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CSCA: Demand and Materials Planning

CSCA · Work through your exam, one topic at a time.

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Demand and Materials Planning

Demand and Materials Planning tests the numbers behind the plan: how demand is forecast and reconciled, how much stock to hold, and how a bill of materials is exploded into timed orders.

Forecasting

  • Exponential smoothing: F(t+1) = α·A(t) + (1−α)·F(t). A high α follows recent demand closely and reacts fast but amplifies noise; low α is stable and slow.
  • Error metrics: MAD (average absolute error), MAPE (percentage; undefined when actual is zero), and bias (mean signed error). Bias signals a broken process; random error signals a noisy one. Tracking signal = running sum of forecast errors ÷ MAD; beyond about ±4 triggers a review.
  • Measure forecast value added against a naive forecast - added complexity must beat "same as last period" to be worth it.

Inventory parameters

  • EOQ = √(2DS/H), where H is unit cost × annual carrying rate.
  • Safety stock = z × σ of demand × √lead time for independent demand; ROP = average demand during lead time + safety stock.
  • Common z values: 90% = 1.28, 95% = 1.65, 99% = 2.33.
  • ABC by annual dollar usage; XYZ by demand variability. AX items get tight control and frequent review.

MRP logic

Inputs are the master production schedule, bills of material and inventory records. Net requirements = gross requirements − on-hand − scheduled receipts + safety stock; then offset by lead time and lot-sized (lot-for-lot, fixed order quantity, period order quantity). MRP II adds capacity checks (RCCP, CRP), and S&OP reconciles demand, supply and finance into one plan each month.

Worked example

Daily demand is 50 units with a standard deviation of 10, lead time is 6 days, target cycle service level 95%. Safety stock = 1.65 × 10 × √6 = 40.4, rounded up to 41. Reorder point = 50 × 6 + 41 = 341 units. If someone instead computes 1.65 × 10 × 6 = 99, they have dropped the square root and will overstock by more than double.

Traps

  • Mixing weekly demand with a lead time expressed in days.
  • Using the z value for cycle service level and calling it fill rate; fill rate is unit-based and is always higher for the same safety stock.
  • Expecting safety stock to absorb forecast bias. It absorbs random error only.
  • Applying MAPE to intermittent demand with zero periods.