Economics Project

T HE ROLE OF PLANNING AND FORECASTING IN

BUSINESS ORGANISATION

SUBMITTED TO-:

Ms KiranBala Das

SUBMITTED BY-:

Amisha Bansal

Roll no-: 22

Date of submission-: 26/08/2013

Name of the university-:

HIDAYATULLAH NATIONAL LAW UNIVERSITY

Place-: Raipur, Chhattisgarh

The Role Of Planning and Forecasting in Business Management

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ACKNOWLEDGEMENT

Firstly, I would be obliged to take the opportunity to thank my subject teacher M/s Kiran Bala

Das, faculty, Economics, HNLU for allotting me this topic to work on. She has been very kind in

providing me all the information regarding the project topic and also guiding me through the

completion of this project. I would also like to thank my friends who have provided me unfailing

support.

Lastly I would also like to thank the library and the IT dept. which have been of great help

during this project.

Amisha Bansal

Semester- I

Roll no. – 22

Section - B


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Tables of content

Acknowledgement…………………………………………………………………………2

Research Methodology……………………………………………………………………..4

Datatype……………………………………………………………………………………..4

Introduction…………………………………………………………………………………5

Forecasting techniques……..……………………………………………………………..7

Business Plam…………………………………………………………………..…………11

Qualitative forcasting models………………………………………………………….16

Importance of planning………………………………………………………………….20

Conclusion…………………………………………………………………………………22

Bibliography……………………………………………………………………………….23


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RESEARCH METHODOLOGY

The nature of the project is purely descriptive and analytical. It is purely based on data collected

from books, acts, journals and web sources.

Books and other reference as guided by Faculty have been primarily helpful in giving this project

a firm structure. Websites, dictionaries and articles have also been referred.

Footnotes have been provided wherever needed, either to acknowledge the source or to point to

a particular provision of law.

DATATYPE-:

The data collected are of two types-: a) Primary data

b) Secondary data

Primary data is the data that is collected directly from the source, whereas, secondary data is the

data that is collected from various sources in an indirect manner.

I have used secondary data for my project.


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Introduction

A plan is a determined course of action for achieving a specific objective. An individual may

prepare a plan for his journey or tour or for a family function. Similarly, a business unit may

prepare a plan to achieve a particular objective. It is called a business plan which includes

production plan, sales plan, and so on. A business unit prepares a master plan for the whole unit.

Such master plan is again divided into departmental plans for actual execution. Planning is a

process of thanking to action. It is a means to achieve well defined objectives. Business plan and

business planning move together.

Planning is the primary function of management and occupies the first position in the

management process. It is the starting point of the whole management process as other

management functions are related to planning function. Planning, in simple words, means to

decide the objectives clearly and to prepare a plan. Thereafter to take suitable steps for the

execution of the plan. Planning function is performed by managers at all levels. It is deciding the

objective to be achieved and taking suitable follow-up steps for achieving the same.

Planning is, now, universally accepted as a key/passport to success, progress and prosperity in

business as well as in all other aspects of life. It acts as a base of all purposeful human activities.

The concept of planning is old enough. Planning was advocated by Confucius almost 2500 years

ago. He said "A man who does not think and plan long ahead will find trouble right at his door".

Thus, planning is the centre around which all business activities move.

In planning, various business problems are studied, decisions are taken regarding the future

course of action and business activities are adjusted accordingly. Thus, planning means deciding

in advance the objectives to be achieved and preparing plans/programs for achieving them. In

other words, planning is the process of foreseeing desired objectives - anticipating problems and

developing solutions. It serves as a core of the whole management process.


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Planning bridges the gap from where we are to where we want to go. In the absence of planning,

events are left to chance. A plan is to-day's projection for tomorrow's activity.1

Forecasting is a process of predicting or estimating the future based on past and present data.

Forecasting provides information about the potential future events and their consequences for the

organization. It may not reduce the complications and uncertainty of the future. However, it

increases the confidence of the management to make important decisions. Forecasting is the

basis of premising. Forecasting uses many statistical techniques. Therefore, it is also called as

Statistical Analysis. A commonplace example might be estimation of some variable of interest at

some specified future date. Prediction is a similar, but more general term. Both might refer to

formal statistical methods employing time series, cross-sectional or longitudinal data, or

alternatively to less formal judgmental methods. Usage can differ between areas of application:

for example, in hydrology, the terms "forecast" and "forecasting" are sometimes reserved for

estimates of values at certain specific future times, while the term "prediction" is used for more

general estimates, such as the number of times floods will occur over a long period.

Risk and uncertainty are central to forecasting and prediction; it is generally considered good

practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be

up to date in order for the forecast to be as accurate as possible.

1 Pinson, Linda. (2004). Anatomy of a Business Plan: A Step-by-Step Guide to Building a Business and Securing Your Company’s Future (6th Edition). Page 20. Dearborn Trade: Chicago, USA.


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FORECASTING TECHNIQUES

1 Linear regression

Least squares linear regression is a method of fitting a straight line to a set of points on a graph.

Typical pairs of graph axes could include:

• total cost v volume produced2

• quantity sold v selling price

• quantity sold v advertising spend.

The general formula for a straight line is y = ax +b. So, ‘y’ could be total cost and ‘x’ could be

volume. ‘a’ gives the slope or gradient of the line (eg how much the cost increases for each

additional unit), and ‘b’ is the intersection of the line on the y axis (the cost that would be

incurred even if production were zero).

You must be aware of the following when using linear regression:

2 Small Business Notes business plan outline for small business start-up


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• The technique guarantees to give the best straight line possible for any set of points. You could

supply a set of people’s ages and their telephone numbers and it would purport to a straight-line

relationship between these. It is, therefore, essential to investigate how good the relationship is

before relying on it. See later when the coefficients of correlation and determination are

discussed.

• The more points used, the more reliable the results. It is easy to draw a straight line through two

points, but if you can draw a straight line through 10 points you might be on to something.

• A good association between two variables does not prove cause and effect. The association

could be accidental or could depend on a third variable. For example, if we saw a share price rise

as a company’s profits increase we cannot, on that evidence alone, conclude that an increase in

profits causes an increase in share price. For example, both might increase together in periods of

economic optimism.

• Extrapolation is much less reliable than interpolation. Interpolation is filling the gaps within the

area we have investigated. So, if we know the cost when we make 10,000 units and the cost

when we make 12,000 units, we can probably make a reasonable estimate of the costs when we

make 11,000 units. Extrapolation, on the other hand, is where you use data to predict

what will occur in areas outside the region you have investigated. We have no experimental data

for those areas and therefore run the risk that things might change there. For example, if we have

never had production of more than 12,000 units, how reliable will estimates of costs be when

output is 15,000 units? Overtime might have to be paid, machines might break down, more

production errors might be made.

• Remove other known effects, such as inflation, before performing the analysis, or the results

are likely to be distorted.

2 The coefficients of correlation and determination


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The coefficients of correlation (r) and determination (r2) measure howgood a fit the linear

regression line is. If r = 1, there is perfect positive correlation, meaning that all the points will fit

on a straight line, and as one variable increases so does the other. If r = -1, there is perfect3

negative correlation meaning that all the points will fit on a straight line, and as one variable

increases the other decreases. If r = 0 there is no correlation and the two variables show no

association (age and telephone numbers).

The coefficient of determination, r2, is similar but is, perhaps, easier to understand. If r2 is 80%

(or 0.8) this implies that 80% of the changes in one variable can be explained by changes in the

other. Note carefully: this does not mean that 80% of the changes in one is caused by 80% of

changes in the other. Even good correlation does not prove cause and effect.

3 Time series analysis

A time series shows how an amount changes over time. For example, sales for each month,

profits for a number of years, market share over each quarter. Because strategic management

inevitably implies trying to look into the future, time series analysis is extremely important. Very

often the starting point for predictions will be based on historical patterns of growth or decline,

or a recognition that, in the past, amounts seem to have varied randomly. Time series are often

analysed by using moving averages.

3 Tufts University non-profit business plan


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In the following table, column 3 shows the readings (sales units) for each quarter for three years.

Time series analysis usually recognises four effects:

• A trend. This is the underlying growth or decline in an amount. For example, sales of a product

could show increases year-on-year. To find a trend first decide on a likely periodicity or

seasonality. For Example , 6 for the trading days of the week, 4 for seasons of the year.

Then ensure that the average is centred on a ‘season’. Above it has been assumed there are four

seasons, so 4-part averages are first calculated : 1,250 = (2,000 + 900 + 1,000 + 1,100)/4. That

average is between seasons 2 and 3. To obtain a centred average, average with the next one:

1,144 = (1,250 + 1,038)/2. Here, the 8-point moving averages move up and down implying no

strong trend.


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• Seasonal variations. These are variations which repeat fairly consistently within a period of no

more than a year. For example, although the trend could be increasing, sales in summer could

always be higher than sales in winter. Variations are identified by the differences between the 4actual results and the trend figures. Again, this table has been designed to show no stable

seasonable variations and all seasons show both positive and negative effects.

• Cyclical variations. These are variations which repeat over longer than a year. For example,

economic boom and depression.

• Random variations. Unexpected changes in what might be expected. For example, a very cold

winter could provoke much larger than normal sales of certain products. Time series analysis

usually concentrates on the first two effects. Once again, if must be emphasized that even if a

strong trend has been identified there is no guarantee that this will continue in the future. For

example, a product life cycle curve might show a strong growth trend early in a product’s life,

but then at some point, growth will fall off, and probably even further in the future the trend will

show decline. Any prediction, even if based on a large amount of historical data and using

recognized and sophisticated techniques, can still be prove to be very different to the actual

results that occur. Judgment has always to be applied when assessing how much to believe the

results.

4 State of Louisiana, USA government agency operational plan


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The Business Plan

Here are a few tips on the actual writing of the plan:

Do’s

Try to keep it fewer than twenty pages, exclusive of the appendix.

Use bullet points and numbered lists wherever possible.

Use, but do not overuse, graphs, diagrams and photographs.

Have a neutral third party read the plan – especially someone unfamiliar withyour

industry.

Include a table of contents and page numbers in your plan.

Don’ts

Avoid big words and long sentences. They only serve to confuse the reader.

Don’t use technical words and unnecessary jargon. If you need to introduce a

technical term, then you should define it.

Avoid using acronyms and initials to express words is another common error.

You may be very familiar with the acronym but your reader might not. 5

6Business leaders and economists are continually involved in the process of trying to forecast, or

predict, the future of business in the economy. Business leaders engage in this process because

much of what happens in businesses today depends on what is going to happen in the future. For

example, if a business is trying to make a decision about developing a revolutionary new

automobile, it would be nice to know whether the economy is going to be in a recession or

5 Tasmanian government project management knowledge base government project plan6 Pinson, Linda. (2004). Anatomy of a Business Plan: A Step-by-Step Guide to Building a Business and Securing Your Company’s Future (6th Edition). Page 20. Dearborn Trade: Chicago, USA.Tricia Bisoux, "Funny Business", BizEd, November/December, 2002


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whether it will be booming when the automobile is released to the general public. If there is a

recession, consumers will not buy the automobile unless it can save them money, and the

manufacturer will have spent millions or billions of dollars on the development of a product that

might not sell.

The process of attempting to forecast the future is not new. Most ancient civilizations used some

method for predicting the future. Today, computers with elaborate programs are often used to

develop models to forecast future economic and business activity. Contemporary models of

economic and business forecasting have been developed in the last century. Today's forecasting

models are considerably more statistical than they were hundreds of years ago when the stars,

and other mystical methods, were used to predict the future. Almost every large business or

government agency performs some type of formalized forecasting.

Forecasting in business is closely related to understanding the business cycle. The foundations of

modern forecasting were laid in 1865 by William Stanley Jevons, who argued that manufacturing

had replaced agriculture as the dominant sector in English society. He studied the effects of

economic fluctuations of the limiting factors of coal production on economic development.

Forecasting has become big business around the world. Forecasters try to predict what the stock

markets will do, 7what the economy will do, what numbers to pick in the lottery, who will win

sporting events, and almost anything one might name. Regardless of who does it, forecasting is

done to identify what is likely to happen in the future so as to be able to benefit most from the

events.

QUALITATIVE FORECASTING MODELS7 Boston College, Carroll School of Management, Business Plan Project The business school advises students that "To create a robust business plan, teams must take a comprehensive view of the enterprise and incorporate management-practice knowledge from every first-semester course." It is increasingly common for business schools to use business plan projects to provide an opportunity for students to integrate knowledge learned through their courses.


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Qualitative forecasting models have often proven to be most effective for short-term projections.

In this method of forecasting, which works best when the scope is limited, experts in the

appropriate fields are asked to agree on a common forecast. Two methods are used frequently.

Delphi Method. This method involves asking various experts what they anticipate will happen

in the future relative to the subject under consideration. Experts in the automotive industry, for

example, might be asked to forecast likely innovative enhancements for cars five years from

now. They are not expected to be precise, but rather to provide general opinions.

Market Research Method. This method involves surveys and questionnaires about people's

subjective reactions to changes. For example, a company might develop a new way to launder

clothes; after people have had an opportunity to try the new method, they would be asked for

feedback about how to improve the processes or how it might be made more appealing for the

general public. This method is difficult because it is hard to identify an appropriate sample that is

representative of the larger audience for whom the product is intended.

QUANTITATIVE FORECASTING MODELS

Three quantitative methods are in common use.

Time-Series Methods. This forecasting model uses historical data to try to predict future events.

For example, assume that you are interested in knowing how long a recession will last. You

might look at all past recessions and the events leading up to and surrounding them and then,

from that data, try to predict how long the current recession will last.

A specific variable in the time series is identified by the series name and date. If gross domestic

product (GDP) is the variable, it might be identified as GDP2000.1 for the first-quarter statistics

for the year 2000. This is just one example, and different groups might use different methods to

identify variables in a time period.

Many government agencies prepare and release time-series data. The Federal Reserve, for

example, collects data on monetary policy and financial institutions and publishes that data in the


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Federal Reserve Bulletin. These data become the foundation for making decisions about

regulating the growth of the economy.

Time-series models provide accurate forecasts when the changes that occur in the variable's

environment are slow and consistent. When large-degree changes occur, the forecasts are not

reliable for the long term. Since time-series forecasts are relatively easy and inexpensive to

construct, they are used quite extensively.

The Indicator Approach. The U.S. government is a primary user of the indicator approach of

forecasting. The government uses such indicators as the Composite Index of Leading, Lagging,

and Coincident Indicators, often referred to as Composite Indexes. The indexes predict by

assuming that past trends and relationships will continue into the future. The government indexes

are made by averaging the behavior of the different indicator series that make up each composite

series.

The timing and strength of each indicator series relationship with general business activity,

reflected in the business cycle, change over time. This relationship makes forecasting changes in

the business cycle difficult.8

Econometric Models. Econometric models are causal models that statistically identify the

relationships between variables and how changes in one or more variables cause changes in

another variable. Econometric models then use the identified relationship to predict the future.

Econometric models are also called regression models.

There are two types of data used in regression analysis. Economic forecasting models

predominantly use time-series data, where the values of the variables change over time.

Additionally, cross-section data, which capture the relationship between variables at a single

point in time, are used. A lending institution, for example, might want to determine what

influences the sale of homes. It might gather data on home prices, interest rates, and statistics on

the homes being sold, such as size and location. This is the cross-section data that might be used

8 "Ten Big Questions"


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with time-series data to try to determine such things as what size home will sell best in which

location.

An econometric model is a way of determining the strength and statistical significance of a

hypothesized relationship. These models are used extensively in economics to prove, disprove,

or validate the existence of a casual relationship between two or more variables. It is obvious that

this model is highly mathematical, using different statistical equations.

For the sake of simplicity, mathematical analysis is not addressed here. Just as there are these

qualitative and quantitative forecasting models, there are others equally as sophisticated;

however, the discussion here should provide a general sense of the nature of forecasting models.

THE FORECASTING PROCESS

When beginning the forecasting process, there are typical steps that must be followed. These

steps follow an acceptable decision-making process that includes the following elements:

1. Identification of the problem. Forecasters must identify what is going to be forecasted, or

what is of primary concern. There must be a timeline attached to the forecasting period.

This will help the forecasters to determine the methods to be used later.

2. Theoretical considerations. It is necessary to determine what forecasting has been done

in the past using the same variables and how relevant these data are to the problem that is

currently under consideration. It must also be determined what economic theory has to

say about the variables that might influence the forecast.

3. Data concerns. How easy will it be to collect the data needed to be able to make the

forecasts is a significant issue.

4. Determination of the assumption set. The forecaster must identify the assumptions that

will be made about the data and the process.


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5. Modeling methodology. After careful examination of the problem, the types of models

most appropriate for the problem must be determined.

6. Preparation of the forecast. This is the analysis part of the process. After the model to be

used is determined, the analysis can begin and the forecast can be prepared.

7. Forecast verification. Once the forecasts have been made, the analyst must determine

whether they are reasonable and how they can be compared against the actual behavior of

the data.

Each of the seven steps has substages; however, the steps that have been presented are the major

concerns to the forecaster. Those with a deep interest in forecasting might pursue more in-depth

treatments.

FORECASTING CONCERNS

Forecasting does present some problems. Even though very detailed and sophisticated

mathematical models might be used, they do not always predict correctly. There are some who

would argue that the future cannot be predicted at all period!

Some of the concerns about forecasting the future are that (1) predictions are made using

historical data, (2) they fail to account for unique events, and (3) they ignore co-evolution

(developments created by our own actions). Additionally, there are psychological challenges

implicit in forecasting. An example of a psychological challenge is when plans based on

forecasts that use historical data become so confining as to prohibit management freedom. It is

also a concern that many decision makers feel that because they have the forecasting data in hand

they have control over the future.

Regardless of the opponents to forecasting, the U.S. government, investment analysts, business

managers, economists, and numerous others will continue to use forecasting techniques to


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predict the future. It is imperative for the users of the forecasts to understand the information and

use the results as they are intended.

Importance Of Planning

Planning helps an organization chart a course for the achievement of its goals. The process

begins with reviewing the current operations of the organization and identifying what needs to be

improved operationally in the upcoming year. From there, planning involves envisioning the

results the organization wants to achieve, and determining the steps necessary to arrive at the

intended destination--success, whether that is measured in financial terms, or goals that include

being the highest-rated organization in customer satisfaction.

Efficient Use of Resources9

All organizations, large and small, have limited resources. The planning process provides the

information top management needs to make effective decisions about how to allocate the

resources in a way that will enable the organization to reach its objectives. Productivity is

maximized and resources are not wasted on projects with little chance of success.

Establishing Goals

Setting goals that challenge everyone in the organization to strive for better performance is one

of the key aspects of the planning process. Goals must be aggressive, but realistic. Organizations

cannot allow themselves to become too satisfied with how they are currently doing--or they are

likely to lose ground to competitors. The goal setting process can be a wake-up call for managers

that have become complacent. The other benefit of goal setting comes when forecast results are

compared to actual results. Organizations analyze significant variances from forecast and take

action to remedy situations where revenues were lower than plan or expenses higher.

Managing Risk And Uncertainty

9 "Ten Big Questions"


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Managing risk is essential to an organization’s success. Even the largest corporations cannot

control the economic and competitive environment around them. Unforeseen events occur that

must be dealt with quickly, before negative financial consequences from these events become

severe. Planning encourages the development of “what-if” scenarios, where managers attempt to

envision possible risk factors and develop contingency plans to deal with them. The pace of

change in business is rapid, and organizations must be able to rapidly adjust their strategies to

these changing conditions.

Team Building

Planning promotes team building and a spirit of cooperation. When the plan is completed and

communicated to members of the organization, everyone knows what their responsibilities are,

and how other areas of the organization need their assistance and expertise in order to complete

assigned tasks. They see how their work contributes to the success of the organization as a whole

and can take pride in their contributions. Potential conflict can be reduced when top management

solicits department or division managers’ input during the goal setting process. Individuals are

less likely to resent budgetary targets when they had a say in their creation.

Creating Competitive Advantages10

Planning helps organizations get a realistic view of their current strengths and weaknesses

relative to major competitors. The management team sees areas where competitors may be

vulnerable and then crafts marketing strategies to take advantage of these weaknesses. Observing

competitors’ actions can also help organizations identify opportunities they may have

overlooked, such as emerging international markets or opportunities to market products to

completely different customer groups.

10 Pennsylvania Business Plan Competition - competition intended to teach economic principles to K-12 students


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Conclusion


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Planning is of paramount importance both for an organisation and an economy. Sound plans are

essential to effective management, because they serve as guides to all management

functions.Lack of well-defined objectives and priorities is the common cause of failure. 'Failure

to plan is planning to fail.Every organisation exists to achieve certain objectives:(Planning

concentrates attention on the dominant goals of the organisation. It forces the members of the

organisation not to get lost in the maze of routine activities and lose sight' of the broad objectives

for which the organisation was established."Plans alone cannot make an enterprise successful.

Action is required; the enterprise must operate. Plans can, however, focus attention on

purposes.They can forecast which actions will tend toward the ultimate objective which tends

away and which are merely irrelevant. Sound Planning avoids the danger of means becoming

ends in themselves. Planning provides a rational approach to predefined objectives. It secures

unity of purpose and action.Uncertainly and risks are inevitable and planning cannot eliminate

them. But planning enables an organisation to cope with uncertainty and change.Although the

exact future can seldom be predicted and factors beyond control may interfere with the best-laid

plans, without planning events are left to chance. (With the help of planning, an enterprise can

predict future opportunities and threats and make due provision for them) Instead of leaving

future events to chance, they can be made to occur in a desired manner, planning seeks to

minimize risk while taking advantage of opportunities.Planning helps to identify potential threats

and opportunities. It also keeps management alert to the changing environment of business. In

this way planning provides additional strength to the organisation for survival and growth in the

face of turbulence.Planning saves an organisation from drifting and avoids aimless activities. It

directs human 11efforts into endeavors that contribute to the accomplishment of goals.

1111 Planning provides the basis for control. Plans serve as standards or

benchmarks for the evaluation of actual performance. Sound planning enables

management to control the events rather than be con Small Business Notes business plan

outline for small business start-up


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Bibliography1. Pinson, Linda. (2004). Anatomy of a Business Plan: A Step-by-Step Guide to Building a Business and

Securing Your Company’s Future (6th Edition). Page 20. Dearborn Trade: Chicago, USA.

2. Small Business Notes business plan outline for small business start-up

3. Tufts University non-profit business plan

4. State of Louisiana, USA government agency operational plan

5. Tasmanian government project management knowledge base government project plan

6. Boston College, Carroll School of Management, Business Plan Project The business school advises

students that "To create a robust business plan, teams must take a comprehensive view of the enterprise


Page 22

and incorporate management-practice knowledge from every first-semester course." It is increasingly

common for business schools to use business plan projects to provide an opportunity for students to

integrate knowledge learned through their courses.

7. Eric S. Siegel, Brian R. Ford, Jay M. Bornstein (1993), 'The Ernst & Young Business Plan Guide' (New

York: John Wiley and Sons)

8. Harvard Business School Press-Pocket Mentor, " Creating a Business Plan"

9. "Ten Big Questions"

10. Pennsylvania Business Plan Competition - competition intended to teach economic principles to K-12

students

11. Tricia Bisoux, "Funny Business", BizEd, November/December, 2002

12. www.wikipedia.org


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Documents

Economics Project