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Techniques for environmental forecasting 

The term business environment means the totality of all individuals, institutions, and other forces that are outside of a business that but that potentially affects its performance. In business decisions, particularly strategic ones, need a clear identification of the relevant and detailed and in-depth analysis of them to understand their impact and implications for the organization, and the foremost importance in the organization is forecasting.

Forecasting is a decision-making tool used by many businesses to help in budgeting, planning, and estimating future growth. In the simplest terms, forecasting is the attempt to predict future outcomes based on past events and management insight. Forecasting starts with certain assumptions based on the management’s experience, knowledge, and judgment.

According to the author Francis cherubim “Forecasting is a way of estimating the future events that have a major impact on the enterprise. It is a technique whereby managers try to predict the future characteristic of the organizational environment and hence make decisions today that will help the firm deal with the environment of tomorrow”.

The objective of forecasting is to produce better forecasts. But in the broader sense, the objective is to improve organizational performance more revenue, more profit, increased customer satisfaction. Better forecasts, by themselves, are of no inherent value if those forecasts are ignored by management or otherwise not used to improve organizational performance.

In environmental Forecasting, there are some steps involved. They are:

1. Identification of Relevant Environment Variables

2. Collection of Information

3. Selection of Forecasting

4. MonitoringIdentification of Relevant Environmental Variables The first step in environmental forecasting is the identification of the environment variables critical to the firm.

All environmental variables do not have the same relevance to all the industries of firms. A variable that is relevant to one industry may not be relevant for another. To envision the future environment is it essential to identify the critical environmental variable and to predict their future trends. The omission of any critical variable will affect the assessment of the future environment and strategies. Collection of Information The key environmental variables having been determined, the next important step is a collection of the needed information. This involves identification of the sources of information, types of information to be collected, selection of methods of data collection and collection of information. Selection of Forecasting Technique The choice of forecasting technique depends on such considerations as the nature of the forecasting decision the amount of accuracy of available information the accuracy required the time available the importance of the forecast, the cost, and the competence and interpersonal relationships of manager and forecaster. Monitoring The characteristics of the variables or their trends may undergo changes. A new variable may emerge as critical or the relevance of certain variables may decline. Sometimes the changes may be very significant so to call for are forecasting.

There are four types of forecasting1. Economic Forecasts2. Social Forecasts3. Political Forecasts4. Technological ForecastsThese is the different types of forecasting. Economic forecasts The fact that environment is a very critical determinant of business prospects underscores the importance of economic forecasts. Important economic factors often considered include general economic conditions, GDP growth rate, per capita income, distribution of income, structural changes in GDP, investment and output trends in different sectors and subsectors industries, prices trends etc. Social Forecasts There are a number of social factors which have profound in business.

It is very essential to forecast the possible changes in the relevant social variables. Important factors include population growth/decline, the age structure of the population, ethnic composition of population occupational pattern, lifestyle, income levels, expenditure pattern, social attitudes etc. Political Forecast Political forecast has an important part in envisioning properly the future scenario of business. Political forecasts also cover industrial policy, commercial policy, and fiscal policy. Some political changes are sudden and unpredictable. Changes in the relative power of political parties, changes in the internal power structure of parties, political alliances, and political ideologies etc. International political developments are also important. Technological Forecast Technological forecast encompass not only technological innovations but also the pace and extent of diffusion and penetration of technologies and their implications.

There are two main techniques of forecasting. They are:

Qualitative Forecasting:

1. Salesforce composite

2. Customer Evaluation

3. Executive opinion

4. Delphi Technique

5. Anticipatory Surveys

Quantitative Forecasting:

1. Time Series Analysis

2. Regression Modelling

3. Econometric Modelling

These are the main techniques of forecasting. Qualitative Forecasting Techniques

Qualitative forecasting is an estimation methodology that uses expert judgment, rather than numerical analysis. This type of forecasting relies upon the knowledge of highly experienced employees and consultants to provide insights into future outcomes. Sales Force CompositeThe Sale Force Composite Method is a sale forecasting method wherein the sales agents forecast the sales in their respective territories, which is then consolidated at branch/region/area level, after which the aggregate of all these factors is consolidated to develop an overall company sales forecast. The sales force composite method is the bottom-up approach where the sales force gives their opinion on sales trend to the top management. Since, the salesmen are the people, who are very close to the market, can give a more accurate sales prediction on the basis of their experience with the direct customers. Under the sales force composite method, a forecast of sales is determined by combining the sales predictions of experienced salespeople. Because salespeople are in constant contact with customers, they are often in a position to accurately forecast sales.

Customer Evaluation

This method is similar to the sales force composite except that it goes to customers for estimates of what the customers expect to buy. Individual customer estimates are then pooled to obtain a total forecast. This method works best when a small number of customers make up a large percentage of total sales. Drawbacks are that the customer may not be interested enough to do a good job and that the method has no provisions for including new customers. Executive opinion method of forecasting using a composite forecast prepared by a number of individual experts. The experts form their own opinions initially from the data given and revise their opinions according to the others’ opinions. Finally, the individuals’ final opinions are combined. With this method, several managers get together and devise a forecast based on their pooled opinions. Advantages of this method are simplicity and low cost. The major disadvantage is that the forecast is not necessarily based on facts. Delphi TechniqueThe Delphi Technique refers to the systematic forecasting method used to gather opinions of the panel of experts on the problem being encountered, through the questionnaires, often sent through the mail. In other words, a set of opinions pertaining to a specific problem, obtained in writing usually through questionnaires from several experts in the specific field is called as a Delphi technique. The objective of a Delphi technique is to reach to the most accurate answer by decreasing the number of solutions each time the questionnaire is sent to the group of experts. The experts are required to give their opinion every time the questionnaire is received, and this process continues until the issues are narrowed, responses are focused, and the consensus is reached. Anticipatory SurveysIn this method, mailed questionnaires, telephone interviews, or personal interviews are used to forecast customer intentions. An anticipatory survey is a form of sampling, in that those surveyed are intended to represent some larger population. Potential drawbacks of this method are that stated intentions are not necessarily carried out and that the sample surveyed does not represent the population. This method is usually accompanied by medium costs and not much complexity.

Quantitative Forecasting

TechniquesQuantitative forecasting techniques. An approach to forecasting where historical demand data is used to project future demand. Extrinsic and intrinsic techniques are typically used. Time-Series AnalysisThis technique forecasts future demand based on what has happened in the past. The basic idea of time-series analysis is to fit a trend line to past data and then to extrapolate this trend line into the future. Sophisticated mathematical procedures are used to derive this trend line and to identify and seasonal or cyclical fluctuations. Usually, a computer program is used to do the calculations required by a time-series analysis. Trend Analysis ForecastingTrend analysis uses a variety of statistical tools, all of which are accessible to business owners. At the most basic level, you can plot data points for visual identification of trends to clarify relationships between variables and identify “outliers,” or random points that don’t fit a pattern. Data points can then be converted into moving averages to smooth random fluctuations. A business owner can use spreadsheet software to “fit” trend lines on charted data or build regression models. These allow her to include more variables to predict sales more accurately and to forecast the impact of rising interest rates and seasonal changes.

Seasonal Analysis Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes that recur every calendar year. Any predictable change or pattern in a time series that recurs or repeats over a one-year period can be said to be seasonal. Regression ModellingRegression modeling is a mathematical forecasting technique in which an equation with one or more input variables is derived to predict another variable. The variable being predicted is called the dependent variable. The input variables used to predict the dependent variable are called independent variables. The general idea of regression modeling is not to determine how changes in the independent variables affect the dependent variable. Once the mathematical relationship between the independent variables and the dependent variable has been determined, future values for the dependent variable can be forecast based on known or predicted values of the independent variables. The mathematical calculations required to derive the equation are extremely complex and almost always require the use of a computer. Regression modeling is relatively complex and expensive. Econometric ModellingEconometric modeling is one of the most sophisticated methods of forecasting. In general, econometric models attempt to mathematically model an entire economy. Most econometric models are based on numerous regression equations that attempt to describe the relationships between the different sectors of the economy. Very few organizations are capable of developing their own econometric models. Those organizations that do use econometric models usually hire the services of consulting groups or company that specializes in econometric modeling. This method is very expensive and complex and is, therefore, primarily used only by very large organizations.

In nowadays all companies using times series modeling because this modeling is fascinating with practical relevance and impact. Time series techniques work on numerical data collected over a considerable period of time. Time series is a popular proven approach that is widely used in different segments such as finance, economics, e-commerce, environment etc.

Forecasting methods to evaluate potential results stemming from their decision. The most notable advantage of quantitative forecasting methods is that the projections rely on the strength of past data. The chief advantage of qualitative methods that is the main source of data derives from the experiences of qualified executives and employees.

The primary disadvantages of forecasting are the same as that of any other method of predicting the future. No one can be absolutely sure what the future holds. Any unforeseen factors can render a forecast useless, regardless of the quantity of data.

1. The selection of the variable included in the predictive model.

2. The selection of functional form for linking these predictor variables to the variables being predicted.

3. The estimation of the correct value for the predictor variables.

The future success of any business depends heavily on its management to forecast well. Judgemental forecasting methods often play important role in this process. The ability to forecast well enhanced if historical data are available to help guide the development of a statistical forecasting method.

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