1. Chapter 2 – Business Forecasting Takesh Luckho

2. What is Business Forecasting?  Forecasting is about predicting the future as accurately as possible, given all the information available including historical data and knowledge of any future events that might impact the forecasts.  Hence, Business forecasting is the process used to estimate or predict future patterns of a firm using business data.  Purpose of Forecasting Short–Run: help to forecast seasonal patters which is important for inventory management and pricing policy Long–Run: help in proper capital planning which is important for HRM and investment decision making

3. What is Demand Forecasting?  Estimation of demand for a product in a future period is termed as Demand forecast.  Purpose of Demand forecasting: Better planning and allocation of resources Appropriate production scheduling Inventory control Determining appropriate pricing policies Setting s les targets and establishing controls and incentives. Planning a new unit or expanding existing one Planning long term financial requirements Planning Human Resource Development strategies.

4. Steps involved in forecasting  Identification of objective Assume you are a retail firm that want to maximise sales of your product  Determining the nature of goods under consideration. Type of good: normal/inferior good  Selecting a proper method of forecasting. Surveys/mathematical forecasting  Interpretation of results. Graphical or mathematical representation

5. Level of Forecasting  Recall: Forecasting can be done on a short-run, medium-run and long-run basis  Forecasting can be done at different levels: Macroeconomic forecasting – how the main macroeconomic variables will behave. E.g Price level (inflation), Economic Growth Industry demand forecasting – how the industry will be moving in the future. Firm demand forecasting decisions – like sales forecasting Product line forecasting – which product to produce with the firms limited resources. General purpose or specific purpose forecast – taking general or specific decision related to the firm Forecast of established product or a new product Commodity forecast – forecasting the demand pattern of goods (like capital goods, durable goods, ect)

6. Methods of forecasting  Two main methods used in business forecasting are: Qualitative approach – subjective approach (no mathematical model) based on value judgement of consumers/producers/opinion polls/experts  Done when past data are not available  Done for medium and long term decisions Quantitative approach – using historical/past data to forecast future decisions  Make use of mathematical/statistical models  Done for short and medium term decisions

7. Qualitative Forecasting  Tools for qualitative forecasting Survey techniques – choosing a sample from a general population so as to get the necessary data/information Opinion polls – targets a specific sample of people  Consumer surveys – e.g to know their future consumer plans  Sales force opinion – getting the opinion of people who are closest to the market.  Expert opinion – solicitating the opinion of experts on certain decision making problems

8. Quantitative Forecasting (cont’d)  Tools for quantitative forecasting Trend projection –fit a trend line or curve either graphically or by means of a statistical technique known as the Least Squares method. Trend forecasting can be done by:  Plotting of the data on the graph and estimating where the trend line lies  Using statistical formulae (least square method) to find the trend line which best fits the available data.  Using past data to predict the future (time series analysis)

9. Quantitative Forecasting (cont’d) Barometric method - used to forecast or anticipate short term changes in economic activity by using leading economic indicators. E.g of such indicators are  Leading economic variables – that anticipate turning point in business cycle. E.g. Successive negative quarterly growth rate might be a sign of future recession  Coincident indicators: These are indicators which move in step or coincide with movements in general economic activity or business cycle.  Lagging indicator: These are indicators which lag the movements in economic activity or business cycle.

10. Quantitative Forecasting (cont’d) Regression analysis/Econometric Forecasting  Econometric is an economics sub-discipline that deals with the development and application of statistical methods to economic data.  Types of models Single equation model Y = a + b 1 X 1 +b 2 X 2 Multiple equation model Y = a + b 1 X 1 +b 2 X 2 X 1 = c +d 1 Z 1 + d 2 Z 2 X 2 = e +f 1 M 1 + f 2 M 2

11. Quantitative Forecasting (cont’d) Input/output forecasting - introduced by Prof. Leontief. Input and output analysis allow us to trace through all inter industry input and outputs flow though out the economy and to determine the total increase of all the inputs required to meet the increased demand.  Utilise Input/Output tables to make a forecast.  Type of input/output matrix Direct Requirement Matrix Total Requirement Maxtrix  Economic simulation method like CGE (Computable General Equilibrium) model also make use of Input/Output tables. CGE I/O matrix is know as the SAM (Social Accounting Matrix)

12. Quantitative Forecasting (cont’d) Shortcoming of I/O forecasting:  Assumption that direct and total coefficients are fixed and thus do not allow for import subsitution  I/O tables are available with a time lag of many years and while the input output coefficients do not change very rapidly they can become very biased

13. Fluctuations in Time Series Data – Reasons for increases or decrease in long-run data Cyclical fluctuations  Expansion or contraction that re-occurs every several years. Seasonal variation  regularly fluctuations during the year. E.g a typical factor could be weather and social customs Irregular or random variation  Resulting from unique events like wars, natural disasters or strikes.

14. Smoothing Techniques  Smoothing - predicts feature value of time series on the basis of some average of its past value only. Moving Average Smoothing – the forecasted value of a time series in a given period is equal to the average value of the time series in a number of previous periods.  Disadvantage: give equal weightage different periods in the past Exponential Smoothing

15. Smoothing Techniques (cont’d)  Exponential Smoothing - According to exponential smoothing method more recent the data the more relevant it is for forecasting and therefore it would be more appropriate to give more weightage to recent observations

16. Risks in Demand Forecasting Overestimation of Demand – mostly due to unforeseen events like war Underestimation of Demand – due to inadequate market analysis These problems can be avoided through:  Carefully defining the market for the product to include all potential users of the market and considering the possibility of product substitution.  Dividing total industry demand into its components and analyzing each component separately.  Forecasting the main driver or user of the product in each segment of the market and projecting how they are likely to change in the future.