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Book Contents

Ch. 4
Designing and Developing Decision Support Systems

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Decision-Oriented Diagnosis

Increasing decision-making effectiveness through changes in how decisions are made should be the major objective for any DSS project (cf., Stabell in Bennett 1983, p. 225). Stabell proposes a decision-oriented design approach for DSS. He argues the pre-design description and diagnosis of decision-making is the key to securing a decision-oriented approach to DSS development.

The diagnosis of current decision-making and the specification of changes in decision processes are the activities that provide the key input to the design of the DSS. Diagnosis is the identification of problems or opportunities for improvement in current decision behavior. Diagnosis involves determining how decisions are currently made, specifying how decisions should be made, and understanding why decisions are not made as they should be. A specification of changes in decision processes involves choosing what specific improvements in decision behavior are to be achieved. These statements of improvements provide the objectives for the DSS development.

Diagnosis of a decision process involves completing the following three activities:

  1. Collecting data on current decision-making using techniques such as interviews, observations, questionnaires and historical records;
  2. Establishing a coherent description of the current decision process;
  3. Specifying a norm for how decisions should be made.

These activities are interdependent and provide feedback for the analyst. In many DSS development projects it is not feasible to perform a full-scale diagnosis of decision-making. A shortened study is often necessary due to cost considerations, limited access to managers, or other organizational constraints. As a consequence, DSS analysts should develop the ability to produce diagnosis after only limited exposure to a decision situation.

DSS Audit Plan

Step 1.

Define the decisions, decision processes and related business processes that will be audited. Define the authority of the auditor, purpose of the audit, scope of the audit, timing of the audit, and resources required to perform the audit. Identify a primary contact.

Step 2.

Examine the formal design of the process. Diagram the process and specify criteria, etc. Is the design effective and efficient?

Step 3.

Examine the actual use of the decision process. Observe the process. Interview decision makers and collect data. Is the process implemented and used as intended?

Step 4.

Assess performance of the actual decision process. What works? Can cycle time be reduced? Are decisions appropriate? Timely? Cost effective? Is the process producing value in meeting business objectives? If not, why?

Step 5.

Reporting and recommendations. Summarize steps 1-4 in a written report. Discuss what is working well and what needs to be improved. Develop recommendations for improving the process. Hold an exit meeting with decision makers.

Table 4.1. A DSS Audit Plan.

A related diagnostic activity is conducting a DSS Audit. In general, it can be very useful to audit operational and managerial decision processes. An audit can be a first step in identifying opportunities to redesign business processes and include new Decision Aids and Decision Support Systems in business processes. In some situations, an audit can suggest changes in decision technologies that can improve performance and reduce costs. When an audit is complete the central questions should be how can we do better and what changes should have the highest priority. Table 4.1 identifies the 5 steps in a DSS Audit.

Diagnosis for some projects focuses on identifying what is assumed by decision-makers in the decision situation and on what is defined by decision-makers as the range of available remedial actions. Focusing on assumptions and actions is appropriate if building a Model-Driven DSS is a possibility, but not when the focus is on a Data-Driven DSS.

Rockart (1979) identified an approach for defining decision-making data needs that is appropriate for Data-Driven DSS and especially Executive Information Systems. Rockartís Critical Success Factors (CSF) Design Method focuses on individual managers and on each manager's current hard and soft information needs. A CSF analysis can be beneficial in identifying "the limited number of areas in which results, if they are satisfactory, will insure successful competitive performance for the organization". If organizational goals were to be attained, then these key areas of activity - usually three to six factors - would need careful and consistent attention from management.

Good diagnosis is difficult, but DSS diagnosis involves skills that can be developed and sharpened. Both managers and MIS staff need to work on completing the diagnosis task. Does diagnosis always provide sufficient information for specifying a DSS? In most cases the diagnosis does provide sufficient information for specifying several alternative designs. DSS design usually involves a number of difficult tradeoffs. The first tradeoff is whether the DSS should support both the existing process and a prescribed new process. There is also a trade-off in the extent of the capabilities of the DSS and the scope of the process the DSS is designed to support. In most cases the initial version of a DSS focuses on either extensive capabilities for a narrow scope process or few capabilities for a broad scope process.

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