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Chapter 13 Decision Support Systems MANAGEMENT INFORMATION SYSTEMS 8/E Raymond McLeod, Jr. and George Schell Copyright 2001 Prentice-Hall, Inc. 13-1.

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Presentation on theme: "Chapter 13 Decision Support Systems MANAGEMENT INFORMATION SYSTEMS 8/E Raymond McLeod, Jr. and George Schell Copyright 2001 Prentice-Hall, Inc. 13-1."— Presentation transcript:

1 Chapter 13 Decision Support Systems MANAGEMENT INFORMATION SYSTEMS 8/E Raymond McLeod, Jr. and George Schell Copyright 2001 Prentice-Hall, Inc. 13-1

2 Simon’s Types of Decisions n Programmed decisions –repetitive and routine –have a definite procedure n Nonprogrammed decisions –Novel and unstructured –No cut-and-dried method for handling problem n Types exist on a continuum 13-2

3 Simon’s Problem Solving Phases n Intelligence – Searching environment for conditions calling for a solution n Design – Inventing, developing, and analyzing possible courses of action n Choice – Selecting a course of action from those available n Review – Assessing past choices 13-3

4 Definitions of a Decision Support System (DSS) General definition - a system providing both problem-solving and communications capabilities for semistructured problems Specific definition - a system that supports a single manager or a relatively small group of managers working as a problem-solving team in the solution of a semistructured problem by providing information or making suggestions concerning specific decisions. 13-4

5 The DSS Concept n Gorry and Scott Morton coined the phrase ‘DSS’ in 1971, about ten years after MIS became popular n Decision types in terms of problem structure –Structured problems can be solved with algorithms and decision rules –Unstructured problems have no structure in Simon’s phases –Semistructured problems have structured and unstructured phases 13-5

6 Degree of problemstructure The Gorry and Scott Morton Grid Management levels Structured Structured Semistructured Unstructured Unstructured Operational control controlManagement Strategicplanning Accounts receivable Order entry Inventory control Budget analysis-- engineered costs Short-term forecasting Tanker fleet mix Warehouse and factory location Production scheduling Cash management PERT/COST systems Variance analysis-- overall budget Budget preparation Sales and production Mergers and acquisitions New product planning R&D planning 13-6

7 Alter’s DSS Types n In 1976 Steven Alter, a doctoral student built on Gorry and Scott-Morton framework –Created a taxonomy of six DSS types –Based on a study of 56 DSSs n Classifies DSSs based on “degree of problem solving support.” 13-7

8 Levels of Alter’s DSSs n Level of problem-solving support from lowest to highest –Retrieval of information elements –Retrieval of information files –Creation of reports from multiple files –Estimation of decision consequences –Propose decisions –Make decisions 13-8

9 Importance of Alter’s Study n Supports concept of developing systems that address particular decisions n Makes clear that DSSs need not be restricted to a particular application type 13-9

10 Retrieve information elements Analyze entire files Prepare reports from multiple files Estimate decision consequen- ces Propose decisions Degree of problem solving support Degree of complexity of the problem-solving system LittleMuch Alter’s DSS Types Make decisions 13-10

11 Three DSS Objectives 1. Assist in solving semistructured problems 2. Support, not replace, the manager 3. Contribute to decision effectiveness, rather than efficiency Based on studies of Keen and Scott-Morton 13-11

12 GDSS software MathematicalModels Other group members group members Database GDSSsoftware Environment Individual problem problem solvers solvers Decision support system Environment Environment Legend : DataInformation Communication A DSS Model Reportwritingsoftware 13-12

13 Database Contents n Used by Three Software Subsystems –Report writers »Special reports »Periodic reports »COBOL or PL/I »DBMS –Mathematical models »Simulations »Special modeling languages –Groupware or GDSS 13-13

14 Group Decision Support Systems n Computer-based system that supports groups of people engaged in a common task (or goal) and that provides an interface to a shared environment. n Used in problem solving n Related areas –Electronic meeting system (EMS) –Computer-supported cooperative work (CSCW) –Group support system (GSS) –Groupware 13-14

15 How GDSS Contributes to Problem Solving n Improved communications n Improved discussion focus n Less wasted time 13-15

16 GDSS Environmental Settings n Synchronous exchange –Members meet at same time –Committee meeting is an example n Asynchronous exchange –Members meet at different times –E-mail is an example n More balanced participation. 13-16

17 GDSS Types n Decision rooms –Small groups face-to-face –Parallel communication –Anonymity n Local area decision network –Members interact using a LAN n Legislative session –Large group interaction n Computer-mediated conference –Permits large, geographically dispersed group interaction 13-17

18 Smaller Larger GROUP SIZE Face-to- face Dispersed Decision Room Local Area Decision Network Legislative Session Computer- Mediated Conference MEMBERPROXIMITY Group Size and Location Determine GDSS Environmental Settings 13-18

19 Groupware n Functions –E-mail –FAX –Voice messaging –Internet access n Lotus Notes –Popular groupware product –Handles data important to managers 13-19

20 Main Groupware Functions IBM TeamWARE Lotus Novell IBM TeamWARE Lotus Novell Function Workgroup Office Notes GroupWise X = standard featureO = optional feature3 = third party offering 13-20

21 Artificial Intelligence (AI) The activity of providing such machines as computers with the ability to display behavior that would be regarded as intelligent if it were observed in humans. 13-21

22 History of AI n Early history –John McCarthy coined term, AI, in 1956, at Dartmouth College conference. –Logic Theorist (first AI program. Herbert Simon played a part) –General problem solver (GPS) n Past 2 decades –Research has taken a back seat to MIS and DSS development 13-22

23 Areas of Artificial Intelligence Expertsystems AIhardware Robotics Perceptive systems systems (vision, (vision, hearing) hearing) Neural networks Natural language language Learning Artificial Intelligence 13-23

24 Appeal of Expert Systems n Computer program that codes the knowledge of human experts in the form of heuristics n Two distinctions from DSS –1. Has potential to extend manager’s problem- solving ability –2. Ability to explain how solution was reached 13-24

25 Know- ledge base User interface Instructions & information Solutions & explanations Knowledge Inference engine Problem Domain Expert and knowledge engineer Development engine Expertsystem An Expert System Model 13-25

26 Expert System Model n User interface –Allows user to interact with system n Knowledge base –Houses accumulated knowledge n Inference engine –Provides reasoning –Interprets knowledge base n Development engine –Creates expert system 13-26

27 User Interface n User enters: –Instructions –Information n Expert system provides: –Solutions –Explanations of »Questions »Problem solutions } Menus, commands, natural language, GUI 13-27

28 Knowledge Base n Description of problem domain n Rules –Knowledge representation technique –‘IF:THEN’ logic –Networks of rules »Lowest levels provide evidence »Top levels produce 1 or more conclusions »Conclusion is called a Goal variable. 13-28

29 Evidence Conclusion Evidence Conclusion A Rule Set That Produces One Final Conclusion 13-29

30 Rule Selection n Selecting rules to efficiently solve a problem is difficult n Some goals can be reached with only a few rules; rules 3 and 4 identify bird 13-30

31 Inference Engine n Performs reasoning by using the contents of knowledge base in a particular sequence n Two basic approaches to using rules –1. Forward reasoning (data driven) –2. Reverse reasoning (goal driven) 13-31

32 Forward Reasoning (Forward Chaining) n Rule is evaluated as: –(1) true, (2) false, (3) unknown n Rule evaluation is an iterative process n When no more rules can fire, the reasoning process stops even if a goal has not been reached Start with inputs and work to solution 13-32

33 Rule 1 Rule 3 Rule 3 Rule 2 Rule 2 Rule 4 Rule 4 Rule 5 Rule 5 Rule 6 Rule 6 Rule 7 Rule 7 Rule 8 Rule 8 Rule 9 Rule 9 Rule 10 Rule 10 Rule 11 Rule 11 Rule 12 IF A THEN B IF C THEN D IF M THEN E IF K THEN F IF G THEN H IF I THEN J IF B OR D THEN K IF E THEN L IF K AND L THEN N IF M THEN O IF N OR O THEN P F IF (F AND H) OR J THEN M IF (F AND H) OR J THEN M The Forward ReasoningProcess T T T T T T T T T F T Legend: First pass Second pass Third pass 13-33

34 Reverse Reasoning Steps (Backward Chaining) ¶ Divide problem into subproblems · Try to solve one subproblem ¸ Then try another Start with solution and work back to inputs 13-34

35 T Rule 1 Rule 2 Rule 3 Rule 9 Rule 11 Legend: Problems to be solved Step 4 Step 3 Step 2 Step 1 Step 5 IF A THEN B IF B OR D THEN K IF K AND L THEN N IF N OR O THEN P IF C THEN D IF M THEN E IF E THEN L IF (F AND H) OR J THEN M IF M THEN O IF M THEN O T The First Five Problems Are Identified Rule 7 Rule 10 Rule 12 Rule 8 13-35

36 If K Then F Legend: Problems to be solved If G Then H If I Then J If M Then O Step 8 Step 9 Step 7Step 6 Rule 4 Rule 5 Rule 11 Rule 6 T IF (F And H) Or J Then M T Rule 9 T T Rule 12 T If N Or O Then P The Next Four Problems Are Identified 13-36

37 Forward Versus Reverse Reasoning n Reverse reasoning is faster than forward reasoning n Reverse reasoning works best under certain conditions –Multiple goal variables –Many rules –All or most rules do not have to be examined in the process of reaching a solution 13-37

38 Development Engine n Programming languages –Lisp –Prolog n Expert system shells –Ready made processor that can be tailored to a particular problem domain n Case-based reasoning (CBR) n Decision tree 13-38

39 Expert System Advantages n For managers –Consider more alternatives –Apply high level of logic –Have more time to evaluate decision rules –Consistent logic n For the firm –Better performance from management team –Retain firm’s knowledge resource 13-39

40 Expert System Disadvantages n Can’t handle inconsistent knowledge n Can’t apply judgment or intuition 13-40

41 Keys to Successful ES Development n Coordinate ES development with strategic planning n Clearly define problem to be solved and understand problem domain n Pay particular attention to ethical and legal feasibility of proposed system n Understand users’ concerns and expectations concerning system n Employ management techniques designed to retain developers 13-41

42 Neural Networks n Mathematical model of the human brain –Simulates the way neurons interact to process data and learn from experience n Bottom-up approach to modeling human intuition 13-42

43 The Human Brain n Neuron -- the information processor –Input -- dendrites –Processing -- soma –Output -- axon n Neurons are connected by the synapse 13-43

44 Soma (processor ) Axon Synapse Dendrites (input) Axonal Paths (output) Simple Biological Neurons 13-44

45 Evolution of Artificial Neural Systems (ANS) n McCulloch-Pitts mathematical neuron function (late 1930s) was the starting point n Hebb’s learning law (early 1940s) n Neurocomputers –Marvin Minsky’s Snark (early 1950s) –Rosenblatt’s Perceptron (mid 1950s) 13-45

46 Current Methodology n Mathematical models don’t duplicate human brains, but exhibit similar abilities n Complex networks n Repetitious training –ANS “learns” by example 13-46

47 y1y1 y2y2 y3y3 y n-1 y w1w1 w2w2 w3w3 w n-1 Single Artificial Neuron 13-47

48 The Multi-Layer Perceptron Y n2 IN n OUT n OUT 1 IN 1 Y1Y1Y1Y1 Input Layer OutputL ayer 13-48

49 Knowledge-based Systems in Perspective n Much has been accomplished in neural nets and expert systems n Much work remains n Systems abilities to mimic human intelligence are too limited and regarded as primitive 13-49

50 Summary [cont.] n AI –Neural networks –Expert systems n Limitations and promise 13-50

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