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 the demand planning literature as how to measure and represent historical demand, since the historical demand forms the basis of forecasting. Should we use the history of outbound shipments or customer orders or a combination of the two to proxy for 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 effects
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 increase reflects 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 influencing demand. These promotions can be modeled with intervention models or use a consensus process to aggregate intelligence using internal collaboration with the Sales and Marketing functions.
Demand Forecasting Methodology
To obtain the most accurate forecasting results, market response forecasting and time series forecasting are used together to predict a retailer’s demand.
Market response forecasting
To accurately predict the consequences of your choices, you must factor how the market will respond to each decision. For example, you need to forecast how consumers will react to various prices. Such a model will look like a textbook demand curve, as shown in Figure 1. The curve itself—labeled D1—predicts demand at different prices, holding other variables constant. At the regular price of $2.65, it predicts weekly demand for about 1,800 boxes of cereal in a specific market. If this item were discounted to $1.95, without any other stimuli changing, demand would increase to about 5,000 units. The relationship between the quantity demanded and the price offered is expressed as the price elasticity of demand. Given the price elasticity, and a forecast of demand at a particular price, it is possible to forecast demand at alternative prices.1 In Figure 1, the curve labeled D2 predicts demand for the same item, in the same market, over the same price range, but under different conditions. These conditions can include changes in seasonal demand (i.e. during the holiday season or summer vacation), display prominence, and competitive...