Lecture-9. Importance of forecasting: Almost every organization, large or small, public or private uses. Types of forecasts:

Transcription

1 Purpose of forecasting: The main purpose of modeling a time series is to make forecasts which are then are used directly for making decisions, such as developing staff schedules for running a production facility. They might also be used as part of a mathematical model for a more complex decision analysis. Importance of forecasting: Almost every organization, large or small, public or private uses forecasting either explicitly or implicitly, because almost every organization must plan to meet the conditions of the future for which it has imperfect knowledge. Forecasts are needed in finance, marketing and production areas, in government and profit-seeking organizations etc. Some questions that explain the importance of forecasting procedures in different situations are 1. What revenue might the state government expect over the next 2-year period? 2. What factors can we identify that will help explain the variability in monthly unit sales? 3. Will there be a recession? If so, when it will begin, how severe will it be,and where will it end? 4. What is a year-by-year prediction for the total loan-balance of the bank over next decade? Types of forecasts: Forecasting might be classified as long-term or short-term. Long-term Forecasts: Long-term forecast is necessary to set the general course of an organization for the long-run, thus they are really important for top management. Short-term Forecasts: Short-term forecasts are used to design immediate strategies and are used by middle management and first line managers to meet the needs of immediate future. Forecasting in terms of position can also be classified as micro or macro continum, that is on the extent to which they involve small details versus large summary values. Micro forecast: Micro forecasting (especially in context of economics thatswhy also called microeconomic forecast) is used to predict important variables for an individual company or perhaps for one component of a company. For example, monthly company sales, unit sales for one of a company s stores and absent hours per employee per month in a factory etc. Macro forecasts: Macro forecasting (especially in context of economics thatswhy also called macroeconomic forecast) is used to predict important variables for the entire economy of a country or for the global economy. For example unemployment rate, gross domestic product or

2 the prime interest rate etc. Economic policy is based on projections of these type of economic indicators, thatswhy much work has been done in evaluating methods for doing this kind of overall economic forecasting. Methods of forecasting: There are two types of forecasting procedures i.e. qualitative or quantitative Qualitative forecasting: When forecasts or predictions about the future relied on personal judgment or opinion of an analyst, then it is called qualitative forecasting. In some forecasting situations, the analyst supplements the data analysis process by injecting qualitative forecasting if he recognized that the past history is not an accurate predictor of the future. For instance, if the historical data are few in number. In the extreme case, it may be the analyst s opinion that no historical data are directly relevant to the forecasting process. Under these conditions, forecasts base purely on the opinions of experts and are used to formulate the forecast or scenario for the future. For example,the introduction of a new technology in market. Few of the well-known judgmental or qualitative forecasting methods for the latter case are: Delphi method: In the first round of this method, the experts reply in writing to the questions posed by the investigating team. The team then summarizes the comments of the participants and mail them back. Participants then able to read the reactions of the others and to either defend their original views or modify them based on views of others. This process continues until the investigators are satisfied that many viewpoints have been developed and carefully considered. At the conclusion of the process,the investigation team should have good insight into the future and can begin to plan their organization s posture accordingly. It should be noted that the objective of the Delphi technique is not to produce a single answer at the end. Instead, it attempts to produce a relatively narrow spread of opinions the range in which opinions of the majority of experts lie. Scenario Writing: Under the scenario writing approach, the forecaster starts with different sets of assumptions. For each set of assumptions, a likely scenario of the business outcome is charted out. Thus, the forecaster generates several different future scenarios (corresponding to the different sets of assumptions). The decision maker or business person is presented with the different scenarios, and has to decide which scenario is most likely to prevail. User expectations approach: The user expectations method relies on answers from customers regarding their intent to purchase the product during the forecasting time period. This method

3 works best when attempting to estimate current market potential as well as to forecast demand, because it does not take into account the company's marketing and advertising efforts which may affect consumers' intent to buy. Quantitative forecasting: Quantitative forecasting methods are used when historical data on variables of interest are available these methods are based on an analysis of historical data concerning the time series of the specific variable of interest. There are two major categories of quantitative forecasting methods. Time series method: This method uses the past trend of a particular variable in order to make a future forecast of the variable. Different methods used for forecasting of univariate time series method are Naïve models, Exponential smoothing, Decomposition and Box-Jenkins methodology. Causal method: This method of quantitative forecasting techniques also uses historical data. But in forecasting future values of a variable, the forecaster examines the cause-and-effect relationships of the variable with other relevant variables such as the level of consumer confidence, changes in consumers' disposable incomes, the interest rate at which consumers can finance their spending through borrowing, and the state of the economy represented by such variables as the unemployment rate. Thus, this category of forecasting techniques uses past time series on many relevant variables to produce the forecast for the variable of interest. Forecasting steps for quantitative method: The following five steps are used in the forecasting process : 1. Problem formulation and data collection. 2. Data manipulation and cleaning. 3. Model building and evaluation. 4. Model implementation (the actual forecast). 5. Forecast evaluation. Selection of Forecasting technique: The selection of which type of forecasting to use depends on several factors: (1) The degree of accuracy required if the decisions that are to be made on the basis of the sales forecast have high risks attached to them, then it stands to reason that the forecast should be prepared as accurately as possible. However, this involves more cost.

4 (2) The availability of data and information - in some markets there is a wealth of available sales information (e.g. clothing retail, food retailing, holidays); in others it is hard to find reliable, up-to-date information (3) The time horizon that the sales forecast is intended to cover. For example, are we forecasting next weeks sales, or are we trying to forecast what will happen to the overall size of the market in the next five years? (4) The position of the products in its life cycle. For example, for products at the introductory stage of the product life cycle, less sales data and information may be available than for products at the maturity stage when time series can be a useful forecasting method. Limitations of the forecasting: Few of the limitations of forecasting are: 1. A good forecast cost money. 2. A good forecast need lot of time. 3. Forecasts are estimates. 4. Changes in fundamental conditions can cause the forecast to vary from actual results. 5. Incorrect forecasts leads to big problems. Naive forecasting models are based exclusively on historical observation of sales or other variables such as earnings and cash flows being forecast. They do not attempt to explain the underlying causal relationships that produce the variables being forecast. Naive models may be classified into two groups. One group consists of simple projection models. These models require inputs of data from recent observations, but no statistical analysis is performed. The second group is comprised of models that, while naive, are complex enough to require a computer. Traditional methods such as classical decomposition, moving average, and exponential smoothing models are some examples. The advantages of naive forecasting models are that they are inexpensive to develop, store data, and operate. The disadvantages are that they do not consider possible causal relationships that underlie the forecasted variable. How it is computed: A simple example of a naive model type is to use the actual sales of the current period as the forecast for the next period. Let us use as the forecast value and the symbol as the actual value. Then: If trends are considered, then: This model adds the latest observed absolute period-to-period change to the most recent observed level of the variable. If it is desirable to incorporate the rate of change rather than the absolute amount, then:

5 If seasonal pattern is strong, an appropriate forecast equation for quarterly data might be If seasonal period is 12, the forecast for next period is. If trend and seasonality for quarterly data is considered, then