Chapter 10 Decision Support Systems

James A. O'Brien, and George Marakas. Management Information Systems with MISource 2007, 8 th ed. Boston, MA: McGraw-Hill, Inc., 2007. ISBN: 13 9780073323091

Decision Support in Business

  Companies are investing in data-driven decision support application frameworks to help them respond to  Changing market conditions  Customer needs This is accomplished by several types of  Management information  Decision support  Other information systems Chapter 10 Decision Support Systems 2

Levels of Managerial Decision Making

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Information Quality

  Information products made more valuable by their attributes, characteristics, or qualities  Information that is outdated, inaccurate, or hard to understand has much less value Information has three dimensions  Time  Content  Form Chapter 10 Decision Support Systems 4

Attributes of Information Quality

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Decision Structure

   Structured (operational)  The procedures to follow when decision is needed can be specified in advance Unstructured (strategic)  It is not possible to specify in advance most of the decision procedures to follow Semi-structured (tactical)  Decision procedures can be pre-specified, but not enough to lead to the correct decision Chapter 10 Decision Support Systems 6

Decision Support Systems

Management Information Systems Decision support provided Information form and frequency

Provide information about the performance of the organization Periodic, exception, demand, and push reports and responses

Information format

Prespecified, fixed format

Information processing methodology

Information produced by extraction and manipulation of business data

Decision Support Systems

Provide information and techniques to analyze specific problems Interactive inquiries and responses Ad hoc, flexible, and adaptable format Information produced by analytical modeling of business data Chapter 10 Decision Support Systems 7

Decision Support Trends

 The emerging class of applications focuses on  Personalized decision support  Modeling  Information retrieval  Data warehousing  What-if scenarios  Reporting Chapter 10 Decision Support Systems 8

Business Intelligence Applications

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Decision Support Systems

  Decision support systems use the following to support the making of semi-structured business decisions  Analytical models   Specialized databases A decision maker’s own insights and judgments  An interactive, computer-based modeling process DSS systems are designed to be ad hoc, quick-response systems that are initiated and controlled by decision makers Chapter 10 Decision Support Systems 10

DSS Components

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DSS Model Base

  Model Base  A software component that consists of models used in computational and analytical routines that mathematically express relations among variables Spreadsheet Examples  Linear programming  Multiple regression forecasting  Capital budgeting present value Chapter 10 Decision Support Systems 12

Applications of Statistics and Modeling



Supply Chain

: simulate and optimize supply chain flows, reduce inventory, reduce stock outs 

Pricing

: identify the price that maximizes yield or profit 

Product and Service Quality

: detect quality problems early in order to minimize them 

Research and Development

: improve quality, efficacy, and safety of products and services Chapter 10 Decision Support Systems 13

Management Information Systems

 The original type of information system that supported managerial decision making  Produces information products that support many day-to-day decision-making needs  Produces reports, display, and responses  Satisfies needs of operational and tactical decision makers who face structured decisions Chapter 10 Decision Support Systems 14

Management Reporting Alternatives

    Periodic Scheduled Reports  Prespecified format on a regular basis Exception Reports  Reports about exceptional conditions  May be produced regularly or when an exception occurs Demand Reports and Responses  Information is available on demand Push Reporting  Information is pushed to a networked computer Chapter 10 Decision Support Systems 15

Online Analytical Processing

 OLAP  Enables managers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives  Done interactively, in real time, with rapid response to queries Chapter 10 Decision Support Systems 16

Online Analytical Operations

   Consolidation  Aggregation of data  Example: data about sales offices rolled up to the district level Drill-Down  Display underlying detail data  Example: sales figures by individual product Slicing and Dicing  Viewing database from different viewpoints  Often performed along a time axis Chapter 10 Decision Support Systems 17

Geographic Information Systems

   DSS uses geographic databases to construct and display maps and other graphic displays Supports decisions affecting the geographic distribution of people and other resources Often used with Global Positioning Systems (GPS) devices Chapter 10 Decision Support Systems 18

Data Visualization Systems

  Represents complex data using interactive, three-dimensional graphical forms (charts, graphs, maps) Helps users interactively sort, subdivide, combine, and organize data while it is in its graphical form Chapter 10 Decision Support Systems 19

    

Using Decision Support Systems

Using a decision support system involves an interactive analytical modeling process   Decision makers are not demanding pre-specified information They are exploring possible alternatives What-If Analysis  Observing how changes to selected variables affect other variables Sensitivity Analysis  Observing how repeated changes to a single variable affect other variables Goal-seeking Analysis  Making repeated changes to selected variables until a chosen variable reaches a target value Optimization Analysis  Finding an optimum value for selected variables, given certain constraints Chapter 10 Decision Support Systems 20

Data Mining

  Provides decision support through knowledge discovery  Analyzes vast stores of historical business data  Looks for patterns, trends, and correlations  Goal is to improve business performance Types of analysis  Regression  Decision tree  Neural network  Cluster detection  Market basket analysis Chapter 10 Decision Support Systems 21

Analysis of Customer Demographics

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Market Basket Analysis

  One of the most common uses for data mining  Determines what products customers purchase together with other products Results affect how companies  Market products  Place merchandise in the store  Lay out catalogs and order forms  Determine what new products to offer  Customize solicitation phone calls Chapter 10 Decision Support Systems 23

Executive Information Systems

    Combines many features of MIS and DSS Provide top executives with immediate and easy access to information Identify factors that are critical to accomplishing strategic objectives (critical success factors) So popular that it has been expanded to managers, analysis, and other knowledge workers Chapter 10 Decision Support Systems 24

Features of an EIS

 Information presented in forms tailored to the preferences of the executives using the system  Customizable graphical user interfaces  Exception reports  Trend analysis  Drill down capability Chapter 10 Decision Support Systems 25

Enterprise Information Portals

 An EIP is a Web-based interface and integration of MIS, DSS, EIS, and other technologies  Available to all intranet users and select extranet users  Provides access to a variety of internal and external business applications and services  Typically tailored or personalized to the user or groups of users  Often has a digital dashboard  Also called

enterprise knowledge portals

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Dashboard Example

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Enterprise Information Portal Components

Chapter 10 Decision Support Systems 28

Enterprise Knowledge Portal

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Case 2 Automated Decision Making

  Automated decision making has been slow to materialize  Early applications were just solutions looking for problems, contributing little to improved organizational performance A new generation of AI applications  Easier to create and manage  Decision making triggered without human intervention  Can translate decisions into action quickly, accurately, and efficiently Chapter 10 Decision Support Systems 30

Case 2 Automated Decision Making

  AI is best suited for  Decisions that must be made quickly and frequently, using electronic data  Highly structured decision criteria  High-quality data Common users of AI  Transportation industry  Hotels  Investment firms and lenders Chapter 10 Decision Support Systems 31

Case Study Questions

   Why did some previous attempts to use artificial intelligence technologies fail?

 What key differences of the new AI-based applications versus the old cause the authors to declare that automated decision making is coming of age?

What types of decisions are best suited for automated decision making?

What role do humans plan in automated decision-making applications?

 What are some of the challenges faced by managers where automated decision-making systems are being used?

 What solutions are needed to meet such challenges?

Chapter 10 Decision Support Systems 32

Artificial Intelligence (AI)

  AI is a field of science and technology based on  Computer science  Biology  Psychology  Linguistics  Mathematics  Engineering The goal is to develop computers than can simulate the ability to think  And see, hear, walk, talk, and feel as well Chapter 10 Decision Support Systems 33

Attributes of Intelligent Behavior

 Some of the attributes of intelligent behavior  Think and reason  Use reason to solve problems  Learn or understand from experience  Acquire and apply knowledge  Exhibit creativity and imagination  Deal with complex or perplexing situations  Respond quickly and successfully to new situations  Recognize the relative importance of elements in a situation  Handle ambiguous, incomplete, or erroneous information Chapter 10 Decision Support Systems 34

Domains of Artificial Intelligence

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Cognitive Science

  Applications in the cognitive science of AI  Expert systems  Knowledge-based systems  Adaptive learning systems  Fuzzy logic systems  Neural networks  Genetic algorithm software  Intelligent agents Focuses on how the human brain works and how humans think and learn Chapter 10 Decision Support Systems 36

Robotics

  AI, engineering, and physiology are the basic disciplines of robotics  Produces robot machines with computer intelligence and humanlike physical capabilities This area include applications designed to give robots the powers of  Sight or visual perception  Touch  Dexterity  Locomotion  Navigation Chapter 10 Decision Support Systems 37

Natural Interfaces

  Major thrusts in the area of AI and the development of natural interfaces  Natural languages  Speech recognition  Virtual reality Involves research and development in  Linguistics  Psychology  Computer science  Other disciplines Chapter 10 Decision Support Systems 38

Latest Commercial Applications of AI

  Decision Support  Helps capture the

why

as well as the

what

engineered design and decision making Information Retrieval of  Distills tidal waves of information into simple presentations  Natural language technology  Database mining Chapter 10 Decision Support Systems 39

Latest Commercial Applications

 

of AI

Virtual Reality  X-ray-like vision enabled by enhanced-reality visualization helps surgeons  Automated animation and haptic interfaces allow users to interact with virtual objects Robotics  Machine-vision inspections systems  Cutting-edge robotics systems  From micro robots and hands and legs, to cognitive and trainable modular vision systems Chapter 10 Decision Support Systems 40

Expert Systems

 An Expert System (ES)  A knowledge-based information system  Contain knowledge about a specific, complex application area  Acts as an expert consultant to end users Chapter 10 Decision Support Systems 41

Components of an Expert System

  Knowledge Base  Facts about a specific subject area  Heuristics that express the reasoning procedures of an expert (rules of thumb) Software Resources  An

inference engine

processes the knowledge and recommends a course of action 

User interface programs

communicate with the end user 

Explanation programs

process to the end user explain the reasoning Chapter 10 Decision Support Systems 42

Components of an Expert System

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Methods of Knowledge Representation

  Case-Based  Knowledge organized in the form of cases  Cases are examples of past performance, occurrences, and experiences Frame-Based  Knowledge organized in a hierarchy or network of frames  A frame is a collection of knowledge about an entity, consisting of a complex package of data values describing its attributes Chapter 10 Decision Support Systems 44

Methods of Knowledge

 

Representation

Object-Based  Knowledge represented as a network of objects  An object is a data element that includes both data and the methods or processes that act on those data Rule-Based  Knowledge represented in the form of rules and statements of fact  Rules are statements that typically take the form of a premise and a conclusion (If, Then) Chapter 10 Decision Support Systems 45

Expert System Application Categories

  Decision Management  Loan portfolio analysis  Employee performance evaluation  Insurance underwriting Diagnostic/Troubleshooting  Equipment calibration  Help desk operations  Medical diagnosis  Software debugging Chapter 10 Decision Support Systems 46

Expert System Application Categories

   Design/Configuration  Computer option installation  Manufacturability studies  Communications networks Selection/Classification  Material selection  Delinquent account identification  Information classification  Suspect identification Process Monitoring/Control Chapter 10 Decision Support Systems 47

Expert System Application Categories

 Process Monitoring/Control  Machine control (including robotics)  Inventory control  Production monitoring  Chemical testing Chapter 10 Decision Support Systems 48

Benefits of Expert Systems

 Captures the expertise of an expert or group of experts in a computer-based information system  Faster and more consistent than an expert  Can contain knowledge of multiple experts  Does not get tired or distracted  Cannot be overworked or stressed  Helps preserve and reproduce the knowledge of human experts Chapter 10 Decision Support Systems 49

Limitations of Expert Systems

 The major limitations of expert systems  Limited focus  Inability to learn  Maintenance problems  Development cost  Can only solve specific types of problems in a limited domain of knowledge Chapter 10 Decision Support Systems 50

Developing Expert Systems

 Suitability Criteria for Expert Systems 

Domain

: the domain or subject area of the problem is small and well-defined 

Expertise

: a body of knowledge, techniques, and intuition is needed that only a few people possess 

Complexity

: solving the problem is a complex task that requires logical inference processing 

Structure

: the solution process must be able to cope with ill-structured, uncertain, missing, and conflicting data and a changing problem situation 

Availability

: an expert exists who is articulate, cooperative, and supported by the management and end users involved in the development process Chapter 10 Decision Support Systems 51

Development Tool

 Expert System Shell  The easiest way to develop an expert system  A software package consisting of an expert system without its knowledge base  Has an inference engine and user interface programs Chapter 10 Decision Support Systems 52

Knowledge Engineering

 A knowledge engineer  Works with experts to capture the knowledge (facts and rules of thumb) they possess  Builds the knowledge base, and if necessary, the rest of the expert system  Performs a role similar to that of systems analysts in conventional information systems development Chapter 10 Decision Support Systems 53

Neural Networks

 Computing systems modeled after the brain’s mesh-like network of interconnected processing elements (neurons)  Interconnected processors operate in parallel and interact with each other  Allows the network to learn from the data it processes Chapter 10 Decision Support Systems 54

Fuzzy Logic

 Fuzzy logic  Resembles human reasoning  Allows for approximate values and  inferences and incomplete or ambiguous data Uses terms such as “very high” instead of precise measures  Used more often in Japan than in the U.S.

 Used in fuzzy process controllers used in subway trains, elevators, and cars Chapter 10 Decision Support Systems 55

Example of Fuzzy Logic Rules and Query

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Genetic Algorithms

 Genetic algorithm software  Uses Darwinian, randomizing, and other mathematical functions  Simulates an evolutionary process, yielding increasingly better solutions to a problem  Being uses to model a variety of scientific, technical, and business processes  Especially useful for situations in which thousands of solutions are possible Chapter 10 Decision Support Systems 57

Virtual Reality (VR)

 Virtual reality is a computer-simulated reality  Fast-growing area of artificial intelligence  Originated from efforts to build natural, realistic, multi-sensory human-computer interfaces  Relies on multi-sensory input/output devices  Creates a three-dimensional world through sight, sound, and touch  Also called

telepresence

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Typical VR Applications

 Current applications of virtual reality  Computer-aided design  Medical diagnostics and treatment  Scientific experimentation  Flight simulation  Product demonstrations  Employee training  Entertainment Chapter 10 Decision Support Systems 59

Intelligent Agents

 A software surrogate for an end user or a process that fulfills a stated need or activity  Uses built-in and learned knowledge base to make decisions and accomplish tasks in  a way that fulfills the intentions of a user Also call

software robots

or

bots

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User Interface Agents



Interface Tutors

– observe user computer operations, correct user mistakes, provide  hints/advice on efficient software use

Presentation Agent

s – show information in a  variety of forms/media based on user preferences

Network Navigation Agents

– discover paths to information, provide ways to view it based  on user preferences

Role-Playing

– play what-if games and other roles to help users understand information and make better decisions Chapter 10 Decision Support Systems 61

Information Management Agents



Search Agents

– help users find files and databases, search for information, and suggest and find new types of information products, media, resources 

Information Brokers

– provide commercial services to discover and develop information  resources that fit business or personal needs

Information Filters

– Receive, find, filter, discard, save, forward, and notify users about products received or desired, including e-mail, voice mail, and other information media Chapter 10 Decision Support Systems 62

Case 3 Centralized Business Intelligence

  A reinventing-the-wheel approach to business intelligence implementations can result in  High development costs  High support costs  Incompatible business intelligence systems A more strategic approach  Standardize on fewer business intelligence tools  Make them available throughout the organization, even before projects are planned Chapter 10 Decision Support Systems 63

Case 3 Centralized Business Intelligence

  About 10 percent of the 2,000 largest companies have a business intelligence competency center  Centralized or virtual  Part of the IT department or independent Cost reduction is often the driving force behind creating competency centers and consolidating business intelligence systems  Despite the potential savings, funding for creating and running a BI center can be an issue Chapter 10 Decision Support Systems 64

Case Study Questions

   What is business intelligence?

 Why are business intelligence systems such a popular business application of IT?

What is the business value of the various BI applications discussed in the case?

Is the business intelligence system an MIS or a DSS?

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Case 4 Robots, the Common Denominator

 In early 2004, 22 patients underwent complex laparoscopic operations  The operations included colon cancer procedures and hernia repairs  The primary surgeon was 250 miles away  A three-armed robot was used to perform the procedures  Left arm, right arm, camera arm Chapter 10 Decision Support Systems 66

Case 4 Robots, the Common Denominator

 Automakers heavily use robotics  Ford has a completely wireless assembly factory  It also have a completely automated body shop  BMW has two wireless plants in Europe and is setting one up in the U.S.

 Vehicle tracking and material replenishment are automated as well Chapter 10 Decision Support Systems 67

Case Study Questions

   What is the current and future business value of robotics?

Would you be comfortable with a robot performing surgery on you?

The robotics being used by Ford Motor Co. are contributing to a streamlining of its supply chain  What other applications of robots can you envision to improve supply chain management beyond those described in the case?

Chapter 10 Decision Support Systems 68