2. INTRO TO MANAGEMENT SUPPORT SYSTEMS IS 340 BY CHANDRA S. AMARAVADI

3. 2 IN THIS PRESENTATION.. l Introduction to MSS l Decisions & types of decisions l DSS l EIS l GDSS

4. 3 INTRO TO MSS

5. 4 INTRODUCTION (FYI) l More competition l Globalization l Complexity More decision making (D.M)

6. 5 MANAGEMENT SUPPORT SYSTEMS MSS: collection of tools/systems to support managerial activity. Characteristics (FYI): u Interactive u Customizable u Model based u Support rather than automate

7. 6 MANAGEMENT SUPPORT SYSTEMS Evolution TPReportingDSS ES GDSS EIS DSS AI MSS Data Mining Note: ES – Expert Systems, AI – Artificial Intelligence EIS – Executive Information Systems; DSS – Decision Support Systems

8. 7 n Whether to approve a loan? n Whether to promote an employee? n How much of an increase to allocate to employees? n Where to advertise? Allocation to media? n How to finance a capital expansion project? n How much to produce? When to produce? n What products to produce? What markets? n What production techniques to use? EXAMPLES OF DECISIONS

9. 8 TYPES OF DECISIONS When to produce? What products? Structured problem (routine) Unstructured problem (non-routine) Types of Decisions

10. 9 DECISION MAKING STYLES Structured Unstructured AnalyticalIntuitive {focus on methods & models} {focus on cues, trial & error} D.M. Styles

11. 10 Intelligence Design ChoiceDecision ! THE IDC MODEL OF DECISION MAKING

12. 11 THE IDC MODEL OF DECISION MAKING Introduced by Herbert Simon, the IDC consists of The following stages: Intelligence -- Identification of problem information Design -- Identification of alternative solutions Choice -- Choosing a solution which optimizes D.M. criteria

13. 12 DECISION SUPPORT SYSTEMS

14. 13 A system that supports structured and semi-structured decision making by managers in their own personalized way. DECISION SUPPORT SYSTEMS

15. 14 CLASSICAL DSS ARCHITECTURE Note: model is an abstract representation of a problem Dialog management Model management Data management User interface Capabilities for creating & linking models Capabilities for managing & accessing data Database

16. 15 DSS ANALYSIS CAPABILITIES u “What - if “ u Sensitivity u Goal-seeking u Optimization

17. 16 What if - change one or more variables Sensitivity - change one variable Goal seeking - finding a solution to satisfy constraints Optimization- find best solution under a given set of constraints DSS ANALYSIS CAPABILITIES

18. 17 u Financial e.g. portfolio, NPV u Statistical e.g. : forecasting u Marketing e.g. : product mix, advertising u Production e.g. capacity planning, inventory u Simulation e.g. production process, bank tellers etc. DSS MODELS (FYI)

19. 18 BANK EXAMPLE Tellers Que1 Que2 Que3 Que4 Arrival of Customers Departure of Customers Customers Waiting Tellers

20. 19 SIMULATION MODEL Customer Arrives Joins Que Is processed Customer leaves PURPOSE: Identify # of tellers needed, service time

21. 20 CASE OF THE S.S. KUNIANG (FYI) l Ship ran aground off the coast of Florida l Owners wanted to sell it l Coast guard was the authority l NEES, a utility company; needs coal l Buy ship or not? How much to bid?

22. 21 l Already has a $70 m, 36,250 ton self-loading; sister vessel? l To have crane or not l Crane would increase repair cost, but reduce turnaround time l Coal from Egypt or PA? lJones act l Buy a barge? DECISION COMPLICATIONS lKuniang (w crane), lKuniang (no crane), lGeneral dynamics vessel, or ltug barge Options are (FYI)

23. 22 l Capacity of General Dynamics 2.5 m tons/yr Needed capacity: 4 m tons/yr l The Jones Act gave priority to the Kuniang in U.S. ports if repair cost > than 3 times boat’s salvage value l Affects round-trip time l Decision hinges on whether the C.G. would value ship > $ 5 million l If ship valued > $5 million, install crane (+$36 m) l Cargo capacity reduced to 40,000 tons, but round trip time is decreased l How much to bid? DECISION CONSTRAINTS (FYI)

24. 23 Capital cost Capacity Round trip (coal) Round trip (Egypt) Operating cost/day Fixed cost/day Revenue/trip coal Revenue/trip Egypt General Dynamics $70 mil. 36,250 tons 5.15 days 79 days $18,670 $2,400 $304,500 $2,540,000 Tug Barge $32 mil 30,000 tons 7.15 days 134 days $12,000 $2,400 $222,000 $2,100,000 Kuniang (Gearless) Bid+$15mil 45,750 tons 8.18 days 90 days $23,000 $2,400 $329,400 $3,570,000 Kuniang (Self-loader) Bid+$36mil 40,000 tons 5.39 days 84 days $24,300 $2,700 $336,000 $2,800,000 DATA FOR THE 4 OPTIONS (FYI)

25. 24 DECISION TREE OF HOW MUCH TO BID Bid $7mil Win Lose Salvage=scrap Salvage=bid Sister Ship Tug/Barge Gearless Self-Unloader Total Cost 43 22 43 28 NPV -1.35 5.8 -1.35 3.2 2.1 -0.6 0.7 ? 0.5 Note: NPV calculations are based on projections from previous slide Decision Outcome

26. 25  NEES ended up bidding $6.7 million for the Kuniang, but lost to a bid of $10 million  Coast Guard valued ship as scrap metal  Decision tree a useful tool; parameters unknown

27. 26 DSS APPLICATIONS l Cash forecasting l Fire-fighting l Portfolio selection l Evaluate lending risk l Event scheduling l School location l Police beat

28. 27 DATA MINING

29. 28 DATA MINING e.g. sequence/association, classification, and clustering Search for relationships and global patterns that exist in large databases but are hidden in the vast amounts of data.

30. 29 u Predicting the probability of default for consumer loans u Predicting audience response to TV advertisements u Predicting the probability that a cancer patient will respond to radiation therapy. u Predicting the probability that an offshore well is going to produce oil

31. 30 Sequence Activities which occur after each other e.g. car and loan Associations activities/purchases which occur together e.g. bread and jam. Classification An analysis to group data into classes e.g. pepsi and coke drinkers

32. 31 EXECUTIVE INFORMATION SYSTEMS

33. 32 Systems to support unstructured decision making by executives EXECUTIVE INFORMATION SYSTEMS

34. 33 EIS ARCHITECTURE Costs: $50,000 - $100,000 Development time: about 1 month Internal Databases EIS Workstation FedStats Medline Does more information lead to better quality decisions?

35. 34 EIS CAPABILITIES Ease of use Drill down capabilities- view data at increasing levels of detail Filtering Status Monitoring User friendliness

36. 35 COLLABORATIVE SYSTEMS (GDSS)

37. 36 An interactive computer based system which facilitates solution of unstructured problems by a set of D.M. working together as a group. Other terms - GDSS, Electronic Meeting Systems. COLLABORATIVE SYSTEMS

38. 37 CURRENT BUSINESS TRENDS (FYI) l More competition l Shift towards flat/virtual organizations l More mergers [industry consolidations] l Globalization of markets and products l More strategic alliances Is it necessary for org. decisions to be made in groups? Why cannot it be handled by individuals? Group D.M.

39. 38 CHARACTERISTICS OF GROUP D.M. l Participants of equal rank l 5-20 l Time limits l Requires knowledge from participants

40. 39 Database Org Memory Screen A GDSS System A repository of the D.M. process. A GROUP DECISION SUPPORT SYSTEM

41. 40 GROUP DECISION SUPPORT SYSTEMS

42. 41 Process losses GDSS - Process gains + GDSS THEORY A GDSS minimizes process losses and maximizes process gains

43. 42 n Time n Anonymity n Democratic participation n Satisfaction n Record of decision ADVANTAGES OF GDSS

44. 43 THE END