Demand forecasting

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.

Necessity for forecasting demand

Often forecasting demand is confused with forecasting sales. But, failing to forecast demand ignores two important phenomena. There is a lot of debate in demand-planning literature about how to measure and represent historical demand, since the historical demand forms the basis of forecasting. The main question is whether we should use the history of outbound shipments or customer orders or a combination of the two as proxy for the demand.

Stock effects

The effects that inventory levels have on sales. In the extreme case of stock-outs, demand coming into your store is not converted to sales due to a lack of availability. Demand is also untapped when sales for an item are decreased due to a poor display location, or because the desired sizes are no longer available. For example, when a consumer electronics retailer does not display a particular flat-screen TV, sales for that model are typically lower than the sales for models on display. And in fashion retailing, once the stock level of a particular sweater falls to the point where standard sizes are no longer available, sales of that item are diminished.

Market response effect

The effect of market events that are within and beyond a retailer’s control. Demand for an item will likely rise if a competitor increases the price or if you promote the item in your weekly circular. The resulting sales a change in demand as a result of consumers responding to stimuli that potentially drive additional sales. Regardless of the stimuli, these forces need to be factored into planning and managed within the demand forecast.

In this case demand forecasting uses techniques in causal modeling. Demand forecast modeling considers the size of the market and the dynamics of market share versus competitors and its effect on firm demand over a period of time. In the manufacturer to retailer model, promotional events are an important causal factor in demand. These promotions can be modeled with intervention models or use a consensus to aggregate intelligence using internal collaboration with the Sales and Marketing functions.

Methods

No demand forecasting method is 100% accurate. Combined forecasts improve accuracy and reduce the likelihood of large errors. Reference class forecasting was developed by professor Bent Flyvbjerg, University of Oxford, to reduce error and increase accuracy in forecasting, including in demand forecasting. Daniel Kahneman, Nobel Prize winner in economics, calls Flyvbjerg's counsel to use reference class forecasting to de-bias forecasts, "the single most important piece of advice regarding how to increase accuracy in forecasting.” Other experts have shown that rule-based forecasts produce more accurate results than combined forecasts.

Methods that rely on qualitative assessment

Forecasting demand based on expert opinion. Some of the types in this method are,

• Unaided judgment
• Prediction market
• Delphi technique
• Game theory
• Judgmental bootstrapping
• Simulated interaction
• Intentions and expectations surveys
• Conjoint analysis
• jury of excecutive method

Methods that rely on quantitative data

• Discrete Event Simulation
• Extrapolation
• Reference class forecasting
• Quantitative analogies
• Rule-based forecasting
• Neural networks
• Data mining
• Causal models
• Segmentation

some of the other methods

a) time series projection methods this includes:

• moving average method
• exponential smoothing method
• trend projection methods

b) casual methods this includes:

• chain-ratio method
• consumption level method
• end use method

Ex post studies of demand forecasts

Ex post studies compare actual with predicted outcomes of forecasts. Such studies generally find demand forecasts to be highly inaccurate. For instance, a statistically valid study of demand forecasts in 210 large public works projects, led by Oxford University professor Bent Flyvbjerg, found that for rail projects the average demand (passenger) forecast was overestimated by a full 106 percent. For roads, half of all demand (vehicle) forecasts were wrong by more than 20 percent; a fourth of forecasts were wrong by more than 40 percent.

See also

• Supply and demand
• Demand chain
• Calculating demand forecast accuracy
• inventory proportionality
• Reference class forecasting