Certified Supply Chain Professional CSCP Practice CSCP Demand Management and Forecasting 1 — Questions and Answers
Question 1: Which demand forecasting technique uses a weighted average of past observations, where more recent data points receive greater weight than older ones?
- Simple moving average
- Exponential smoothing (Correct answer)
- Linear regression
- Delphi method
Correct answer: Exponential smoothing
Exponential smoothing applies exponentially decreasing weights to past observations, giving more influence to recent data. This makes it more responsive to demand shifts than a simple moving average, which weights all periods equally.
Question 2: A supply chain manager calculates the Mean Absolute Deviation (MAD) for two forecasting models. Model A has a MAD of 45 and Model B has a MAD of 72. What does this indicate?
- Model B is more accurate because larger MAD values indicate better fit
- Model A is more accurate because it has a lower average forecast error (Correct answer)
- Model A has more bias than Model B
- The models cannot be compared using MAD alone
Correct answer: Model A is more accurate because it has a lower average forecast error
MAD measures the average absolute difference between forecasted and actual values. A lower MAD means smaller average errors, so Model A's MAD of 45 indicates it produces more accurate forecasts than Model B's MAD of 72.
Question 3: Which of the following is an example of an INDEPENDENT demand item in a manufacturing environment?
- A sub-assembly required to build a finished product
- A raw material driven by a bill of materials explosion
- A finished good sold directly to end customers (Correct answer)
- A component replenished via MRP logic
Correct answer: A finished good sold directly to end customers
Independent demand is driven by external market forces and customer orders, not derived from the demand for another item. Finished goods sold to end customers represent independent demand, while components and sub-assemblies driven by bills of materials represent dependent demand.
Question 4: A retailer shares its point-of-sale data directly with a supplier so the supplier can replenish stock automatically. This practice is best described as:
- Demand sensing
- Vendor-managed inventory (VMI) (Correct answer)
- Sales and operations planning (S&OP)
- Safety stock optimization
Correct answer: Vendor-managed inventory (VMI)
Vendor-managed inventory (VMI) is an arrangement where the supplier takes responsibility for replenishing the buyer's inventory using shared demand data such as point-of-sale information. This reduces the bullwhip effect and improves replenishment accuracy.
Question 5: The bullwhip effect in a supply chain is PRIMARILY caused by:
- Seasonal demand fluctuations at the consumer level
- Order quantity variability amplifying as it moves upstream through the supply chain (Correct answer)
- Inaccurate safety stock calculations at distribution centers
- Poor transportation scheduling between suppliers and manufacturers
Correct answer: Order quantity variability amplifying as it moves upstream through the supply chain
The bullwhip effect occurs when small variations in consumer demand get progressively amplified as orders are placed upstream through the supply chain. Each tier buffers uncertainty by ordering more, causing suppliers to see highly volatile demand even when end-consumer demand is relatively stable.
Question 6: When using a causal forecasting model, a company correlates housing starts data with the demand for its home appliance products. Housing starts in this model would be classified as:
- A dependent variable
- A lagging indicator
- A leading indicator (Correct answer)
- A seasonal index
Correct answer: A leading indicator
In causal forecasting, a leading indicator is a variable that changes before the forecasted item changes, allowing it to predict future demand. Housing starts precede appliance purchases, making them a leading indicator that helps forecast demand for home appliances.
Certified Supply Chain Professional CSCP Practice CSCP Demand Management and Forecasting 1
Which demand forecasting technique uses a weighted average of past observations, where more recent data points receive greater weight than older ones?