Enhancing Management Decision-making For The Digital Firm

1. Chapter EnhancingManagementDecision-makingFor The Digital Firm

2. Objectives • How can information systems help individual managers make better decisions when the problems are non-routine and constantly changing? • How can information systems help people working in a group make decisions more efficiently?

3. Objectives • Are there any special systems that can facilitate decision-making among senior managers? Exactly what can these systems do to help high-level management? • What benefits can systems that support management decision-making provide for the organization as a whole?

4. Management Challenges • Building information systems that can actually fulfill executive information requirements • Create meaningful reporting and management decision-making processes

5. Decision-Support Systems (DSS) • Computer system at the management level of an organization • Combines data, analytical tools, and models • Supports semi-structured and unstructured decision-making

6. Figure 13-1 Systems and Technologies for Business Intelligence

7. Decision-Making Levels: • Senior management • Middle management and project teams • Operational management and project teams • Individual employees

8. Types of Decisions • Unstructured decisions: • Novel, non-routine decisions requiring judgment and insights • Examples: Approve capital budget; decide corporate objectives

9. Structured decisions: • Routine decisions with definite procedures • Examples: Restock inventory; determine special offers to customers • Semistructured decisions: • Only part of decision has clear-cut answers provided by accepted procedures • Examples: Allocate resources to managers; develop a marketing plan

10. Figure 13-2 Information Requirements of Key Decision-Making Groups in a Firm

11. Systems for Decision Support • There are four kinds of systems that support the different levels and types of decisions: • Management Information Systems (MIS) • Decision-Support Systems (DSS) • Executive Support Systems (ESS) • Group Decision-Support Systems (GDSS)

12. Decision Making in the Real World • In the real world, investments in decision-support systems do not always work because of • Information quality: Accuracy, integrity, consistency, completeness, validity, timeliness, accessibility • Management filters: Biases and bad decisions of managers • Organizational inertia: Strong forces within organization that resist change

13. Trends in Decision Support and Business Intelligence • Detailed enterprise-wide data • Broadening decision rights and responsibilities • Intranets and portals • Personalization and customization of information • Extranets and collaborative commerce • Team support tools

14. Figure 13-3 Stages in Decision Making

15. Management Information Systems: • Primarily address structured problems • Provides typically fixed, scheduled reports based on routine flows of data and assists in the general control of the business

16. DSS • Support semistructured and unstructured problems • Greater emphasis on models, assumptions, ad-hoc queries, display graphics • Emphasizes change, flexibility, and a rapid response

17. Types of Decision-Support Systems • Model-driven DSS • Primarily stand-alone systems • Use a strong theory or model to perform “what-if” and similar analyses

18. Types of Decision-Support Systems • Data-driven DSS • Integrated with large pools of data in major enterprise systems and Web sites • Support decision making by enabling user to extract useful information • Data mining: Can obtain types of information such as associations, sequences, classifications, clusters, and forecasts

19. Figure 13-4 Overview of a Decision-Support System (DSS)

20. Components of DSS • DSS database: A collection of current or historical data from a number of applications or groups • DSS software system: Contains the software tools for data analysis, with models, data mining, and other analytical tools • DSS user interface: Graphical, flexible interaction between users of the system and the DSS software tools

21. Model: • An abstract representation that illustrates the components or relationships of a phenomenon • Statistical models • Optimization models • Forecasting models • Sensitivity analysis: Models that ask “what-if” questions repeatedly to determine the impact of changes in one or more factors on the outcomes

22. Figure 13-5 Sensitivity Analysis

23. Decision-Support Systems (DSS) • Associations: Occurrences linked to a single event • Sequences: Events linked over time

24. Decision-Support Systems (DSS) • Classification: Recognizing patterns that describe the group to which an item belongs • Clustering: Similar to classification when no groups have yet been defined. Discovers different groupings within data

25. Business Value of DSS • Providing fine-grained information for decisions that enable the firm to coordinate both internal and external business processes much more precisely • Helping with decisions in • Supply chain management • Customer relationship management • Pricing Decisions • Asset Utilization • Data Visualization: Presentation of data in graphical forms, to help users see patterns and relationships • Geographic Information Systems (GIS): Special category of DSS that display geographically referenced data in digitized maps

26. Decision-Support Systems (DSS) Cargo revenue optimization of Continental Airlines

27. DSS for Pricing Decisions • By analyzing several years of sales data for similar items, the software estimates a “seasonal demand curve” for each item and predicts how many units would sell each week at various prices. • The software uses sales history to predict how sensitive customer demand will be to price changes

28. DSS for Supply Chain Management • Can help firms model inventory stocking levels, production schedules, or transportation plans • Can provide firms with information on key performance indicators such as lead time, cycle time, inventory turns, or total supply chain costs

29. DSS for Customer Relationship Management • Uses data mining to guide decisions • Consolidates customer information into massive data warehouses • Uses various analytical tools to slice information into small segments

30. Figure 13-6 DSS for Customer Analysis and Segmentation

31. Predictive Analysis • Use of datamining techniques, historical data, and assumptions about future conditions to predict outcomes of events

32. Web-Based Customer Decision-Support Systems • DSS based on the Web and the Internet can support decision making by providing online access to various databases and information pools along with software for data analysis • Some of these DSS are targeted toward management, but many have been developed to attract customers. • Customer decision making has become increasingly information intensive, with Internet search engines, intelligent agents, online catalogs, Web directories, e-mail, and other tools used to help make purchasing decisions. • Customer decision-support systems (CDSS) support the decision-making process of an existing or potential customer.

33. Group Decision-Support System (GDSS): • An interactive computer-based system used to facilitate the solution of unstructured problems by a set of decision makers working together as a group.

34. Components of GDSS • Hardware • conference facility, • audiovisual equipment, etc. • Software tools • Electronic questionnaires, • brainstorming tools, • voting tools, etc. • People • Participants, • trained facilitator, • support staff

35. Overview of a GDSS Meeting • In a GDSS electronic meeting, each attendee has a workstation. • The workstations are networked and are connected to • the facilitator’s console, which serves as the facilitator’s workstation and • control panel, and • to the meeting’s file server. • All data that the attendees forward from their workstations to the group are collected and saved on the file server.

36. The facilitator is able to project computer images onto the projection screen at the front of the room. • Many electronic meeting rooms have seating arrangements in semicircles and are tiered in legislative style to accommodate a large number of attendees. • The facilitator controls the use of tools during the meeting.

37. Figure 13-7 Group System Tools Group Interaction

38. How GDSS can Enhance Group Decision-Making • Traditional decision-making meetings support an optimal size of three to five attendees. GDSS allows a greater number of attendees. • Enable collaborative atmosphere by guaranteeing contributor’s anonymity. • Enable nonattendees to locate organized information after the meeting.

39. How GDSS Can Enhance Group Decision Making • Can increase the number of ideas generated and the quality of decisions while producing the desired results in fewer meetings • Can lead to more participative and democratic decision making

40. Organizational Memory • Store learning from an organization’s history that can be used for decision making and other purposes

41. Executive Support Systems (ESS): • ESS can bring together data from all parts of the firm and enable managers to select, access, and tailor them as needed. • It tries to avoid the problem of data overload so common in paper reports. • The ability to drill down is useful not only to senior executives but also to employees at lower levels of the firm who need to analyze data. • Can integrate comprehensive firmwide information and external data in timely manner • Inclusion of modeling and analysis tools usable with a minimum of training

42. Executive Support Systems (ESS): • Monitor organizational performance • Track activities of competitors • Spot problems • Identify opportunities • Forecast trends

43. The Role of Executive Support Systems in the Organization • Brings together data from the entire organization • Allows managers to select, access, and tailor data • Enables executive and any subordinates to look at the same data in the same way

44. Drill Down • The ability to move from summary data to lower and lower levels of detail

45. Developing ESS: • Ease of use • Facility for environmental scanning • External and internal sources of information to be used for environmental scanning

46. Benefits of Executive Support Systems • Analyzes, compares, and highlights trends • Provides greater clarity and insight into data • Speeds up decision-making

47. Benefits of Executive Support Systems • Improves management performance • Increases management’s span of control • Better monitoring of activities

48. ESS for Competitive Intelligence • Identify changing market conditions • Formulate responses • Track implementation efforts • Learn from feedback

49. Balanced Scorecard • Model for analyzing firm performance that supplements traditional financial measures with measurements from additional business perspectives, such as customers, internal business processes, and learning and growth

50. Strategic performance management tools for enterprise systems • SAP: Web-enabled mySAP.com™, Management Cockpit • PeopleSoft: Web-enabled Enterprise Performance Management (EPM)