are not correct, as they either refer to a different parameter (beta) or a different scenario (high alpha value):

A low beta value would be used for data with a trend component, but a stable demand does not have a trend component. A low beta value would produce a smooth and stable trend forecast that does not react to random noise or outliers.

A high beta value would also be used for data with a trend component, but a stable demand does not have a trend component. A high beta value would produce a responsive and dynamic trend forecast that reflects the recent changes in the data.

A high alpha value would be used for data with a high variability or uncertainty, but a stable demand does not have these characteristics. A high alpha value would produce a responsive and dynamic level forecast that reflects the recent changes in the data. References:

[CPIM Part 2 - Section A - Topic 3 - Demand Management]

Exponential Smoothing for Time Series Forecasting

What is alpha and beta in exponential smoothing?

Value of alpha and beta in Holt’s exponential smoothing method

Sharing of demand data with a supplier is an example of collaboration between suppliers and operations which would give more lead time visibility. This is because it allows the supplier to plan and adjust their production and delivery schedules according to the customer’s needs, reducing the risk of stockouts or excess inventory. It also enables the supplier to provide feedback and suggestions to improve the demand planning process and the accuracy of the forecasts. Sharing of demand data is a key component of supplier relationship management (SRM) and collaborative planning, forecasting, and replenishment (CPFR). References :=

CPIM Exam Content Manual, Module 4: Supply, Section 4.2: Supplier Relationship Management, p. 15

Essentials of Supply Chain Management, Chapter 6: Demand Planning and Forecasting, Section 6.4: Collaborative Planning, Forecasting, and Replenishment, pp. 101-103