Fast Changing Economic Scenario Commerce Essay


The present day managers have to deal with an increasingly uncertain business environment due to the fast changing economic scenario. Decision-making becomes difficult in such uncertainty. Needless to mention that the uncertainty in business environment is due to the complex behaviour of market related variables like demand, market share, people's perception and factors affecting demand in the present day as a result of recent policy changes and market forces. The need of the hour for a manager is to know the behaviour of the market related variables, their interrelationship and future movement. The most important aspects for a manager in the present day is to know process of estimation of demand and demand forecasting, factors affecting demand and identification of target groups using scientific tools. Thanks to global competition, demand situation in the economy is no longer certain. Gone are the days of certainty longer PLC's & the low competitive intensity, today the overall environment has become dynamic. Demand has become uncertain, PLC's have shortened & competition is intensified. Therefore in such a situation, firms are increasingly realizing that understanding demand, planning demand & linking supply with demand pays.

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Forecast of future demand is essential for all strategic decisions in the supply chain. If the supply chain begins with a forecast that is substantially in error, in terms of timing or quantity, the ramification will be felt throughout the entire process. This is why forecasting has assumed significant importance and commitment to it seems to be increasing day by day

Forecasting practices are characterized by some interesting insights into changes in techniques. Research indicates that during the 1980s, despite the growing availability of computer-based forecasting systems, companies continued to rely predominantly on subjective techniques. Since the mid-'90s, companies have started using computer-based forecasting systems. What is surprising is that even among the community of those who use these models, forecast accuracy has not increased.

Why has demand forecasting acquired such a significant place today? Are forecasts reviewed and agreed upon by key departments in the organization? Are right statistical methods used in forecasting the demand for product? What horizons and time periods are used for long-term and short-term forecasting? How are statistical and judgmental considerations to be combined? These are a few questions which need to be answered in order to understand the state of forecasting in Indian Companies.

Causal & time series models have given way to rolling plans. With the changing nature of businesses and increasing complexity due to the changing nature of demand, this shift from quantitative models is understandable. But what is found surprising was that even where causal and time series models would have been appropriate, information technology-based sales force composites were used blindly. Forecasting is not owned as yet by any department, and thus a consensual approach is yet to be evolved leading to a budget-driven demand planning.

What these companies probably forget is that not all demand has become unpredictable; these are situations where demand follows a detectable and predictable pattern. Forecasting methods & models needs to be applied intelligently today to make forecast business significant. Indian firms seem to have lost their direction. Forecasting methods of companies seem to be dictated by supply chain requirements and a technology with little understanding of when, where and what to forecast. The appropriate choice of a technique depends upon the inherent uncertainty in the business environment and the factors which cause this uncertainty.

Many inventory systems cater for uncertain demand. The inventory parameters in these systems require estimates of the demand and forecast error distributions. The two stages of these systems, forecasting and inventory control, are often examined independently. Most studies tend to look at demand forecasting as if this were an end in itself , or at stock control models as if there were no preceding stages of computation. Nevertheless, it is important to understand the interaction between demand forecasting and inventory control since this influences the performance of the inventory system. This integrated process is shown in the following figure:

The decision-maker uses forecasting models to assist him or her in decision-making process. The decision-making often uses the modeling process to investigate the impact of different courses of action retrospectively; that is, "as if" the decision has already been made under a course of action. That is why the sequence of steps in the modeling process, in the above figure must be considered in reverse order. For example, the output (which is the result of the action) must be considered first.

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It is helpful to break the components of decision making into three groups: Uncontrollable, Controllable, and Resources (that defines the problem situation). As indicated in the above activity chart, the decision-making process has the following components:

Performance measure (or indicator, or objective): Measuring business performance is the top priority for managers. Management by objective works if you know the objectives. Unfortunately, most business managers do not know explicitly what it is. The development of effective performance measures is seen as increasingly important in almost all organizations. However, the challenges of achieving this in the public and for non-profit sectors are arguably considerable. Performance measure provides the desirable level of outcome, i.e., objective of your decision. Objective is important in identifying the forecasting activity. The following table provides a few examples of performance measures for different levels of management:


Performance Measure


  Return of Investment, Growth, and Innovations


 Cost, Quantity, and Customer satisfaction


 Target setting, and Conformance with standard

Performance Measure

Clearly, if you are seeking to improve a system's performance, an operational view is really what you are after. Such a view gets at how a forecasting system really works; for example, by what correlation its past output behaviors have generated. It is essential to understand how a forecast system currently is working if you want to change how it will work in the future. Forecasting activity is an iterative process. It starts with effective and efficient planning and ends in compensation of other forecasts for their performance

What is a System? Systems are formed with parts put together in a particular manner in order to pursue an objective. The relationship between the parts determines what the system does and how it functions as a whole. Therefore, the relationships in a system are often more important than the individual parts. In general, systems that are building blocks for other systems are called subsystems

The Dynamics of a System: A system that does not change is a static system. Many of the business systems are dynamic systems, which mean their states change over time. We refer to the way a system changes over time as the system's behavior. And when the system's development follows a typical pattern, we say the system has a behavior pattern. Whether a system is static or dynamic depends on which time horizon you choose and on which variables you concentrate. The time horizon is the time period within which you study the system. The variables are changeable values on the system.

Resources: Resources are the constant elements that do not change during the time horizon of the forecast. Resources are the factors that define the decision problem. Strategic decisions usually have longer time horizons than both the Tactical and the Operational decisions.

Forecasts: Forecasts input come from the decision maker's environment. Uncontrollable inputs must be forecasted or predicted.

Decisions: Decisions inputs ate the known collection of all possible courses of action you might take.

Interaction: Interactions among the above decision components are the logical, mathematical functions representing the cause-and-effect relationships among inputs, resources, forecasts, and the outcome.

Interactions are the most important type of relationship involved in the decision-making process. When the outcome of a decision depends on the course of action, we change one or more aspects of the problematic situation with the intention of bringing about a desirable change in some other aspect of it. We succeed if we have knowledge about the interaction among the components of the problem.

There may have also sets of constraints which apply to each of these components. Therefore, they do not need to be treated separately.

Actions: Action is the ultimate decision and is the best course of strategy to achieve the desirable goal

Decision-making involves the selection of a course of action (means) in pursue of the decision maker's objective (ends). The way that our course of action affects the outcome of a decision depends on how the forecasts and other inputs are interrelated and how they relate to the outcome.

Statistical Forecasting:

The selection and implementation of the proper forecast methodology has always been an important planning and control issue for most firms and agencies. Often, the financial well-being of the entire operation rely on the accuracy of the forecast since such information will likely be used to make interrelated budgetary and operative decisions in areas of personnel management, purchasing, marketing and advertising, capital financing, etc. For example, any significant over-or-under sales forecast error may cause the firm to be overly burdened with excess inventory carrying costs or else create lost sales revenue through unanticipated item shortages. When demand is fairly stable, e.g., unchanging or else growing or declining at a known constant rate, making an accurate forecast is less difficult. If, on the other hand, the firm has historically experienced an up-and-down sales pattern, then the complexity of the forecasting task is compounded.

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There are two main approaches to forecasting. Either the estimate of future value is based on an analysis of factors which are believed to influence future values, i.e., the explanatory method, or else the prediction is based on an inferred study of past general data behaviour over time, i.e., the extrapolation method. For example, the belief that the sale of doll clothing will increase from current levels because of a recent advertising blitz rather than proximity to Christmas illustrates the difference between the two philosophies. It is possible that both approaches will lead to the creation of accurate and useful forecasts, but it must be remembered that, even for a modest degree of desired accuracy, the former method is often more difficult to implement and validate than the latter approach.