Supply Chain Planning and Demand Management
Domain 2 tests how demand signals become supply plans. Expect forecasting method selection, forecast error measurement, S&OP/IBP mechanics, inventory policy calculations, and the link from independent demand to MRP and capacity.
Demand planning
Forecast at the level and time bucket you will use: product family for capacity, SKU for replenishment. Qualitative methods (sales composite, Delphi, market research) suit new products; quantitative methods (moving average, exponential smoothing, regression) suit stable history. Measure both error and bias: MAD and MAPE show magnitude; tracking signal reveals systematic bias. Bullwhip effect causes include forecast updating, order batching, promotions, and shortage gaming. Counter with shared POS data, CPFR, VMI, stable pricing, and allocation based on historical sales.
Supply planning and inventory
S&OP is a monthly cycle: demand review, supply review, reconciliation, executive approval. IBP adds financial and strategic alignment. Independent demand is forecast; dependent demand is calculated by MRP. For a single-stage item, reorder point = expected demand during lead time + safety stock. Safety stock = z × standard deviation of demand during lead time when only demand varies. EOQ balances ordering and carrying costs; its assumptions (constant demand, known costs) rarely hold exactly.
Worked example: Daily demand averages 100 units with standard deviation 20; lead time is 9 days; target cycle service level is 95%, z = 1.65. Demand during lead time = 900. Its standard deviation = 20 × √9 = 60. Safety stock = 1.65 × 60 = 99. Reorder point = 900 + 99 = 999 units. If lead time itself varies, combine demand and lead-time variability; the simple formula understates the buffer.
Traps: using 1.96 for a one-tailed 95% cycle service level (1.65 is correct); ignoring lead-time variability; forecasting at SKU level when capacity is planned by family; treating a promotion as baseline demand; using MAPE when actual demand can be zero.