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Decision Making

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					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
                   N
             EV   Poutcomei  payoff i 
                   i 1


       Gives the average value of the decision if it
        were made repeatedly
       Uses all the information concerning events
        and their 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



   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
                        Boom 60%
                                                 8M

                           Steady 30%
                                                     1M
                                 Recession 10%
   Project A                                     -10M

                      Boom 60%                   -2M
         Project B
                            Steady 30%
                                                 +6M
                                Recession 10%
                                                 +12M

                     Boom 60%                    +16M
     Project C
                          Steady 30%
                                                 0
                             Recession 10%
                                                 -26M
Example
                                  Boom 60%
                                                           8M

                     4.1             Steady 30%
                                                               1M
                                           Recession 10%
   Project A                                               -10M

                                Boom 60%                   -2M
         Project B
                         1.8          Steady 30%
                                                           +6M
                                          Recession 10%
                                                           +12M

                               Boom 60%                    +16M
     Project C
                     7              Steady 30%
                                                           0
                                       Recession 10%
                                                           -26M
Example
                                      Boom 60%
                                                               8M

                         4.1             Steady 30%
                                                                   1M
                                               Recession 10%
       Project A                                               -10M

                                    Boom 60%                   -2M
             Project B
 4.1                         1.8          Steady 30%
                                                               +6M
                                              Recession 10%
                                                               +12M

                                   Boom 60%                    +16M
         Project C
                         7              Steady 30%
                                                               0
                                           Recession 10%
                                                               -26M

				
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