Demand planning

The process starts with a statistical baseline: forecasting models project each item or family from history, trend, and seasonality. Planners then layer on what the model cannot see (a distributor launch, a customer ramping a new program, a planned promotion) and reconcile the inputs into a consensus locked during the S&OP demand review. Sales input gets screened for bias on the way in, since quota math reliably pushes it optimistic.

Demand planning predicts demand; demand management shapes it, steering customers with pricing, promotions, and quoted lead times toward what the supply chain can actually deliver. Mixing the two corrupts both: a forecast bent to match supply is no longer a forecast, it is a wish with a spreadsheet.

The discipline is in measurement. Track forecast accuracy and bias at the lag that matters (the lead time of your longest component, not last week), feed the measured error into safety stock sizing, and make sure MRP consumes the consensus number rather than a side spreadsheet someone trusts more.

Frequently asked questions

What is demand planning?

Demand planning produces the consensus forecast of future demand that supply decisions run on. It combines a statistical baseline built from sales history with market input from sales, marketing, and key customers, reconciling them into one number per product or family per period. That number feeds S&OP, the master schedule, and material planning.

What is the difference between demand planning and demand management?

Demand planning predicts demand; demand management shapes it, steering customers with pricing, promotions, and quoted lead times toward what the supply chain can deliver. Mixing the two corrupts both, because a forecast bent to match supply is no longer a forecast. Keeping the prediction honest and the shaping explicit lets each discipline do its job.

How does the demand planning process work?

Demand planning starts with a statistical baseline projected from history, trend, and seasonality. Planners then layer on what the model cannot see, such as a distributor launch or a customer ramping a new program, and reconcile the inputs into a consensus number locked during the S&OP demand review. Sales input gets screened for bias on the way in, since quota math reliably pushes it optimistic.

How do you measure demand planning performance?

Track forecast accuracy and bias at the lag that matters, which is the lead time of your longest component rather than last week. Feed the measured error into safety stock sizing, and make sure MRP consumes the consensus number instead of a side spreadsheet someone trusts more. One planner who tracked input streams separately found the sales overlay running 12 to 18% high for six straight months; discounting future overlays by the trailing bias improved three-month-lag accuracy from 71% to 82%.

Why should sales forecasts be screened for bias?

Sales input to demand planning is valuable because it carries market knowledge the statistical model cannot see, but quota incentives push it optimistic in a consistent, measurable way. Tracking each input stream separately reveals the bias, and discounting overlays by their trailing error keeps real market intelligence while stripping out the wishful volume.