Demand Forecasting

Almost every operating decision in a business depends on an expectation of demand. Purchasing buys materials for expected orders; production schedules to expected volumes; the warehouse holds stock for expected sales; the call centre rosters staff for expected calls; finance forecasts cash from expected revenue.

The demand forecast is the single assumption on which these plans rest, and errors in it propagate through every one of them: forecast too high and the business carries excess inventory, idle capacity and cash tied up in stock; forecast too low and it runs out, loses sales, expedites at premium cost and disappoints customers. Forecast accuracy is therefore an economic quantity, and improving it is worth money.

Forecasting methods fall into three families. Qualitative methods rely on judgement: sales force estimates, expert panels, customer surveys and structured techniques such as the Delphi method, which are used where there is little history, for new products, or where the future is expected to differ from the past.

Time-series methods project the pattern in past demand forward: moving averages smooth out noise, exponential smoothing weights recent periods more heavily, and decomposition separates the level, trend and seasonality so that each can be projected. Causal methods model demand as a function of the factors that drive it, such as price, advertising, weather, economic activity or the customer's own sales, using regression and related techniques.

In practice a good process combines them: a statistical baseline from the time series, adjusted by causal factors where they are known, and overridden by judgement where there is specific information the statistics cannot see. The forecast must be produced at the right level of detail and horizon for the decisions it supports.

A factory scheduling next week needs a forecast by product and day; a board planning capacity needs one by product family and quarter over three years. Forecasts at aggregate levels are more accurate than forecasts at detailed levels, because the errors in individual items partly cancel out, and forecasts for the near future are more accurate than for the distant future.

A business typically maintains a hierarchy of forecasts reconciled to each other, and reforecasts on a rolling basis as new information arrives. Measuring accuracy is what turns forecasting from guesswork into a managed process.

The common measures are mean absolute percentage error, which shows the typical size of the error regardless of direction, and bias, which shows whether the forecast is systematically too high or too low. Bias is the more damaging fault, because a consistently optimistic forecast builds inventory month after month, and it usually has an organisational cause: sales teams forecasting to their targets, or managers reluctant to forecast a decline.

Tracking accuracy by product, by forecaster and by method identifies where the process is weak, and comparing the accuracy of the judgemental overrides with the statistical baseline shows whether the judgement is adding value or subtracting it. The financial connection runs through inventory, service and capacity.

Safety stock, the buffer held against demand exceeding the forecast, is set by the variability of the forecast error: the less accurate the forecast, the more safety stock is needed for a given service level, and the more cash is tied up. Lost sales from under-forecasting cost the gross margin on the sale and sometimes the customer.

Capacity built for an over-forecast is depreciated whether or not it is used. A sales and operations planning process, in which sales, operations and finance agree a single forecast and the plans that flow from it, is the standard way of connecting the forecast to the decisions and of holding the organisation to one set of numbers.