CCME - Lesson 3.pdf

PART 3: Demand estimation and forecasting MANAGERIAL ECONOMICS Demand Estimation and Demand Forecasting In Demand estimating, manager attempts to quantify the links or relationship between the level of demand and the variables which are determinants to it and is generally used in designing pricing...

PART 3: Demand estimation and forecasting MANAGERIAL ECONOMICS Demand Estimation and Demand Forecasting In Demand estimating, manager attempts to quantify the links or relationship between the level of demand and the variables which are determinants to it and is generally used in designing pricing strategy of the firm. Firm can charge a price that the market will be ready to wear to sell its product. Over estimation of demand may lead to an excessive price and lost sales whereas under estimates may lead to setting of low price resulting in reduced profits. Demand Estimation and Demand Forecasting In Demand forecasting, managers forecast the most likely future demand of a product so that he can make necessary arrangement for the various factor of production i.e labor, raw material, machines, money etc. Demand forecasting tells the expected level of demand at some future date on the basis of past and present information. It helped in production planning, new product development, capacity enhancement or new schemes etc. Demand forecasting is generally used for short term estimation as well as long term forecasting. Why Demand Forecasting? ✓ In short run demand forecasting helps in determining the optimum level of output at various periods to avoid under or over production. ✓ It helps in better inventory management, of buying inputs and control its inventory level which cuts down overall cost of production. A balanced pricing policy can be formulated to suit short run and seasonal variations in demand. ✓ It helps the company to set realistic sales targets for each individual salesman and for the firm as a whole. Why Demand Forecasting? ✓ It helps in advance financial planning required for achieving the budgeted production and sales and to raise the required funds well in advance at reasonable cost. ✓ It also helps the firm in evolving a suitable labor policy and to determine the exact number of workers to be employed in the short run. Demand Forecasting Process 1. Specifying the objective of Demand Forecasting While forecasting demand one may have different objectives like quantity and composition of demand, price to be quoted, production planning, inventory planning or capital budgeting, short or long term demand, firm’s market share etc. Thus, the objective for which demand is to be estimated must be clearly defined at first stage. Demand Forecasting Process 2. Determining the nature of goods The next step in demand forecasting is identification of type of goods as different type of goods such as consumer goods, capital goods, industrial goods, durable and nondurable goods; perishable or seasonal goods have different type of demand pattern. This will help in applying write approach to demand estimation process. Demand Forecasting Process 3. Determining the time perspective Depending upon the nature of goods and firm’s objective, the demand can be forecasted for short term as well as for long term. In short term many of the determinants of demand may remain constant or not to be change significantly but in long run these determinants may change significantly. Thus, while forecasting demand one has to define the time span for the forecast. The time period is normally divided into short run up to 3 to 6 months, medium term up to 2 or 3 years and long term beyond 3 or 5 years. Demand Forecasting Process 4. Determining the level of forecasting Demand forecasting may be undertaken at micro or firm level, industry level, macro level or international level. It may be done for product line forecasting, general or specific purpose or for established or new products. There are different factors that influence the demand at different level of forecasting. Thus, one must specify the level of forecasting beforehand. Demand Forecasting Process 5. Selection of proper method or technique of forecasting Economists have developed different methods or techniques for forecasting. However, these methods are not suitable for all type of demand forecasting due to the type or objectives of forecasting, data requirement, availability of data and time frame. One has to select an appropriate method for demand forecasting to achieve stated objectives. It also depends upon the purpose, knowledge and experience of the forecaster. Demand Forecasting Process 6. Data Collection and modification Depending upon the objective and method of forecasting next step in demand forecasting is to collect the required data. There are different method of collection of primary data like observation, interview, survey or questionnaire, focus group discussion methods etc. Data can also be collected from various secondary sources but, this data required modification as it may not be available in the required mode. Demand Forecasting Process 7. Data analysis and estimations Once the data has been collected and method of data analysis has been finalized the next step in demand forecasting is analysis of data and interpretations of results. The Efficiency of estimation depends upon the efficiency with which it has been analyzed and interpretive. Sometimes, estimation required support from background factors which has not been used in estimation process. One mist frequently revised the estimates depending upon the changed business conditions. Determinants of Demand Forecasting Different type of goods has different determinants. Broadly, goods can be classified as Capital goods, Durable and Non-durable consumer goods and factors determines the demand of theses goods are discussed below. Demand Forecasting Process Capital Goods i.e factory building, machinery, equipment, tools etc., have derived demand as demand of these goods depend upon demand of consumer goods industry growth rate, level of capacity utilization, wage rate and size of the market The demand for these goods comprises of replacement demand and new demand and one should consider Growth potential of the Industry, per unit consumption norms and velocity of their use in estimating the demand of capital goods. Demand Forecasting Process Demand for Consumer Durable goods i.e. residential building, car, refrigerators, furniture, readymade garments, TV, Computer etc. depend upon social status, prestige, level of money income, obsolescence rate, maintenance costs, availability of road, petrol, supply of electricity, family size, age-sex distribution and credit facilities. One should consider the trends of these factors while estimating the demand of consumer durables. Demand Forecasting Process Nondurable consumer goods are consumed once only i. e. milk, food, vegetables, fruits, medicines etc. and their demand is effected by disposable income or purchasing power of the household, price elasticity (own price or price of substitute and complimentary goods) and demographic variables. Methods of Demand Forecasting 1. Survey Methods Survey methods are generally used in short run and estimating the demand for new products. In survey methods information about the future purchase plans of potential buyers are collected through direct interview of potential consumers or expert’s opinions. The different approaches under survey methods are: 1. Survey Methods A. Consumers’ Survey method Under this method, efforts are made to collect the relevant information directly from the consumers with regard to their future purchase plans. It is one of the oldest methods of demand forecasting. It is also called as “Opinion surveys”. Under this method, the intentions of the consumer are recorded by trained, reliable and experienced investigators, through personal interviews, e-mail or post surveys and telephonic interviews. 1. Survey Methods B. Collective Opinion Method (Sale Force Opinion or Reaction Survey Method) Another variant of the survey method is Collective Opinion Method also known as “Sales – force polling” or “Opinion poll method” or “Reaction Survey Method”. In this method, instead of customers, salesmen, marketing manager, production manager, professional experts and the market consultants and others are asked to express their considered opinions about the volume of sales expected in the future. It is very simple method and does not involve the use of any statistical techniques and take advantage of collective wisdom of salesmen and managers. 1. Survey Methods C. Expert’s Opinion Method or Delphi Method It is a variant of opinion poll and survey method of demand forecasting. Under this method, outside experts are appointed. They are supplied with all kinds of information, statistical data and posed questions relating to an underlying forecasting problem. The management requests the experts to express their considered opinions and views about the expected future sales of the company. 1. Survey Methods D. Market Studies and Experiments Under this method, companies first select some markets or cities having similar features i.e population, income culture, social or religious factors etc., then carry out the market experiments by changing prices, quality, packing, advertisement expenditure or other controllable demand determinants under the assumption that other things remain contestant. Methods of Demand Forecasting 2. Statistical Methods In statistical methods historical or cross-sectional data are used to forecast the future probable demand of a particular product by applying statistical models and mathematical, equations. These methods are considered to be superior techniques of demand estimation. 2. Statistical Methods A. Trend Projection Method In this method a data set of past sales are taken at specified time, generally at equal intervals to depict the historical pattern under normal conditions. On the basis of derived historical pattern, the future sales of a company are project. The main aspect of this method lies in the use of time series and changes in time series occur due to following reasons: 1. Secular Trend: Secular Trend also known as long term trend indicate the general tendency and direction in which graph of a time series move in relatively over a long period of time. This can be upward or downward trend, depending upon the behavior. 2. Statistical Methods 2. Seasonal Trends: This trend reflects the changes in sales a company due to change in various seasons or climates or due to festival season or sales clearance season etc. These changes are repetitive in nature and related to twelve months period. 3. Cyclical Trends: These trends reflect the change in the demand for a product during diverse phases of a business cycle i.e growth, boom, maturity, depression, revival, etc. 4. Random or irregular trends: These changes arise randomly or irregularly due to unforeseen events such as famines, earth quakes, floods, natural calamities, strikes, elections and crises. These changes take place only in the short run and have their own impact on the sales of a firm. These trends cannot be predicted. 2. Statistical Methods In trend projection method real problem is to separate and measure each of these trends separately. In order to estimate the future demand of the product the impact of seasonal, cyclical and irregular trends are eliminated from the data and only secular trend is used. The trend in the time series can be eliminated by using any of the following method; I. Graphical Method, II. The method of semi average, III. Moving average method and IV. The least square method 2. Statistical Methods I. Graphical Method It is simplest method of trend projection. In this method periodical sales data is plotted on a graph paper and a line is drawn through the plotted points. Then a free hand line is drawn passing through as many points as possible. general trends whereas the actual trend line will show the seasonal, cyclical and irregular trend. I. Graphical Method This has been illustrated with the help of table-1 and figure-1. I. Graphical Method This has been illustrated with the help of table-1 and figure-1. 2. Statistical Methods II. The Semi average method In this method, first of all time series data of sale is divided into two equal parts and thereafter, separate average sale is calculated for each half. The two values of averages are plotted on graph corresponding to the time period. A straight line is then drawn by joining these two points. This line become the trend line and is used to forecast future sale. 2. Statistical Methods III. Moving average Method Moving average method is very widely used in practice. Under this method, moving average is calculated. One has to decide what moving year average – 3year or 5year or 7year should be taken up and it depends upon the periodicity of the data. It is determined by plotting the data on the graph paper and noticing the average time interval of successive peaks or trough. After deciding the moving year average, moving average of the given sales data is calculated and these averages are plotted on the graph paper to fit the trend. It has been explained with help of following example; III. Moving average Method III. Moving average Method 2. Statistical Methods IV. The least square method Fitting trend equation or popularly known as least square method is a scientific, formal and popular method of projecting the trend line. In this method a trend line is fitted with the help of straight line regression equation IV. The least square method Regression Analysis Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables. It can be utilized to assess the strength of the relationship between variables and for modeling the future relationship between them. Regression Analysis Regression Analysis Regression analysis includes several variations, such as linear, multiple linear, and nonlinear. The most common models are simple linear and multiple linear. Nonlinear regression analysis is commonly used for more complicated data sets in which the dependent and independent variables show a nonlinear relationship. Regression analysis offers numerous applications in various disciplines, including finance. Regression Analysis – Linear Model Assumptions Linear regression analysis is based on six fundamental assumptions: 1. The dependent and independent variables show a linear relationship between the slope and the intercept. 2. The independent variable is not random. 3. The value of the residual (error) is zero. 4. The value of the residual (error) is constant across all observations. 5. The value of the residual (error) is not correlated across all observations. 6. The residual (error) values follow the normal distribution. Regression Analysis – Simple Linear Regression Simple linear regression is a model that assesses the relationship between a dependent variable and an independent variable. The simple linear model is expressed using the following equation: Y = a + bX + ϵ Where: Y – Dependent variable X – Independent (explanatory) variable a – Intercept b – Slope ϵ – Residual (error) Regression Analysis – Multiple Linear Regression Multiple linear regression analysis is essentially similar to the simple linear model, with the exception that multiple independent variables are used in the model. The mathematical representation of multiple linear regression is: Y = a + bX1 + cX2 + dX3 + ϵ Where: Y – Dependent variable X1, X2, X3 – Independent (explanatory) variables a – Intercept b, c, d – Slopes ϵ – Residual (error) Regression Analysis – Multiple Linear Regression Multiple linear regression follows the same conditions as the simple linear model. However, since there are several independent variables in multiple linear analysis, there is another mandatory condition for the model: Non-collinearity: Independent variables should show a minimum correlation with each other. If the independent variables are highly correlated with each other, it will be difficult to assess the true relationships between the dependent and independent variables.