Supply Chain Planning and Coordination
This domain covers demand forecasting, the bullwhip effect, sales and operations planning, and material requirements planning. Expect one forecast-error calculation and questions on why variability grows upstream.
Forecasting methods
Qualitative: Delphi, market survey, sales-force composite. Quantitative: moving average, weighted moving average, exponential smoothing with smoothing constant alpha, and causal regression. Higher alpha makes exponential smoothing more responsive to recent demand; lower alpha smooths more. Accuracy is measured by MAD, MAPE and bias.
Coordination concepts
The bullwhip effect is the amplification of demand variability moving upstream. Causes: demand forecast updating from order data, order batching, price-promotion forward buying, and shortage gaming under rationing. Remedies: sharing point-of-sale data through EDI, vendor-managed inventory, CPFR, everyday-low-price policies, shorter lead times and allocation based on past sales. S&OP is a monthly cycle: demand plan, supply plan, reconciliation, financial alignment, executive authorisation. MRP explodes the bill of materials against the master production schedule net of inventory and lead times; DRP does the same for distribution. JIT and kanban pull material on consumption.
Worked example
Demand for the last three months was 200, 180 and 160; weights 0.5, 0.3, 0.2 give a forecast of (0.5x200)+(0.3x180)+(0.2x160) = 100+54+32 = 186. If actual demand turns out to be 190, the error is 4 units and MAPE is 4/190 = 2.1%. The reasoning: recent months dominate, and error metrics let you compare methods rather than judge them by feel.
Traps that cost marks
- Claiming a lower alpha makes exponential smoothing more responsive.
- Describing the bullwhip effect as rising demand; it is rising variability.
- Confusing MRP (material planning from a BOM) with DRP (distribution planning).
- Listing order batching as a remedy for the bullwhip effect rather than a cause.