decion making -mis

8/3/2019 Decion Making -MIS

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Decision-Making An overview

Team-based decision making

Increased information sharing

Daily feedback

Self-empowerment

Shifting responsibility towards teams

Elimination of middle management


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Different Classes of Decisions

Structured ware house location

Semi-structured Introduction of newproduct

Unstructured R&D Planning


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Decision Making

Process of choosing amongstalternative courses of action for thepurpose of attaining a goal or goals.

The four phases of the decisionprocess are: Intelligence

Design Choice

implementation


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Models Used for DSS

Iconic

Small physical replication of system

Analog Behavioral representation of system

May not look like system

Quantitative (mathematical) Demonstrates relationships betweensystems


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Phases of Decision-Making

Simons original three phases:

Intelligence

Design

Choice

He added fourth phase later:

Implementation

Book adds fifth stage:

Monitoring


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Decision-Making IntelligencePhase

Scan the environment

Analyze organizational goals

Collect data

Identify problem

Categorize problem

Programmed and non-programmed

Decomposed into smaller parts

Assess ownership and responsibility forproblem resolution


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Decision-Making Design Phase

Develop alternative courses of action

Analyze potential solutions

Create model

Test for feasibility Validate results

Select a principle of choice

Establish objectives

Incorporate into models Risk assessment and acceptance

Criteria and constraints


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Decision-Making Choice Phase

Principle of choice Describes acceptability of a solution approach

Normative Models

Optimization Effect of each alternative

Rationalization More of good things, less of bad things

Courses of action are known quantity

Options ranked from best to worse

Suboptimization Decisions made in separate parts of organization

without consideration of whole


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Descriptive Models

Describe how things are believed tobe

Typically, mathematically based

Applies single set of alternatives

Examples: Simulations

What-if scenarios Cognitive map

Narratives


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Developing Alternatives

Generation of alternatives

May be automatic or manual

May be legion, leading to informationoverload

Scenarios

Evaluate with heuristics

Outcome measured by goal attainment


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Problems

Satisficing is the willingness to settlefor less than ideal.

Form of suboptimization

Bounded rationality

Limited human capacity

Limited by individual differences and

biases

Too many choices


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Decision-Making Choice Phase

Decision making with commitment toact

Determine courses of action

Analytical techniques

Algorithms

Heuristics

Blind searches

Analyze for robustness


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Decision-MakingImplementation Phase

Putting solution to work

Vague boundaries which include:

Dealing with resistance to change

User training

Upper management support


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Decision Support Systems

Intelligence Phase

Automatic

Data Mining

Expert systems, CRM, neural networks

Manual

OLAP

KMS

Reporting

Routine and ad hoc


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Design Phase

Financial and forecasting models

Generation of alternatives by expertsystem

Relationship identification through OLAPand data mining

Recognition through KMS Business process models from CRM,

ERP, and SCM


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Choice Phase

Identification of best alternative

Identification of good enough alternative

What-if analysis

Goal-seeking analysis

May use KMS, CRM, ERP, and SCM

systems


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Implementation Phase

Improved communications

Collaboration

Training

Supported by KMS, expert systems


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Decision-Making In Humans

Temperament

Hippocrates personality types

Myers-Briggs Type Indicator

Kiersey and Bates Types and

Motivations

Birkmans True Colours

Gender


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Decision-Making In Humans

Cognitive styles What is perceived?

How is it organized?

Subjective Decision styles

How do people think?

How do they react? Heuristic, analytical, autocratic,

democratic, consultative


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Problem Searching

Problem is defined as the differencebetween expected and reality:

Desired/Expected Actual / Reality = Difference

(Problem)


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Problem

A sales Manager who has set a salestarget of Rs. 5 lakh in one particularmonth. And he could achieve only Rs.

4 lakh worth of sales for that particularmonth. Find out the differencebetween a expected model and

reality.


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Value Assessment

Expected value= Outcome X Probability

Two choices offered for a decision which wouldgive higher expected value:

(a) 2% probability of profit of Rs. 1,00,000

(b) 80% probability of profit of Rs. 10,000

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