decision theory

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 1

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.


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.


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


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


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


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


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,


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.


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

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

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


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:

.


(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.


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.


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


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


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 =


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.


(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


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


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.


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


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


(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


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.


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)


(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.


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.


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)


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:


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.


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.


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,


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.


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


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


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.


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.

***************************************************


RISK MANAGEMENT SYSTEM (HOME PAGE)


RISK MANAGEMENT SYSTEM

WORKFLOW


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


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


http://www.businessdictionary.com/definition/decision-theory.html

http://www.google.com/url?url=http://www.mindtools.com/pages/main/newMN_TED.htm&rct=j&ei=3mXeS4DwJYv80wSVyZHIBw&sa=X&oi=breadcrumbs&resnum=10&ct=result&cd=1&ved=0CCsQ6QUoAA&q=decision+analysis+problems&usg=AFQjCNFP6VJVi1dR1dZYfZLDZFrY6pKi-g

http://home.ubalt.edu/ntsbarsh/opre640a/partix.htm

http://www.envisionsoftware.com/.../Normative_Decision_Making_Theory.html

http://www.envisionsoftware.com/.../Normative_Decision_Making_Theory.html

http://www.cse.unr.edu/~bebis/CS679/Handouts/DHS2.11Revised.pdf

http://www.springerlink.com/index/R456425111457PK7.pdf

http://www.morehouse.edu/facstaff/ajohnson/ai.../6.825-lecture-19.pdf

http://www.rapidmore.com/rapidshare.php?...decision+theory...brief+introduction

http://www.stat.tamu.edu/~hart/632/Bayes2

http://www.mendeley.com/.../decision-theory-a-brief-introduction

http://www.answers.com/topic/decision-theory

http://www.infra.kth.se/~soh/decisiontheory.pdf