Assignment on the topic:

Submitted by: Sindhu S.M

Roll number: 0930

Submitted to: Mrs. Bharathi karanth

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Content

SI.NO PARTICULARS PAGE NO.

1Introduction

Definition

Need & importance

3

2 use of forecasts 5

3 Which area and why

to use forecast

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4 Features &

assumptions

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5 Selection of

forecasting techniques

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6 Techniques of

forecasting

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7 Limitations of

forecasting

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8 bibliography 23

Environmental forecasting

Introduction:

After collecting relevant information on environmental factors to be

analyzed, forecasting of their likely behaviour in many cases is required as

information collected shows the present pattern. For this purpose

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forecasting is required which has become the general pattern throughout

the world. Forecasting is the process of estimating the relevant events of

future, based on the analysis of their past and present behaviour.

Definition:

Estimating the intensity, nature, and timing of the external forces that

may effect the performance of a firm, disrupt its plans, or force a change

in its strategies.

Need / importance of environmental forecasting:

Today, changes are rapid and too frequent and in a way quite necessary

for overall growth of economy. There have been quantum changes from

1970 onwards and today in the business world anything that is consistent

is only change. In the times to come when changes would predatory, it

would be crucial for managers to invent new ways of surviving in the

ever changing business environment. They would have to build up build

up the capacity of the firm to face the onslaught of changes by being

more agile and flexible for adapting themselves to changes. They would

have to find out new ways of creating opportunities of profitability and

growth. New rules of business will have to be written to meet over-

increasing expectations of drivers of business. To be prepared for such

on going eventualities, managers will have to prepare themselves for

really understanding the remote and the immediate environments of

business and mechanisms of changes that affect their industry.

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The changes have not only affected smaller companies but also the giants

of various industries. In fact the organizational models those are

available with us today for giant companies prove as their handicap in

their process of adaptation due to their large inertia and consequent

slower response towards changes in environment. Hence there is an

urgent need for companies shed-off extra inertia and develop agility since

these large corporations will also be involved in the transition process.

For example: the situation that the automobile industry is facing today is

due to incorrect business environment forecasting for which many giants

are paying a price through under-utilized manufacturing capacity, piling

up of inventories and the locked up capital and operating cash.

Forecasting does not promise flexibility, it enhances the capability of

reacting faster at low costs.

Use of Forecasts

Plans are often confused with forecasts. Plans are sets of actions to help

deal with the future. Forecasting (or predicting) is concerned with

determining what the future will be. A plan is an input to the forecasting

model. If the forecasts arc undesirable, then one might change the plan,

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which, in turn, could change the forecast. The point to remember is that

good plans depend on good forecasts. In practice, forecasts are sometimes

used to motivate people. More properly, people should be motivated by

plans (e.g., “meet this plan. And we will pay you a bonus of 25 percent”).

Decisions have often been made, before any formal forecasting has been

done. In such cases, the forecast serves little purpose other than to annoy

people if it conflicts with their decision or to please them if it supports their

decision. For the forecast to be used effectively, it should be prepared

before decisions arc made. Not only the expected outcome, but also other

likely outcomes (such as the best and worst outcomes) should be forecast. If

the worst outcome poses too much risk, forecasts should be made for

alternative interventions.

Which Area   and Why Use Forecasts:

Forecasts are needed for marketing, production, purchasing, manpower,

and financial planning. Further, top management needs forecasts for

planning and implementing long-term strategic objectives and planning for

capital expenditures.

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1. Marketing managers use sales forecasts to determine optimal

sales force allocations, set sales goals, and plan promotions

and advertising. Market share, prices, and trends in new

product development are also required.

2. Production planners need forecasts in order to: schedule

production activities, order materials, establish inventory

levels and plan shipments.

3. In planning for capital investments, predictions about future

economic activity are required so that returns or cash inflows

accruing from the investment may be estimated. Forecasts are

needed for money and credit conditions and interest rates so

that the cash needs of the firm may be met at the lowest

possible cost. Forecasts also must be made for interest rates, to

support the acquisition of new capital, the collection of

accounts receivable to help in planning working capital needs,

and capital equipment expenditure rates to help balance the

flow of funds in the organization.

4. Sound predictions of foreign exchange rates are increasingly

important to managers of multinational companies.

5. Long-term forecasts are needed for the planning of changes

in the company’s capital structure. Decisions on issuing stock

or debt to maintain the desired financial structure require

forecasts of money and credit conditions.

6. The personnel department requires a number of forecasts in

planning for human resources. Workers must be hired,

trained, and provided with benefits that are competitive with

those available in the firm’s labor market. Also, trends that

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affect such variables as labor turnover, retirement age,

absenteeism, and tardiness need to be forecast for planning

and decision making.

7. Managers of nonprofit institutions and public administrators

also must make forecasts for budgeting purposes. Hospital

administrators forecast the healthcare needs of the community.

In order to do this efficiently, a projection has to be made of:

growth in absolute size of population, changes in the number of

people in various age groupings, and varying medical needs

these different age groups will have.

8. Universities forecast student enrollments, cost of operations,

and, in many cases, the funds to be provided by tuition and by

government appropriations.

9. The service sector, which today accounts for two-thirds of the

U.S. gross domestic product, including banks, insurance

companies, restaurants, and cruise ships, needs various

projections for its operational and long-term strategic

planning. The bank has to forecast: Demands of various loans

and deposits Money and credit conditions so that it can

determine the cost of money it lends.

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Common Features and Assumptions Inherent in Forecasting :

As pointed out, forecasting techniques are quite different from each other.

But four features and assumptions underlie the business of forecasting.

They are:

Forecasting techniques generally assume that the same underlying

causal relationship that existed in the past will continue to prevail in the

future. In other words, most of our techniques are based on historical data.

Forecasts are rarely perfect. Therefore, for planning purposes,

allowances should be made for inaccuracies. For example, the company

should always maintain a safety stock in anticipation of a sudden depletion

of inventory.

Forecast accuracy decreases as the time period covered by the

forecast (i.e., the time horizon) increases. Generally speaking, a long-term

forecast tends to be more inaccurate than a short-term forecast because of

the greater uncertainty.

Forecasts for groups of items tend to be more accurate than

forecasts for individual items, because forecasting errors among items in a

group tend to cancel each other out. For example, industry forecasting is

more accurate than individual firm forecasting.

Selection of Forecasting Method :

The choice of a forecasting technique is influenced significantly by the stage

of the product life cycle and sometimes by the firm or industry for which a

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decision is being made. In the beginning of the product life cycle, relatively

small expenditures are made for research and market investigation.

During the first phase of product introduction, these expenditures start to

increase. In the rapid growth stage, considerable amounts of money are

involved in the decisions, so a high level of accuracy is desirable. After the

product has entered the maturity stage, the decisions are more routine,

involving marketing and manufacturing. These are important

considerations when determining the appropriate sales forecast technique.

After evaluating the particular stages of the product and firm and industry

life cycles, a further probe is necessary. Instead of selecting a forecasting

technique by using whatever seems applicable, decision makers should

determine what is appropriate.

Some of the techniques are quite simple and rather inexpensive to develop

and use. Others are extremely complex, require significant amounts of time

to develop, and may be quite expensive. Some are best suited for short-term

projections, others for intermediate- or long-term forecasts.

What technique or techniques to select depends on six criteria:

What is the cost associated with developing the forecasting model,

compared with potential gains resulting from its use? The choice is one of

benefit-cost trade-off.

How complicated are the relationships that are being forecasted?

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Is it for short-run or long-run purposes?

How much accuracy is desired?

Is there a minimum tolerance level of errors?

How much data are available? Techniques vary in the amount of

data they require.

 

Quantitative models work superbly as long as little or no systematic

change in the environment takes place. When patterns or relationships do

change, by themselves, the objective models are of little use.

It is here where the qualitative approach, based on human judgment, is

indispensable. Because judgmental forecasting also bases forecasts on

observation of existing trends, they too are subject to a number of

shortcomings. The advantage, however, is that they can identify systematic

change more quickly and interpret better the effect of such change on the

future.

Techniques of forecasting

Econometric Models:

Econometric models use information about causal relationships to make

forecasts. The causal relationships should be specified by using domain

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knowledge (i.e., information that a manager has about the problem). Well

established theories should also be used: thus, we know that income should,

in most cases, be positively related to demand for an item, and price should

he negatively related. Given a description of the product and market, we

can also use prior research to determine the approximate magnitude of the

relationship. So if a community makes it more expensive to pollute, one

would expect less pollution if the plan is properly designed, and perhaps

more graft if the plan is poorly designed. Theory or domain knowledge can

he used to identify key variables, specify the direction and form of the

relationships, and set limits on the values that coefficients may take.

While extrapolations assume that everything continues as in the past,

econometric models assume only that the relationships will remain

constant. Given an estimate of the relationship of the causal variables to the

dependent variable, one must forecast changes in the causal variables in

order to calculate a forecast for the variable of interest.

Econometric methods arc most useful where

(1) Large changes are expected;

(2) A priori information about relationships is strong;

(3) Good data are available; and

(4) Causal factors arc easier to forecast than the variable of interest. These

conditions are often encountered in forecasting

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One of the major advantages of econometric methods, in comparison with

other forecasting methods, is that alternate interventions can be compared

with one another. In effect, one is comparing results in an objective way

with an attempt to hold all other influences constant.

The technology for econometric methods has become much more

complicated since the least-absolute value method was introduced in 1757,

followed by the least-squares method in 1805. But highly complex

procedures are not easily understood by decision makers. Worse, little

validation research has been conducted on complex procedures. What has

been done suggests that complexity seldom leads to improved accuracy.

Expert Systems:

Expert systems seem ideal for cases involving environmental forecasting.

One can draw upon the expertise of the best experts. If econometric models

have been developed, the resulting information about relationships could be

incorporated into the expert system. Further refinements could he made by

quantifying experts’ rules by judgmental bootstrapping. Information about

citizen responses could be incorporated by using conjoint studies. Thus,

expert systems allow for the systematic and explicit integration of all extant

knowledge about a situation. Expert systems are being used for a variety of

problems. Unfortunately, information about the predictive validity of expert

systems is limited but positive.

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Extrapolation:

Extrapolation involves making statistical projections using only the

historical values for a time series; it is an appropriate tool to use when the

factors will continue to operate as they have in the past. Furthermore, if one

has little understanding of the causal factors, it might be best to use

extrapolation.

Extrapolation has some useful characteristics. For one thing, it is fairly

reliable. If agreement can be reached on the definition and length of the

time series and on the statistical procedure, the same forecast will be

achieved irrespective of who makes the forecast. Extrapolation can also be

relatively simple and inexpensive. The opportunities for the introduction of

biases in extrapolation are limited. Perhaps the major potential source of

bias is that extrapolative forecasts can differ substantially depending on the

time period examined. This bias can be reduced by selecting long time

series and by comparing forecasts when different starting and ending points

are used. Another source of bias associated with extrapolative forecasts

involves the selection of the extrapolation method. To combat this bias, one

should use simple, easily understood methods and preferably more than one

method.

Judgmental Methods:

Judgmental forecasting involves methods that process information by

experts, rather than by quantitative methods. The experts might have access

to data, and their approach might be structured, but the final forecasts arc

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the result of some process that goes on in their heads. Before discussing

tools that aid judgmental forecasting, it is important to mention one tool

that is widely used and well accepted, but which typically harms accuracy

and leads to an unwarranted gain in confidence. The culprit is the

traditional (unstructured) group meeting. Besides the biases inherent in

unstructured meetings (such as the influence of the boss), the group’s

information is likely to be poorly used. Judgmental forecasts are susceptible

to various biases. To reduce biases, one should select unbiased experts (i.e.,

those who have nothing to gain from a forecast that is either too high or too

low). In addition, care should be given to how the forecasting problem is

formulated. Questions should be structured to use the judges’ knowledge

most effectively, pre-tested to ensure that the experts understand them, and

worded in different ways to see if that affects the forecasts. Such procedures

are particularly important when forecasting sensitive issues, such as the

effects of global warming. The use of structured procedures can greatly

improve the accuracy of judgmental forecasts. Structure is easy to apply

and involves only modest costs. I discuss four structured judgmental

procedures that should be of interest for environmental forecasting: (1) role

playing, which uses subjects to act out relevant interactions to determine

what they would do when affected by an intervention; (2) intention surveys,

which use statements by key participants in the system about what they

expect to do given certain trends or interventions; (3) Delphi. Which uses

expert judgment to forecast trends or the effects of intervention: and (4)

analogies, where experts try to generalize from similar situations. Brief

attention is given to conjoint analysis and to judgmental bootstrapping.

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Role Playing

Role playing involves asking subjects to adopt the viewpoints of groups in a

negotiation situation and having them act out the interactions. When the

interactions of conflicting groups are important to the outcome, role playing

provides a way to simulate this interaction. If new and important

interventions would lead to behaviors that are dependent upon the

interactions among decision makers, then role playing is likely to be more

relevant than intentions. With intentions, decision makers would have to

predict what they would do initially, how they would modify their decisions

in reaction to the decisions made by others, how others would respond to

this reaction, and so on. This chain of events is often too complex for the

respondent, so it makes sense to act it out.

To use role playing to forecast the outcome of an intervention, such as a tax

on air pollution, one would write short descriptions of the problem and of

the roles of key decision makers. Different materials can be prepared to test

alternative interventions. These guidelines should be followed:

- Use props to make the situation realistic.

- Select subjects who can act the role (interestingly, the selection of subjects

does not seem to be a critical aspect for the accuracy of role playing)

- Subjects should receive their roles before they receive any information

about the situation, and they should not step out of their roles.

- Subjects should act as they would act if they were actually in such a role

- Subjects should improvise as needed.

Forecasts would be based on the outcomes of the role-playing sessions.

Ideally, possible outcomes can be identified in advance. However, if the

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range of possible outcomes is uncertain, one should leave the materials

open-ended and ask research assistants to code the outcome, of the role -

playing sessions. If the session does not lead to an outcome, one can ask the

players to predict what would have happened had it continued to a

conclusion. The standard error of this estimate can then be obtained by

using the formula for the standard error of a proportion, with the number of

role-playing sessions as the sample size. Prediction intervals would be

expected to be larger than this estimate because of possible response biases.

Intention Surveys:

Intention studies are surveys of individuals about what actions they plan to

lake in a given situation or, if lacking a plan, what they expect to do. Such

surveys are useful for predicting the outcomes of interventions. When a

situation depends on the decisions of many people (such as with the trash

collection for a community), surveys arc much more expensive than Delphi.

However, they provide the perspective of those who will actually be making

decisions. In addition, one could have presented this situation to consumers

and asked them how they would respond.

Tools for surveys have been improving since the 1936. When interventions

would create large changes and where the behavior of decision makers is

dependent upon decisions by others, respondents may find it difficult to

predict how they would behave. Surveys are of less value in such cases.

Given all the ways that intentions or expectations may be wrong, it should

not be surprising to find that sampling error alone provides a poor way to

estimate prediction intervals.

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Delphi:

Delphi involves the use of experts to make independent anonymous

forecasts. Delphi goes beyond expert surveys in that it is conducted for two

or more rounds. After the first round of forecasts, each expert receives a

Quantitative summary of the group’s forecasts. In addition, anonymous

explanations of their choices might be provided by the experts. Typically,

two rounds are sufficient; however, if the cost associated with error is high,

conducting three or four rounds may be worthwhile. Delphi is usually

conducted by mail and honoraria are paid to the participating experts. The

primary criterion for the selection of experts for a Delphi panel is that they

be unbiased. Delphi requires only a few experts. The number of experts

should be at least five but seldom more than 20. Delphi studies can be

relatively inexpensive to conduct. This approach may be much less

expensive than surveys that obtain information of individuals’ intentions or

expectations. Delphi is relevant when data are lacking, the quality of the

data are poor, or experts disagree with one another. As a result, Delphi is

applicable when new interventions arc proposed or where a trend has

recently undergone a shock. Nevertheless, judgments tend to be too

conservative in the face of rapid change. In particular, judgment

underestimates exponential growth and exponential growth is common in

environmental problems.

This type of method is useful and quite effective for long-range forecasting.

The technique is done by questionnaire format and eliminates the

disadvantages of group think. There is no committee or debate. The experts

are not influenced by peer pressure to forecast a certain way, as the answer

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is not intended to be reached by consensus or unanimity. Low reliability is

cited as the main disadvantage of the Delphi method, as well as lack of

consensus from the returns

Analogies:

To forecast the outcome of interventions, it is common for experts to search

for cases where similar Interventions have been conducted at different times

or in different geographic areas and then to generalize from them.

Conjoint Analysis:

Conjoint analysts can be used to predict what strategy would be accepted.

For example, one could propose different possible plans that would have

various effects. The effects could be varied according to an experimental

design. Once a model is developed, predictions can be made for changes in

the design.

Judgmental Bootstrapping:

Experts could be asked to predict the reactions to various possible

interventions. A model could then be developed by regressing these

predictions on the various elements of the intervention.

Business barometers:

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In physical science, a barometer is used to measure the atmospheric

pressure. In the same way, index numbers are used to measure the stage of

economy between two or more periods. These index numbers are the device

to study the trend, seasonal fluctuations, cyclical movements and irregular

fluctuations. These index numbers, when used in conjunction with one

another or combined with one or more, provide indications as to the

direction in which the economy is heading. Thus, with the help of business

activity index numbers, it becomes easy to forecast the future course of

action. However, it should be borne in mind that business barometers have

their own limitations and they are not sure road to success.

Time series analysis:

It involves decomposition of historical series into various components, viz.,

trend, seasonal variations, cyclical variations and random variations. Time

series analysis uses index numbers but it is different from barometric

technique, the future is predicted from the indicating series which serve as

barometers of economic change. In time series analysis, the future is taken

as some sort of an extension of the past. However, time series analysis

should be used as a basis for forecasting when data are available for a long

period of time and tendencies disclosed by trend and seasonal factors are

fairly clear and stable.

Regression analysis:

Regression analysis is meant to disclose the relative movements of two or

more inter related series. It is used to estimate the changes in one variable

as a result of specified changes in other variable or variables. In economic

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and business situations, there is multiple causation and a number of factors

affect a business phenomenon simultaneously. Regression analysis helps in

isolating the effects of such factors to a great extent. Generally the

regression and correlation analysis is used for processing the statistical

data and deriving a generalized mathematical relationship which, subject to

a certain error, can be used for forecasting the expected values of the

dependent variables in future if the values of independent variables are

known.

Survey method:

Field surveys can be conducted to gather information on the intensions of

the concerned people; for example information can be collected through

surveys about the likely expenditures of consumers on various items. Both

quantitative and qualitative information can be collected. Such information

may throw useful light on the attitudes of the consumers in regard to

various items of expenditure and consumption. On the basis of such survey,

demand for various goods can be projected. To limit the cost and time, the

survey may be restricted to a sample from the prospective consumers.

Survey method is suitable for forecasting demand both of existing and new

products.

Limitations of forecasting

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No doubt forecasting is an essential ingredient, but it should not be

concluded that forecasting is the only element which goes into planning and

other areas of the organizational process. Forecasting provides base for

assuming the behaviours of certain events which may not be fully true

because forecasting has the following limitations:

1. based on assumptions:

Forecasting is based on certain assumptions. It merely suggests that if

an event has happened this way in the past, it will happen that way in the

future. The basic assumption behind this is that events do not change

haphazardly and speedily but change on a regular pattern. This

assumption may not hold good. In fact there are various factors which

go into determining the occurrence of an event. The behaviour of all

these factors may not be similar. A change in a particular factor may be

so unpredictable and important that it may affect the total business

situation.

2. not absolute truth:

Forecasts are not always true; they merely indicate the trend of future

happenings. This is so because the factors which are taken into account

for making forecasts are affected by human factor which is highly

unpredictable. Various techniques of forecasting suggest the relationship

among various known facts. They can project the future trends but

cannot guarantee that this would happen in future. More is the period of

forecasting, higher is the degree of forecasting, higher is the degree of

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error. Therefore it has been commented that “the only thing you can be

sure about any forecast is that it will contain some error.”

3. time and cost factor:

Time and cost factor is also an important aspect of forecasting. While

the above factors speak of limitations inherent in forecasting, time and

cost factor suggests the degree to which an organization will go for

formal forecasting. For making forecast of any event, certain

information and data are required. Some of these may be in highly

disorganized form; some may be in qualitative form. The collection of

information and conversion of qualitative data into quantitative ones

involves lot of time and money. Therefore managers have to trade off

between the cost involved in forecasting and resultant benefits. This is

the reason why most of the smaller organizations do not go for formal

system of forecasting.

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Bibliography:

1. Prasad, L.M. Strategic Management. New Delhi. Sultan Chand & Sons, 2009.

2. Cherunilam, Francis. Business Environment. Mumbai. Himalaya Publishing House, 2008.

3. Lomash, Sukal and Mishra, P.K. Business Policy and Strategic Management. New Delhi. Vikas Publishing House, 2007.

4. Internet

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