Unit 1.pptx

WHAT IS QUANTITATIVE
TECHNIQUES ?
QUANTITATIVE
ANALYSIS

Introduction:
• Decision Science is the application that uses scientific approach and solves
the management problems. It also helps managers to make best decisions.
Decision science includes a large number of mathematically oriented
techniques. These techniques can be either developed within field of decision
science or taken from other disciplines. Decision science is a recognized and
established discipline in business. Decision science is a technique which is
mainly used within business for increasing their efficiency and productivity.

Introduction:
In various surveys of businesses, many indicate that they use decision science
techniques, and most rate the results to be very good. Decision science is also
known as operations research, quantitative techniques, quantitative analysis and
management sciences. It is largely used in daily routine of most programs of
business organization.

Meaning and Definition of Quantitative
Techniques :
• The term Decision Science / Quantitative Techniques (QT) /Operations
Research (OR) describes the discipline that is focused on the application of
Information Technology (IT) for well-versed decision-making.
• Quantitative techniques are those statistical and programming techniques:
which support the decision making process especially related to industry and
business. QT takes into consideration the elements of qualities such as use
of numbers, symbols and other mathematical expressions.
• QT is basically helpful enhancement to judgment and intuition.

Techniques :
• Quantitative techniques assess planning factors and alternatives as and when
they arise rather than suggest courses of action.
• Quantitative, techniques may be defined as those techniques which provide
the decision maker with a systematic and powerful means of analysis and
help, based on quantifiable data, in exploring policies for achieving pre-
determined goals. ''Quantitative techniques are mainly appropriate to
problems of complex business enterprises".

Techniques :
• QT can be considered as the scientific approach to managerial decision
making. This approach starts from raw data and after manipulation or
processing, information is produced which is valuable for making decision.
• The main aim of quantitative analysis is the processing and manipulating of
raw data into meaningful information. For increasing the use of quantitative
analysis, computer can be used as an instrument.

Techniques :
• According to C.R. Kothari :
• "Quantitative Techniques may be defined as those technique which provide
the decision maker with a systematic and powerful means of analysis and
help, based on quantitative in exploring policies for achieving per-determined
goals”.
• Quantitative Techniques are the devices developed on the basis of
mathematical and statistical models.

Role of Quantitative Techniques
in Decision Making :
• The major roles of quantitative technique are as follows :
• It provides a tool for scientific analysis.
• It offers solutions for various business problems.
• It enables proper deployment of resources.
• It supports in minimising waiting and servicing costs.
• It helps the management to decide when to buy and what is the procedure of
buying.
• It helps in reducing the total processing time necessary for performing a set of jobs.

Characteristics of Quantitative Techniques:

1) Decision-Making :
• Decision-making or problem solving constitutes the major working of operations
research: Managerial decision-making is considered to be a general systematic
process of operations research (OR).
2) Scientific Approach :
• Like any other research, operations research also emphasizes on the overall
approach and takes into account all the significant effects of the system. It
understands and evaluates them as a whole. It takes a scientific approach towards
reasoning. It involves the methods defining the problem, its formulation, testing and
analyzing of the results obtained.

3) Objective-Oriented Approach :
• Operations Research not only takes the overall view of the problem, but also
endeavours to arrive at the best possible (say optimal) solution to the
problem in hand. It takes an objective-oriented approach. To achieve this, it
is necessary to have a defined measure of effectiveness which is based on the
goals of the organisation. This measure is then used to make a comparison
between alternative solutions to the problem and adopt the best one.

4) Inter-Disciplinary Approach :
• No approach can be effective, if taken singly. OR is also inter-disciplinary in
nature. Problems are multi-dimensional and approach needs a team work.
For example, managerial problems are affected by economic, sociological,
biological, psychological, physical and engineering aspect. A team that plans
to arrive at a solution, to such a problem, needs people who are specialists in
areas such as mathematics, engineering, economics, statistics, management,
etc.

Scope of Quantitative Techniques :
The following
are the scope
of quantitative
techniques in
different areas :

1) Industry :
• Industrial management deals with a series of problems, starting right from the purchase of raw materials till
the dispatch of final products. The management is ultimately interested in overall understanding of the
methods, of optimising profits. Therefore, to take decision on scientific basis, operations research team has
to think about various alternative methods, to produce goods and obtaining returns in each case.
• Not only this, the operations research study should also suggest possible changes in the overall structure like
installation of a new machine or introduction to automation, etc., for optimising the results. Many industries
have gained immensely by applying operations research in various tasks. For example, operations research
can be used in the fields of manufacturing and production, blending and product mix, inventory
management, for forecasting demand, sale and purchase, for repair and maintenance jobs, for scheduling
and sequencing planning, and also for scheduling and control of projects.

2) Developing Economies :
• OR is applicable to both developing and developed economies. But a lot of
scope exists in developing economies, for building up an operations research
approach towards planning. The basic idea is to orient the planning, to achieve
maximum growth per capital income in minimum time; considering the goals and
restrictions of the country. Poverty and hunger are the core problems faced by
many countries of Asia and Africa. Therefore, people like statisticians,
economists, technicians, administrators, politicians and agriculture experts can
work in conjunction, to solve this problem with an operations research approach.

3) Agriculture Industry :
• Operations research approach has a huge scope in agriculture sector Population
explosion has led to scarcity of food. Optimum allocation of land for various
crops in accordance with climatic conditions is a challenge for many countries.
Also, each developing country is facing the problem of optimal distribution of
water from several water bodies. These areas of concern hold a great scope for
scientific research.

4) Organisation :
• Organisation, big or small, can adopt operations research approach effectively.
Operational productivity of organisations have improved by using quantitative
techniques. Techniques of operations research, can be applied to minimise cost,
and maximise benefit for decisions. For example, a departmental store faces
problem like, employing additional sales girls, or purchasing an additional van,
etc.

5) Business and Society :
• Businesses and society can directly be benefited from operations research. For
example, hospitals, clinics etc. Operations research methods can be applied directly
to solve administrative problems such as minimising the waiting time of outdoor
patients.
• Similarly, the business of transport can also be benefited by
applying simulation methods. Such methods, can help to regulate train arrivals and
their running timings. Queuing theory, can be applied to minimise congestion and
passengers waiting time.

• These methods are increasingly being applied in L.I.C. workplaces. It helps in
deciding the premium rates of various policies. Industries such as petroleum,
paper, chemical, metal processing, aircraft, rubber, mining and textile have been
extremely benefited by its use.

Nature of Quantitative Techniques :

1) Quality of Solution :
• Quantitative techniques helps in improving the quality of solution but may not necessarily
result in a perfect solution. It helps to find the best possible solution to the problem under
consideration.
2) Goal-Oriented Optimum Solution :
• Quantitative techniques is sensitive about the optimization theory. It aims at identify the best
possible course of action or solution under given constraints.

3) Use of Models :
• Quantitative techniques uses models built by quantitative measurement, It
also derives a solution from the model using one or more of the diversified
mathematical techniques. A decision can be arrived, either by conducting
experiments on it or by mathematical analysis. The objective is to assess the
organisation to determine its policy, and actions scientifically and optimise its
results.

4) Require Willing Executives :
• Quantitative techniques needs a group of individuals having diverse
backgrounds and skills to evaluate and analyse the costs, pros and cons of
the alternative solutions of the problem. Willingness to participate in such
experimental process is must for the executives. This will empower the
decision-makers, to be objective in selecting the best possible solution.

5) Reduces Complexity :
• Quantitative techniques attempts to minimise the complexity of business
operation by helping managers to correct a difficult function or process. It
also attempts to innovate easy solutions of costly and complicated functions,
compared to actual experimental practice.

Importance of Decision Science /
Quantitative Techniques :
1) Better Control :
• For large organisations, it is practically impossible to continuously supervise
every routine work. A QT approach comes handy and gives an analytical and
quantitative basis to identify the problem area. QT approach is most
frequently adopted with production scheduling and inventory replenishment.

2) Better Systems :
• For example, Problems identifying the best location for factories or decision
on whether to open a new warehouse, etc., are often been studied and
analysed by QT approach. This approach helps to improve the existing
system such as, selecting economical means of transportation, production
scheduling, job sequencing, or replacing old machinery.

3) Better Decisions :
• QT models help in improved decision-making and thereby reduce the risk of wrong
decisions. QT approach gives the executive an improved insight into the problem
and thereby improve decision-making.
4) Better Co-ordination :
• QT models help in co-ordination of different or various divisions of an
organisation.

Limitations of Quantitative Techniques :
1) Dependence on an Electronic Computer :
• QT approach is mathematical in nature. QT techniques try to find out an
optimal solution to a problem, by taking all the factors into consideration.
The need of computers become unavoidable because these factors are
enormous (huge), it requires huge calculations to express them in quantity
and to establish relationships among them.

2) Non-Quantifiable Factors :
• One of the drawbacks of QT techniques is that they provide a solution only
when all the elements related to a problem are quantified. Since all relevant
variables may not be quantified, they do not find a place in QT models.
3) Wrong Estimation :
• Certain assumptions and estimates are made for assigning quantitative values
to factors involved in QT, so that a quantitative analysis can be done. If such
estimates are wrong. the result can be misleading.

4) Involves Time and Cost :
• Operations research is a costly affair. An organisation needs to invest time,
money and effort into QT to make it effective. Professionals need to be
hired to conduct constant research. For better research outcomes, these
professionals must constantly review the rapidly changing business scenarios.
5) Implementation :
• The complexities of human relations and behavior must be taken into
account while implementing QT decisions, as it is a very delicate task.

Applications of Quantitative Techniques :
Uses, scope and
applications of
quantitative
techniques in
managerial
decision-making
are as follows :

1) Finance, Budgeting and Investment :
•Long range capital requirements, cash flow analysis, investment
portfolios and dividend policies.
•Credit policies, credit risks and procedures for delinquent account.
•Procedures to deal with complaints and claim.

2) Marketing :
•Selection of product, its timing and competitive actions.
•Cost and time-based decision for advertising media.
•Rate of calling an account and requirement of number of salesmen,
etc.
•Market research effectiveness.

3) Physical Distribution :
•Size of warehouses, distribution centre, retail outlets, etc., and their
location.
•Policy for distribution.
4) Purchasing, Procurement and Exploration :
•Buying rules.
•Determining purchase timing and its quantity.
•Policies for bidding and analysis of vendor.
•Replacement policies of equipment.

5) Personnel :
•Manpower requirement forecasting, recruitment policies and
assignment of job.
•Suitable personnel selection considering age and skills, etc.
•For each service centre determining the optimum number of
persons.

6) Production :
•Proper allocation of machines for scheduling and sequencing the
production.
•Optimum product mix calculation.
•Selecting production plant sites along with its location and design.

7) Research and Development :
•Alternative designs evaluation and its reliability.
•Developed projects control.
•Multiple research projects co-ordination.
•Required determination of time and cost.

Quantitative Techniques in Decision
Making
Various quantitative techniques for decision making are:-
• 1. Mathematical Programming 2. Cost Analysis (Break-Even Analysis)
• 3. Cost-Benefit Analysis 4. Linear Programming 5. Capital Budgeting
• 6. Inventory Management 7. Expected Value 8. Decision Tree
• 9. Simulation 10. Queuing or Waiting Line Theory 11. Game Theory
• 12. Information Theory 13. Preference Theory/Utility Theory and Few
Others.

Technique # 1. Mathematical
Programming:
• Besides the calculus, there are other management science techniques which
can be employed to resolve a variety of decision problems. One such
technique is Mathematical Programming which is useful whenever several
factors constrain the choice of strategies. Consider the inventory problem. If
the objective is simply to minimize total cost, there are no constraints which
limit our choice of strategies.
• If there are constraints, they might limit either the space in which inventory
can be placed, the funds which can be spent on inventory, or the maximum
number of orders that can be placed by the purchasing department.

• This being the case, it would have become a problem in constrained
minimization and mathematical programming techniques could be used to
find a solution. The constraints create the environment within which
decision makers strive to maximize or minimize the objectives to be
achieved.
• This is the essence of mathematical programming: Constrained
maximization or minimization. It becomes an intuitively appealing
framework for the analysis of many types of business problems. The
difficult task, however, is shouldered by the model builder, who must abstract
from the environment those important elements that are to be incorporated
in the mathematical model. Linear programming techniques such as Simplex
method, graphical method etc., make the mathematical models to solve them.

Technique # 2. Cost Analysis (Break-Even
Analysis):
• Managers want to make money. The objective of the break-even analysis is
to decide the optimum break-even point, that is, where profits will be
highest. In making decisions, managers must pay a great deal of attention to
the profit opportunities of alternative courses of action. This obviously
requires that the cost implications of those alternatives are assessed. An
important aspect of such cost analysis is that made between fixed and
variable costs.

• A cost can be classified as being fixed or variable in relation to changes in the
level of activity within a given period. (In the long run, of course, all costs
are variable). Fixed costs are those which remain fixed irrespective of the
volume of production or sales. For example, a managing director’s salary will
not vary (change) with the volume of goods produced during any year. Road
tax payable for a car will not vary with its annual mileage covered. Insurance
premiums, rent charges, R&D costs are a few other typical examples of fixed
costs.

• Variable costs vary or change in response to changes in, say, volume of
production or sales or any other similar activity. Sales commissions in relation
to sales levels, petrol costs in relation to miles travelled and labour, costs in
relation to hours worked are obvious examples.
• Mixed costs are of hybrid nature, being partly fixed and partly variable. An
example is found in telephone charges – the rental element is a fixed cost,
whereas charges for calls made are a variable cost. Separating fixed and
variable costs.

• The total cost at any level of operations is the sum of a fixed cost component and a
variable cost component. The importance of separating variable costs from fixed
costs stems from the different behaviour patterns of each, which have a significant
bearing on their control. Variable Costs must be controlled in relation to the level of
activity, whilst fixed costs must be controlled in relation to time.
• From a decision-making point of view, it is also important to know whether or not
a particular cost will vary as a result of a given decision. By adding graphically
variable cost to the fixed cost for different levels of activity (e.g. number of goods
produced), a total cost curve can be drawn.

• If a revenue curve is super-
imposed on the same graph
(Fig. 18.2) the result is the
break-even chart which
depicts the profits/loss
picture for several possible
cost-revenue situations at
different levels of activity.
• In particular, break-even
analysis is useful as a
background information
device for reviewing overall
cost and profit levels, but it
can also be used in
connection with special
decisions such as selecting a
channel of distribution or
make or buy decisions.

Technique # 3. Cost-Benefit Analysis:
• Cost-benefit analysis is a mathematical technique for decision-making. It is a
quantitative technique used to evaluate the economic costs and the social benefits
associated with a particular course of action. In this technique, an effort is made to
identify all costs and benefits, not only those that may be expressed in rupees, but
also the less easily calculated effects of a given decision.
• In general, this technique (which is fairly complicated) is advocated for use in
decisions on public projects, in which social costs and social benefits as well as
actual out-of-pocket costs should be taken into account. What counts as a benefit or
loss to one part of the economy—to one or more persons or groups- does not
necessarily count as a benefit or loss to the economy as a whole.

• And in cost-benefit analysis we are concerned with the economy as a whole,
with the welfare of a defined society and not any smaller part of it. But cost-
benefit analysis may also be applicable to a single company, for in many
cases, it is advisable to place a value on costs and benefits that are not
ordinarily expressed in rupees.
• Somewhat similar to cost-benefit analysis is the cost-effectiveness analysis,
which is analysis to determine the least expensive way of reaching an
objective or of obtaining the greatest possible value from a given
expenditure.

Technique # 4. Linear Programming:
• Linear programming is a quantitative technique used to determine the
optimal mix of limited resources for maximizing profits or minimizing costs.
Linear programming is an extension of break-even analysis that is very useful
in analyzing complex problems. Linear programming involves the solution of
linear equations and is appropriate when the manager must allocate scarce
resources to competing projects.

Technique # 5. Capital Budgeting:
• A manager relies heavily on linear programming when he allocates resources
to competing projects. Similarly Capital budgeting provides a set of
techniques a manager can use to evaluate the relative attractiveness of
various projects in which a lump payment is made to generate a stream of
earnings over a future period.
• Examples of capita! budgeting projects include an investment in a new
machine that will increase future profits by reducing costs, an investment of
a sum of money into an advertising campaign to increase future sales (and
profits) etc.

• In essence, capital budgeting techniques provide management with a useful
method for analyzing the profitability of potential investments that have
dissimilar earnings characteristics. Without these techniques, it would be
nearly impossible to weigh the advantages of dissimilar investments.

Technique # 6. Inventory Management:
• In quest to make money, a manager should employ his resources as
efficiently as possible. Inventory management involves determining and
controlling the amount of raw material an organization should keep in stock
to operate effectively and efficiently.
• Efficient management of inventory requires balancing several conflicting
goals. The first goal is 10 Keep inventories as small as possible to minimize
the amount of warehouse space and the amount of money tied up in
inventories.

• This goal is in conflict with the need to fill all customer requirements, to
optimize the number of orders placed, and to take advantage of the
economies of long production runs and quantity discounts. To solve
inventory problems, the manager can use the economic order quantity
(EOQ) model. This model can be expressed as a mathematical formula. The
solution of EOQ formula tells the manager how many items he should
purchase, and how often.

Technique # 7. Expected Value:
• To understand expected value model, it is important to comprehend the
concept of probability which refers to the likelihood that an event will
happen. Mathematically, probability is expressed as a fraction or percentage.
• For example, there is a 30% (or 0.3) probability that it will rain tomorrow.
Probabilities may be established empirically, by observing some phenomenon
over time. When several courses of action are available and the outcome of
each is uncertain, the decision maker can use probabilities to select his final
choice.

Technique # 8. Decision Tree:
• Another increasingly useful tool for management decision-makers is the so
called decision tree. This is basically a conceptual map of possible decisions
and outcomes in a particular situation. It is useful in cases where a manager is
required to make a number of sequential decisions i.e., where earlier
decisions will affect later ones.

A simple decision tree appears below:
The diagram focuses attention on
outcomes or consequences as well as
decisions. These outcomes can be further
elaborated in terms of their probability
and their anticipated pay off. It is also
possible to add a time dimension to the
whole diagram, so that, for example in
Fig. 18.3 the period from decision point 1
to decision point 2 could be one year.
These additional features help to make the
use of decision trees a salutary exercise
for managers.

Technique # 9. Simulation:
• Simulation techniques are especially applicable to what if problems, in which a
manager or technician wants to know, If we do this, what will happen. Simulation
can, of course, be conducted by the manipulation of physical models. For example,
one might have a physical model of a machine and actually keep on increasing its
speed to determine at what point it would begin to jam, fly apart or walk across the
floor.
• With no loss, one may, instead, use a mathematical model in which each of the
terms represents one of the variables, and observe the effect on the others when
different values are given to one or more of the terms. With the help of a
computer, it is possible to examine what will happen in an enormous number of
cases-without spending a prohibitive amount of time.

• Because large electronic computers have become easily accessible in recent
years, management can simulate complex situations in order to determine the
best course of action. Simulation is the process of building, testing and
operating models of real-world phenomena through the use of mathematical
relationships that exist among critical factors.
• This technique is useful for solving complex problems that cannot be readily
solved by other techniques. A simulation model can be deterministic if the
manager knows exactly the value of the factors he employs in the equations.

• However, simulation is essentially probabilistic, since the manager typically must estimate the
future values of these factors. Simulation is very helpful in engineering and design problems,
where the medium may be either the mathematical model or a diagram on a screen (VDU)
connected to the computer. In the latter case, the engineer-designer can modify the design
by using a light pen. The technique is equally applicable to management decision-making.
• It is obviously much cheaper, safer and easier to experiment with a mathematical model or
diagrammatic simulator than to experiment with real machines or even physical models of
machines. In some cases, however the variables that one manipulates are not exact quantities
but probabilities. Then what are known as Monte Carlo techniques must be used. These
make it possible to stretch as far as possible such few actual data as are available to begin
with.

Technique # 10. Queuing or Waiting Line
Theory:
• Queuing theory is an O.R. technique which aids the manager in making decisions
involving the establishment of service facilities to meet irregular demands. Cost
problems arise when there are more service facilities available than are needed, or
when too few facilities are available and consequently, long waiting lines form.
• For example, in a battery of machines, breakdowns will occur randomly, and
whenever the maintenance service falls below that demanded by the breakdowns, a
waiting line of unrepaired machines forms. This idle capacity is a cost that has to be
balanced against the costs of keeping maintenance services available.

• Queuing theory is applied to any situation producing a need to balance the cost of
increasing available service against the cost of letting units wait. To arrive at the best
number of service facilities, the manager and the O.R. team must first determine (in
the example above) the breakdown rate and the time required to service each
machine.
• These data can then be used to construct a mathematical model of the problem,
which can become extremely complex. Simulation methods are widely used to solve
waiting line problems. Simulation is a systematic, trial and error procedure for
solving waiting line & problems that are too complex for easy mathematical analysis.

• Reasonably good solutions may often be obtained by simulating important
elements of the problem. A widely used method of simulating business
problems in which events occur with assigned or computed probabilities is
known as the Monte Carlo Method. This method utilizes the mathematics of
probability, and is often run on the computer.

Technique # 11. Game Theory:
• Game theory is a technique of operations research. This provides a basis for
determining, under specified conditions, the particular strategy that will result in
maximum gain or minimum loss, no matter what opponents do or do not do. (An
opponent would be the enemy general in military application, or a competitor in a
business situation etc.)
• The simplest application of the game theory is the two-person, zero-sum game, in
which there are only two players and one player can gain only at the expense of the
other. These two conditions are generally fulfilled when two armies are opposing
each other. In business they are fulfilled only in special cases.

• Assume, a company has only one competitor and the size of the market is
fixed; thus every gain in sales by one company means an equal loss in sales
for the other. In an expanding market, both the companies could gain, in a
declining market, one could gain at the expense of the other. Game theory
has the greatest practical usefulness in planning sales promotion strategies.

A Company who wishes to increase its sales may do so
by using one or more of such techniques as:
• A reduction in product price,
• An increase in number of salesmen, and
• A rise in its advertising budget
The company must consider what the rival can do to nullify the effect of any
of these techniques. The company therefore asks itself questions like these.

Assuming we decide to increase our share of market
by cutting prices, what will actually happen if:
• Our rival also cuts prices,
• He increases the number of his salesmen,
• He raises his advertising budget or
• He uses a combination of all three of these tactics?
By evaluating each one of these possibilities, the company can ascertain the greatest possible damage the rival can
inflict. This will reveal either the minimum gain the company is assured of or the maximum loss it can suffer.
In real life, however, there are more than two competitors and the demand for most products is not stable or fixed.
If all competitors cut prices, the market for all may be increased and possibly all may gain. Or, if the market remains
the same, all may lose. Therefore the losses of one do not necessarily equal the gains of another.

Game Models:
• The next quantitative decision making model consists of game models or
competitive strategies. These models are derived from game theory which
provides many useful insights into situations involving elements of
competition.
• Decision situations are of a game nature when a rational opponent (e.g., a
competitor in the market) is involved, so that resulting effects are dependent
on the specific strategies selected by the decision maker and his opponent.
This assumes that the opponent will carefully consider what the decision
maker may do before he selects his own strategy.

Technique # 12. Information Theory:
• A central element in all decision making is the process of obtaining, using and
disseminating information. Information theory is a rigorous mathematical effort to
solve problems in communication engineering. Since information theory deals with
the flow of information and communication net-works, it has important
implications for organization design and for man-machine relationships.
• Information theory provides a means of measuring the information content of
both symbolic and verbal languages and relating the characteristics of an efficient
communication system to the information content of messages transmitted. This
body of theory has been of great use in the design of communication systems and
computers.

Technique # 13. Preference Theory/Utility
Theory:
• One of the interesting and practical supplements of modern decision theory is (the
work that has been done and) the techniques developed to supplement statistical
probabilities with analysis of individual preferences in the assumption or avoidance
of risk. While referred to here as preference theory, it is more classically denoted
Utility theory. It might seem reasonable that if we had a 60% chance of a decision
being the right one, we would take it.
• But this is not necessarily true, since the risk of being wrong is 40% and a manager
might not wish to take this risk, particularly if the penalty for being wrong is severe,
whether in terms of monetary losses, reputation or job security. If we doubt this,
we might ask ourselves whether we would risk, say Rs. 40,000 on the 60% chance
that we might make Rs. 100,000.

• We might readily risk Rs. 4 on a chance of making Rs. 10, and gamblers have been known to
risk much more on a lesser chance of success. Therefore, in order to give probabilities
practical meaning in decision making, we need better understanding of the individual
decision maker’s aversion to, or acceptance of risk. This varies not only with people but also
with the size of the risk, with the level of managers in an organization and according to
whether the funds involved are personal or belong to a company.
• Higher level managers are accustomed to taking larger risks than lower-level managers. The
same top manager who may take a decision involving risks of millions of rupees for a
company would not like to do that with his own personal fortune. Moreover, the same
manager willing to opt for a 75% risk in one case might not be willing to, in another.

• For example, he may go for a large advertising program where the chances
of success are 70%, but might not decide in favour of an investment in plant
and machinery unless the probabilities for success were higher. In other
words, attitudes toward risk vary with events, as well as with people and
positions.
• Most of us are gamblers when small stakes are involved, but soon take on
the role of risk averters when the stakes rise. Many managers are risk averters
and thereby miss opportunities.

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