Certified Supply Chain Professional CSCP Practice CSCP Demand Management and Forecasting 1

Question 1

Which demand forecasting technique uses a weighted average of past observations, where more recent data points receive greater weight than older ones?

Accepted Answer

Exponential smoothing

Answer

Simple moving average

Answer

Linear regression

Answer

Delphi method

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?

Accepted Answer

Model A is more accurate because it has a lower average forecast error

Answer

Model B is more accurate because larger MAD values indicate better fit

Answer

Model A has more bias than Model B

Answer

The models cannot be compared using MAD alone

Question 3

Which of the following is an example of an INDEPENDENT demand item in a manufacturing environment?

Accepted Answer

A finished good sold directly to end customers

Answer

A sub-assembly required to build a finished product

Answer

A raw material driven by a bill of materials explosion

Answer

A component replenished via MRP logic

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:

Accepted Answer

Vendor-managed inventory (VMI)

Answer

Demand sensing

Answer

Sales and operations planning (S&OP)

Answer

Safety stock optimization

Question 5

The bullwhip effect in a supply chain is PRIMARILY caused by:

Accepted Answer

Order quantity variability amplifying as it moves upstream through the supply chain

Answer

Seasonal demand fluctuations at the consumer level

Answer

Inaccurate safety stock calculations at distribution centers

Answer

Poor transportation scheduling between suppliers and manufacturers

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:

Accepted Answer

A leading indicator

Answer

A dependent variable

Answer

A lagging indicator

Answer

A seasonal index