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

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

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

3. INTRO TO MSS

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

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

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

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

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

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

10. THE IDC MODEL OF DECISION MAKING Intelligence Design Choice Decision !

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

12. DECISION SUPPORT SYSTEMS

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

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

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

16. DSS ANALYSIS CAPABILITIES 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

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

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

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

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

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

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

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

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

25. CONCLUSIONS (FYI) • 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

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

27. DATA MINING

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

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

30. DATA MINING ANALYSES 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

31. EXECUTIVE INFORMATION SYSTEMS

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

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

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

35. COLLABORATIVE SYSTEMS (GDSS)

36. COLLABORATIVE SYSTEMS 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.

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

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

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

40. GROUP DECISION SUPPORT SYSTEMS

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

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

43. THE END