This is an important tool in optimizing business profitability through efficient supply chain management.
Demand forecasting methods are divided into two major categories, qualitative and quantitative methods: Demand forecasting may be used in resource allocation, inventory management, assessing future capacity requirements, or making decisions on whether to enter a new market.
[4] Nevertheless, understanding customer needs is an indispensable part of any industry in order for business activities to be implemented efficiently and more appropriately respond to market needs.
These include, but are not limited to, waste reduction, optimized allocation of resources, and potentially large increases in sales and revenue.
[9] Forecasting demand can be broken down into seven stage process, the seven stages are described as: The first step to forecast demand is to determine a set of objectives or information to derive different business strategies.
These objectives are based on a set of hypotheses that usually come from a mixture of economic theory or previous empirical studies.
For example, a manager may wish to find what the optimal price and production amount would be for a new product, based on how demand elasticity affected past company sales.
[10] In this stage it is important to define the type of variables that will be used to forecast demand.
An example of a model for forecasting demand is M. Roodman's (1986) demand forecasting regression model for measuring the seasonality affects on a data point being measured.
M. Roodman's demand forecasting model is based on linear regression and is described as:
These observations are used to derive relevant statistics, characteristics, and insight from the data.
[12] The data points that may be collected using time series data may be sales, prices, manufacturing costs, and their corresponding time intervals i.e., weekly, monthly, quarterly, annually, or any other regular interval.
The subset of data points may not be observable or feasible to determine but can be a practical method for adding precision to the demand forecast model.
Using the linear regression model as an example of estimating parameters, the following steps are taken: Linear regression formula: The first step is to find the line that minimizes the sum of the squares of the difference between the observed values of the dependent variable and the fitted values from the line.
The method for omitting these variables is described below: Calculating demand forecast accuracy is the process of determining the accuracy of forecasts made regarding customer demand for a product.
[14][15] Understanding and predicting customer demand is vital to manufacturers and distributors to avoid stock-outs and to maintain adequate inventory levels.
In order to maintain an optimized inventory and effective supply chain, accurate demand forecasts are imperative.
Forecast accuracy in the supply chain is typically measured using the Mean Absolute Percent Error or MAPE.
This is the same as dividing the sum of the absolute deviations by the total sales of all products.
, where A is the actual value and F the forecast, is also known as WAPE, or the Weighted Absolute Percent Error.
The only problem is that for seasonal products you will create an undefined result when sales = 0 and that is not symmetrical.
This means that you can be much more inaccurate if sales are higher than if they are lower than the forecast.
So sMAPE also known as symmetric Mean Absolute Percentage Error, is used to correct this.
In this situation, a business may consider MASE (Mean Absolute Scaled Error) as a key performance indicator to use.
[16] Another metric to consider, especially when there are intermittent or lumpy demand patterns at hand, is SPEC (Stock-keeping-oriented Prediction Error Costs).
SPEC takes into account temporal shifts (prediction before or after actual demand) or cost-related aspects and allows comparisons between demand forecasts based on business aspects as well.
The results should describe what is trying to be achieved and determine if the theory or hypothesis is true or false.
In relation to the example provided in the first stage, the model should show the relationship between demand elasticity of the market and the correlation it has to past company sales.
The final step is to then forecast demand based on the data set and model created.
Regarding the estimation of the chosen variable, a regression model can be used or both qualitative and quantitative assessments can be implemented.