1. Analyzing Business Decision Processes
Focus First on Identifying Business Decision Processes and then on Designing DSS

2. Decision Processes
Decision making is the most important part of a manager’s job
When does a decision process begin and end?
Design begins with understanding an existing decision process
Decision making is a dynamic process
Decisions are made by managers at all levels
Reference - Power (2008)

3. Decision Making: Introduction and Definitions
Characteristics of decision making
Groupthink
Decision makers are interested in evaluating what-if scenarios
Experimentation with the real system may result in failure
Experimentation with the real system is possible only for one set of conditions at a time and can be disastrous
Changes in the decision making environment may occur continuously, leading to invalidating assumptions about the situation

4. Decision Making: Introduction and Definitions
Characteristics of decision making
Changes in the decision making environment may affect decision quality by imposing time pressure on the decision maker
Collecting information and analyzing a problem takes time and can be expensive. It is difficult to determine when to stop and make a decision
There may not be sufficient information to make an intelligent decision
Information overload

5. Decision Making: Introduction and Definitions
The action of selecting among alternatives

6. Decision Making: Introduction and Definitions
Phases of the decision process
Intelligence
Design
Choice
Problem solving
A process in which one starts from an initial state and proceeds to search through a problem space to identify a desired goal. It includes the 4th phase of the decision process
Implementation

7. Decision Making: Introduction and Definitions
Decision making disciplines
Behavioral
Scientific
Successful decision
Effectiveness
The degree of goal attainment. Doing the right things
Efficiency
The ratio of output to input. Appropriate use of resources. Doing the things right

8. Decision Making: Introduction and Definitions
Decision style and decision makers
Decision style
The manner in which a decision maker thinks and reacts to problems. It includes perceptions, cognitive responses, values, and beliefs
Autocratic
Democratic
Consultative

9. Decision Making: Introduction and Definitions
Decision style and decision makers
Different decision styles require different types of support
Individual decision makers need access to data and to experts who can provide advice
Groups need collaboration tools

10. What is your Decision Style?
Decision Styles
Style is the manner in which a manager makes decisions.
The effect of a particular style depends on problem context, perceptions of the decision maker, and his/her own set of values.
The complexity of these intertwine in the formation of decision style.
What is your Decision Style?

11. Decision Style Model

12. Decision Style Categories
Directive – combines a high need for problem structure with a low tolerance for ambiguity. Often these are decisions of a technical nature that require little information.
Analytical – greater tolerance for ambiguity and tends to need more information.
Conceptual – high tolerance for ambiguity but tends to be more a “people person”.
Behavioral – requires low amount of data and demonstrates relatively short-range vision. Is conflict-averse and relies on consensus.

13. Types of Problems Structured Unstructured
Described in numbers or numerical objectives
Specific computational techniques may be available
Unstructured
Objectives are hard to quantify
Usually not possible to model the situation
Require more creativity and subjective judgment
Reference - Power (2008)

14. Decision Situation Categories
Routine/Recurring
Programmed
Repetitive
Ex: Placing an inventory order
Non-Routine
Infrequent
Non-programmed

15. Matching DSS to Situations
Reference - Power (2008)

16. Computerized Support
DSS should be used when managers are in decision situations characterized by one or more of the following:
Complexity
Risk and Uncertainty
Multiple Stakeholders
Large amount of information (company data)
Rapid change in information
Reference - Power (2008)

17. Phases of the Decision-Making Process

18. General Decision Process Model
Data-driven DSS
Model-driven DSS
Reference - Power (2008)

19. IBM Credit Corporation Business Decision Process
Hammer and Champy (1993)
According to Hammer and Champy (1993, p ), IBM Credit Corporation had a business decision process that evaluated customer's requests for financing that included the following five steps:
Step 1. A salesperson called in a request for financing, which was recorded on paper by 1 of 14 clerical staff members "sitting around a conference room table in Old Greenwich, Connecticut". This step initiated the process.
Step 2. Someone physically walked the paper request to the credit department, where a specialist entered the request into a computer and checked the credit status of the customer. The result was written on the credit repot. Then, the paper- based credit report was delivered to the business practices department.
Step 3. The business practices department used a different computer system to modify a standard loan agreement according to any special requests made by the customer. The document was attached to the original request and delivered to the pricer.
Step 4. The pricer keyed all the information into a PC spreadsheet and determined the appropriate interest rate. This figure was written onto the other forms and delivered to the clerical group.
Step 5. The clerical group converted all paper documents into a quote letter and delivered it to the sales representative using FedEx.

20. Phases of the Decision-Making Process
Intelligence phase
The initial phase of problem definition in decision making
Design phase
The second decision-making phase, which involves finding possible alternatives in decision making and assessing their contributions

21. Phases of the Decision-Making Process
Choice phase
The third phase in decision making, in which an alternative is selected
Implementation phase
The fourth decision-making phase, involving actually putting a recommended solution to work

22. Decision Making: The Intelligence Phase
Problem (or opportunity) identification: some issues that may arise during data collection
Data are not available
Obtaining data may be expensive
Data may not be accurate or precise enough
Data estimation is often subjective
Data may be insecure
Important data that influence the results may be qualitative

23. Decision Making: The Intelligence Phase
Problem (or opportunity) identification: some issues that may arise during data collection
Information overload
Outcomes (or results) may occur over an extended period
If future data is not consistent with historical data, the nature of the change has to be predicted and included in the analysis

24. Decision Making: The Intelligence Phase
Problem classification
The conceptualization of a problem in an attempt to place it in a definable category, possibly leading to a standard solution approach
Problem decomposition
Dividing complex problems into simpler subproblems may help in solving the complex problem
Problem ownership
The jurisdiction (authority) to solve a problem

25. Decision Making: The Design Phase
The design phase involves finding or developing and analyzing possible courses of action
Understanding the problem
Testing solutions for feasibility
A model of the decision-making problem is constructed, tested, and validated

26. Decision Making: The Design Phase
Modeling involves conceptualizing a problem and abstracting it to quantitative and/or qualitative form
Models have:
Decision variables
Principle of choice

27. Decision Making: The Design Phase
Decision variables
A variable in a model that can be changed and manipulated by the decision maker. Decision variables correspond to the decisions to be made, such as quantity to produce, amounts of resources to allocate, and so on
Principle of choice
The criterion for making a choice among alternatives

28. Decision Making: The Design Phase
Normative models
Models in which the chosen alternative is demonstrably the best of all possible alternatives
Optimization
The process of examining all the alternatives and proving that the one selected is the best
Suboptimization
An optimization-based procedure that does not consider all the alternatives for or impacts on an organization

29. Decision Making: The Design Phase
Descriptive model
A model that describes things as they are
Simulation
An imitation of reality
Narrative is a story that helps a decision maker uncover the important aspects of the situation and leads to better understanding and framing

30. Decision Making: The Design Phase
Good enough or satisficing
Satisficing
A process by which one seeks a solution that will satisfy a set of constraints. In contrast to optimization, which seeks the best possible solution, satisficing simply seeks a solution that will work well enough

31. Decision Making: The Design Phase
Good enough or satisficing
Reasons for satisficing:
Time pressures
Ability to achieve optimization
Recognition that the marginal benefit of a better solution is not worth the marginal cost to obtain it

32. Decision Making: The Design Phase
Developing (generating) alternatives
In optimization models the alternatives may be generated automatically by the model
In most MSS situations it is necessary to generate alternatives manually (a lengthy, costly process); issues such as when to stop generating alternatives are very important
The search for alternatives usually occurs after the criteria for evaluating the alternatives are determined
The outcome of every proposed alternative must be established

33. Optimizing Versus Satisficing
Optimizing strategies
search here
Satisficing strategies
search here

34. Decision Making: The Design Phase
Measuring outcomes
The value of an alternative is evaluated in terms of goal attainment
Risk
One important task of a decision maker is to attribute a level of risk to the outcome associated with each potential alternative being considered

35. Decision Making: The Design Phase
Scenario
A statement of assumptions about the operating environment of a particular system at a given time; a narrative description of the decision-situation setting
Scenarios are especially helpful in simulations and what-if analyses

36. Decision Making: The Design Phase
Scenarios play an important role in MSS because they:
Help identify opportunities and problem areas
Provide flexibility in planning
Identify the leading edges of changes that management should monitor
Help validate major modeling assumptions
Allow the decision maker to explore the behavior of a system through a model
Help to check the sensitivity of proposed solutions to changes in the environment

37. Decision Making: The Design Phase
Possible scenarios
The worst possible scenario
The best possible scenario
The most likely scenario
The average scenario

38. Decision Making: The Design Phase
Errors in decision making
The model is a critical component in the decision-making process
A decision maker may make a number of errors in its development and use
Validating the model before it is used is critical
Gathering the right amount of information, with the right level of precision and accuracy is also critical

39. Decision Making: The Choice Phase
Solving a decision-making model involves searching for an appropriate course of action
Analytical techniques (solving a formula)
Algorithms (step-by-step procedures)
Heuristics (rules of thumb)
Blind searches

40. Decision Making: The Choice Phase
Analytical techniques
Methods that use mathematical formulas to derive an optimal solution directly or to predict a certain result, mainly in solving structured problems
Algorithm
A step-by-step search in which improvement is made at every step until the best solution is found

41. Decision Making: The Choice Phase
Heuristics
Informal, judgmental knowledge of an application area that constitutes the rules of good judgment in the field. Heuristics also encompasses the knowledge of how to solve problems efficiently and effectively, how to plan steps in solving a complex problem, how to improve performance, and so forth

42. Decision Making: The Choice Phase
Sensitivity analysis
A study of the effect of a change in one or more input variables on a proposed solution
What-if analysis
A process that involves asking a computer what the effect of changing some of the input data or parameters would be

43. Decision Making: The Implementation Phase
Generic implementation issues important in dealing with MSS include:
Resistance to change
Degree of support of top management
User training

44. Decision Making: The Implementation Phase

45. How Decisions Are Supported
Support for the intelligence phase
The ability to scan external and internal information sources for opportunities and problems and to interpret what the scanning discovers
Web tools and sources are extremely useful for environmental scanning
Web browsers provide useful front ends for a variety of tools (OLAP, data mining, data warehouses)
Internal data sources may be accessible via a corporate intranet
External sources are many and varied

46. How Decisions Are Supported
Support for the design phase
The generation of alternatives for complex problems requires expertise that can be provided only by a human, brainstorming software, or an ES

47. How Decisions Are Supported
Support for the choice phase
DSS can support the choice phase through what-if and goal-seeking analyses
Different scenarios can be tested for the selected option to reinforce the final decision
KMS helps identify similar past experiences
CRM, ERP, and SCM systems are used to test the impacts of decisions in establishing their value, leading to an intelligent choice
An ES can be used to assess the desirability of certain solutions and to recommend an appropriate solution
A GSS can provide support to lead to consensus in a group

48. How Decisions Are Supported
Support for the implementation phase
DSS can be used in implementation activities such as decision communication, explanation, and justification
DSS benefits are partly due to the vividness and detail of analyses and reports

49. Defining Success of a Decision
Success is a function of its quality and of how a decision is implemented
Decision quality is judged by a decision’s
Compatibility with existing constraints
Its timeliness
Its incorporation of the optimal amount of information
A successful implementation
Avoids conflict of interest
Makes sure the decision is understood by everyone
Benefits outweigh the risks
DSS can impact success
Reference - Power (2008)

50. How Decisions Are Supported
New technology support for decision making
Mobile commerce (m-commerce)
Personal devices
Personal digital assistants [PDAs]
Cell phones
Tablet computers
Laptop computers

51. Impediments to Decision Success
Tradition and Bias
“We have always done it that way.”
Lack of Knowledge
DSS and expert systems can reduce this
Improper Use of Decision Aids
DSS can hinder “good” decision-making
Reference - Power (2008)