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  1. Decision Support Systems Concepts Week 5

  2. DSS Configurations • Many configurations exist; based on • management-decision situation • specific technologies used for support • DSS have three basic components • Data • Model • User interface • (+ optional) Knowledge

  3. DSS Configurations • Each component • has several variations; are typically deployed online • Managed by a commercial of custom software • Typical types: • Model-oriented DSS • Data-oriented DSS

  4. DSS Description • An early definition of DSS • A system intended to support managerial decision makers in semistructured and unstructured decision situations • meant to be adjuncts to decision makers (extending their capabilities but not replacing their judgment) • aimed at decisions that required judgment or at decisions that could not be completely supported by algorithms • would be computer based; operate interactively; and would have graphical output capabilities…

  5. DSS Description • A DSS is typically built to support the solution of a certain problem (or to evaluate a specific opportunity). This is a key difference between DSS and BI applications • BI systems monitor situations and identify problems and/or opportunities, using variety of analytic methods • The user generally must identify whether a particular situation warrants attention • Reporting/data warehouse plays a major role in BI • DSS often has its own database and models

  6. DSS Description • DSS is an approach (or methodology) for supporting decision making • uses an interactive, flexible, adaptable computer-based information system (CBIS) • developed (by end user) for supporting the solution to a specific nonstructured management problem • uses data, model and knowledge along with a friendly (often graphical; Web-based) user interface • incorporate the decision maker's own insights • supports all phases of decision making • can be used by a single user or by many people

  7. A Web-Based DSS Architecture

  8. DSS Characteristics and Capabilities

  9. DSS Characteristics and Capabilities • Business analytics implies the use of models and data to improve an organization's performance and/or competitive posture • Web analytics implies using business analytics on real-time Web information to assist in decision making; often related to e-Commerce • Predictive analytics describes the business analytics method of forecasting problems and opportunities rather than simply reporting them as they occur

  10. DSS Classifications • AIS SIGDSS Classification for DSS • Communications-driven and group DSS • Data-driven DSS • Document-driven DSS • Knowledge-driven DSS • Model-driven DSS

  11. Communications-Driven and Group DSS • Use computer, collaboration, and communication technologies to support groups in tasks that may or may not involve decision making • Examples: • Support meetings • KMS developed around communities of practice

  12. Data-Driven DSS • Primarily involved with data and processing • DB organization plays a major role in structure • Features strong report generation and query capabilities

  13. Document-Driven DSS • Rely on knowledge coding, analysis, search and retrieval for decision support • KMS

  14. Knowledge-Driven DSS and ES • Involve the application of knowledge technologies to address specific decision support needs • Example: AI-based DSS and ES

  15. Model-Driven DSS • Developed around one or more optimization or simulation models • Most common end-user tool Excel

  16. Compound (or Hybrid) DSS • Include 2 or more of the major categories • Data-driven can feed a model-driven DSS

  17. DSS Classifications • Holsapple and Whinston's Classification • The text-oriented DSS • The database-oriented DSS. • The spreadsheet-oriented DSS • The solver-oriented DSS • The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications) • The compound DSS

  18. Brief Example: Advanced Scout • Allows NBA coaches and league officials organize and interpret the data collected at every game • Can review countless stats: shots attempted, shots blocked, assists made, personal fouls, etc. • Can detect patterns; patterns found are linked to video of the game • Helps coaches mine through and analyze a lot of data

  19. Components of DSS • Data Management Subsystem • Includes the database that contains the data • Database management system (DBMS) • Can be connected to a data warehouse • Model Management Subsystem • Model base management system (MBMS) • User Interface Subsystem • Knowledgebase Management Subsystem • Organizational knowledge base

  20. Design and Development of DSS Focus on the decision, then build or buy?

  21. Overview of Design and Development Approaches Traditional system analysis and design, SDLC An iterative, rapid prototyping, or “quick-hit” approach Managers develop their own personal DSS, End-User DSS Development Design and Development of DSS, D. J. Power 21

  22. Investigate Alternative Design and Development Approaches Building effective DSS is important and expensive Choose an approach that increases the chances the DSS will be used Building a DSS is a difficult task; people vary so much in terms of their personalities, knowledge and ability, the jobs they hold, and the decision they make Design and Development of DSS, D. J. Power 22

  23. Methodology • SDLC the standard • Alternatives • Prototyping • End-user development • Involve quickly constructing a portion of the DSS then testing, improving, and expanding

  24. A Decision-Oriented Design Approach Pre-design description and diagnosis of decision making Diagnosis of current decision – making  Identification of problems or opportunities for improvement in current decision behavior  Determine how decisions are currently made Design and Development of DSS, D. J. Power

  25. Decision – orientation is the key Specify changes in decision processes  Determine what specific improvements in decision behavior are to be achieved  Flowchart the process Design and Development of DSS, D. J. Power

  26. 3 Diagnostic Steps Collect data on current decision-making  Use interviews, observations, and historical records Establish a coherent description of the current decision process Specify a norm for how decisions should be made Design and Development of DSS, D. J. Power

  27. Decision Process Audit Plan Step 1: What will be audited and by whom Step 2: Examine and diagram process Step 3: Observe and collect data Step 4: Assess performance Step 5: Reporting and recommendations Design and Development of DSS, D. J. Power

  28. 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.

  29. DSS Audit Plan Step 2 • Examine the formal design of the process. Diagram the process and specify criteria, etc. Is the design effective and efficient?

  30. DSS Audit Plan 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?

  31. DSS Audit Plan 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?

  32. DSS Audit Plan 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.

  33. Reaching a Diagnosis Focus on identifying what is assumed by decision-makers in the decision situation Focus on what is defined by decision-makers as the range of available remedial actions How can decision-making be improved? Design and Development of DSS, D. J. Power

  34. Critical Success Factors Design Method for a Data-Driven DSS • Focus on individual managers and on their current hard and soft information needs • It identifies "the limited number of areas in which results, if they are satisfactory, will insure successful competitive performance for the organization" (Rockart, 1979) • 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.

  35. Conduct a feasibility study Issues  Objectives  DSS Scope and Target Users  Anticipated DSS Impacts  Major Alternatives Conclusions Build versus Buy Design and Development of DSS, D. J. Power

  36. If build, then choose a DSS Development Approach SDLC A rapid prototyping approach End-user DSS development Design and Development of DSS, D. J. Power

  37. 7 Step SDLC Approach Confirm user requirements Systems analysis System design Programming Testing Implementation Use and Evaluation Design and Development of DSS, D. J. Power

  38. SDLC Project plans must be carefully prepared Determine the needs of potential users Identify the outputs that fulfill those needs Technical requirements should follow logical requirements and design steps If in-house development is not chosen, a request – for – proposal [RFP] may be required Design and Development of DSS, D. J. Power

  39. SDLC In many situations a full-scale SDLC is too rigid for DSS, especially a DSS where requirements are changing rapidly User requirements agreed upon at the first stage of the process are hard to change Design and Development of DSS, D. J. Power

  40. 5 Step Rapid Prototyping Process 1. Identify user requirement 2. Develop a first iteration DSS prototype 3. Evolve and modify the next DSS prototype 4. Test and return to step 3 if needed 5. Full-scale implementation Design and Development of DSS, D. J. Power

  41. How is a prototype developed? DSS analyst sits down with potential users and develops requirements Analyst develops a prototype User use the prototype, react to, comment on, and eventually approve Missing features are added later Design and Development of DSS, D. J. Power

  42. More on Prototyping Once approved, the prototype can be expanded in the development environment or used as a specification for a DSS developed in a language like Java, C, or C++ Compared with the SDLC approach, prototyping seems to improve user-developer communication Design and Development of DSS, D. J. Power

  43. End-User DSS Development Puts the responsibility for building and maintaining a DSS on the manager who builds it Major advantages 1) person who wants computer support will be involved in creating it 2) fast 3) lower cost Design and Development of DSS, D. J. Power

  44. End-User Development Concerns End-users may select an inappropriate software development product End-user may have limited expertise in the use of the product and the IT group may have limited ability to support End-user development Errors during End-user DSS development are common Design and Development of DSS, D. J. Power

  45. End-User Development Concerns Unnecessary databases are sometimes developed by the end-users for their DSS DSS may have limited testing and limited documentation End-user databases may be poorly constructed and difficult to maintain End-users rarely follow a systematic development process Design and Development of DSS, D. J. Power

  46. DSS project Management Assign DSS project manager Tasks include diagnosis, a feasibility study, and a definition of the objectives and scope of the proposed project The larger the scope of the project the more important it is to receive widespread agreement and sponsorship of the project Design and Development of DSS, D. J. Power

  47. DSS Project Management Once the project is approved then a methodology and project plan needs to be developed Outsourced – process needs to be developed for creating RFP’s and then evaluating proposals In-house – development and technical tools need to be resolved Design and Development of DSS, D. J. Power

  48. DSS Project Management DSS project manager should identify tasks that need to be completed, resources that are needed and project deliverables Deliverables are especially important for monitoring the progress of the project Design and Development of DSS, D. J. Power

  49. DSS Project Participants DSS Project Manager or DSS analyst Expert who makes the technical decisions about the software and hardware to use Executive Sponsor Senior manager who has the influence to help resolve major resource issues and potential problems Potential DSS users Users are often non-technical people in functional areas of a business like marketing and finance Design and Development of DSS, D. J. Power

  50. DSS Project Participants DSS Builder or analyst Technical Support Staff DW Architect, Data Quality Analyst Toolsmith/Specialist Focus on the tools and technologies that will be used in the construction of the DSS Network Specialists, Database Administrator Design and Development of DSS, D. J. Power