1. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 Supporting Business Decision Making Week 2 Dr. Jocelyn San Pedro School of Information Management & Systems Monash University

2. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 2 Lecture Outline  Decisions and Decision Makers  Decision Effectiveness and Efficiency  Effective Decision Making in BI Environment  Approaches to Problem Solving  Quantitative-centred approach  Decision-centred approach  Approaches to Problem Finding  Problem-centred approach  Opportunity-centred approach  Summary

3. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 3 Learning Objectives  Gain an understanding of decision making process and nature of decision makers  Have initial understanding of types of problems that can use BI in their solutions  Have knowledge of approaches to problem solving and problem finding to facilitate effective decision making in BI environment  Explore some BIS for their capability to facilitate effective decision making

4. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 4 Decisions and Decision Makers adopted from Marakas (2003) Decision Support Systems, 2 nd ed, Prentice Hall www.prenhall.com/marakas

5. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 5 Decisions and Decision Makers Classes of Decision Makers

6. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 6 Decisions and Decision Makers  Decision Maker Classifications  Individual decision makers can be a single person or a computer system.  Multiple decision makers can be:  groups where all members have a say in the decisions,  teams where members support a single decision maker, or  organizational where global agreement is needed.

7. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 7 Decisions and Decision Makers  Decision Style  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 own set of values.  The complexity of these intertwine in the formation of decision style.

8. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 8 Decisions and Decision Makers Decision Style Model

9. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 9 Decisions and Decision Makers  Decision Style Categories (Marakas, 2003)  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.

10. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 10 Decisions and Decision Makers  Decision Style in BIS Design  Key issues are the decision maker’s reaction to stress and the method in which problems are usually solved.  For example, to best serve a directive type who does not handle stress well, the interface needs to allow the decision maker to control the system without tedious input.  For an analytic type, the DSS needs to allow access to many data sources which the decision maker will analyze.

11. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 11 Decision effectiveness and efficiency

12. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 12 Decision Effectiveness & Efficiency  Effectiveness is the degree to which goals are achieved  Doing the right thing!  Efficiency is a measure of the use of inputs (or resources) to achieve outputs  Doing the thing right!  BIS emphasize effectiveness  Often: several non-quantifiable, conflicting goals

13. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 13 Decision Effectiveness & Efficiency  How can BIS help in improving the effectiveness of decision making?  Easier access to information  Keen insight into the relationships of presented facts  Faster problem recognition and identification  Easier access to computing tools  Greater ability to evaluate large choice sets

14. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 14 Decision Effectiveness & Efficiency  How can BIS help in improving the efficiency of decision making?  Reduction in decision costs  Reduction in decision time  Better quality in feedback supplied

15. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 15 Effective Decision Making in Business Intelligence Environment

16. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 16 Effective Decision Making in Business Intelligence Environment  The need to rethink decision making as a way of capitalising on BI  BI activities include problem finding, problem solving, and finding related opportunities

17. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 17 Effective Decision Making in Business Intelligence Environment Types of problems that can use BI in their solution  Well-structured problems  all elements can be identified and quantified  Time frame is usually short duration  e.g., production allocation –time and costs of production for next month can be identified

18. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 18 Effective Decision Making in Business Intelligence Environment  Semi-structured  Contains both well-structured and unstructured elements  Time frame can range from short run to long run  e.g., weather forecasting  Scientific method of forecasting (use of numerical weather prediction models)  Criteria for making intelligent forecast decisions need to be left to the forecaster’s judgment

19. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 19 Effective Decision Making in Business Intelligence Environment Unstructured problems  Significant parameters of the problem cannot be identified (i.e., problem solving under conditions of uncertainty)  Time frame is generally too long (e.g. beyond 5 years)  Generally large number of unknowns  Mathematical or statistical models generally inappropriate  Need for general knowledge, meaningful experience, know-how, intuition, judgment and past experience  May use of rules of thumb (i.e., heuristic methods)– resorting to trends, patterns, evaluations, educated guesses, hypotheses, and the like  e.g. determining personnel needs 10 years from now

20. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 20 Approaches to Problem Solving

21. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 21 Approaches to Problem Solving  Decision-centred approach  Semi-structured and unstructured problems  Search for limited number of acceptable solutions (satisficing solutions), from which the solution is selected  Quantitative-centred approach  Scientific method  Well-structured problems  Use of mathematical models that optimise performance (optimal solutions)  e.g., maximise profits, minimise costs

22. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 22 Decision-centred approach Simon’s Model of Problem Solving Marakas, (2002)

23. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 23 Decision-centred approach  Intelligence phase – the decision maker looks for indications that a problem exists  Design phase – alternatives are formulated and analyzed  Choice phase – one of the alternatives is selected and implemented

24. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 24 Decision-centred approach Rational Decision Making  Many decision strategies attempt to find optimal solutions.  In many circles, this is considered to be rational behavior.  It is not always possible to optimize. Some problems have only qualitative solutions. Others may be quantitative but have multiple objectives at odds with others.  In such situations, rational behavior would be to choose a “good” solution

25. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 25 Decision-centred approach Bounded Rationality  Simon argued that people don’t always optimize because it is often impractical to consider all possible solutions to a problem.  He notes that we often “simplify reality” by looking for a solution that is acceptable, a strategy he called satisficing.  When people make rational decisions that are bounded by often uncontrollable constraints, he notes that they are operating inside bounded reality.

26. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 26 Decision-centred approach Example: Deciding whether to take or not to take umbrella http://www.sis.pitt.edu/~dsl/

27. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 27 Decision-centred approach Strategy: Use of Influence Diagrams  Influence diagram - a simple method of graphing the components of a decision and linking them to show the relationships between them  based on the premise that humans are reasonably capable of framing a decision problem, listing possible decision options, determining relevant factors, and quantifying uncertainty and preferences, but are rather weak in combining this information into a rational decision Chance Decision Utility

28. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 28 Decision-centred approach  The goal of influence diagram modeling is choosing such a decision alternative that will lead to the highest expected gain utility.  Chance node - random variable representing uncertain quantities that is relevant to the decision problem (quantified by conditional probability distributions)  Decision node - represents a variable that is under control of the decision maker and model the decision alternatives available to the decision maker.  Utility node – a measure of desirability of the outcomes of the decision process

29. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 29 Decision-centred approach Decision Model http://www.sis.pitt.edu/~dsl/

30. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 30 Decision-centred approach Using Genie – Setting the evidence

31. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 31 Decision-centred approach Updating the Influence Diagram

32. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 32 Decision-centred approach Setting the Decision

33. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 33 Decision-centred approach Calculating Utility

34. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 34 Quantitative-centred approach 1. Formulation 2. Solution 3. Interpretation Render, Stair, and Balakrishnan, (2003)

35. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 35 Quantitative-centred approach Example: Goal Seeking – XYZ Real Estate Agents  The Problem: The XYZ Real Estate Agents wants to determine the number of properties to sell by the end of year 2004 in order to achieve a target average of 600 properties sold from 2000 to 2004. YearNumber of Properties Sold 2000200 2001100 2002900 2003950 2004? Average600 Known parameters Unknown Goal

36. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 36 Quantitative-centred approach Example: Using Excel to formulate the decision model.

37. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 37 Quantitative-centred approach Example: Using Goal Seek to find solution.

38. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 38 Quantitative-centred approach The Solution

39. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 39 Quantitative-centred approach What-if Analysis

40. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 40 Quantitative-centred approach Other examples  Unit scheduling or registration  Find “best” schedule that satisfies student time preferences, course program, unit availability, staff availability, etc.  Allocate + - assists both students as well as Unit coordinators  How much can borrow?  On-line loan calculators

41. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 41 Approaches to Problem Finding

42. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 42 Approaches to Problem Finding  Problem-centred approach  Examining the environment using with the idea of looking into the future  Projecting into the future, determining important problems and bringing them back to the present time to examine their cause-and-effect relationships  Opportunity-centred approach  Identifying opportunities that generally come from problems uncovered  Not necessarily related to future problems, but can relate to current opportunities

43. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 43 Approaches to Problem Finding Basic PhasesProblem- centred Opportunity- centred SearchGeneration Evaluation Exploration IdentificationValidation Establish boundaries Selection Examine boundaries Solution and Implementatio n Use steps from Problem solving process Use steps from problem-solving process

44. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 44 Approaches to Problem Finding Example - Chemical products company  Current product sold to retail and industrial  laundry plants - contribute 25% of total sales  dry-cleaning plants contribute 25% of total sales  Problem: Government regulations on dry-cleaning industry due to hazardous waste  Problem-centred:  Change in sales and marketing strategy?  Opportunity: Examine environmental issues in more depth to identify future opportunities  Biodegradable products and implications?  Breaking into the home market?

45. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 45 Approaches to Problem Finding http://www.mindjet.com/us/index.php Creative Computer Software to support problem finding

46. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 46 Summary  Decisions and Decision Maker  Various types of problems in BI environment  Problem-Solving  Problem-Finding  BI activities include problem finding, problem solving, and finding related opportunities to facilitate effective decision making

47. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 47 References  Marakas, G.M. (2002). Decision support systems in the 21st Century. 2nd Ed, Prentice Hall  Render, B. Stair, R. and Balakrishnan, N. (2003) Managerial Decision Modeling with Spreadsheets, Prentice Hall.  Thierauf, R. (2001) Effective business intelligence systems, Quorum Books.

48. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 48 Questions? Jocelyn.sanpedro@sims.monash.edu.au School of Information Management and Systems, Monash University T1.28, T Block, Caulfield Campus 9903 2735