1. Enhancing Management Decision-making For The Digital Firm
Chapter
Enhancing Management Decision-making For 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. Systems and Technologies for Business Intelligence
Figure 13-1

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

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. Stages in Decision Making
Figure 13-3

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

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. Sensitivity Analysis
Figure 13-5

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. DSS for Customer Analysis and Segmentation
Figure 13-6

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, , 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 Software tools People 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. Group System Tools
Group Interaction
Figure 13-7

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)

51. Activity-Based Costing
Model for identifying all the company activities that cause costs to occur while producing a specific product or service so that managers can see which products or services are profitable or losing money and make changes to maximize firm profitability

52. Management Challenges:
Building systems that can actually fulfill Executive Information Requirements
Changing management thinking to make better use of systems for decision support
Organizational resistance

53. Management Opportunities:
Decision-support systems provide opportunities for increasing precision, accuracy, and rapidity of decisions and thereby contributing directly to profitability

54. Solution Guidelines: Flexible Design and Development:
Users must work with IS specialists to identify a problem and a specific set of capabilities that will help them arrive at decisions about the problem.
The system must be flexible, easy to use, and capable of supporting alternative decision options.
Training and Management Support:
User training, involvement, and experience; top management support; and length of use are the most important factors in the success of management support systems.