1. 1 MANAGEMENT SUPPORT SYSTEMS PART I IS524 BY CHANDRA S. AMARAVADI

2. 2 Introduction to MSS Decisions & types of decisions DSS BI/EIS Collaborative systems Social networking IN THIS PRESENTATION..

3. 3 INTRO TO MSS

4. 4 INTRODUCTION More competition More products/services More promotions/payment options Globalization More decision making (D.M) The complexity of business is increasing by the day:

5. 5 MSS Evolution TPReportingDSS GIS GDSS BI/EIS DW MSS DSS – Decision Support Systems, BI – Business Intelligence, DW – Data Warehouse, EIS – Executive Information Systems, GIS – Geographical Information systems, GDSS – Group Decision Support Systems, MSS – Management Support Systems. Note: Collab- orative Dbms are back ends for problems led to EVOLUTION OF MSS

6. 6 MSS: collection of tools/systems to support managerial activity. Characteristics: intended for all levels of decisions interactive customizable some MSS are model based What does managerial activity mean? MANAGEMENT SUPPORT SYSTEM

7. 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 Design a training program for employees. n What products to produce? What markets? n What production techniques to use? n How to deal with reducing sales? Types of Decisions structured un-structured EXAMPLES OF DECISIONS

8. 8 Structured Unstructured Analytical Intuitive focus on methods & models. focus on cues, trial & error. D.M. Styles Decision making tends to be individualistic DECISION MAKING STYLES

9. 9 Intelligence Design ChoiceDecision ! Steps in D.M. for individual/group (rational model) INDIVIDUAL MODEL OF D.M.(IDC)

10. 10 Introduced by Herbert Simon, the IDC models steps in individual (and rational) decision making and consists of stages: Intelligence -- Identification of problem and constraints Design -- Identification of alternative solutions Choice -- Choosing a solution which optimizes D.M. criteria THE IDC MODEL OF DECISION MAKING

11. 11 Rational model Bureaucratic model Political model Garbage can Overall method of D.M. employed by organizations ORGANIZATIONAL MODELS OF D.M

12. 12 Rational model Decisions made for rational reasons Maximization of utility Bureaucratic model Decisions made under complex set of constraints Multiple parties involved; extended time periods Satisficing (sub-optimal solutions) Political model D.M. subject to politics Maximize utility of particular stakeholders Survival of the strongest! The “power maxim” Garbage can Problems and solutions coming together unexpectedly! ORGANIZATIONAL MODELS OF D.M

13. 13 Bounded rationality human cognitive limitations Anchoring and adjustment biases estimates based & adjusted on recent values Localized search/Uncertainty avoidance search for solutions in known vicinities. prefer alternative which is more certain Heuristics substitute to the d.m. process Constraints if alternatives are large and time constraints NO IDEAL DECISIONS BECAUSE

14. 14 DECISION SUPPORT SYSTEMS

15. 15 A system that supports structured and semi-structured decision making by managers in their own personalized way e.g. to decide projects, investments etc. DECISION SUPPORT SYSTEMS

16. 16 Decisions Int rate = 5% Growth = 10% IRR = 15% Decision Parameters Decision Support System Decision Utility Dialogue Management Model Management Data Management Decision Model Decision Data OVERVIEW OF DSS

17. 17 Depicts architecture or components of a DSS. Dialog Management Model Management Data Management CLASSICAL DSS ARCHITECTURE

18. 18 Basic conceptsDescription Decision Choice made in a business situation. Decision parameter Variable in a decision such as revenue growth. Decision model Abstract representation of a decision e.g. NPV or LP model. Decision data Data used in the decision, example data on inventory levels. Decision utility What outcomes are valued by D.M. e.g. reduce costs, increase sales etc. Dialog management The user interface through which the problem is entered. Model management The interface that supports creation and linking of models to decision parameters. Data management Facilities to manage decision data e.g. sort/filter data. DSS BASIC CONCEPTS

19. 19 “What - if “ Sensitivity Goal-seeking Optimization These can be thought of as problem solving modes in a DSS or what a DSS can do. DSS ANALYSIS CAPABILITIES

20. 20 What if - change one or more variables. What if sales were to increase by 10% instead of 15%? Sensitivity - Change one variable such as int. rates or growth rate Goal seeking - finding a solution to satisfy constraints. What level of sales would satisfy profit target? Optimization- find best solution under a given set of constraints. What level of sales would maximize profit? DSS ANALYSIS CAPABILITIES

21. 21 DSS MODELS

22. 22 Linear programming (LP) Integer Programming Non-linear programming models Graph models (e.g. PERT) DECISION MAKING UNDER CERTAINTY DECISION MAKING UNDER RISK Decision tree Bayesian Analysis Queing, Discrete Event Simulation Markov DSS Models are classified based on level of uncertainty as follows: CLASSIFICATION OF MODELS

23. 23 Causal models Influence diagrams Strategic Assumption Surfacing & Testing (SAST) DECISION MAKING UNDER UNCERTAINTY CLASSIFICATION OF MODELS

24. 24 An aircraft manufacturing company needs to decide how many of two-engine (a2), three-engine (a3) and four-engine (a4) models to produce (monthly) in order to maximize its profits. The profit contributions and constraints are as follows: Profit contribution -- a2: $500,000 a3: 650,000 a4:900,000 a2, a3, a4 require two, three and four engines respectively maximum # of available engines are 60 per month a2, a3, a4 require 135, 175 and 245 seats respectively there is a limit of 3500 seats per month the plant capacity is limited to 30 aircraft of any type Formulate an LP model of the situation above. LINEAR PROGRAMMING MODEL

25. 25 This slide is intentionally blank.

26. 26 Ship ran aground Owners wanted to sell it Sealed bid Coast guard was the authority Scrap value ($5m) Repair cost ($15m) D.M.UNDER RISK CASE OF SS.KUNIANG

27. 27 Utility company needs coal 4m tons/year Purchased a $70m General Dynamics vessel Capacity 36,250 tons (self loading) Bid for Kuniang? How much? NEW ENGLAND ELECTRIC SYSTEM

28. 28 Type of coal: PA or Egypt? Jones Act and round trip time Exception to Jones Act Coast guard valuation Self unloader? reduces cargo capacity Buy a sister vessel? Tug barge? DECISION COMPLICATIONS

29. 29 Kuniang (w crane), Kuniang (w.out crane), General dynamics vessel, or tug barge Options are DECISION OPTIONS

30. 30 Capital cost Capacity Round trip (PA) Round trip (Egypt) Operating cost/day Fixed cost/day Revenue/trip PA 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

31. 31 Bid $7mil Win Lose Salvage=scrap Salvage=bid Sister Ship Tug/Barge Gearless Self-Unloader Total Cost 43 22 43 22 NPV -1.35 5.8 -1.35 5.8 2.1 -0.6 0.7 ? 0.5 Note: NPV calculations are based on projections from previous slide Decision Outcome DECISION TREE OF HOW MUCH TO BID

32. 32  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 Under what conditions is the decision tree approach useful? CONCLUSIONS

33. 33 stock Price earnings + costs - A model that depicts causality i.e. causes & effects ‘+’ : is proportional ‘-’ : is inversely proportional Used to depict complex decision situations. *Also known as cause maps CAUSAL MODELS*

34. 34 Depict the relationship between development of society, economy, technology and jobs using causal maps. What assumptions are relevant here? DISCUSSION

35. 35 Russian submarine named after Kursk city Oscar II class, largest nuclear attack sub sank in Barents sea in Aug 2000 naval exercise firing torpedos inferior quality torpedos defective weld explosion oxygen regeneration unit problem second explosion killed most of crew 23 sailors remaining for 15 days Norway and Britain offer help THE KURSK TRAGEDY ANALYSING A DECISION SITUATION

36. 36 DSS APPLICATIONS & TRENDS

37. 37 Cash forecasting Fire-fighting Portfolio selection Evaluate lending risk Event scheduling School location movie forecasting (movie guru) These are some examples of DSS applications: DSS APPLICATIONS

38. 38 BI systems (formerly EIS) Geographical Information Systems (GIS) Collaborative Systems (extension of GDSS) Expert Systems Data mining/warehousing The DSS concept has been extended as follows: EXTENSIONS TO DSS

39. 39 BI SYSTEMS (AKA EXECUTIVE INFORMATION SYSTEMS)

40. 40 BI System: Systems that provide information to executives on the business environment. Does more information lead to better quality decisions? Executive Dashboard: An interface that displays information needed to effectively run an enterprise. An intuitive easy-to-navigate graphical display Customizable Information from multiple sources, departments, or markets Automatic updates (if possible) BI SYSTEMS & DASHBOARDS

41. 41 BI Capabilities Ware- house OLAP Dashboard Sales data Dashboard Score cards/metrics Analysis Reports Production data Accounting data BI ARCHITECTURE

42. 42 DASHBOARD

43. 43 DASHBOARD

44. 44 SCORE CARDS/METRICS

45. 45 ANALYSIS

46. 46 REPORTS

47. 47 COLLABORATIVE SYSTEMS

48. 48 Technologies to support groups in achieving a common goal: Group decision Support Systems are a specialization of collaborative systems. strategic planning develop reports research design software development COLLABORATIVE SYSTEMS

49. 49 Decision making (GDSS) brainstorming, alternative eval., voting etc. Document sharing share documents e.g. Google docs, pbWorks, Domino Design web-based design environments shared objects Collaborative systems provide support for: COLLABORATIVE SYSTEMS

50. 50 Supports the 22 nd century organization Reduces travel Time savings Improves end product (?) ADVANTAGES OF COLLABORATIVE SYSTEMS

51. 51 Definition: Use of IT to support social interaction Example Twitter, Face book, Linked-in etc. Useful when social system and work system interlinked e.g. contacts on Linked-in, Gore-tex Can be a valuable marketing tool exploit contacts -- example of iPhone identify opinion leaders Sentiment analysis SOCIAL NETWORKING

52. 52 What type of decision making is supported by DSS? What would be examples of typical decisions supported by a DSS? What is a decision model? Can unstructured decision making be supported with models? How can an Excel spreadsheet package be viewed as a DSS? What types of D.M. styles are usually followed by executives? Would an intuitive D.M. prefer a DSS or EIS why? Why are collaborative systems increasingly being used? What sort of features are provided by collaborative systems? Does the quality of a product improve because of increased collaboration? In what way is Social Networking an extension of Collaborative Systems? DISCUSSION QUESTIONS

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