Demand Forecasting & Supply Planning Flashcards
7 cards from real CIM practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Demand Forecasting & Supply Planning flashcards as text
A product with highly erratic, low-volume demand (e.g., spare parts ordered once every few months) is best described as having:
Answer: Lumpy or intermittent demand
Lumpy or intermittent demand is characterized by irregular, infrequent order occurrences with high variability between periods of zero demand.
In demand planning, a 'consensus forecast' is best achieved through:
Answer: Combining statistical forecasts with cross-functional business input (S&OP)
A consensus forecast integrates statistical models with input from sales, marketing, finance, and operations through the S&OP process to produce a single agreed-upon number.
When safety stock is set using the 'days of supply' method rather than a statistical approach, the main risk is:
Answer: Safety stock does not account for actual demand variability
The days-of-supply method applies a fixed coverage rule without considering actual demand variability, potentially resulting in over- or under-stocking.
Which planning tool calculates time-phased net requirements for components based on the master production schedule and bill of materials?
Answer: MRP (Material Requirements Planning)
MRP uses the MPS, BOM, and inventory records to calculate when and how much of each component to order or produce.
A supply planner reduces the reorder point for a high-velocity item. The most likely consequence is:
Answer: Higher risk of stockouts
Lowering the reorder point means replenishment is triggered later, reducing the time buffer and increasing the probability of running out of stock before the order arrives.
Which forecasting performance indicator signals that the cumulative sum of forecast errors is consistently drifting in one direction?
Answer: Tracking signal
The tracking signal monitors cumulative forecast error (RSFE) relative to MAD; a value outside the control limits (±4–6) signals systematic bias requiring model adjustment.
Demand sensing differs from traditional demand forecasting primarily because it:
Answer: Uses near-real-time data (POS, shipments) to generate short-horizon, high-accuracy forecasts
Demand sensing leverages real-time or daily signals like point-of-sale data and shipment data to produce highly accurate short-term (1–10 day) forecasts.