Decision Making Decision-making is based on

information Information is used to:

Identify the fact that there is a problem in the first place

Define and structure the problem Explore and choose between different

possible solutions Evaluate the effectiveness of the decision

Value of Information The value of information used in

decision making is: (value of the outcome with the

Information) – (value of the outcome without the Information)

Types of Decision H. A. Simon classified decisions into

Programmed decisions Non-Programmed decisions

Classified according to the extent to which decision making can be pre-planned

These are the extremes of a continuous range of decision types

Programmed Decisions Also known as Structured Decisions Characteristics

Repetitive, routine, known rules or procedures, often automated, can be delegated to low levels in the organisation, often involve things rather than people

Examples - Inventory control decisions, machine loading decisions, scheduling.

Non-Programmed Decisions Also known as Unstructured Decisions Characteristics

Novel, non-routine, rules not known, high degree of uncertainty, cannot be delegated to low levels, more likely to involve people.

Examples - Acquisitions, mergers, launching new products, personnel appointments.

Semi-Structured Decisions The most common type of decision May be partially automated

Empowerment Authority to take decisions is being

delegated down the line especially in modern service industries

This process is called empowerment and should enable an organisation to take a variety of decisions more quickly, thus providing a more flexible service

Empowerment Decisions should be made:

At the lowest possible level, which accords with their nature

As close to the scene of the action as possible

at the level that ensures none of the activities and objectives are forgotten

Empowerment Enabled by systems such as

Customer Relationship Management (CRM)

Gives call centre staff specialist knowledge about any customer

Expert Systems Assists non-experts in making complex

decisions

Uncertainty Uncertainty arises from incomplete

information due to: Incomplete forecasting models Conflicting data from external sources Lack of time Internal data on particular problem not

collated The uncertainty of an outcome is

expressed as a probability

Rational Decision Making The rational model of decision

making is a mechanistic approach to decision making

It assumes perfect knowledge of all factors surrounding the decision

Rational vs. Real Decisions ‘Users tend to explain their actions in

terms of rational behaviour, whereas their actual performance may be governed by intuition rather than by rational analysis. Studies of managers at work have shown that there is a discrepancy between how managers claim to take decisions and their actual observed decision-making behaviour’.

Argyris and Schon

Payoff Matrices The standard way to analyse simple

decision problems These are constructed as follows:

Identify all available options Identify events which cause an outcome (states

of nature) Estimate the likelihood of each state of nature Estimate the value/payoff of each outcome Determine the expected value for each option Choose the option with the highest expected

value

Example A company must decide on one of

three development projects, A, B or C They have identified three possible

events relating to market conditions that will effect this decision

Event Probability

Boom 60%

Steady State 30%

Recession 10%

Example The profit and loss figures (potential payoff)

for the three products under the possible market conditions have been forecast as:

Decision

Event Project A Project B Project C

Boom 60% +8M -2M +16M

Steady State 30% +1M +6M 0

Recession 10% -10M +12M -26M

Which one of the above projects should the company run?

Decision Criteria In order to evaluate the alternatives,

managers use a number of different criteria: Equally Likely

The consequences of each decision are summed and the result divided by the number of events

Useful if probabilities are not known Maximax

Determine the highest possible profit from each strategy and choose that with the highest overall profit - Usually high risk, but high gain

Example

Decision

Event Project A Project B Project C

Boom 60% +8M -2M +16M

Steady State 30% +1M +6M 0

Recession 10% -10M +12M -26M

Preferred Project is? Equally Likely Maximax

Decision Criteria Minimax

Choose that action with the smallest maximum possible loss, or the largest minimum profit.

Low risk, low gain. Maximum Likelihood

Choose the most likely event and then choose the best strategy for that event.

Low risk, low gain. Does not make full use of available information.

Example

Decision

Event Project A Project B Project C

Boom 60% +8M -2M +16M

Steady State 30% +1M +6M 0

Recession 10% -10M +12M -26M

Preferred Project is? Minimax Max Likelihood

Example

Decision

Event Project A Project B Project C

Boom 60% +8M -2M +16M

Steady State 30% +1M +6M 0

Recession 10% -10M +12M -26M

Decision Criteria Expected Value

A weighted average of all outcomes The weights are probabilities

Gives the average value of the decision if it were made repeatedly

Uses all the information concerning events and their likelihood

N

iii payoffoutcomePEV

1

Example

Decision

Event Project A Project B Project C

Boom 60% +8M -2M +16M

Steady State 30% +1M +6M 0

Recession 10% -10M +12M -26M

Calculate EV for each option/choice Project A (8M*0.6)+(1M*0.3)+(-10M*0.1) = 4.1M Project B (-2*0.6)+(6*0.3)+(12*0.1) = 1.8 Project C (16*0.6)+(0*0.3)+(-26*0.1) = 7.0

Preferred Project is?C

Example 2

Alternative A Alternative B Alternative C

Outcome: Proby Profit Proby Profit Proby Profit

Optimistic 0.2 5000 0.3 4000 0.1 3000

Most Likely 0.6 7500 0.5 7000 0.7 6500

Pessimistic 0.2 9000 0.3 9500 0.2 10000

Decision Criteria Expected Value

Uses all the information concerning events and their likelihood

Does not take into account decision-makers attitude to risk

Does not reflect the actual outcomes in the figures

Can the company afford to lose 26M?

Decision Trees Not all decisions will be taken in

isolation A decision will have an effect of

future events and outcomes An outcome in turn may effect

future decision making

Decision Trees Decision trees provide a means of

structuring the decision making process to allow for alternative futures

Decision Tree Two types of Node Decision Node

Represent decision points Decision are made by the

organisation Outcome Node

Linked to possible outcomes These are uncontrollable

Example

Project A

Project B

Project C

Boom 60%

Steady 30%

Recession 10%

Boom 60%

Boom 60%

Steady 30%

Steady 30%

Recession 10%

Recession 10%

8M

1M

-10M

-2M

+6M

+12M

+16M

0

-26M

Example

Project A

Project B

Project C

4.1

1.8

7

Boom 60%

Steady 30%

Recession 10%

Boom 60%

Boom 60%

Steady 30%

Steady 30%

Recession 10%

Recession 10%

8M

1M

-10M

-2M

+6M

+12M

+16M

0

-26M

Example

4.1

Project A

Project B

Project C

4.1

1.8

7

Boom 60%

Steady 30%

Recession 10%

Boom 60%

Boom 60%

Steady 30%

Steady 30%

Recession 10%

Recession 10%

8M

1M

-10M

-2M

+6M

+12M

+16M

0

-26M