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Demand Forecasting

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What is a demand forecast?

A demand forecast is the prediction of what will happen to your company's existing product sales. It would be best to determine the demand forecast using a multi-functional approach. The inputs from sales and marketing, finance, and production should be considered. The final demand forecast is the consensus of all participating managers. You may also want to put up a Sales and Operations Planning group composed of representatives from the different departments that will be tasked to prepare the demand forecast.

Determination of the demand forecasts is done through the following steps:

•  Determine the use of the forecast

•  Select the items to be forecast

•  Determine the time horizon of the forecast

•  Select the forecasting model(s)

•  Gather the data

•  Make the forecast

•  Validate and implement results

The time horizon of the forecast is classified as follows:


Forecast Horizon





Usually less than 3 months, maximum of 1 year

3 months to 3 years

More than 3 years


Job scheduling, worker assignments

Sales and production planning, budgeting

New product development, facilities planning

How is demand forecast determined?

There are two approaches to determine demand forecast – (1) the qualitative approach, (2) the quantitative approach. The comparison of these two approaches is shown below:


Qualitative Approach

Quantitative Approach


Used when situation is vague & little data exist (e.g., new products and technologies)

Used when situation is stable & historical data exist

(e.g. existing products, current technology)


Involves intuition and experience

Involves mathematical techniques


Jury of executive opinion

Sales force composite

Delphi method

Consumer market survey

Time series models

Causal models


Qualitative Forecasting Methods

Your company may wish to try any of the qualitative forecasting methods below if you do not have historical data on your products' sales.

Qualitative Method


Jury of executive opinion

The opinions of a small group of high-level managers are pooled and together they estimate demand. The group uses their managerial experience, and in some cases, combines the results of statistical models.

Sales force composite

Each salesperson (for example for a territorial coverage) is asked to project their sales. Since the salesperson is the one closest to the marketplace, he has the capacity to know what the customer wants. These projections are then combined at the municipal, provincial and regional levels.

Delphi method

A panel of experts is identified where an expert could be a decision maker, an ordinary employee, or an industry expert. Each of them will be asked individually for their estimate of the demand. An iterative process is conducted until the experts have reached a consensus.

Consumer market survey

The customers are asked about their purchasing plans and their projected buying behavior. A large number of respondents is needed here to be able to generalize certain results.

Quantitative Forecasting Methods

There are two forecasting models here – (1) the time series model and (2) the causal model. A time series is a s et of evenly spaced numerical data and is o btained by observing responses at regular time periods. In the time series model , the forecast is based only on past values and assumes that factors that influence the past, the present and the future sales of your products will continue.

On the other hand, t he causal model uses a mathematical technique known as the regression analysis that relates a dependent variable (for example, demand) to an independent variable (for example, price, advertisement, etc.) in the form of a linear equation. The time series forecasting methods are described below:

Time Series Forecasting Method


Naïve Approach

Assumes that demand in the next period is the same as demand in most recent period; demand pattern may not always be that stable

For example:

If July sales were 50, then Augusts sales will also be 50


Time Series Forecasting Method


Moving Averages (MA)

MA is a series of arithmetic means and is used if little or no trend is present in the data; provides an overall impression of data over time

A simple moving average uses average demand for a fixed sequence of periods and is good for stable demand with no pronounced behavioral patterns.


F 4 = [D 1 + D2 + D3] / 4

F – forecast, D – Demand, No. – Period

(see illustrative example – simple moving average)

A weighted moving average adjusts the moving average method to reflect fluctuations more closely by assigning weights to the most recent data, meaning, that the older data is usually less important. The weights are based on intuition and lie between 0 and 1 for a total of 1.0


WMA 4 = (W) (D3) + (W) (D2) + (W) (D1)

WMA – Weighted moving average, W – Weight, D – Demand, No. – Period

(see illustrative example – weighted moving average)

Exponential Smoothing

The exponential smoothing is an averaging method that reacts more strongly to recent changes in demand by assigning a smoothing constant to the most recent data more strongly; useful if recent changes in data are the results of actual change (e.g., seasonal pattern) instead of just random fluctuations

F t + 1 = a D t + (1 - a ) F t


F t + 1 = the forecast for the next period

D t = actual demand in the present period

F t = the previously determined forecast for the present period

•  = a weighting factor referred to as the smoothing constant

(see illustrative example – exponential smoothing)

Time Series Decomposition

The time series decomposition adjusts the seasonality by multiplying the normal forecast by a seasonal factor

(see illustrative example – time series decomposition)


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