1.Decision Support Systems Theory Decision Support Systems

2 Management Roles (by Mintzberg (1980)) Interpersonal –Figurehead –Leader –Liaison Informational –Monitor –Disseminator –Spokesperson Decisional –Entrepreneur –Disturbance Handler –Resource Allocator –Negotiator The Nature of Managers ’ Work To perform these roles, managers need information to be delivered efficiently end effectively by computers.

3 Managerial Decision Making and Information Systems Management is a process by which organizational goals are achieved through the use of resources Resources: Inputs Goal Attainment: Output Measuring Success: Productivity = Outputs / Inputs

4 Timely decisions (Speed) Productivity – Increase the production – Reduce the cost Technical support Quality support Competitive edge: BPR Cognitive limits may limit ones problem solving capabilities The Need for Computerized Decision Support

5 Management Support Systems (MSS): “ Application of technologies to support management tasks (esp. decision making) ” – Decision Support Systems (DSS) – Group Support Systems (GSS) – Enterprise (Executive) Information Systems (EIS) – Enterprise Resource Planning (ERP) and Supply-Chain Management (SCM) – Knowledge Management (KM) Systems – Expert Systems (ES) – Artificial Neural Networks (ANN) – Hybrid Support Systems – Intelligent DSS and Agents Decision Support Technologies

6 Since the 1960s Repetitive in nature High level of structure Can abstract and analyze them, and classify them into prototypes Solve with quantitative formulas or models Management Science (MS) / Operations Research (OR) Computer Support for Structured Decisions

7 Management Science Scientific approach is a step-by-step process to automate the managerial decision making 1. Define problem 2. Classify problem 3. Construct mathematical model 4. Find and evaluate potential solutions 5. Choose and recommend a solution Modeling: Transforming the real-world problem into an appropriate abstract structure

8 Decision Support Systems Concept DSS are interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems Gorry and Scott Morton (1989). Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. Keen and Scott Morton (1978) There is NO universally accepted definition of DSS –Umbrella term vs. Narrow definition

9 Why Use DSS? Perceived benefits –decision quality –improved communication –cost reduction –increased productivity –time savings –improved customer and employee satisfaction

10 Group Support Systems (GSS) Decisions often made by groups Supports group work, anytime, anyplace Also called – Groupware – Electronic meeting systems – Collaborative computing

11 Executive Information Systems (EIS) Also called –Executive Support Systems (ESS) –Mid- 1980s, was expensive (appealing to large companies), now affordable to smaller companies Characteristics of EIS – Provides an organizational view – Serves the information needs of executives – Customized user seductive interface – Timely and effective tracking and control – Filter, compress, and track critical data – Identify problems / opportunities

12 Expert Systems (ES) Experts solve complex problems Experts have specific knowledge and experience Expert systems mimic human experts ES performance comparable to or better than experts in a specialized and usually narrow problem area

13 Help automate various tasks Increase productivity and quality Learn how you work and adjust accordingly Software Agents

14 Artificial Neural Networks (ANN) Mathematical models of the human brain ANN learn patterns in data ANN can work with partial, incomplete, or inexact information

15 Capture and reuse knowledge at the organizational level Knowledge repository for storage Organizational impacts can be dramatic Knowledge Management Systems

16 Cutting Edge Intelligent Systems Genetic Algorithms Search algorithms that mimic an evolutionary behavior Fuzzy Logic Continuous logic (NOT just True / False) Rough Sets, Support Vector Machines, etc.

17 Decision Making vs. Problem Solving Decision making and problem solving are interchangeable Are they the same? Simon’s 4 Phases of Decision Making 1.Intelligence 2.Design 3.Choice 4.Implementation

18 Models Major component of DSS A model is a simplified representation or abstraction of reality Degrees of Model Abstraction: – Iconic (Scale) Models – Analog (Symbolic) Models – Mathematical (Quantitative) Models

19 Benefits of Models  Time compression  Easy model manipulation  Low cost of construction  Low cost of making mistakes  Can model risk and uncertainty  Can model large and extremely complex systems with infinite number of solutions  Enhance and reinforce learning, and enhance training.

20 The Modeling Process: A Preview How Much to Order for the Ma-Pa Grocery? Bob and Jan: How much bread to stock each day? Solution Approaches – Trial-and-Error – Simulation – Optimization – Heuristics

21 Systematic Decision-Making Process (Simon, 1977) Intelligence Design Choice Implementation Modeling is Essential to the Process Decision Making Process

22 The Intelligence Phase Scan the environment to identify problem situations or opportunities Collect all related data/information While finding the Problem … – Identify/Examine organizational goals and objectives – Determine whether they are being met – Explicitly define the problem Classify the problem – Programmed versus Nonprogrammed

23 Divide a complex problem into (easier to solve) sub- problems (a.k.a. Chunking) Some seemingly poorly structured problems may have some highly structured sub-problems Problem Ownership The outcome of intelligence Phase: Problem Statement Problem Decomposition

24 The Design Phase Generating, developing, and analyzing possible courses of actions Includes Understanding the problem Generating and testing solution alternatives for feasibility: Usually a model is constructed, tested, and validated Modeling Conceptualization of the problem Abstraction to quantitative and/or qualitative forms

25 The Design Phase Mathematical model – Identify variables – Establish equations describing their relationships – Simplifications through assumptions – Balance model simplification and the accurate representation of reality Modeling: an art and science

26 Quantitative Modeling Topics Model Components Model Structure Selection of a Principle of Choice (Criteria for Evaluation) Developing (Generating) Alternatives Predicting Outcomes Measuring Outcomes Scenarios

27 Optimization Problems n Linear programming n Goal programming n Network programming n Integer programming n Transportation problem n Assignment problem n Nonlinear programming n Dynamic programming n Stochastic programming n Investment models n Simple inventory models n Replacement models (capital budgeting) n Tools: LONGO, LINDO, AMPL, MPX, Solver, etc.

28 The Principle of Choice What criteria to use? – Chose between … High risk versus Low risk Optimize versus Satistice Normative vs. Descriptive models

29 Descriptive Models Describe things as they are, or as they are believed to be Extremely useful in DSS for evaluating the consequences of decisions and scenarios No guarantee a solution is optimal Often a solution will be good enough Examples: – Simulation, Queuing, Petri nets, Markov chain – Scenario analysis, Financial planning

30 Satisficing (Good Enough) Most human decision makers will settle for a good enough solution Tradeoff: time and cost of searching for an optimum versus the value of obtaining one Bounded Rationality – Humans have a limited capacity for rational thinking, therefore they generally construct and analyze a simplified model of a real situation. – Bounded rationality: why many models are descriptive, not normative (Simon, 1977)

31 Developing (Generating) Alternatives In Optimization Models: Alternatives are generated automatically by the model. Not for the rest of the MSS situation! Manual, lengthy process Involves Searching and Creativity Issue: When to ?

32 The Choice Phase The CRITICAL act - decision made here! Search, evaluation, and recommending an appropriate solution to the model Specific set of values for the decision variables in a selected alternative The problem is considered solved only after the recommended solution to the model is successfully implemented

33 There is nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things (Machiavelli, 1500s) *** The Introduction of a Change *** Important Issues – Resistance to change – Degree of top management support – Users ’ roles and involvement in system development – Users ’ training Implementation Phase

34 How Decisions are Supported Specific MSS technologies relationship to the decision making process (Figure 2.11)

35 DSS Definitions Earlier DSS definition indicated that – A system intended to support managerial decision making – They are meant to be an adjunct to decision makers to extend their capabilities but not to replace their judgment – The system is computer-based, would operate interactively, and would have a GUI Little (1970) “ Model-based set of procedures for processing data and judgments to assist a manager in his decision making ” – … must be simple, robust, adaptive, complete, and user friendly.

36 DSS Components Subsystems: 1. Data Management 2. Model Management 3. User Interface 4. Knowledge-based (Management) 5. The User (Figure 3.2)

37 The Data Management Subsystem Major Components: DSS database Database management system Data directory Query facility (Figure 3.3)

38 DSS Classification Alter ’ s Output Classification (1980) – Degree of action implication of system outputs (supporting decision) Data-oriented Data- or Model-orineted Model-oriented Holsapple and Whinston ’ s Classification 1. Text-oriented DSS 2. Database-oriented DSS 3. Spreadsheet-oriented DSS 4. Solver-oriented DSS 5. Rule-oriented DSS 6. Compound/Hybrid DSS

39 Intelligent DSS Rule-oriented DSS types – Descriptive – Procedural – Reasoning – Linguistic – Presentation – Assimilative Will be covered later in the course (Chapters 6 and 18)

Past, Present and Future of DSS DSS are computer technology solutions that can be used to support complex decision making and problem solving. Evolved from two main area of research:  The theoretical studies of organizational decision making (Simon, Cyert, March, … ) conducted at Carnegie Institute of Technology during 1950s and 1960s.  The technical work (Garrity, Ness, … ) carried out at MIT in 1960s. DSS have evolved significantly since 1970s

Past, Present and Future of DSS Decision-Making Process in DSS

Past, Present and Future of DSS DSS development areas: –Data Warehouse –OLAP –Data Mining –Web-based DSS –Collaborative Support Systems GSS and Virtual Teams –Optimization-based DSS Models Formulation, Solution, Analysis

Past, Present and Future of DSS What to expect from DSS in the next decade and beyond: –Less compromises in the complexity and comprehensiveness of DSS tools and techniques (more and more sophisticated users) – DSS technology of the future will be enhanced by Mobile tools, mobile e-services, and wireless technologies –More advanced models and tools for multiple-criteria decision making –Use of intelligent systems and soft computing –Better EIS tools will enhance use of DSS tools in critical decision making at all levels of management –Better integration with intelligent data mining and software agents