Demand Forecasting By Prof. Jharna Lulla

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.

Forecasting Steps Identification of the objective Determining the nature of goods under consideration Selecting a proper method for forecasting Interpretation of results

Role of Macro-level forecasting in demand forecasts Various macro parameters found useful for demand forecasting: National income and per capita income. Investment. Population growth. Taxation. Credit policy.

Purposes of Forecasting Purposes of short-term forecasting Appropriate production scheduling. Reducing costs of purchasing raw materials. Determining appropriate price policy Setting sales targets and establishing controls and incentives. Evolving a suitable advertising and promotional campaign. Forecasting short term financial requirements. Purposes of long-term forecasting Planning of a new unit or expansion of an existing unit. Planning long term financial requirements. Planning man-power requirements.

Qualitative Methods or the Survey Method Definition: To obtain information about the intentions of the spenders through collecting experts’ opinion or by conducting interviews with the consumers.

Types of Qualitative methods Expert Opinion Method: Panel consensus Delphi method Hunch method Consumer Survey Method: Complete Enumeration Survey Sample Survey End-Use Method

Expert Opinion Methods Panel Consensus method: If the forecast is based on the opinion of several experts then the approach is called forecasting through the use of panel consensus Advantages: This kind of forecasting minimizes individual deviations and personal biases. Disadv: Some powerful individual could have influenced the consensus.

Expert Opinion Methods Delphi method: In this method a panel of experts is individually presented a series of questions pertaining to the forecasting problem. Responses acquired from the experts are analyzed by an independent party that will provide the feedback to the panel members. Based on the responses of other individuals, each expert is then asked to make a revised forecast. This process continues till a consensus is reached or until further iterations generate no change in estimates. This method thus takes care of the disadvantage of panel consensus method.

Consumer Survey methods Definition: The most direct method of estimating demand in the short- run is to conduct the survey of buyers’ intentions. The consumers are directly approached and are asked to give their opinions about the particular product. It is of three types: Complete Enumeration method Sample Survey method End-Use Method

Consumer Survey methods Complete Enumeration method: This method is based on a complete survey of all the consumers for the commodity under consideration. It resembles the Census Data Collection which considers the entire population. For Example: if there are n consumers and their probable demands for commodity X in the forecast period are x1, x2, x3 ……xn, the sales forecast will be : X= X1 + X2 + X3 …………….+Xn

Consumer Survey methods Sample Survey method: Few consumers are selected to represent the entire population of the consumers of the commodity consumed. The total demand for the product in the market is then projected on the basis of the opinion collected from the sample. This method gives good results especially for new products and brands. The sample size should neither be too small or too big.

Consumer Survey method End Use survey: A commodity that is used for the production of some other finally consumable food is also known as an intermediary good. A survey is planned of the intermediary users and the estimated demands from all segments of intermediary users are added.

Econometric model An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining a particular economic phenomena quantitatively and qualitatively formulated

Trend Method These are generally based on analysis of past sales patterns. This method is used in case the sales data of the firm under consideration relates to different time periods. There are five techniques of the trend or mechanical extrapolation method:

Trend Method 1. Fitting a trend line by Observation: this method is elementary, easy and quick. It requires merely the plotting of actual sales data on a chart and then estimating by observation where the trend lines lies.

Trend Method 2. Trend through least square method: This method is a mathematical procedure for fitting a line to a set of observed data points (S,T). This technique uses statistical formulas rather than visual estimates, to find the trend line which best fits the available data. Example Sales: a=b ( year number) Or S= a+BT Where a and b have been calculated from past data and T is the number of year for which forecsat is to be done

Trend Method 3. Time- Series Analysis: This is an extension of Linear regression which attempts to build seasonal and cyclical variations into the estimating equation. Thus, Sales = A (trend) + b (season) + c (cycle)+ d Where a,b,c and d are constants calculated from past data.

Trend Method 4. Moving Average and Annual Difference Method: A moving average of order K is obtained by adding yearly demands for successive years of K number of years and dividing it by K. Thus the moving avearge of order 5 at year t=1/5 (Dt-2+ Dt-1+ Dt + Dt+1 + Dt+2) where Dt is demand in years t.

Trend Method 5. Exponential Weighted Moving Average Method: A progressively smaller weight is given to the more distant as compared to a more recent past years.

Barometric Techniques It is based on the idea that future can be predicated from certain events occuring in the present. The barometric techniques involve statistical indicators, usually time series which when combined in certain ways provide indications of the direction of change in the economy or specific industries.

Barometric Techniques Leading indicators: These tend to reflect future market changes. Coincident Indicators: These are the indicators which coincide with or fall behind general economic activity or market trends. Lagging Indicators: Lagging indicators confirm long-term trends, but they do not predict them. Example manufacturer’s stock level and consumer credit outstanding.

Regression Analysis It is the method undertaken to measure the relationship between two variables where correlation appears to exist Regression Analysis Equation : B= Unknown parameters X= Independent variable Y=Dependent variable

Example : Y= 0.1 + 0.109 X where Y= sales of tractors in thousand units X= Index of farm income For X=200 we get Y = 21.9 Therefore we can forecast a sale of 21,900 units

Limitations(Qualitative) Method Limitation Application Expert opinion May be affected by influence of one person Can be used when extensive statistical calculation is not required Complete enumeration survey Costly and time consuming Can be used for census collection Sample survey method Only a few consumers views are taken into account Can be used when a high degree of accuracy is not required End use survey It requires complex and diverse calculations Can be used for forecasting the demand of primary products like milk.

Limitations(Quantitative) Methods Limitations Applications Time series True results as long as time series projection moves in the same direction Used only if data exhibits a persistent growth trend. Econometric model Assumes relationships established in the past will continue in future Provides useful results when statistical data is reliable Input output analysis To help understand inter- industry relation Exponential smoothing Uses only recent data for analysis Can be used in the fashion industry. Regression analysis Useful only if the relation between the statistical data of variables can be found can be used when there is a relation between two variables.

The End Thank You