1. Implementing and Integrating Management Support Systems

2. Implementing and Integrating MSS
Introducing MSS into organizations and use them for their intended purpose
The issue of implementation
Integration of MSS Technologies

3. 1. Opening Vignette: INCA Expert Systems for the SWIFT Network
The Society for Worldwide Interbank Financial telecommunication (SWIFT) network
Automated international message-processing and message transmission services between financial institutions on all continents
A real-time decision–making system—INCA (Intelligent Network Controller Assistant)
Network control
Dealing with events (node failures, open communication links, etc.)

4. Opening Vignette (cont.)
INCA
could not degrade or fail
could be introduced online only once
Not possible
Rapid prototyping refinements
Incremental extraction of knowledge from the experts
Solution
Quick, on-schedule development with tight quality control
A modular prototyping approach
User and expert involvement
User training plans
An object-oriented paradigm for automated event handling
Introduced in modular phases to minimize risks
Maintained by the internal system support group

5. 2.1 Implementation: An Overview
Opening vignette: INCA - major points
about systems implementation
Standard methods would not work
Custom implementation methods to be designed, tested, and implemented
Users must be involved in every phase of the development
Management support is crucial (not mentioned)
Experts must be cooperative
Criteria for success were clearly defined
Large-scale, real-time ES can be developed on schedule and be very reliable

6. 2.2 Introduction MSS systems implementation is not always successful
Implementation is an ongoing process
preparing an organization for the new system
Introducing the system to assure success
MSS implementation is complex
MSS are linked to tasks that may significantly change the manner in which organizations operate

7. 2.3 What Is Implementation?
Simplistically define
Getting a newly developed or significantly changed, system to be used by those for whom it was intended
MSS implementation
It is an ongoing process during the entire development of the system.
It can have partial implementation.
一開始
The definition of implementation is complicated because implementation is long, involved process with vague boundaries.
Partial implementation:
At one place in the system may precipitate compensatory and negative impacts.
Others reasons are budget reduction or cost overruns.

8. 2.4 Measuring Implementation Success
Five independent criteria for success.
Others measures for judging the success of MSS include the following.
Additional measures of success in evaluating DSS.

9. 2.5 MSS Implementation Failures
Usually a closely held secret in many organizations
Expected synergy of human and machine not developed
1. Especially when millions of dollars have been spend on uncompleted or incorrect systems.

10. 3.1 Major Issues of Implementation
Models of Implementation
Success factors of implementation (figure 18.1)
implementation
Organizational
factors
External
environment
Values and
ethics
User
involvement
Process
Behavioral
Technical
Project-
related

11. 3.2 Technical Factors
Relate to the mechanics of the implementation procedure (Table 18.1)
Level of complexity
System response time and reliability
Inadequate functionality
Lack of equipment
Lack of standardization
Network problems
Mismatch of hardware and/or software
Low level of technical capacity of the project team
Two categories
Technical constraints
Technical problems
Several of major importance are listed in table18.1
Can be classified in two categories
Network speed is a technical constraints , and budget is a technical problem.

12. 3.3 Behavioral Factors
The way people perceive systems and how people behave in accepting systems
Table 18.2
Decision styles
Need for explanation
Organizational climate
Organizational expectations
Resistance to change (user resistance)
Reasons (Ex.change indecision-making approach)
Strategies for dealing (Ex.participative strategies)
The resisters (Focus 18.3)

13. 3.4 Process Factors The process of developing and implementing MSS
Top management support (one of the most important)
Management and user commitment
Institutionalization
Length of time users have been using computers and MSS
User Involvement

14. 3.5 Organizational Factors
Competence (skills) and organization of the MSS team
Adequacy of Resources
Relationship with the information systems department
Organizational politics

15. 3.6 Other organizational factors
Values and ethics
Project goals
Implementation process
Possible Impact on other systems
External environment
Legal factors
Social factors
Economic factors
Political factors (e.g., government regulations)
Other factors
Positive or negative

16. 4.1 Implementation Strategies
Implementation Strategies for DSS
Major Categories
Divide the project into manageable pieces
Keep the solution simple
Develop a satisfactory support base
Meet user needs and institutionalize the system

17. 4.2 Expert System Implementation
Quality of the system
Cooperation of the expert(s)
Conditions justifying the need for a particular ES
Other factors
Commitment on the part of management
User involvement
The characteristics of the knowledge engineer

18. 5.1 What Is Systems Integration and Why Integrate?
Two General Types of Integration
Functional Integration
(Our primary focus)
Different support functions are provided as a single system
A single, consistent interface and can switch from one task to another and back again
Physical Integration
Packaging hardware, software, and communication features required together for functional integration

19. 5.2 Why Integrate? Two Major Objectives for MSS Software Integration
Enhancements of basic tools
Increasing the applications’ capabilities
Integrating DSS and ES provides mutual benefits (Table 18.5)
Database and database management system
Models and model base management system
Interface
System capabilities (synergy)
Two General Types of Integration
Different systems (e.g., ES and DSS)
Same type systems (e.g., multiple ES)

20. 6.1 Generic Models for MSS Integration
Two different levels (figure 18.2)
Across different MSS
Within MSS
Hybrids of different technologies
Supporting different phases or activities in decision making
Solve repetitive and/or dependent decision problems
Facilitate integration by assisting in the transformation of the outputs for one system to the inputs to another system

21. 7. Models of ES and DSS Integration
Names ranging from expert support systems to intelligent DSS
Different Models：
Expert Systems attached to DSS components
ES as a separate DSS component
ES output as input to a DSS
DSS output as input to ES
Feedback

22. 7. Models of ES and DSS Integration
Sharing in the decision-making process
Specification of objectives, parameters, probabilities.
Retrieval and management of data.
Generation of decision alternatives.
Inference of consequences of decision alternatives.
Assimilation of verbal, numeric, and graphical information.
Evaluation of sets of consequences
Explanation and implementation of decisions.
Strategy formulation.

23. 8. Integrating EIS, DSS, and ES, and Global Integration
Information generated by EIS is used as an input to DSS.
DSS feeds back to EIS.
A possible interpretation and explanation capability performed by ES.
See Fig 18.6 (P.753)

24. 9. Intelligent DSS Active (Symbiotic) DSS Understanding the domain.
Formulating problems.
Relating a problem to a solver.
Interpreting results
Explaining results and decisions

25. 9. Intelligent DSS Self-evolving DSS
Automatically adapt to the evolution of its users.
Dynamic menu
Dynamic user interface
Intelligent model base management system

26. 9. Intelligent DSS Problem Management (table 18.6) Problem finding.
Problem representation.
Information surveillance.
Solution generation
Solution evaluation

27. 10. Intelligent modeling and model management
Tasks require considerable expertise
Potential benefits could be substantial
Integration implementation is difficult and slow

28. 10. Intelligent modeling and model management
Issue in model management
Problem diagnosis and selection of models.
Construction of models.
Use of models.
Interpretation of results.

29. 10. Intelligent modeling and model management
Quantitative models
Human experts often use quantitative models to support their experience and expertise.
ES contributions can be demonstrated by examining the work of a consultant. (7 steps ,P.758)
ES can be used as an intelligent interface between user and quantitative models.
There are several commercial systems assist with statistical analysis.

30. 11. Examples of integrated systems
Manufacturing
Marketing
Engineering
Software Engineering
Financial services
Retailing
Commodities trading
Property casualty insurance industry decision making

31. 12.Problems and issues in integration
Need for integration
Justification and cost-benefit analysis
Architecture of integration
People problems
Finding appropriate builders
Attitudes of employees in the IS department

32. 12.Problems and issues in integration
Development process
Organizational impacts
Data structure issues
Data issues
Connectivity