decision theory
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Assignment No.2
Quantitative Techniques
(5564)
Col MBA/MPA
DECISION
THEORY
Fayyaz Ahmed Kayani
Roll No. AD593483
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
Semester: Autumn 2009
ACKNOWLEDGEMENT
No one writes alone. So I would like to thanks all those who
helped and assisted a great source in completion of
assignment. Assigned topic was a new for me and it was not
possible to accomplish it without their magnificent support.
They have been a source of knowledge for me as they helped
me much in understanding the assigned Topic. I especially
thank to my honorable tutor who guided me in every juncture.
I also pay my gratitude to Department of Business
Administration, AIOU, Islamabad for their marvelous selection
of issues for MBA students through which they are gaining
treasure of knowledge after completion of given task for their
future.
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
DECISION THEORY
INTRODUCTION
Every day we, are humans, make many decisions; and occasionally we
make an important decision that can have immediate and/or long-term
effects on our lives. Such decisions as where to attend school, whether to
rent or buy, whether your company should accept a merger proposal, and so
on, are important decisions for which we would prefer to make correct
choice.
The success or failure that an individual or organization experiences,
depends to a large extent on the ability of making appropriate decisions.
Making of a decision requires an enumeration of feasible and viable
alternatives (courses of action or strategies), the projection of consequences
associated with different alternatives, and the measure of effectiveness (or
an objective) to identify best alternative to be used.
Everyone engages in the process of making decisions on a daily basis.
Some of these decisions are quite easy to make and almost automatic. Other
decisions can be very difficult to make and almost debilitating. Likewise, the
information needed to make a good decision varies greatly. Some decisions
require a great deal of information whereas others much less. Sometimes
there is not much if any information available and hence the decision
becomes intuitive, if not just a guess. Many, if not most, people make
decisions without ever truly analyzing the situation and the alternatives that
exist. There is a subjective and intrinsic aspect to all decision making, but
there are also systematic ways to think about problems to help make
decisions easier. The purpose of decision analysis is to develop techniques to
aid the process of decision making, not replace the decision maker.
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Earlier, the decisions were taken subjectively based on the skill,
experience and intuition of the decision maker. But in today’s world of
dynamism, the decision making has become very complex, particularly in
business, marketing and management because they involve a number of
interactive variables (factors) whose values and relationships cannot be
determined accurately. In such situations, mere intuition and expertise of the
decision maker are inadequate and we require well considered judgment and
analysis based on the use of several quantitative techniques and even
computers in solving problems. It is in this context that we need a full-
fledged decision theory which provides a sound and scientific basis for
improved decision making.
Decision making is the essence of management. In general, the
process of making decisions calls for (i) identifying the alternatives, (ii)
gathering all the relevant information about them, and (iii) selecting
the best alternative on the basis of some criterion.
The decision theory, also called the decision analysis, is used to
determine optimal strategies where a decision-maker is faced with several
decision alternatives and an uncertain, or risky, pattern of future events. To
recapitulate, all decision-making situations are characterized by the fact that
two or more alternative courses of action are available to the decision-maker
to choose from. Further, a decision may be defined as the selection by the
decision-maker of an act, considered to be best according to some pre-
designated standard, from among the available options.
When analyzing the decision making process, the context or environment of
the decision to be made allows for a categorization of the decisions based on
the nature of the problem or the nature of the data or both. There are two
broad categories of decision problems: decision making under certainty and
decision making under uncertainty.
THEORETICAL QUESTIONS ABOUT DECISIONS
The following are examples of decisions and of theoretical problems that
they give rise to.
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Shall I bring the umbrella today? – The decision depends on something
which I do not know, namely whether it will rain or not.
I am looking for a house to buy. Shall I buy this one? – This house
looks fine, but perhaps I will find a still better house for the same price if I go
on searching. When shall I stop the search procedure?
Am I going to smoke the next cigarette? – One single cigarette is no
problem, but if I make the same decision sufficiently many times it may kill
me.
The court has to decide whether the defendant is guilty or not. –
There are two mistakes that the court can make, namely to convict an
innocent person and to acquit a guilty person. What principles should the
court apply if it considers the first of these mistakes to be more serious than
the second?
A committee has to make a decision, but its members have different
opinions. – What rules should they use to ensure that they can reach a
conclusion even if they are in disagreement? Almost everything that a
human being does involves decisions. Therefore, to theorize about decisions
is almost the same as to theorize about human activities. However, decision
theory is not quite as all-embracing as that. It focuses on only some aspects
of human activity. In particular, it focuses on how we use our freedom. In the
situations treated by decision theorists, there are options to choose between,
and we choose in a non-random way.
Our choices, in these situations, are goal-directed activities. Hence, decision theory is
concerned with goal-directed behaviour in the presence of options.
A Truly Interdisciplinary Subject
Modern decision theory has developed since the middle of the 20th century
through contributions from several academic disciplines. Although it is now
clearly an academic subject of its own right, decision theory is typically
pursued by researchers who identify themselves as economists, statisticians,
psychologists, political and social scientists or philosophers. There is some
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division of labour between these disciplines. A political scientist is likely to
study voting rules and other aspects of collective decision-making. A
psychologist is likely to study the behaviour of individuals in decisions, and a
philosopher the requirements for rationality in decisions. However, there is a
large overlap, and the subject has gained from the variety of methods that
researchers with different backgrounds have applied to the same or similar
problems.
Normative and Descriptive Theories
The distinction between normative and descriptive decision theories is, in
principle, very simple. A normative decision theory is a theory about how
decisions should be made, and a descriptive theory is a theory about how
decisions are actually made.
The “should” in the foregoing sentence can be interpreted in many ways.
There is, however, virtually complete agreement among decision scientists
that it refers to the prerequisites of rational decision-making. In other words,
a normative decision theory is a theory about how decisions should be made
in order to be rational. This is a very limited sense of the word “normative”.
Norms of rationality are by no means the only – or even the most important –
norms that one may wish to apply in decision-making. However, it is practice
to regard norms other than rationality norms as external to decision theory.
Decision theory does not, according to the received opinion, enter the scene
until the ethical or political norms are already fixed. It takes care of those
normative issues that remain even after the goals have been fixed. This
remainder of normative issues consists to a large part of questions about
how to act in when there is uncertainty and lack of information. It also
contains issues about how an individual can coordinate her decisions over
time and of how several individuals can coordinate their decisions in social
decision procedures.
If the general wants to win the war, the decision theorist tries to tell him how
to achieve this goal. The question whether he should at all try to win the war
is not typically regarded as a decision-theoretical issue. Similarly, decision
theory provides methods for a business executive to maximize profits and for
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an environmental agency to minimize toxic exposure, but the basic question
whether they should try to do these things is not treated in decision theory.
Although the scope of the “normative” is very limited in decision theory, the
distinction between normative (i.e. rationality-normative) and descriptive
interpretations of decision theories is often blurred. It is not uncommon,
when you read decision-theoretical literature, to find examples of disturbing
ambiguities and even confusions between normative and descriptive
interpretations of one and the same theory. Probably, many of these
ambiguities could have been avoided. It must be conceded, however, that it
is more difficult in decision science than in many other disciplines to draw a
sharp line between normative and descriptive interpretations. This can be
clearly seen from consideration of what constitutes a falsification of a
decision theory. It is fairly obvious what the criterion should be for the
falsification of a descriptive decision theory.
ELEMENTS OF DECISION MAKING
Decision Maker: The entity responsible for making the decision. This may
be a single person, a committee, company, and the like. It is viewed here as
a single entity, not a group.
Alternatives: A finite number of possible decision alternatives or courses of
action available to the decision maker. The decision maker generally has
control over the specification and description of the alternatives. These
alternatives are also called courses of action (actions, acts or strategies) and
are known to the decision-maker.
States of Nature: The scenarios or states of the environment that may
occur but are not under control of the decision maker. These are the
circumstances under which a decision is made. The states of nature are
mutually exclusive events and exhaustive. This means that one and only one
state of nature is assumed to occur and that all possible states are
considered.
Payoff or Outcome: Outcomes are the measures of net benefit, or payoff,
received by the decision maker. This payoff is the result of the decision and
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the state of nature. Hence, there is a payoff for each alternative and
outcome pair. The measures of payoff should be indicative of the decisions
maker’s values or preferences. The payoffs are generally given in a payoff
matrix in which a positive value represents net revenue, income, or profit
and a negative value represents net loss, expenses, or costs. This matrix
yields all alternative and outcome combinations and their respective payoff
and is used to represent the decision problem.
General form of payoff matrix
STEPS OF DECISION MAKING PROCESS
The decision making process involves the following steps:
1. Identify and define the problem.
2. Listing of all possible future events, called states of nature, which can
occur in the context of the decision problem. Such events are not
under the control of decision-maker because these are erratic in
nature.
3. Identification of all the courses of action (alternatives or decision
choices) which are available to the decision-maker. The decision-maker
has control over these courses of action.
4. Expressing the payoffs resulting from each pair of course of action and
state of nature. These payoffs are normally expressed in a monetary
value.
5. Apply an appropriate mathematical decision theory model to select
best course of action from the given list on the basis of some criterion
(measure of effectiveness) that results in the optimal (desired) payoff.
TYPES OF DECISION-MAKING ENVIRONMENTS
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To arrive at a good decision it is required to consider all available data, an
exhaustive list of alternatives, knowledge of decision environment, and use
of appropriate quantitative approach for decision-making. In this section four
types of decision-making environments: Certainty, uncertainty, risk and
conflict have been described. The knowledge of these environments helps in
choosing appropriate quantitative approach for decision-making.
Type 1 - Decision-Making under Certainty
The process of choosing an act or strategy when the state of nature is
completely known is called decision making under certainty. The decision-
maker has the complete knowledge (perfect information) of consequence of
every decision choice (course of action or alternative) with certainty.
Obviously, he will select an alternative that yields the largest return (payoff)
for the known future (state of nature). In such situation, each act will only
result in one event and the outcome of the act can be predetermined with
certainty. Hence, such situations are also termed as deterministic situations.
For example, the decision to purchase either National Saving Certificate
(NSC); or deposit in National Saving Scheme is one in which it is reasonable
to assume complete information about the future because there is no doubt
that the Pakistani government will pay the interest when it is due and the
principal at maturity. In this decision-model, only one possible state of nature
(future) exists.
Type 2 - Decision-Making under Risk
In this case the decision-maker has less than complete knowledge with
certainty of the consequence of every decision choice (course of action)
because it is not definitely known which outcome will occur. This means
there is more than one state of nature (future) and for which he makes an
assumption of the probability with which each state of nature will occur. For
example, probability of getting head in the toss of a coin is 0.5. Decision-
making under risk is a probabilistic decision situation, in which more than
one state of nature exists and the decision-maker has sufficient information
to assign probability values to the likely occurrence of each of these states.
The probabilities of various outcomes may be determined objectively from
the past data. Knowing the probability distribution of the states of nature,
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the best decision is to select that course of action which has the largest
expected payoff value. The expected (average) payoff of an alternative is the
sum of all possible payoffs of that alternative weighted by the probabilities of
those payoffs occurring. However, past records may not be available to
arrive at the objective probabilities. In many cases the decision-maker may,
on the basis of his experience and judgment, be able to assign subjective
probabilities to the various outcomes. The problem can then be solved as
decision problem under risk.
Under conditions of risk, the most popular decision criterions for
evaluating the alternative is the expected monetary value/expected
opportunity loss of the expected payoff.
(i) Expected monetary value (EMV)
More generally, the decision-making in situations of risk is on the basis
of the expectation principle, with the event probabilities assigned,
objectively or subjectively as the case may be, the expected pay-off for
each strategy is calculated by multiplying the pay-off values with their
respective probabilities and then adding up these products. The
strategy with the highest expected pay-off represents the optimal
choice. It goes without saying that in problems involving pay-off matrix
in terms of costs, optimal strategy is that corresponding to which the
expected value is the least.
(ii) Expected Opportunity Loss (EOL)
An alternative approach to maximizing expected monetary value (EMV)
is to minimize the expected opportunity loss (EOL), also called
expected value of regret. The EOL is defined as the difference between
the highest profit (or payoff) for a state of nature and the actual profit
obtained for the particular course of action taken. In other words, EOL
is the amount of payoff that is lost by not selecting the course of action
that has the greatest payoff for the state of nature that actually occurs.
The course of action due to which EOL is minimum, is recommended.
Since EOL is an alternative decision criterion for decision-making
under risk, therefore, the results will always be the same as those
obtained by EMV criterion discussed earlier.
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The steps for calculating EOL are summarized as follows:
(a)Prepare a profit (cost) table for each course of action and state of
nature combination along with the associated probabilities.
(b)For each state of nature calculate the opportunity loss (OL) values
by subtracting each payoff from the maximum payoff for that
outcome. (For each state of nature calculate the opportunity loss
(OL) values by subtracting the minimum payoff for that outcome
from each payoff.)
(c) Calculate EOL for each course of action by multiplying the
probability of each state of nature with the OL value and then
adding the values.
(d)Select a course of action for which the EOL value is minimum.
(iii) Expected value of perfect information (EVPI)
The expected profit with perfect information is the expected return, in
the long run, if we have perfect information before a decision is made.
The Expected Value of Perfect Information (EVPI) may be defined as
the maximum amount one would be willing to pay, to acquire perfect
information as to which event would occur. EPPI represents the
maximum obtainable EMV with perfect information as to which event
will actually occur (as calculated before information is received). If EMV
represents the maximum obtainable EMV without perfect information,
perfect information would increase expected profit from EMV up to the
value of EPPI, so the amount of that increase would be equal to EVPI.
Thus, we have
EVPI = EPPI – EMV
Type 3 - Decision-Making under Uncertainty
In this case the decision-maker is unable to specify the probabilities
with which the various states of nature (futures) will occur. However, this is
not the case of decision-making under ignorance, because the possible
states of nature are known. Thus, decisions under uncertainty are taken
even with less information than decisions under risk. For example, the
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probability that Mr. X will be the prime minister of the country 15 years from
now is not known.
The decision situations where there is no way in which the decision-
maker can assess the probabilities of the various states of nature are called
decisions under uncertainty. In such situations, the decision-maker has no
idea at all as to which of the possible states of nature would occur nor has he
a reason to believe why a given state is more, or less, likely to occur as
another. With probabilities of the various outcomes unknown, the actual
decisions are based on specific criteria. The several principles which may be
employed for taking decisions in such conditions include (i) Laplace
Criterion, (ii) Maximin or Minimax Criterion, (iii) Maximax or Minimin
Criterion, (iv) Savage Criterion, (v) Hurwicz Criterion (or Criterion of
Realism).
Such situations are frequent in business and management. Will the new
plant or industrial unit be successful? Will the new product be able to
compete with others in the market? How much to produce and stock to get
maximum returns?
(i) Optimism (Maximax (Profit) or Minimin (Cost)) Criterion
In this criterion the decision-maker ensures that he should not miss
the opportunity to achieve the largest possible profit (maximax) or
lowest possible cost (minimin). Thus, he selects the alternative
(decision choice or course of action) that represents the maximum of
the maxima (or minimum of the minima) payoffs (consequences or
outcomes). The working method is summarized as follows:
(a)Locate the maximum (or minimum) payoff values corresponding to
each alternative (or course of action), then
(b)Select an alternative with best anticipated payoff value (maximum
for profit and minimum for cost).
Since in this criterion the decision-maker selects an alternative with
largest (or lowest) possible payoff value, it is also called an optimistic
decision criterion.
(ii) Pessimism (Maximin (Profit) or Minimax (Cost)) Criterion
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This principle is adopted by pessimistic decision-makers who are
conservative in their approach. Using this approach, the minimum pay-
offs resulting from adoption of various strategies are considered and
among these values the maximum one is selected. It involves,
therefore, choosing the best (the maximum) profit from the set of
worst (the minimum) profits.
When dealing with the costs, the maximum cost associated with each
alternative is considered and the alternative which minimizes this
maximum cost is chosen. In this context, therefore, the principle is
used minimax-the best (the minimum cost) of the worst (the maximum
cost).
The working method is summarized as follows:
(a)Locate the minimum (or maximum in case of profit) payoff value in
case of loss (or cost) data corresponding to each alternative, then
(b)Select an alternative with best anticipated payoff value (maximum
for profit and minimum for loss or cost).
Since in this criterion the decision-maker is conservative about the
future and always anticipates worst possible outcome (maximum for
profit and minimum for loss or cost), it is called a pessimistic decision
criterion. This criterion is also known as Wald’s criterion.
(iii) Equal probabilities (Laplace) Criterion
Since the probabilities of states of nature are not known, it is
assumed that all states of nature will occur with equal probability, i.e.
each state of nature is assigned an equal probability. As states of
nature are mutually exclusive and collectively exhaustive, so the
probabilities of each of these must be . The
working method is summarized as follows:
(a)Assign equal probability value to each state of nature by using the
formula:
.
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(b)Compute the expected (or average) payoff for each alternative
(course of action) by adding all the payoffs and dividing by the
number of possible states of nature or by applying the formula:
(c) Select the best expected payoff value (maximum for profit and
minimum for cost).
This criterion is also known as the criterion of insufficient reason
because, except in a few cases, some information of the likelihood of
occurrence of states of nature is available.
(iv) Coefficient of optimism (Hurwicz) Criterion
This criterion suggests that a rational decision-maker should be
neither completely optimistic nor pessimistic and, therefore must
display a mixture of both. Hurwicz, who suggested this criterion,
introduced the idea of a coefficient of optimism (denoted by ) to
measure the decision-maker’s degree of optimism. This coefficient lies
between 0 and 1, where 0 represents a complete pessimistic attitude
about the future and 1 a complete optimistic attitude about the future.
Thus, if is the coefficient of optimism, then (1 ) will represent the
coefficient of pessimism.
In case of profits, the Hurwicz approach suggests that the decision-
maker must select an alternative that maximizes H (Criterion of
realism) = (Maximum in column) + (1 ) (Minimum in column)
The working method is summarized as follows:
(a) Decide the coefficient of optimism (alpha) and then coefficient of
pessimism (1 ).
(b) For each alternative select the largest and lowest payoff value and
multiply these with and (1 ) values, respectively. Then
calculate the weighted average, H by using above formula.
(c) Select an alternative with best anticipated weighted average
payoff value.
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In the case of costs, the principle works like this. The minimum of the
costs for each course of action is multiplied by (the indicator of
the degree of optimism of the decision maker), and the maximum of
the costs for each alternative is multiplied by(1 ). Then the sum
of the products for each action strategy is obtained the alternative
for which the sum is the least is selected.
(v) Regret savage criterion
This criterion is also known as opportunity loss decision criterion or
minimax regret decision criterion because decision-maker feels regret
after adopting a wrong course of action (or alternative) resulting in an
opportunity loss of payoff. Thus, he always intends to minimize this
regret. The working method is summarized as follows:
(a) From the given payoff matrix, develop an opportunity loss (or
regret) matrix as follows:
(i) Find the best payoff corresponding to each state of nature, and
(ii)Subtract all other entries (payoff values) corresponding to each
state of nature from this value.
(b) For each course of action (strategy or alternative) identify the
worst or
maximum regret value. Record this number in a new row.
(c) Select the course of action (alternative) with the smallest
anticipated
opportunity loss value.
In the case of costs, the principle works like this.
(a) From the given payoff matrix, develop an opportunity loss (or
regret) matrix as follows:
(i) Find the worst payoff corresponding to each state of nature, and
(ii)Subtract all other entries (payoff values) corresponding to each
state of nature from this value.
(b) For each course of action (strategy or alternative) identify the
best or minimum regret value. Record this number in a new row.
(c) Select the course of action (alternative) with the greatest
anticipated opportunity loss value.
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Type 4 - Decision-Making under Conflict
In many situations, neither states-of-nature are completely known nor are
they completely uncertain. Partial knowledge is available and therefore it
may be termed as decision-making under ‘partial uncertainty’. An example
of this is the situation of conflict involving two or more competitors
marketing the same product.
Some Examples related to Different Decision-Making Environments
Example 1: A food product company is contemplating the introduction of a
revolutionary new product with new packaging or replace the existing
product at much higher price (S1) or a moderate change in the composition
of the existing product with a new packaging at a small increase in price (S2)
or a small change in the composition of the existing product except the word
‘New’ with a negligible increase in price (S3). The three possible states of
nature or events are: (i) high increase in sales (N1), (ii) no change in sales
(N2) and (iii) decrease in sales (N3). The marketing department of the company worked out
the payoffs in terms of yearly net profits for each of the strategies of three events (expected sales). This
is represented in the following table:
States of Nature
StrategiesS1 S2 S3
N1 7,00,000
5,00,000
3,00,000
N2 3,00,000
4,50,000
3,00,000
N3 1,50,000
0 3,00,000
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Which strategy should the concerned executive choose on the basis of the
following?
(a)Maximin criterion (b) Maximax criterion
(c) Minimax regret criterion (d) Laplace criterion
Solution: The payoff matrix is rewritten as follows:
(a) Maximin Criterion
States of Nature StrategiesS1 S2 S3
N1 7,00,000
5,00,000
3,00,000
N2 3,00,000
4,50,000
3,00,000
N3 1,50,000
0 3,00,000
Column (minimum)
1,50,000
0 3,00,000 Maximin
The maximum of column minima is 3,00,000. Hence, the company should
adopt strategy S3.
(b) Maximax Criterion
States of Nature StrategiesS1 S2 S3
N1 7,00,000 5,00,000 3,00,000N2 3,00,000 4,50,000 3,00,000N3 1,50,000 0 3,00,000Column (maximum)
7,00,000Maximax
5,00,000 3,00,000
The maximum of column maxima is 7,00,000. Hence, the company should
adopt strategy S1.
(c) Minimax Regret Criterion (opportunity loss in case of profits)
States of Nature
StrategiesS1 S2 S3
N1 7,00,000 7,00,000 = 0
7,00,000 5,00,000 = 2,00,000
7,00,000 3,00,000 = 4,00,000
N2 4,50,000 3,00,000 = 1,50,000
4,50,000 4,50,000 = 0
4,50,000 3,00,000 = 1,50,000
N3 3,00,000 1,50,000 = 1,50,000
3,00,000 0 = 3,00,000
3,00,000 3,00,000 = 0
Column (maximum)
1,50,000Minimax regret
3,00,000 4,00,000
Hence, the company should adopt minimum opportunity loss strategy, S1.
(d)Laplace Criterion
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Since, we do not know the probabilities of states of nature, assume that they are equal. For this
example, we would assume that each state of nature has a probability 1/3 of occurrence. Thus,
Strategy Expected Return (Rs)S1 (7,00,000 + 3,00,000 + 1,50,000)/3 =
3,83,333.33S2 (5,00,000 + 4,50,000 + 0)/3 = 3,16,666.66S3 (3,00,000 + 3,00,000 + 3,00,000)/3 =
3,00,000 Since, the largest expected return is from strategy S1; the executive must
select strategy S1.
Example 2: A Super Bazaar must decide on the level of supplies it must
stock to meet the needs of its customers during Eid days. The exact number of
customers is not known, but it is expected to be in one of the four categories; 300, 350, 400 or 450
customers. Four levels of supplies are thus suggested with level j being ideal (from the viewpoint of
incurred costs) if the number of customers falls in category j. Deviations from the ideal levels results in
additional costs either because extra supplies are stocked needlessly or because demand cannot be
specified. The table below provides these costs in thousands of rupees.
Customer categorySupplies levelA1
A2
A3
A4
E1 7 12
20
27
E2 10
9 10
25
E3 23
20
14
23
E4 32
24
21
17
(a) Which level of inventory is chosen on the basis of (i) Laplace criterion (ii)
minimax criterion (iii) minimin criterion?
(b) Now consider payoff matrix as profit matrix then which level of inventory
is chosen on the basis of Hurwicz criterion
Solution: (a) (i) Laplace Criterion
Since, we do not know the probabilities of states of nature, assume that they are equal. For this
question, we would assume that each state of nature has a probability 1/4 of occurrence. Thus,
Strategy Expected Return (Rs)A1 (7 + 10 + 23 + 32)/4 =
18A2 (12 + 9 + 20 + 24)/4 =
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16.25A3 (20 + 10 + 14 + 21)/4 =
16.25A4 (27 + 25 + 23 + 17)/4 =
23 Since, the lowest expected return is from strategy A2 and A3; the executive
must select strategy A2 or A3.
(ii)Minimax Criterion
States of Nature StrategiesA1
A2
A3 A4
E1 7 12
20 27
E2 10
9 10 25
E3 23
20
14 23
E4 32
24
21 17
Column (maximum)
32
24
21 Minimax
27
The minimum of column maxima is 21. Hence, the company should adopt
strategy A3.
(iii) Minimin Criterion
States of Nature
StrategiesA1 A
2
A3
A4
E1 7 12
20
27
E2 10 9 10
25
E3 23 20
14
23
E4 32 24
21
17
Column (minimum)
7Minimin
9 10
17
The minimum of column minima is 7. Hence, the company should adopt
strategy A1.
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
(b)In the context of profit data, Hurwicz Criterion, HC = (Max Value) + (1 –
) (Min Value). Its value for various strategies is as follows:
State of Nature
Profit from optimal Course of Action(thousand Rs)(1)
(2)
(3)
(4)
(5) (6) (7) (8)
A1 A2 A3 A4 Profit (Max in columns (1, 2, 3 & 4))
0.5 x (5)
Profit (Min in columns (1, 2, 3 & 4))
0.5 x (7)
(6) + (8)
E1 7 12
20
27
32 16 7 3.5 19.5
E2 10
9 10
25
24 12 9 4.5 16.5
E3 23
20
14
23
21 10.5 10 5 15.5
E4 32
24
21
17
27 13.5 17 8.5 22
Since, maximum is 22, so, it is optimal to adopt strategy A4.
Example 3: Al Abbas Ltd has installed a machine costing Rs 4 lacs and is in
the process of deciding on an appropriate number of a certain spare parts
required for repairs. The spare parts cost Rs 4000 each but are available only
if they are ordered now. In case the machine fails and no spares are
available, the cost to the company of mending the plant would be Rs 18000.
The plant has an estimated life of 8 years and the probability distribution of
failures during the time, based on experience with similar machines, is as
follows:
No. of failures during 8-yearly
period
0 1 2 3 4 5
Probability 0.
1
0.
2
0.
3
0.
2
0.
1
0.1
Ignoring any discounting for time value of money, determine the following:
(a)The expected number of failures in the 8-year period.
(b)The optimal number of units of the spare part on the basis of Hurwicz
principle (taking =0.7).
(c) EVPI.
Solution: Since the availability of number of spares at the time of the failure
of any machine is under the control of decision-maker, no. of spares per year
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
is considered as ‘course of action’ (decision choice) and the no. of failures of
machines is uncertain and only known with probability, therefore, it is
considered as a ‘state of nature’ (event).
Let S be the quantity (number of spares to be available). And F is the no. of
failures within one year. It is given that cost of storing a spare is Rs. 4000.
Cost of not storing the spare is Rs. 18000.
Cost function = 4,000S, S F
4,000S + 18000 (F – S), S < F
(a)The expected number of failures in the 8 year period, is given by
State of Nature (F)
Probability
Cost (thousand Rs) Due to Course of Action (purchase)
Expected Cost (thousand Rs) Due to Course of Action
(1) (2) (3) (4) (5) (6) (7) (1) x (2)
(1) x (3)
(1) x (4)
(1) x (5)
(1) x (6)
(1) x (7)
0 1 2 3 4 5 0 1 2 3 4 50 0.10 0 4 8 12 16 20 0 0.4 0.8 1.2 1.6 21 0.20 18 4 8 12 16 20 3.6 0.8 1.6 2.4 3.2 42 0.30 36 22 8 12 16 20 10.8 6.6 2.4 3.6 4.8 63 0.20 54 40 26 12 16 20 10.8 8 5.2 2.4 3.2 44 0.10 72 58 44 30 16 20 7.2 5.8 4.4 3 1.6 25 0.10 90 76 62 48 34 20 9 7.6 6.2 4.8 3.4 2Expected Cost (EC) 41.4 29.2 20.6 17.
417.8 20
(b)In the context of cost data, Hurwicz Criterion, HC = (Min Value) + (1
– ) (Max Value). Its value for various strategies is as follows:
State of Nature
Probability
Cost (thousand Rs) Due to Course of Action
Cost from optimal Course of Action(thousand Rs)
(1) (2)
(3)
(4)
(5)
(6)
(7)
(8) (9) (10) (11)
0 1 2 3 4 5 Cost (Min in columns (2, 3, 5, 6 & 7))
0.7 x (8)
Cost (Max in columns (2, 3, 5, 6 & 7))
0.3 x (10)
(9) + (11)
0 0.05 0 4 8 12 16 20 0 0 90 27 271 0.10 18 4 8 12 16 20 4 2.8 76 22.
825.6
2 0.20 36 22 8 12 16 20 8 5.6 62 18.6
24.2
3 0.30 54 40 26 12 16 20 12 8.4 48 14.4
22.8
4 0.20 72 58 44 30 16 20 16 11.2
34 10.2
21.4
5 0.15 90 76 62 48 34 20 20 14 20 6 20
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
Since, minimum is 20, so, it is optimal to keep 5 spare parts.
(c)
State of Nature
Probability
Cost (thousand Rs) Due to Course of Action
Cost from optimal Course of Action(thousand Rs)
(1) (2) (3) (4) (5) (6) (7) (8) (1) x (8)0 1 2 3 4 5 Cost (Min in (2, 3,
5, 6 & 7))Weighted Cost
0 0.05 0 4 8 12 16 20 0 01 0.10 18 4 8 12 16 20 4 0.82 0.20 36 22 8 12 16 20 8 2.43 0.30 54 40 26 12 16 20 12 2.44 0.20 72 58 44 30 16 20 16 1.65 0.15 90 76 62 48 34 20 20 2Expected Cost with Perfect Information (ECPI) 9.2
Now, EVPI = EC* – ECPI
= 17.4 – 9.2
= 8.2 thousand rupees
Example 4: An investor is given the following investment alternatives and percentage rates of
return.
Investment alternatives
State of Nature (Market Conditions)
Low Medium HighRegular Shares 7% 10% 15%Risky Shares -10% 12% 25%Property -12% 18% 30%
Over the past 300 days, 150 days have been medium market conditions and
60 days have had high market increases. On the basis of these data, state
the optimum investment strategy for the investment.
Solution: According to the given information, the probabilities of low, medium and high market
conditions would be 0.30 (300 – (150 + 60) = 90/300), 0.50 (150/300) and 0.20 (60/300) respectively.
The expected pay-offs for each of the alternatives are calculated and shown in the table below:
Market Conditions
Probability
Profit (Rs) Due to Course of Action
Expected Payoff (Rs) Due to Course of Action
(1) (2) (3) (4) (1) x (2) (1)x (3) (1) x (4)Regular shares
Risky shares
Property
Regular shares
Risky shares
Property
Low 0.30 0.07 0.10 0.15 0.021 0.03 0.045Medium 0.50 –0.10 0.12 0.25 –0.05 0.06 0.125High 0.20 –0.12 0.18 0.30 –0.024 0.036 0.06Expected monetary value (EMV) –0.053 0.126 0.230
Since the expected return of 23% is the highest for property, the investor
should invest in this alternative.
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
Example 5: A company manufactures goods for a market in which the
technology of the product is changing rapidly. The research and
development department has produced a new product which appears to
have potential for commercial exploitation. A further Rs 60,000 is required
for development testing.
The company has 100 customers and each customer might purchase at the
most one unit of the product. Market research suggests that a selling price of
Rs 6000 for each unit with total variable costs of manufacturing and selling
estimate are Rs 2,000 for each unit.
From previous experience, it has been possible to derive a probability
distribution relating to the proportion of customers who will buy the product
as follows:
Proportion of customers
0.04
0.08
0.12
0.16
0.20
Probability 0.10
0.10
0.20
0.40
0.20
Determine the expected opportunity losses, given no other information than
that stated above, and state whether or not the company should develop the
product.
Solution: If p is the proportion of customers who purchase the new product,
the profit is:
(6,000 – 2,000) x 100p – 60,000 = Rs (4,00,000p – 60,000).
Let Ni (I = 1, 2, …, 5) be the possible states of nature, i.e. proportion of the
customers who will buy the new product and S1 (develop the product) and S2
(do not develop the product) be the two courses of action.
The profit values (payoffs) for each pair of N i’s and Sj’s are shown in the
following table:
State of Nature Ni
(Proportion of Customers, p)
Probability
Profit = Rs (4,00,000p – 60,000)Course of Action
Opportunity Loss (Rs) (1) x (2)
(1) x (3)
(1) (2) (3)
S1 S2 S1 S2 S1 S2
0.04 0.1 –44,000
0 0 – (–44,000) = 44,000
0 – 0 = 0 4,400
0
0.08 0.1 –28,000
0 0 – (–28,000) = 28,000
0 – 0 = 0 2,800
0
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
0.12 0.2 –12,000
0 0 – (–12,000) = 12,000
0 – 0 = 0 2,400
0
0.16 0.4 4,000 0 4,000 – 4,000 = 0 4,000 – 0 = 4,000
0 1,600
0.20 0.2 20,000 0 20,000 – 20,000 = 0
20,000 – 0 = 20,000
0 4,000
Expected Opportunity Loss (EOL) 9,600
5,600
(Note: All the entries of column S2 would be 0. Since, we are not developing
anything then no profit will be earned)
Since, the company seeks to minimize the expected opportunity loss, the
company should select course of action S2 (do not develop the product) with
minimum EOL.
Example 6: A retailer purchases cherries every morning at Rs 50 a case and sells them for Rs 80 a
case. Any case remaining unsold at the end of the day can be disposed of next day at a salvage value of
Rs 20 per case (thereafter they have no value). Past sales have ranged from 15 to 18 cases per day. The
following is the record of sales for the past 120 days:
Cases sold 15
16 17 18
Number of days 12
24 48 36
Find how many cases the retailer should purchase per day to maximize his
profit.
Solution: Since number of cherries (in cases) purchased is under the control
of decision-maker, purchase per day is considered as ‘course of action’
(decision choice) and the daily demand of the cherries is uncertain and only
known with probability, therefore, it is considered as a ‘state of nature’
(event).
Let P be the quantity (number of cases of cherries to be purchased). And D is
the demand within a day.
Profit = (80-50) P, D>=P
(80-50)D – (50-20) (P-D), D < P
The resulting profit values and corresponding expected payoffs are
computed in the following table:
States of Nature D (Demand per week)
Probability
Profit (Rs) Due to Courses of Action P (Purchase per day)
Expected Payoff (Rs) Due to Courses of Action (Purchase per Day)
15 16 17 18 15 16 17 18
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
(1) (2) (3) (4) (5) (1)x(2) (1)x(3) (1)x(4) (1)x(5)15 12/120 =
0.1450 420 390 360 45 42 39 36
16 24/120 = 0.2
450 480 450 420 90 96 90 84
17 48/120 = 0.4
450 480 510 480 180 192 204 192
18 36/120 = 0.3
450 480 510 540 135 144 153 162
Expected monetary value (EMV) 450 474 486 474Since the highest EMV of Rs 486 is corresponding to course of action 17, the
retailer must purchase 17 cases of cherries every morning.
Example 7: A company needs to increase its production beyond its existing
capacity. It has narrowed the alternatives to two approaches to increase the
production capacity: (a) expansion, at a cost of Rs 8 million, or (b)
modernization at a cost of Rs 5 million. Both approaches would require the
same amount of time for implementation. Management believes that over
the required payback period, demand will either be high or moderate. Since
high demand considered being somewhat less likely than moderate demand,
the probability of high demand has been set at o.35. If the demand is high,
expansion would gross estimated additional Rs 12 million but modernization
only additional Rs 6 million, due to lower maximum product capability. On
the other hand, if the demand is moderate, the comparable figures would be
Rs 7 million for expansion and Rs 5 million for modernization.
(a)Calculate the profit in relation to various action and outcome
combinations and states of nature.
(b)If the company wishes to maximize its EMV, should it modernize or
expand?
(c) Calculate the EVPI.
(d)Construct the conditional opportunity loss table and also calculate EOL.
Solution: Defining the states of nature: high demand and moderate demand
(over which the company has no control) and courses of action (company’s
possible decisions): Expand and Modernize.
Since the probability that the demand is high estimated at 0.35, the
probability of moderate demand must be (1 – 0.35) = 0.65. The resulting
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
profit values, corresponding expected payoffs and Expected Opportunity
Loss (EOL) values are computed in the following table:
State of Nature (Demand)
Probability
Profit (million Rs) Due to Course of Action
Expected Payoff (million Rs) Due to Course of Action
Profit from optimal Course of Action(million Rs)
Opportunity Loss (million Rs) Due to Course of Action
(1) x (5)
(1) x (6)
(1) (2) (3) (1) x (2)
(1) x (3)
(4) (1) x (4)
(5) (6)
Expand (S1)
Modernize(S2)
Expand (S1)
Modernize(S2)
Profit (Max in (2 & 3))
Weighted Profit
S1 S2 S1 S2
Hig
h
Dem
an
d
(N1)
0.35 12 – 8 = 4
6 – 5 = 1
1.4 0.35 4 1.40 4 – 4 = 0
4 – 1 = 3
0 1.05
Mod
era
te
Dem
an
d 0.65 7 – 8
= –15 – 5 = 0
–0.65 0 0 0 0–(–1) = 1
0 – 0 = 0
0.65
0
Expected monetary value
(EMV)
0.75 0.35
Expected Profit with Perfect Information (EPPI) 1.40
Expected Opportunity Loss (EOL) 0.6
5
1.0
5
(b) Since the highest EMV of Rs 0.75 million is corresponding to course of
action Expand, the company must expand it.
(c) EVPI = EPPI – EMV
=1.40 – 0.75
= Rs. 0.65 Million
(d)Since, the company seeks to minimize the expected opportunity loss
(EOL), the company should select course of action S1 (Expand).
Example 8: A toy manufacturer is considering a project of manufacturing a
dancing doll with three different movement designs. The doll will be sold at
an average of Rs 10. The first movement design using ‘gears and levels’ will
provide the lowest tooling and set up cost of Rs 1,00,000 and Rs 5 per unit of
variable cost. A second design with spring action will have a fixed cost of Rs.
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
1, 60,000 and variable cost of Rs 4 per unit. Yet another design with weights
and pulleys will have a fixed cost of Rs. 3, 00,000 and variable cost of Rs 3
per unit. One of the following demand events can occur for the doll with the
probabilities:
Demand
(units)
Probability
Light demand 25,000 0.10
Moderate demand 1,00,000 0.70
Heavy demand 1,50,000 0.20
(a) Construct a payoff table for the above project.
(b) Which is the optimum design?
(c) How much can be decision-maker afford to pay to obtain perfect
information about the demand?
Solution: Payoff (Profit) = Revenue – Cost
= (Selling Price x no. of units demanded) – (fixed cost + variable cost)
= (Selling Pricexno. of units demanded)–(fixed cost+(no. of units
demandedxper unit cost))
State of Nature (Demand)
Probability
Profit (Rs) Due to Course of Action
Expected Payoff (Rs) Due to Course of Action
(1) (2) (3) (4) (1) x (2) (1) x (3) (1) x (4)Gears & Levels
Spring Action
Weights & Pulleys
Gears & Levels
Spring Action
Weights & Pulleys
Light 0.10 25,000 –10,000 –1,25,000
2,500 –1,000 –12,500
Moderate 0.70 4,00,000 4,40,000
4,00,000 2,80,000 3,08,000 2,80,000
Heavy 0.20 6,50,000 7,40,000
7,50,000 1,30,000 1,48,000 1,50,000
Expected monetary value (EMV) 4,12,500 4,55,000
4,17,500
Since, EMV is largest for spring action, it must be selected.
State of
Nature
Probabilit
y
Profit (Rs) Due to Course of Action Profit from optimal Course
of Action(Rs)
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
(Demand)
(1) (2) (3) (4) (4) (1) x (4)
Gears &
Levels
Spring
Action
Weights &
Pulleys
Profit (Max in
(2, 3 & 4))
Weighted
Profit
Light 0.10 25,000 –10,000 –1,25,000 25,000 2,500
Moderate 0.70 4,00,000 4,40,000 4,00,000 4,40,000 3,08,000
Heavy 0.20 6,50,000 7,40,000 7,50,000 7,50,000 1,50,000
Expected Profit with Perfect Information (EPPI) 4,60,500
The maximum amount of money that the decision-maker would be willing to
pay to obtain perfect information regarding demand for the doll will be EVPI
= EPPI – EMV
=4,60,000 – 4,55,000 = Rs
5,500
DECISION TREE ANALYSIS
Decision-making problems discussed so far have been limited to a
single decision over one period of time, because the payoffs, states of
nature, courses of action and probabilities associated with the occurrence of
states of nature are not subject to change.
However, situations may arise when a decision-maker needs to revise his
previous decisions on getting new information and make a sequence of
several interrelated decisions over several future periods. Thus he should
consider the whole series of decisions simultaneously. Such a situation is
called a sequential or multi period decision process.
Decision tree is a network which exhibits graphically the logical
relationship between the different parts of the complex decision process. It is
a graphic model of each combination of various acts and states of nature {S i,
Aj}; (I = 1, 2, …, m; j = 1, 2, …, n) along with their payoffs, the probability
distribution of the various states of nature and the EMV or EOL for each act.
Decision tree is a very effective device in making decisions in various
diversified problems relating to personnel, investment, portfolios, project
management, new project strategies, etc.
Each combination (Si, Aj) is depicted by a distinct path through the
decision tree. An essential feature of the decision tree is that the flow should
be from left to right in a chronological order.
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
Standard symbols are used in drawing a decision tree.
(i) A square ( ) is used to represent a decision point or decision node at
which the decision maker has to decide about one of the various acts
or alternatives available to him.
(ii) Each act or alternative is shown as a line, representing a branch of the
tree emanating from the square.
(iii) A circle ( ) is used to represent a chance event or chance node
at which various events or states of nature are represented by lines,
which depict the sub-branches of the tree emanating from the circle.
(iv) Each branch of the tree (corresponding to each act or strategy)
has as many sub-branches as the number of events or states of nature.
(v)Along the branches/sub-branches are also shown the probabilities of
various states of nature and the payoffs for each combination (Si, Aj); I
= 1, 2, …, m; j = 1, 2, …, n along with the EMV or EOL for each act.
(vi) As a branch can sub-branch again, we obtain a tree like
structure, which represents the various steps in a decision problem.
Roll Back Technique of Analyzing a Decision Tree
A decision tree is extremely useful in multistage situations which involve a
number of decisions, each depending on the preceding one. At any stage, to
decide about any strategy or act, the decision maker has to take into
consideration all future outcomes that may result from choosing the said act.
Consequently to analyze a decision tree, we start from the end of the tree
(extremely RHS) i.e., we start from the last decision/event node, say D l and
work backwards. This technique of analyzing the decision tree, called the
roll-back technique is explained in the following steps.
1. (a) for each branch of the event node (of D l) we compute the conditional
expected payoffs.
(b) Adding these expected payoffs for each event-nodal branch, we obtain
the EMV for the given path (act or strategy) emanating from the square
(decision node Dl).
(c) The optimal act or strategy at Dl is the one which corresponds to the
highest EMV.
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
2. Next we move to the last but one decision node (Dl-1), make the EMV
analysis as in steps 1 (a), (b) and (c) and then move back to the preceding
decision node (Dl-2) and so on.
3. This roll-back process is continued till we reach the first decision node (D l).
Example 1: A manufacturing company has to select one of the two products
X or Y for manufacturing. Product X requires investment of Rs. 30,000 and
product Y, Rs. 50,000. Market result survey shows high, medium and low
demands with corresponding probabilities and return from sales, (in
thousand rupees), for the two products, as given in the following table.
Demand Probability Return from Sales (‘ooo Rs.)
Product X
Product Y
Product X Product Y
High 0.4 0.3 75 100Medium 0.4 0.4 55 80Low 0.2 0.3 35 70
Construct the appropriate decision tree. What decision the company should
take?
Solution:
Net Payoff (Rs.) Expected Payoff
(Rs.)
75000-
30000=45000
45000 0.4=18000
55000-
30000=25000
25000 0.4=10000
35000-
30000=5000
5000 0.2=1000
Total 29000 (EMV)
100000-
50000=50000
50000 0.3=15000
80000-
50000=30000
30000 0.4=12000
70000-
50000=20000
20000 0.3=6000
Total 33000 (EMV)
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
Example 2: A businessman has two independent investments A and B
available to him but he lacks the capital to undertake both of them
simultaneously. He can choose to take A first and then stop, or if A is
successful then take B, or vice versa. The probability of success for A is 0.7
while for B it is 0.4. Both investments require an initial capital outlay of Rs.
2000; and both return nothing if the venture is unsuccessful. Successful
completion of A will return Rs. 3000 (over cost), and successful completion of
B will return Rs. 5,000 (over cost). Draw and evaluate the decision tree by
the roll back technique and determine the best strategy.
Solution:
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
Decision Node Event Probability
(p)
Conditional Payoff
(in Rs.) P
Expected Payoff
(Rs.) p P
D
3
(i) Accept A Succe
ss
0.7 3000 2100
Failur
e
0.3 -2000 -600
EMV = 1500
(ii) Stop 0
D
2
(i) Accept B Succe
ss
0.4 5000 2000
Failur
e
0.6 -2000 -1200
EMV = 800
(ii) Stop 0
D
1
(i) Accept A Succe
ss
0.7 3000 + 800 = 3800 2660
Failur
e
0.3 -2000 -600
EMV = 2060
(ii) Accept B Succe
ss
0.4 5000 + 1500 =
6500
2600
Failur
e
0.6 -2000 -1200
EMV = 1400
(iii)Do
Nothing
0
From the above table we conclude that the best strategy is to accept
investment A first and if it is successful, then accept the investment B.
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
PRACTICAL STUDY OF THE ORGANIZATION
WITH RESPECT OF THE TOPIC
ORGANIZATION: GLAXOSMITHKLINE Pakistan Limited
SYSTEM STUDIED: RISK MANAGEMENT SYSTEM
In GSK, the Risk Management System is used as proactive approach to
eliminate / reduce the potential risks associated with their business. Decision
theory is used extensively in Risk Management System for scoring the risks
on the basis of likelihood and consequences.
Note : This is only the overview of Risk Management System. Original documents
could not be part of assignment due to their confidentiality.
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
COMPANY INTRODUCTION
GlaxoSmithKline Pakistan Limited was created on January Ist 2002
through the merger of SmithKline and French of Pakistan Limited, Beecham
Pakistan (Private) Limited and Glaxo Wellcome (Pakistan) Limited- standing
today as the largest pharmaceutical company in Pakistan.
As leading international pharmaceutical company they make a real
difference to global healthcare and specifically to the developing world.
Company believes this is both an ethical imperative and key to business
success. Companies that respond sensitively and with commitment by
changing their business practices to address such challenges will be the
leaders of the future. GSK Pakistan operates mainly in two industry
segments: Pharmaceuticals (prescription drugs and vaccines) and consumer
healthcare (over-the-counter- medicines, oral care and nutritional care).
GSK leads the industry in value, volume and prescription market share.
Company is proud of their consistency and stability in sales, profits and
growth. Some of their key brands include Augmentin, Panadol, Seretide,
Betnovate, Zantac and Calpol in medicine and renowned consumer
healthcare brands include Horlicks, Aquafresh, Macleans and ENO.
In addition, company is also deeply involved with our communities and
undertakes various Corporate Social Responsibility initiatives including
working with the National Commission for Human Development (NCHD) for
whom GSK was one of the largest corporate donors. GSK consider it their
responsibility to nurture the environment we operate in and persevere to
extend their support to our community in every possible way. GSK
participates in year round charitable activities which include organizing
medical camps, supporting welfare organizations and donating to /
sponsoring various developmental concerns and hospitals. Furthermore, GSK
maintains strong partnerships with non-government organizations such as
Concern for children, which is also extremely involved in the design,
F a y y a z A h m e d K a y a n i R o l l N o . 5 9 3 4 8 3 Page 11
implementation and replication of models for the sustainable development of
children with specific emphasis on primary healthcare and education.
GSK’s MISSION STATEMENT
Excited by the constant search for innovation, we at GSK undertake
our quest with the enthusiasm of entrepreneurs we value performance
achieved with integrity. We will attain success as a world class global leader
with each and every one of our people contributing with passion and an
unmatched sense of urgency.
Our mission is to improve the quality of human life by enabling people to do
more, feel better and live longer.
Quality is at the heart of everything we do-from the discovery of a molecule
to the development of a medicine.
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RISK MANAGEMENT SYSTEM
Risk management is an essential component of the system of internal control
and governance and is regarded as good management practice throughout
GSK. A systematic, standardized and effective approach to risk management
is required in order to:
Establish a common language and protocols for communicating risks in
order to take right decisions at right time.
Ensure that responsibilities for managing risks are clearly stated,
understood and accepted.
Establish appropriate mechanisms for communication, reporting and
escalation of risks.
Ensure that business objectives are achieved.
SCOPE OF RISK MANAGEMENT PROCESS
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PROCESS STEP ACTIVITIES
Following are the different steps involved in the risk management system:
Establish the Risk Management Organization for the risk assessment area.
Identify, Record and Priorities Scored Risks.
Confirm and Approve Risk Mitigation plans.
Implementation, monitoring and of risk mitigation plans.
Governance and Maintenance.
Figure – Risk Management Process
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Decision Theory comes into play when a risk is going to be scored
(Analyse the risks). Risks are scored on the basis of likelihood and
consequences.
INFORMATION STRUCTURE IN RISK MANAGEMENT SYSTEM
A Risk is the basic record.
Risk requirements now split into three components.
Mandated requirements to progress risks through workflow.
A number of Risk Mitigation Plans may be attached to Risk. A Risk must
have at least one Risk Mitigation Plan.
A number of Action Plans may be attached to each Risk Mitigation Plan.
A Risk Mitigation Plan must have at least one Action Plan.
The diagram below depicts the structure of a Risk Record.
RISK SCORING
Risk scoring is subjective – there is no right or wrong answer it is based on
personal judgment or consensus.
Review the consequence of a risk first and only when this is agreed –
review the associated likelihood of the scored consequence.
The subjectivity on assessment of likelihood is inherently higher than that
for consequence and influenced by individual perception, background,
and local objectives – a team based approach is always used to reach
consensus on likelihood.
The key requirement for the risk management process is that the
significant risks are identified and managed appropriately – the precise
scoring is a secondary consideration.
It is essential that risks assessment area are consistently scored and
prioritized and a group view is required by the Quality management
process to avoid personal bias in scoring.
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The scoring supports the prioritization of risks but, even then, judgment is
required where several risks all have the same score and decisions are
required in terms of resource allocation.
The scoring supports the prioritisation of risks but, even then, judgment
is required where several risks all have the same score and decisions are
required in terms of resource allocation.
Comparisons of numbers of risks on aggregation of risk assessment areas
are of little value – any analysis and trending should focus on topics and
not scores.
Differences in number and ratings of risks across risk assessment areas
should be explored in terms of processes, resources and approach to
generate them.
As with risk description, scoring is based on the current environment
taking into account all controls.
A control can impact the consequence or likelihood. A control should be
considered as something which impacts how severe a risk can become
and not be limited to physical controls, written procedures or failsafe
controls.
Risks should be assessed and scored from a GSK perspective. Hence, the
consequence and likelihood Matrix has been changed, to focus on the
impact of the Regulators detecting risks e.g. observations.
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RISK MANAGEMENT SYSTEM (HOME PAGE)
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RISK MANAGEMENT SYSTEM
WORKFLOW
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RISK IDENTIFICATION TOOLS
5 –Whys
Brainstorming
Surveys
Interviews
FMEA (Failure Mode Effect Analysis)
SWOT (Strengths, Weaknesses, Opportunities & Threats) Analysis
PEST (political, Economic, Socio-Cultural, Technological) Analysis
Kaizen (Continuous Improvement)
GEMBA (Go and See)
Affinity & Fishbone diagrams
Reality Trees
Process flowcharts
Potential Problem Analysis (Kepnor Tregoe)
Benchmarking
Mind maps
IPO
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REFERENCES
1. Quantitative Techniques (AIOU)
2. www.infra.kth.se/~soh/decisiontheory.pdf
3. en.wikipedia.org/wiki/Decision_theory
4. www.answers.com/topic/decision-theory
5. www.mendeley.com/.../decision-theory-a-brief-introduction
6. books.google.com
7. www.stat.tamu.edu/~hart/632/Bayes2
8. www.rapidmore.com/rapidshare.php?...decision+theory...brief+introduction
9. darwin.eeb.uconn.edu/eeb310/lecture.../decision/decision.html
10. www.morehouse.edu/facstaff/ajohnson/ai.../6.825-lecture-19.pdf
11. www.springerlink.com/index/R456425111457PK7.pdf
12. www.cse.unr.edu/~bebis/CS679/Handouts/DHS2.11Revised.pdf
13. www.envisionsoftware.com/.../
Normative_Decision_Making_Theory.html
14. economics.stanford.edu/.../normative-decision-theory
15. home.ubalt.edu/ntsbarsh/opre640a/partix.htm
16. www.mindtools.com › Decision Making
17. www.businessdictionary.com/definition/decision-theory.html
18. encyclopedia2.thefreedictionary.com/decision+theory
19.Lectures delivered by worthy Tutors in the class
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