Chapter 1 Management Support Systems: An Overview

Chapter 1 Management Support Systems: An Overview
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 1 Management Support Systems: An Overview © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Understand how management uses computer technologies. Learn basic concepts of decision-making. Understands decision support systems. Recognize different types of decision support systems used in the workplace. Determine which type of decision support system is applicable in specific situations. Learn what role the Web has played in the development of these systems. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Harrah’s Makes a Great Bet Vignette
Data Warehouse Data Mining Business Intelligence Transaction Processing System Customer Relationship Management Decision Support System © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Mintzberg’s 10 Management Roles
Interpersonal Figurehead Leader Liaison Informational Monitor Disseminator Spokesperson Decisional Entrepreneur Disturbance Handler Resource Allocation Negotiator © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Productivity The ratio of outputs to inputs that measures the degree of success of an organization and its individual parts © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Factors Affecting Decision-Making
New technologies and better information distribution have resulted in more alternatives for management. Complex operations have increased the costs of errors, causing a chain reaction throughout the organization. Rapidly changing global economies and markets are producing greater uncertainty and requiring faster response in order to maintain competitive advantages. Increasing governmental regulation coupled with political destabilization have caused great uncertainty. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

What do Decision Support Systems Offer?
Quick computations at a lower cost Group collaboration and communication Increased productivity Ready access to information stored in multiple databases and data warehouse Ability to analyze multiple alternatives and apply risk management Enterprise resource management Tools to obtain and maintain competitive advantage © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Cognitive Limits The human mind has limited processing and storage capabilities. Any single person is therefore limited in their decision making abilities. Collaboration with others allows for a wider range of possible answers, but will often be faced with communications problems. Computers improve the coordination of these activities. This knowledge sharing is enhanced through the use of GSS, KMS, and EIS. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Management Support Systems
The support of management tasks by the application of technologies Sometimes called Decision Support Systems or Business Intelligence © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Management Support Systems Tools
DSS Management Science Business Analytics Data Mining Data Warehouse Business Intelligence OLAP CASE tools GSS EIS EIP ERM ERP CRM SCM KMS KMP ES ANN Intelligent Agents E-commerce DSS © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision Support Frameworks
Type of Control Type of Decision: Operational Control Managerial Control Strategic Planning Structured (Programmed) Accounts receivable, accounts payable, order entry Budget analysis, short-term forecasting, personnel reports Investments, warehouse locations, distribution centers Semistructured Production scheduling, inventory control Credit evaluation, budget preparation, project scheduling, rewards systems Mergers and acquisitions, new product planning, compensation, QA, HR policy planning Unstructured (Unprogrammed) Buying software, approving loans, help desk Negotiations, recruitment, hardware purchasing R&D planning, technology development, social responsibility plans © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Technologies for Decision-Making Processes
Type of Decision Technology Support Needed Structured (Programmed) MIS, Management Science Models, Transaction Processing Semistructured DSS, KMS, GSS, CRM, SCM Unstructured (Unprogrammed) GSS, KMS, ES, Neural networks © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Technology Support Based on Anthony’s Taxonomy
Type of Control Operational Control Managerial Control Strategic Planning Technology Support Needed MIS, Management Science Management Science, DSS, ES, EIS, SCM, CRM, GSS, SCM GSS, CRM, EIS, ES, neural networks, KMS © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Management Science/Operations Research
Adopts systematic approach Define problem Classify into standard category Construct mathematical model Evaluate alternative solutions Select solution © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Enterprise Information Systems
Evolved from Executive Information Systems combined with Web technologies EIPs view information across entire organizations Provide rapid access to detailed information through drill-down. Provide user-friendly interfaces through portals. Identifies opportunities and threats © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Specialized systems include ERM, ERP, CRM, and SCM Provides timely and effective corporate level tracking and control. Filter, compress, and track critical data and information. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management Systems
Knowledge that is organized and stored in a repository for use by an organization Can be used to solve similar or identical problems in the future ROIs as high as a factor of 25 within one to two years © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Expert Systems Technologies that apply reasoning methodologies in a specific domain Attempts to mimic human experts’ problem solving Examples include: Artificial Intelligence Systems Artificial Neural Networks (neural computing) Genetic Algorithms Fuzzy Logic Intelligent Agents © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Hybrid Support Systems
Integration of different computer system tools to resolve problems Tools perform different tasks, but support each other Together, produce more sophisticated answers Work together to produce smarter answers © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Emerging Technologies
Grid computing Improved GUIs Model-driven architectures with code reuse M-based and L-based wireless computing Intelligent agents Genetic algorithms Heuristics and new problem-solving techniques © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 2 Decision-Making Systems, Models, and Support
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 2 Decision-Making Systems, Models, and Support © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Learn the basic concepts of decision making.
Understand systems approach. Learn Simon’s four phases of decision making. Understand the concepts of rationality and bounded rationality. Differentiate betwixt making a choice and establishing a principle of choice. Learn which factors affect decision making. Learn how DSS supports decision making in practice. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Team-based decision making
Standard Motor Products Shifts Gears Into Team-Based Decision-Making Vignette Team-based decision making Increased information sharing Daily feedback Self-empowerment Shifting responsibility towards teams Elimination of middle management © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision Making Process of choosing amongst alternative courses of action for the purpose of attaining a goal or goals. The four phases of the decision process are: Intelligence Design Choice implementation © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Systems Structure Separated from environment by boundary
Inputs Processes Outputs Feedback from output to decision maker Separated from environment by boundary Surrounded by environment Input Processes Output boundary Environment © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

System Types Closed system Open system Independent Takes no inputs
Delivers no outputs to the environment Black Box Open system Accepts inputs Delivers outputs to environment © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Models Used for DSS Iconic Analog Quantitative (mathematical)
Small physical replication of system Analog Behavioral representation of system May not look like system Quantitative (mathematical) Demonstrates relationships between systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Phases of Decision-Making
Simon’s original three phases: Intelligence Design Choice He added fourth phase later: Implementation Book adds fifth stage: Monitoring © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making Intelligence Phase
Scan the environment Analyze organizational goals Collect data Identify problem Categorize problem Programmed and non-programmed Decomposed into smaller parts Assess ownership and responsibility for problem resolution © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making Design Phase
Develop alternative courses of action Analyze potential solutions Create model Test for feasibility Validate results Select a principle of choice Establish objectives Incorporate into models Risk assessment and acceptance Criteria and constraints © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making Choice Phase
Principle of choice Describes acceptability of a solution approach Normative Models Optimization Effect of each alternative Rationalization More of good things, less of bad things Courses of action are known quantity Options ranked from best to worse Suboptimization Decisions made in separate parts of organization without consideration of whole © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Descriptive Models Describe how things are believed to be
Typically, mathematically based Applies single set of alternatives Examples: Simulations What-if scenarios Cognitive map Narratives © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Developing Alternatives
Generation of alternatives May be automatic or manual May be legion, leading to information overload Scenarios Evaluate with heuristics Outcome measured by goal attainment © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Problems Satisficing is the willingness to settle for less than ideal.
Form of suboptimization Bounded rationality Limited human capacity Limited by individual differences and biases Too many choices © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making Choice Phase
Decision making with commitment to act Determine courses of action Analytical techniques Algorithms Heuristics Blind searches Analyze for robustness © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making Implementation Phase
Putting solution to work Vague boundaries which include: Dealing with resistance to change User training Upper management support © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Source: Based on Sprague, R. H. , Jr
Source: Based on Sprague, R.H., Jr., “A Framework for the Development of DSS.” MIS Quarterly, Dec. 1980, Fig. 5, p. 13. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision Support Systems
Intelligence Phase Automatic Data Mining Expert systems, CRM, neural networks Manual OLAP KMS Reporting Routine and ad hoc © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Design Phase Financial and forecasting models Generation of alternatives by expert system Relationship identification through OLAP and data mining Recognition through KMS Business process models from CRM, RMS, ERP, and SCM © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Choice Phase Identification of best alternative Identification of good enough alternative What-if analysis Goal-seeking analysis May use KMS, GSS, CRM, ERP, and SCM systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Implementation Phase Improved communications Collaboration Training Supported by KMS, expert systems, GSS © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making In Humans
Temperament Hippocrates’ personality types Myers-Briggs’ Type Indicator Kiersey and Bates’ Types and Motivations Birkman’s True Colours Gender © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making In Humans
Cognitive styles What is perceived? How is it organized? Subjective Decision styles How do people think? How do they react? Heuristic, analytical, autocratic, democratic, consultative © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 3 Decision Support Systems: An Overview
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 3 Decision Support Systems: An Overview © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Understand DSS configurations.
Learn characteristics and capabilities of DSS. Understand DSS components. Describe structure of DSS components. Understand how DSS and the Web interact. Learn the role of the user in DSS. Understand DSS hardware and integration. Learn DSS configurations. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Successfully integrates DSS applications
Southwest Airlines Flies in the Face of Competition Through DSS Vignette Successfully integrates DSS applications Ties ERP applications to OLAP, allowing retrieval of financial data Allows access to both financial and operational data © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Systems designed to support managerial decision-making in unstructured problems More recently, emphasis has shifted to inputs from outputs Mechanism for interaction between user and components Usually built to support solution or evaluate opportunities © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS A DSS is a methodology that supports decision-making. It is:
Flexible; Adaptive; Interactive; GUI-based; Iterative; and Employs modeling. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Business Intelligence
Proactive Accelerates decision-making Increases information flows Components of proactive BI: Real-time warehousing Exception and anomaly detection Proactive alerting with automatic recipient determination Seamless follow-through workflow Automatic learning and refinement © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Components of DSS Subsystems: Data management Model management
Managed by DBMS Model management Managed by MBMS User interface Knowledge Management and organizational knowledge base © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Management Subsystem
Components: Database Database management system Data directory Query facility © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Database Interrelated data extracted from various sources, stored for use by the organization, and queried Internal data, usually from TPS External data from government agencies, trade associations, market research firms, forecasting firms Private data or guidelines used by decision-makers © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Database Management System
Extracts data Manages data and their relationships Updates (add, delete, edit, change) Retrieves data (accesses it) Queries and manipulates data Employs data dictionary © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Directory Catalog of all data Contains data definitions
Answers questions about the availability of data items Source Meaning Allows for additions, removals, and alterations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Model Management Subsystem
Components: Model base Model base management system Modeling language Model directory Model execution, integration, and command processor © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Models Strategic Tactical Operational Analytical
Supports top management decisions Tactical Used primarily by middle management to allocate resources Operational Supports daily activities Analytical Used to perform analysis of data © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Placeholder figure 3.5 © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Model Base Management System
Functions: Model creation Model updates Model data manipulation Generation of new routines Model directory: Catalog of models Definitions © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Model Management Activities
Model execution Controls running of model Model command processor Receives model instructions from user interface Routes instructions to MBMS or module execution or integration functions Model integration Combines several models’ operations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

User Interface System Data management and DBMS Knowledge-based system
Model management and MBMS User Interface Management System (UIMS) Natural Language Processor Input Action Languages Output Display Language PC Display Based on Figure 3.6, Schematic View of the User Interface Users Printers, Plotters © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

User Interface Management System
GUI Natural language processor Interacts with model management and data management subsystems Examples Speech recognition Display panel Tactile interfaces Gesture interface © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge-Based Management System
Expert or intelligent agent system component Complex problem solving Enhances operations of other components May consist of several systems Often text-oriented DSS © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Hardware De facto standard Web server with DBMS:
Operates using browser Data stored in variety of databases Can be mainframe, server, workstation, or PC Any network type Access for mobile devices © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Classifications Alter Holsapple and Whinston Intelligent
Extent to which outputs can directly support or determine the decision Data oriented or model oriented Holsapple and Whinston Text oriented, database oriented, spreadsheet oriented, solver oriented, rule oriented, or compound Intelligent © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

(ad hoc analysis) © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Classifications Donovan and Madnick Ad hoc Hackathorn and Keen
Institutional Problems of recurring nature Ad hoc Problems that are not anticipated or are not repetitive Hackathorn and Keen Personal support, group support, or organizational support © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Classifications GSS v. Individual DSS
Decisions made by entire group or by lone decision maker Custom made v. vendor ready made Generic DSS may be modified for use Database, models, interface, support are built in Addresses repeatable industry problems Reduces costs © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Web and DSS Data collection Communications Collaborations
Download capabilities Run on Web servers Simplifies integration problems Increased usability features © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 4 Modeling and Analysis
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 4 Modeling and Analysis © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Understand basic concepts of MSS modeling.
Describe MSS models interaction. Understand different model classes. Structure decision making of alternatives. Learn to use spreadsheets in MSS modeling. Understand the concepts of optimization, simulation, and heuristics. Learn to structure linear program modeling. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Understand the capabilities of linear programming.
Examine search methods for MSS models. Determine the differences between algorithms, blind search, heuristics. Handle multiple goals. Understand terms sensitivity, automatic, what-if analysis, goal seeking. Know key issues of model management. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Promodel simulation created representing entire transport system
Dupont Simulates Rail Transportation System and Avoids Costly Capital Expense Vignette Promodel simulation created representing entire transport system Applied what-if analyses Visual simulation Identified varying conditions Identified bottlenecks Allowed for downsized fleet without downsizing deliveries © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

MSS Modeling Key element in DSS Many classes of models
Specialized techniques for each model Allows for rapid examination of alternative solutions Multiple models often included in a DSS Trend toward transparency Multidimensional modeling exhibits as spreadsheet © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Simulations Explore problem at hand Identify alternative solutions
Can be object-oriented Enhances decision making View impacts of decision alternatives © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Models Algorithm-based models Statistic-based models
Linear programming models Graphical models Quantitative models Qualitative models Simulation models © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Problem Identification
Environmental scanning and analysis Business intelligence Identify variables and relationships Influence diagrams Cognitive maps Forecasting Fueled by e-commerce Increased amounts of information available through technology © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Static Models Single photograph of situation Single interval
Time can be rolled forward, a photo at a time Usually repeatable Steady state Optimal operating parameters Continuous Unvarying Primary tool for process design © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Dynamic Model Represent changing situations Time dependent
Varying conditions Generate and use trends Occurrence may not repeat © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making Certainty Assume complete knowledge
All potential outcomes known Easy to develop Resolution determined easily Can be very complex © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making Uncertainty Several outcomes for each decision
Probability of occurrence of each outcome unknown Insufficient information Assess risk and willingness to take it Pessimistic/optimistic approaches © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision-Making Probabilistic Decision-Making Decision under risk
Probability of each of several possible outcomes occurring Risk analysis Calculate value of each alternative Select best expected value © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Influence Diagrams Graphical representation of model
Provides relationship framework Examines dependencies of variables Any level of detail Shows impact of change Shows what-if analysis © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Influence Diagrams Variables: Decision
Intermediate or uncontrollable Result or outcome (intermediate or final) Decision Arrows indicate type of relationship and direction of influence Certainty Amount in CDs Interest earned Sales Uncertainty Price © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Influence Diagrams Random (risk) Preference
~ Demand Random (risk) Place tilde above variable’s name Sales Sleep all day Graduate University Preference (double line arrow) Get job Ski all day Arrows can be one-way or bidirectional, based upon the direction of influence © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Modeling with Spreadsheets
Flexible and easy to use End-user modeling tool Allows linear programming and regression analysis Features what-if analysis, data management, macros Seamless and transparent Incorporates both static and dynamic models © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision Tables Multiple criteria decision analysis Features include:
Decision variables (alternatives) Uncontrollable variables Result variables Applies principles of certainty, uncertainty, and risk © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Decision Tree Graphical representation of relationships
Multiple criteria approach Demonstrates complex relationships Cumbersome, if many alternatives © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

MSS Mathematical Models
Link decision variables, uncontrollable variables, parameters, and result variables together Decision variables describe alternative choices. Uncontrollable variables are outside decision-maker’s control. Fixed factors are parameters. Intermediate outcomes produce intermediate result variables. Result variables are dependent on chosen solution and uncontrollable variables. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

MSS Mathematical Models
Nonquantitative models Symbolic relationship Qualitative relationship Results based upon Decision selected Factors beyond control of decision maker Relationships amongst variables © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Mathematical Programming
Tools for solving managerial problems Decision-maker must allocate resources amongst competing activities Optimization of specific goals Linear programming Consists of decision variables, objective function and coefficients, uncontrollable variables (constraints), capacities, input and output coefficients © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Multiple Goals Simultaneous, often conflicting goals sought by management Determining single measure of effectiveness is difficult Handling methods: Utility theory Goal programming Linear programming with goals as constraints Point system © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Sensitivity, What-if, and Goal Seeking Analysis
Assesses impact of change in inputs or parameters on solutions Allows for adaptability and flexibility Eliminates or reduces variables Can be automatic or trial and error What-if Assesses solutions based on changes in variables or assumptions Goal seeking Backwards approach, starts with goal Determines values of inputs needed to achieve goal Example is break-even point determination © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Search Approaches Analytical techniques (algorithms) for structured problems General, step-by-step search Obtains an optimal solution Blind search Complete enumeration All alternatives explored Incomplete Partial search Achieves particular goal May obtain optimal goal © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Search Approaches Heurisitic Repeated, step-by-step searches
Rule-based, so used for specific situations “Good enough” solution, but, eventually, will obtain optimal goal Examples of heuristics Tabu search Remembers and directs toward higher quality choices Genetic algorithms Randomly examines pairs of solutions and mutations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Simulations Imitation of reality
Allows for experimentation and time compression Descriptive, not normative Can include complexities, but requires special skills Handles unstructured problems Optimal solution not guaranteed Methodology Problem definition Construction of model Testing and validation Design of experiment Experimentation Evaluation Implementation © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Simulations Probabilistic independent variables
Discrete or continuous distributions Time-dependent or time-independent Visual interactive modeling Graphical Decision-makers interact with simulated model may be used with artificial intelligence Can be objected oriented © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Model-Based Management System
Software that allows model organization with transparent data processing Capabilities DSS user has control Flexible in design Gives feedback GUI based Reduction of redundancy Increase in consistency Communication between combined models © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Model-Based Management System
Relational model base management system Virtual file Virtual relationship Object-oriented model base management system Logical independence Database and MIS design model systems Data diagram, ERD diagrams managed by CASE tools © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition
Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Describe the issues in management of data.
Understand the concepts and use of DBMS. Learn about data warehousing and data marts. Explain business intelligence/business analytics. Examine how decision making can be improved through data manipulation and analytics. Understand the interaction betwixt the Web and database technologies. Explain how database technologies are used in business analytics. Understand the impact of the Web on business intelligence and analytics. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Network of systems that provide knowledge integration and distribution
Information Sharing a Principle Component of the National Strategy for Homeland Security Vignette Network of systems that provide knowledge integration and distribution Horizontal and vertical information sharing Improved communications Mining of data stored in Web-enabled warehouse © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data, Information, Knowledge
Items that are the most elementary descriptions of things, events, activities, and transactions May be internal or external Information Organized data that has meaning and value Knowledge Processed data or information that conveys understanding or learning applicable to a problem or activity © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Raw data collected manually or by instruments Quality is critical
Quality determines usefulness Contextual data quality Intrinsic data quality Accessibility data quality Representation data quality Often neglected or casually handled Problems exposed when data is summarized © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Cleanse data Data integrity issues When populating warehouse
Data quality action plan Best practices for data quality Measure results Data integrity issues Uniformity Version Completeness check Conformity check Genealogy or drill-down © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Data Integration Access needed to multiple sources
Often enterprise-wide Disparate and heterogeneous databases XML becoming language standard © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

External Data Sources Web Commercial databases Intelligent agents
Document management systems Content management systems Commercial databases Sell access to specialized databases © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Database Management Systems
Software program Supplements operating system Manages data Queries data and generates reports Data security Combines with modeling language for construction of DSS © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Database Models Hierarchical Network Relational Object oriented
Top down, like inverted tree Fields have only one “parent”, each “parent” can have multiple “children” Fast Network Relationships created through linked lists, using pointers “Children” can have multiple “parents” Greater flexibility, substantial overhead Relational Flat, two-dimensional tables with multiple access queries Examines relations between multiple tables Flexible, quick, and extendable with data independence Object oriented Data analyzed at conceptual level Inheritance, abstraction, encapsulation © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Database Models, continued
Multimedia Based Multiple data formats JPEG, GIF, bitmap, PNG, sound, video, virtual reality Requires specific hardware for full feature availability Document Based Document storage and management Intelligent Intelligent agents and ANN Inference engines © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Warehouse Subject oriented
Scrubbed so that data from heterogeneous sources are standardized Time series; no current status Nonvolatile Read only Summarized Not normalized; may be redundant Data from both internal and external sources is present Metadata included Data about data Business metadata Semantic metadata © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Architecture May have one or more tiers
Determined by warehouse, data acquisition (back end), and client (front end) One tier, where all run on same platform, is rare Two tier usually combines DSS engine (client) with warehouse More economical Three tier separates these functional parts © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Migrating Data Business rules Data extracted from all relevant sources
Stored in metadata repository Applied to data warehouse centrally Data extracted from all relevant sources Loaded through data-transformation tools or programs Separate operation and decision support environments Correct problems in quality before data stored Cleanse and organize in consistent manner © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Warehouse Design Dimensional modeling Grain Retrieval based
Implemented by star schema Central fact table Dimension tables Grain Highest level of detail Drill-down analysis © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Warehouse Development
Data warehouse implementation techniques Top down Bottom up Hybrid Federated Projects may be data centric or application centric Implementation factors Organizational issues Project issues Technical issues Scalable Flexible © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Marts Dependent Independent Created from warehouse Replicated
Functional subset of warehouse Independent Scaled down, less expensive version of data warehouse Designed for a department or SBU Organization may have multiple data marts Difficult to integrate © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Business Intelligence and Analytics
Acquisition of data and information for use in decision-making activities Business analytics Models and solution methods Data mining Applying models and methods to data to identify patterns and trends © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

OLAP Activities performed by end users in online systems
Specific, open-ended query generation SQL Ad hoc reports Statistical analysis Building DSS applications Modeling and visualization capabilities Special class of tools DSS/BI/BA front ends Data access front ends Database front ends Visual information access systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Mining Organizes and employs information and knowledge from databases Statistical, mathematical, artificial intelligence, and machine-learning techniques Automatic and fast Tools look for patterns Simple models Intermediate models Complex Models © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Mining Data mining application classes of problems
Classification Clustering Association Sequencing Regression Forecasting Others Hypothesis or discovery driven Iterative Scalable © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Tools and Techniques Data mining Text Mining Statistical methods
Decision trees Case based reasoning Neural computing Intelligent agents Genetic algorithms Text Mining Hidden content Group by themes Determine relationships © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Discovery in Databases
Data mining used to find patterns in data Identification of data Preprocessing Transformation to common format Data mining through algorithms Evaluation © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Data Visualization Technologies supporting visualization and interpretation Digital imaging, GIS, GUI, tables, multidimensions, graphs, VR, 3D, animation Identify relationships and trends Data manipulation allows real time look at performance data © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Multidimensionality Data organized according to business standards, not analysts Conceptual Factors Dimensions Measures Time Significant overhead and storage Expensive Complex © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Analytic systems Real-time queries and analysis
Real-time decision-making Real-time data warehouses updated daily or more frequently Updates may be made while queries are active Not all data updated continuously Deployment of business analytic applications © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

GIS Computerized system for managing and manipulating data with digitized maps Geographically oriented Geographic spreadsheet for models Software allows web access to maps Used for modeling and simulations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Web Analytics/Intelligence
Application of business analytics to Web sites Web intelligence Application of business intelligence techniques to Web sites © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 6 Decision Support System Development
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 6 Decision Support System Development © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Understand the concepts of systems development.
Learn PADI, the phases of SDLC. Describe prototyping. Understand which factors lead to DSS success or failure. Learn the importance of project management. Describe the three technology levels of DSS. Understand the learning process involved in DSS development. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Creation of a specialized business portal to solve specific problem
Osram Sylvania Thinks Small, Strategizes Big-Develops the Infonet HR Portal System Vignette Creation of a specialized business portal to solve specific problem Prototype Interactive, Web-based HR portal Think small, strategize big Focus on key problems first Plan to achieve quick small successes Intranet-based portal for hiring, job postings, benefits, bonuses, retirement information © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Systems Development Life Cycle
Four phases Planning Analysis Design Implementation Cyclical Can return to other phases Waterfall model © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Tools Computer-aided software design tools RAD design tools
Upper CASE – Creates systems diagrams Lower CASE Manages diagrams and code Integrated CASE Combination RAD design tools Enterprise class repository and collaboration UML modeling Analysis and design software Code debugging methods Testing and quality assurance tools © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Successful Project Management
Establish a baseline Define scope of project Manage change and scope creep Get support from upper management Establish timelines, milestones, and budgets based on realistic goals Involve users Document everything © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Implementation Failures
Lack of stakeholder involvement Incomplete requirements Scope creep Unrealistic expectations Project champion leaves Lack of skill or expertise Inadequate human resources New technologies © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Evolutionary Disruptors
Development environment Organizational cultural factors Loss of top management support User and analyst attitude User experience Development team capability Development process User education, support, involvement, training © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Project Management Tools
Project management software can allow: Collaboration among disparate teams Resource and program management Portfolio management Web enabled Aggregates and analyses project data © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Alternative Development Methodologies
Parallel development Multiple development on separate systems RAD Quick development allowing fast, but limited functionality Phased development Sequential serial development Prototyping Rapid development of portions of projects for user input and modification Small working model or may become functional part of final system Throwaway prototyping Pilot test or simple development platforms © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Agile Development Rapid prototyping Used for: Heavy user input
Unclear or rapidly changing requirements Speedy development Heavy user input Incremental delivery with short time frames Tend to have integration problems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Development Methodology
Prototyping Iterative design Evolutionary development Middle out process Adaptive design Incremental design © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Prototyping Short steps Immediate stakeholder feedback Iterative
Planning Analysis Design Prototype Immediate stakeholder feedback Iterative In development of prototype Within the system in general Evaluation integral part Control mechanism © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Prototyping Advantages Disadvantages
User and management involvement Learning explicitly integrated Prototyping bypasses information requirement Short intervals between iterations Low cost Improved user understanding of system Disadvantages Changing requirements May not have thorough understanding of benefits and costs Poorly tested Dependencies, security, and safety may be ignored High uncertainty Problem may get lost Reduction in quality Higher costs due to multiple productions © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Change Management Crucial to DSS People resistant to change
Examine cause of change May require organizational culture shift Lewin-Schein change theory steps Unfreeze Create awareness of need for change People support what they help create Move Develop new methods and behaviors Create and maintain momentum Refreeze Reinforce desired changes Establish stable environment © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Technology Levels DSS primary tools DSS generator (engine)
Fundamental elements Programming languages, graphics, editors, query systems DSS generator (engine) Integrated software package for building specific DSS Modeling, report generation, graphics, risk analysis Specific DSS DSS application that accomplishes the work DSS primary tools are used to construct integrated tools that are used to construct specific tools © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Hardware Software PCs to multiprocessor mainframes
Involves multiple criteria Develop in house, outsource, or buy off the shelf Off the shelf software rapidly updated; many on market Prices fluctuate Different tools available © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Team developed DSS requires substantial effort to build and manage
End user developed DSS Decision-makers and knowledge workers develop to solve problems or enhance productivity Advantages Short delivery time User requirements specifications are eliminated Reduced implementation problems Low costs Risks Quality may be low May have lack of documentation Security risks may increase © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS DSS is much more than just a DBMS, MBMS, GUI, interface, and knowledge component © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 7 Collaborative Computing Technologies: Group Support Systems
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 7 Collaborative Computing Technologies: Group Support Systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Understand concepts and fundamentals of groupwork, communications, and collaboration. Examine how computer systems enhance communication and collaboration. Understand the principles and capabilities of GSS. Explore the concepts of time/place frameworks. Learn how GSS interplays with the concepts of process gain and loss, and task gain and loss. See how GSS utilizes parallelism and anonymity. Understand the fundamentals of electronic meetings. Examine GSS’ three technologies. Learn how the Web enables GSS, electronic meetings, and collaborative computing. Explain hoe distance learning is enabled by GSS. Show how GSS enhances creativity. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chrysler Scores with Groupware Vignette
SCORE initiative Identified waste in supply chain Enhanced relationships Accessed through Internet or modem Enhanced communication and collaboration Used good project management principles © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Groupwork Groupwork Collaboration and communication Members can be located in different places and work at different times Information may be located external to the project Allows for rapid solutions May exhibit normal team problems of synergy or conflict Often Internet based Groupware tools support groupwork Work called computer-supported cooperative work Collaborative computing © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Communication Support
No collaboration without communication Internet supplies fast, reliable, inexpensive support Groups need not only communication, but information and knowledge © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Time/Place Communication Framework
Effectiveness of collaborative group depends on Time synchronous or asynchronous transmission of information Place location of participants © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Groupware Software providing collaborative support to groups
Different time/place applications Most use Internet technologies Most offer one or more capabilities Electronic brainstorming Free flow of ideas and comments Electronic conferencing or videoconferencing Group scheduling and calendars Conflict resolution Model building Electronic document sharing Voting services Electronic meeting services also available Enterprise-wide systems expensive in cost and human resources © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Popular Groupware Lotus Notes/Domino Microsoft Netmeeting
Groove Workspace GroupSystems MeetingRoom and OnLine WebEx © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Benefits and Problems Benefits of groupwork Problems in groupwork
Process gains Nominal group technique Delphi method Technology applied as GSS Hardware and software combined to enhance groupwork Collaborative computing Problems in groupwork Process losses inefficient © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

GSS Common group activities with computer assistance
Information retrieval Information sharing Parallelism Anonymity Information use Support participants Improve productivity and effectiveness of meetings More efficient decision-making Increase effectiveness of decisions © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

GSS Technology Deployment
Special purpose decision room Electronic meeting rooms Software operates across LAN Allowed for face-to-face meetings Trained facilitator coordinates meeting Group leader structures meeting with facilitator Multiple use facility General purpose computer lab Effective way to lower costs Web-based groupware with clients Anytime/anyplace meetings with deadlines established Software bought or leased No facility costs Flexible © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

GSS Meeting Process Group leader meets with facilitator to plan meeting structure. Participants meet on computers. Group leader or facilitator poses question. Participants brainstorm by entering comments into computer. Facilitator employs idea organization software to sort comments into common themes. Results are displayed. Facilitator or group leader leads discussion. Themes are prioritized. Highest priority topics are either sent through the process again for further discussion or a vote is taken. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

GSS Meeting Process Standard Process
Exploratory idea generation Idea organization tool Prioritization New idea generation Selection of final idea Success based upon effectiveness, reduction in costs, better decisions, increased productivity © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

GSS and Distance Education
Classroom collaborative computing advantages Brainstorming, chat, discussion boards Distribution of information, lectures Publishes to course site Videoconferenced Consistent materials Textbooks can be bound or electronic s and listservs One-on-one interaction Allows for global classrooms Anytime/anyplace with fixed deadlines Flexible time frame Doesn’t interfere with work shift Low delivery costs with large audiences © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

GSS and Distance Education, continued
Disadvantages: Fewer social interactions Communication problems Students must be self-starters and highly disciplined Classes require major technical and administrative support Technical infrastructure must be reliable Courses may need to be redesigned for online Special training Corporate training online: Allows anytime/anyplace training Lowers costs Decreases time away from jobs Shortens learning process Delivered via Intranet, intranets, extranets, audio and video conferencing © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Creativity Support System
Fundamental human trait Level of achievement Can be learned Organizations recognize value in innovation Stimulated by electronic brainstorming software Free flow idea generation Creative computer programs Smartbots function as facilitators Identify analogies in letter patterns Draw art Write poems Computer programs stimulate human productivity © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 8 Enterprise Information Systems
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 8 Enterprise Information Systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Learn the basic concepts in enterprise information systems. Determine how to extract information needs for a DSS. Compare features and capabilities of EIS and DSS. Learn the relationship between and amongst business intelligence/DSS systems. Understand the capabilities of enterprise information portals. Examine supply chain management issues. Discuss customer relationship management concepts. Understand how the Web impacts EIS, and vice versa. Describe how EIS has improved decision making. Learn emerging and future EIS. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

United States Military Turns to Portals Vignette
Implement Web-based portals to enhance communications Allows quick dispersal of combat intelligence Improve quality of life issues Connect support applications with tactical applications © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Executive information system Computer system that allows executives access to management reports Drill-down capabilities User-friendly Executive support systems Comprehensive executive support system Includes communication, office automation, analysis support, business intelligence Enterprise information systems Corporate-wide system Not restricted to executives Business intelligence © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Information Flows Internal information from functional units
External information from Internet, news media, government Environmental scanning © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Capabilities of Enterprise Information System
Drill-down paths Supported by star or snowflake schemas Critical success factors Strategic, managerial, or operational Sources: organizational, industrial, environmental Types of information monitored: Key problem narratives Highlight charts Top level financials Key factors Detailed key performance indicator responsibility reports © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Capabilities of Enterprise Information System, continued
Status Access Relevance of latest data of key indicators Analysis Built-in analytical functions Integration with DSS products Analysis by intelligent agents Exception reporting Management by exception to standards Navigation of information Large amounts of data can be analyzed Audio and Visual Use of colors and sounds Communications , GSS, news groups, interface with voice mail © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Comparing EIS to DSS EIS DSS Integration
Supports upper management in discovering problems and opportunities Repetitive analysis High speed GUI based DSS Analyzes specific problem or opportunity Ad hoc analysis Effective May have GUI Integration Uses EIS output to launch DSS Data from same places Integrates user roles Third party software © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

EIS Data Access and Use Data usually comes from single warehouse
Advanced data visualization Combines multidimensional analysis with OLAP Spreadsheets and graphics Slice and dice Web ready © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Enterprise Portals Corporate portals
Integrate internal and external applications Web-based interface Effective distribution of information Encourage collaboration Data visualization tools Customized Search engines © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Soft Information Information for questionable sources that is used informally Vague Unofficial News reports and external data sources Predictions and speculations Explanations and justifications Opinions and gut feelings Rumors and hearsay © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Organizational Decisional Support Systems
Focused on organizational task or activity affects several units Cuts across hierarchy layers Cuts across functional groups Computer based Communication technology Can be integrated into a DSS or EIS © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Supply Chains Old New Supply chain management Supply chain
Material flow from sources to finished product and disbursement within the organization Demand chain Order generation, taking, and fulfillment New Flow of material, information, services from suppliers through manufacturer to end user Supply chain management Planning, organization, and coordination of supply chain activities Increase effectiveness Reduce risk Decrease cycle time Improve customer service © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Supply Chains Upstream = suppliers
Internal supply chain = changing inputs to outputs Downstream = distribution © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Value Chains Porter’s value chain model Primary activities
Inbound logistics Operations Outbound logistics Marketing and sales Customer service Support activities Organization’s infrastructure Human resource management Technology development Procurement © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Value System Value chain is part of larger stream called value system
Includes tiers of suppliers Value chains of distributors Buyers Extended supply chain Maximize and optimize total value of chain © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Supply Chain Problems Uncertainties Need to coordinate activities
Demand forecasts Delivery time Quality issues Need to coordinate activities Other issues Poor customer service Obtaining real time data on chain status Cultural problems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Supply Chain Problem Solutions
Inventory management Shipping management Efficient purchasing JIT CRM Collaboration along chain Strategic partnerships Reduce number of intermediaries Outsourcing © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Material Resource Planning
MRP system Production plan for 100% capacity Inventory models Master production schedule Component lists CRP system Added factory and machine capacities MRPII system Added financial and resource planning © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Integration Tangible benefits: Intangible benefits:
Inventory reduction Personnel reduction Improved productivity Cost reductions Increased revenues Delivery improvement Order management Reduction in maintenance Intangible benefits: Visibility of information Improved processes Better customer service Standardization Flexibility Globalization Improved employee satisfaction Increased business performance © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Enterprise Resource Planning
Computer system that integrates all of an organization’s departments and functions Shortens production times Based on value chain view Decreases costs in chain Expensive Increases customer service Single interface Facilitates business process changes Automates key business processes SCM provides intelligent decision support Overlay ERP Advanced planning and scheduling modules © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Enterprise Resource Planning
Options Build your own Off-the-shelf packages Outsource Application Service Providers Problems High failure rate ERP is a formal business process Organization’s processes don’t match the ERP’s Software capability and needs vary © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Customer Resource Management Systems (CRM)
Enterprise approach Communication based Focused on: Customer acquisition Customer retention Customer loyalty Customer profitability Empowers employees Enables one-to-one marketing Allows for proper allocation of resources to each customer class © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

CRM Relationship technologies Data warehouses Foundation for CRM
Business intelligence/business analytics Data mining Predictive analytics determine relationships OLAP Integrated with: GIS = geographical preferences Revenue management optimization software = optimized pricing Data mining workbench = targets promotions © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

CRM Benefits: Issues: Decrease expense of recruiting customer
Reduce sales costs Greater profitability through targeting and segmentation Increase customer retention Increase customer loyalty Improve customer service Customer-focused Issues: Failure to use software Integration Organizational culture Expensive Adapting business processes Retention of employees Training Allocation of time for deployment Commitment from top management © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

CRM Success Indications Often intangible Tangible
Improved customer satisfaction Tangible Reduced reporting cycle Reduced expense of doing business Reduced sales cycle Increased productivity Increased sale Indications Systems used to meet key customer needs Make in-depth analysis of customer costs and potential profits Information linked from disparate business units Employees empowered to handle customers’ problems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Product Lifecycle Management (PLM)
Integrated, information driven Includes all aspects of product’s life Goals Streamline development Increase innovation Requires integration of independent databases Shares information about product among different groups, both inside and outside organization © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

PLM Tracks electronic information about life of product
Links together all required processes Integrates nodules and tools into single application suite Enhances communication and collaboration Product data is central component Repository Specifications, requirements, design documents, manufacturing plans, and support Available to all stakeholders at all times © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

PLM Benefits: Issues: Flexibility Reduced change orders
Improved design Reduced production times Reduced time to market Improved quality control Collaboration Centralized repository Issues: Support from senior management User involvement Training Integration © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Business Process Management Systems (BPM)
Integrates data, applications, and people through business process Streamlined Automates processes Less administration Graphical map of processes Enterprise information portal into business processes Integrates systems Provides view of organization’s health and progress Unifies rules, processes, methods, and workflows Benefits Links legacy systems to newer workflows Issues Forces review of processes © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Business Activity Monitoring Systems (BAM)
Real time systems monitoring specific facility Detects opportunities, problems, and threats Modeling function for solutions Collaboration Fast response Benefits Recognizing and responding to events Allows for quick resolution Issues Senior management support Change in business processes Requires identification of CSFs and proper analytical techniques © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Frontline Decision Support Systems
Frontline decision-making Automate decision processes and push them down the organization or out to partners Empowers employees Incorporates decision-making into daily work Provides right questions to ask Locates needed data Provides metrics for use with data © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Future Developments Hardware and software advances Virtual reality
Three-dimensional image displays Increased utilization of multimedia Increased collaboration Improved communication Automated support Intelligent agents © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 9 Knowledge Management
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 9 Knowledge Management © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Define knowledge.
Learn the characteristics of knowledge management. Describe organizational learning. Understand the knowledge management cycle. Understand knowledge management system technology and how it is implemented. Learn knowledge management approaches. Understand the activities of the CKO and knowledge workers. Describe the role of knowledge management in the organization. Be able to evaluate intellectual capital. Understand knowledge management systems implementation. Illustrate the role of technology, people, and management with regards to knowledge management. Understand the benefits and problems of knowledge management initiatives. Learn how knowledge management can change organizations. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Siemens Knows What It Knows Through Knowledge Management Vignette
Community of interest Repositories Communities of practice Informal knowledge-sharing techniques Employee initiated Created ShareNet Easy to share knowledge Incentives for posting Internal evangelists responsible for training, monitoring, and assisting users Top management support © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management Process to help organization identify, select, organize, disseminate, transfer information Structuring enables problem-solving, dynamic learning, strategic planning, decision-making Leverage value of intellectual capital through reuse © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Data = collection of facts, measurements, statistics
Information = organized data Knowledge = contextual, relevant, actionable information Strong experiential and reflective elements Good leverage and increasing returns Dynamic Branches and fragments with growth Difficult to estimate impact of investment Uncertain value in sharing Evolves over time with experience © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Explicit knowledge Tacit knowledge
Objective, rational, technical Policies, goals, strategies, papers, reports Codified Leaky knowledge Tacit knowledge Subjective, cognitive, experiential learning Highly personalized Difficult to formalize Sticky knowledge © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management Systematic and active management of ideas, information, and knowledge residing within organization’s employees Knowledge management systems Use of technologies to manage knowledge Used with turnover, change, downsizing Provide consistent levels of service © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Organizational Learning
Learning organization Ability to learn from past To improve, organization must learn Issues Meaning, management, measurement Activities Problem-solving, experimentation, learning from past, learning from acknowledged best practices, transfer of knowledge within organization Must have organizational memory, way to save and share it Organizational learning Develop new knowledge Corporate memory critical Organizational culture Pattern of shared basic assumptions © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management Initiatives
Aims Make knowledge visible Develop knowledge intensive culture Build knowledge infrastructure Surrounding processes Creation of knowledge Sharing of knowledge Seeking out knowledge Using knowledge © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management Initiatives
Knowledge creation Generating new ideas, routines, insights Modes Socialization, externalization, internalization, combination Knowledge sharing Willing explanation to another directly or through an intermediary Knowledge seeking Knowledge sourcing © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Approaches to Knowledge Management
Process Approach Codifies knowledge Formalized controls, approaches, technologies Fails to capture most tacit knowledge Practice Approach Assumes that most knowledge is tacit Informal systems Social events, communities of practice, person-to-person contacts Challenge to make tacit knowledge explicit, capture it, add to it, transfer it © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Approaches to Knowledge Management
Hybrid Approach Practice approach initially used to store explicit knowledge Tacit knowledge primarily stored as contact information Best practices captured and managed Best practices Methods that effective organizations use to operate and manage functions Knowledge repository Place for capture and storage of knowledge Different storage mechanisms depending upon data captured © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management System Cycle
Creates knowledge through new ways of doing things Identifies and captures new knowledge Places knowledge into context so it is usable Stores knowledge in repository Reviews for accuracy and relevance Makes knowledge available at all times to anyone Disseminate © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Components of Knowledge Management Systems
Technologies Communication Access knowledge Communicates with others Collaboration Perform groupwork Synchronous or asynchronous Same place/different place Storage and retrieval Capture, storing, retrieval, and management of both explicit and tacit knowledge through collaborative systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Components of Knowledge Management Systems
Supporting technologies Artificial intelligence Expert systems, neural networks, fuzzy logic, intelligent agents Intelligent agents Systems that learn how users work and provide assistance Knowledge discovery in databases Process used to search for and extract information Internal = data and document mining External = model marts and model warehouses XML Extensible Markup Language Enables standardized representations of data Better collaboration and communication through portals © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management System Implementation
Challenge to identify and integrate components Early systems developed with networks, groupware, databases Knowware Technology tools that support knowledge management Collaborative computing tools Groupware Knowledge servers Enterprise knowledge portals Document management systems Content management systems Knowledge harvesting tools Search engines Knowledge management suites Complete out-of-the-box solutions © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management System Implementation
Software packages available Include one or more tools Consulting firms Outsourcing Application Service Providers © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management System Integration
Integration with enterprise and information systems DSS/BI Integrates models and activates them for specific problem Artificial Intelligence Expert system = if-then-else rules Natural language processing = understanding searches Artificial neural networks = understanding text Artificial intelligence based tools = identify and classify expertise © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management System Integration
Database Knowledge discovery in databases CRM Provide tacit knowledge to users Supply chain management systems Can access combined tacit and explicit knowledge Corporate intranets and extranets Knowledge flows more freely in both directions Capture knowledge directly with little user involvement Deliver knowledge when system thinks it is needed © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Human Resources Chief knowledge officer CEO Upper management
Senior level Sets strategic priorities Defines area of knowledge based on organization mission and goals Creates infrastructure Identifies knowledge champions Manages content produced by groups Adds to knowledge base CEO Champion knowledge management Upper management Ensures availability of resources to CKO Communities of practice Knowledge management system developers Team members that develop system Knowledge management system staff Catalog and manage knowledge © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Management Valuation
Asset-based approaches Identifies intellectual assets Focuses on increasing value Knowledge linked to applications and business benefits approaches Balanced scorecard Economic value added Inclusive valuation methodology Return on management ratio Knowledge capital measure Estimated sale price approach © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Metrics Financial Non-financial ROI Perceptual, rather than absolute
Intellectual capital not considered an asset Non-financial Value of intangibles External relationship linkages capital Structural capital Human capital Social capital Environmental capital © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Factors Leading to Success and Failure of Systems
Companies must assess need System needs technical and organizational infrastructure to build on System must have economic value to organization Senior management support Organization needs multiple channels for knowledge transfer Appropriate organizational culture Failure System does not meet organization’s needs Lack of commitment No incentive to use system Lack of integration © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 10 Intelligent Decision Support Systems
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 10 Intelligent Decision Support Systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Describe the basic concepts in artificial intelligence. Understand the importance of knowledge in decision support. Examine the concepts of rule-based expert systems. Learn the architecture of rule-based expert systems. Understand the benefits and limitations of rule based systems for decision support. Identify proper applications of expert systems. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Intelligent Systems in KPN Telecom and Logitech Vignette
Problems in maintaining computers with varying hardware and software configurations Rule-based system developed Captures, manages, automates installation and maintenance Knowledge-based core User-friendly interface Knowledge management module employs natural language processing unit © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Artificial Intelligence
Duplication of human thought process by machine Learning from experience Interpreting ambiguities Rapid response to varying situations Applying reasoning to problem-solving Manipulating environment by applying knowledge Thinking and reasoning © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Artificial Intelligence Characteristics
Symbolic processing Computers process numerically, people think symbolically Computers follow algorithms Step by step Humans are heuristic Rule of thumb Gut feelings Intuitive Heuristics Symbols combined with rule of thumb processing Inference Applies heuristics to infer from facts Machine learning Mechanical learning Inductive learning Artificial neural networks Genetic algorithms © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Development of Artificial Intelligence
Primitive solutions Development of general purpose methods Applications targeted at specific domain Expert systems Advanced problem-solving Integration of multiple techniques Multiple domains © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Artificial Intelligence Concepts
Expert systems Human knowledge stored on machine for use in problem-solving Natural language processing Allows user to use native language instead of English Speech recognition Computer understanding spoken language Sensory systems Vision, tactile, and signal processing systems Robotics Sensory systems combine with programmable electromechanical device to perform manual labor © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Vision and scene recognition Computer intelligence applied to digital information from machine Neural computing Mathematical models simulating functional human brain Intelligent computer-aided instruction Machines used to tutor humans Intelligent tutoring systems Game playing Investigation of new strategies combined with heuristics © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Language translation Programs that translate sentences from one language to another without human interaction Fuzzy logic Extends logic from Boolean true/false to allow for partial truths Imprecise reasoning Inexact knowledge Genetic algorithms Computers simulate natural evolution to identify patterns in sets of data Intelligent agents Computer programs that automatically conduct tasks © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Experts Experts Expertise
Have special knowledge, judgment, and experience Can apply these to solve problems Higher performance level than average person Relative Faster solutions Recognize patterns Expertise Task specific knowledge of experts Acquired from reading, training, practice © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Expert Systems Features
Expertise Capable of making expert level decisions Symbolic reasoning Knowledge represented symbolically Reasoning mechanism symbolic Deep knowledge Knowledge base contains complex knowledge Self-knowledge Able to examine own reasoning Explain why conclusion reached © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Applications of Expert Systems
DENDRAL project Applied knowledge or rule-based reasoning commands Deduced likely molecular structure of compounds MYCIN Rule-based system for diagnosing bacterial infections XCON Rule-based system to determine optimal systems configuration Credit analysis Ruled-based systems for commercial lenders Pension fund adviser Knowledge-based system analyzing impact of regulation and conformance requirements on fund status © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Applications Finance Data processing Marketing Human resources
Insurance evaluation, credit analysis, tax planning, financial planning and reporting, performance evaluation Data processing Systems planning, equipment maintenance, vendor evaluation, network management Marketing Customer-relationship management, market analysis, product planning Human resources HR planning, performance evaluation, scheduling, pension management, legal advising Manufacturing Production planning, quality management, product design, plant site selection, equipment maintenance and repair © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Environments Consultation (runtime) Development

Major Components of Expert Systems
Knowledge base Facts Special heuristics to direct use of knowledge Inference engine Brain Control structure Rule interpreter User interface Language processor © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Additional Components of Expert Systems
Knowledge acquisition subsystem Accumulates, transfers, and transforms expertise to computer Workplace Blackboard Area of working memory Decisions Plan, agenda, solution Justifier Explanation subsystem Traces responsibility for conclusions Knowledge refinement system Analyzes knowledge and use for learning and improvements © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Presentation
Production rules IF-THEN rules combine with conditions to produce conclusions Easy to understand New rules easily added Uncertainty Semantic networks Logic statements © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Inference Engine Forward chaining Backward chaining
Looks for the IF part of rule first Selects path based upon meeting all of the IF requirements Backward chaining Starts from conclusion and hypothesizes that it is true Identifies IF conditions and tests their veracity If they are all true, it accepts conclusion If they fail, then discards conclusion © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

General Problems Suitable for Expert Systems
Interpretation systems Surveillance, image analysis, signal interpretation Prediction systems Weather forecasting, traffic predictions, demographics Diagnostic systems Medical, mechanical, electronic, software diagnosis Design systems Circuit layouts, building design, plant layout Planning systems Project management, routing, communications, financial plans © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

General Problems Suitable for Expert Systems
Monitoring systems Air traffic control, fiscal management tasks Debugging systems Mechanical and software Repair systems Incorporate debugging, planning, and execution capabilities Instruction systems Identify weaknesses in knowledge and appropriate remedies Control systems Life support, artificial environment © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Benefits of Expert Systems
Increased outputs Increased productivity Decreased decision-making time Increased process and product quality Reduced downtime Capture of scarce expertise Flexibility Ease of complex equipment operation Elimination of expensive monitoring equipment Operation in hazardous environments Access to knowledge and help desks © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Benefits of Expert Systems
Ability to work with incomplete, imprecise, uncertain data Provides training Enhanced problem solving and decision-making Rapid feedback Facilitate communications Reliable decision quality Ability to solve complex problems Ease of knowledge transfer to remote locations Provides intelligent capabilities to other information systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Limitations Knowledge not always readily available
Difficult to extract expertise from humans Approaches vary Natural cognitive limitations Vocabulary limited Wrong recommendations Lack of end-user trust Knowledge subject to biases Systems may not be able to arrive at conclusions © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Success Factors Management champion User involvement Training
Expertise from cooperative experts Qualitative, not quantitative, problem User-friendly interface Expert’s level of knowledge must be high © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Types of Expert Systems
Rule-based Systems Knowledge represented by series of rules Frame-based Systems Knowledge represented by frames Hybrid Systems Several approaches are combined, usually rules and frames Model-based Systems Models simulate structure and functions of systems Off-the-shelf Systems Ready made packages for general use Custom-made Systems Meet specific need Real-time Systems Strict limits set on system response times © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 11 Knowledge Acquisition, Representation, and Reasoning
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 11 Knowledge Acquisition, Representation, and Reasoning © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Understand the nature of knowledge.
Learn the knowledge engineering processes. Evaluate different approaches for knowledge acquisition. Examine the pros and cons of different approaches. Illustrate methods for knowledge verification and validation. Examine inference strategies. Understand certainty and uncertainty processing. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Development of a Real-Time Knowledge-Based System at Eli Lilly Vignette
Problems with fermentation process Quality parameters difficult to control Many different employees doing same task High turnover Expert system used to capture knowledge Expertise available 24 hours a day Knowledge engineers developed system by: Knowledge elicitation Interviewing experts and creating knowledge bases Knowledge fusion Fusing individual knowledge bases Coding knowledge base Testing and evaluation of system © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Engineering
Process of acquiring knowledge from experts and building knowledge base Narrow perspective Knowledge acquisition, representation, validation, inference, maintenance Broad perspective Process of developing and maintaining intelligent system © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Engineering Process
Acquisition of knowledge General knowledge or metaknowledge From experts, books, documents, sensors, files Knowledge representation Organized knowledge Knowledge validation and verification Inferences Software designed to pass statistical sample data to generalizations Explanation and justification capabilities © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Sources Documented Undocumented Acquired from
Written, viewed, sensory, behavior Undocumented Memory Acquired from Human senses Machines © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Levels Shallow Deep Surface level Input-output
Problem solving Difficult to collect, validate Interactions betwixt system components © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Categories Declarative Procedural Metaknowledge
Descriptive representation Procedural How things work under different circumstances How to use declarative knowledge Problem solving Metaknowledge Knowledge about knowledge © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Engineers Professionals who elicit knowledge from experts
Empathetic, patient Broad range of understanding, capabilities Integrate knowledge from various sources Creates and edits code Operates tools Build knowledge base Validates information Trains users © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Elicitation Methods Manual Semiautomatic Automatic Based on interview
Track reasoning process Observation Semiautomatic Build base with minimal help from knowledge engineer Allows execution of routine tasks with minimal expert input Automatic Minimal input from both expert and knowledge engineer © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Manual Methods Interviews Structured Unstructured Semistructured
Goal-oriented Walk through Unstructured Complex domains Data unrelated and difficult to integrate Semistructured © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Manual Methods Process tracking Protocol analysis Observation
Track reasoning processes Protocol analysis Document expert’s decision-making Think aloud process Observation Motor movements Eye movements © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Manual Methods Case analysis Critical incident User discussions
Expert commentary Graphs and conceptual models Brainstorming Prototyping Multidimensional scaling for distance matrix Clustering of elements Iterative performance review © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Semiautomatic Methods
Repertory grid analysis Personal construct theory Organized, perceptual model of expert’s knowledge Expert identifies domain objects and their attributes Expert determines characteristics and opposites for each attribute Expert distinguishes between objects, creating a grid Expert transfer system Computer program that elicits information from experts Rapid prototyping Used to determine sufficiency of available knowledge © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Semiautomatic Methods, continued
Computer based tools features: Ability to add knowledge to base Ability to assess, refine knowledge Visual modeling for construction of domain Creation of decision trees and rules Ability to analyze information flows Integration tools © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Automatic Methods Data mining by computers
Inductive learning from existing recognized cases Neural computing mimicking human brain Genetic algorithms using natural selection © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Multiple Experts Scenarios Approaches Experts contribute individually
Primary expert’s information reviewed by secondary experts Small group decision Panels for verification and validation Approaches Consensus methods Analytic approaches Automation of process through software usage Decomposition © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Automated Knowledge Acquisition
Induction Activities Training set with known outcomes Creates rules for examples Assesses new cases Advantages Limited application Builder can be expert Saves time, money © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Difficulties Rules may be difficult to understand Experts needed to select attributes Algorithm-based search process produces fewer questions Rule-based classification problems Allows few attributes Many examples needed Examples must be cleansed Limited to certainties Examples may be insufficient © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Interactive induction Incrementally induced knowledge General models Object Network Based on interaction with expert interviews Computer supported Induction tables IF-THEN-ELSE rules © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Evaluation, Validation, Verification
Dynamic activities Evaluation Assess system’s overall value Validation Compares system’s performance to expert’s Concordance and differences Verification Building and implementing system correctly Can be automated © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Production Rules IF-THEN
Independent part, combined with other pieces, to produce better result Model of human behavior Examples IF condition, THEN conclusion Conclusion, IF condition If condition, THEN conclusion1 (OR) ELSE conclusion2 © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Artificial Intelligence Rules
Types Knowledge rules Declares facts and relationships Stored in knowledge base Inference Given facts, advises how to proceed Part of inference engines Metarules © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Artificial Intelligence Rules
Advantages Easy to understand, modify, maintain Explanations are easy to get. Rules are independent. Modification and maintenance are relatively easy. Uncertainty is easily combined with rules. Limitations Huge numbers may be required Designers may force knowledge into rule-based entities Systems may have search limitations; difficulties in evaluation © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Semantic Networks Graphical depictions Nodes and links
Hierarchical relationships between concepts Reflects inheritance © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Frames All knowledge about object
Hierarchical structure allows for inheritance Allows for diagnosis of knowledge independence Object-oriented programming Knowledge organized by characteristics and attributes Slots Subslots/facets Parents are general attributes Instantiated to children Often combined with production rules © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Knowledge Relationship Representations
Decision tables Spreadsheet format All possible attributes compared to conclusions Decision trees Nodes and links Knowledge diagramming Computational logic Propositional True/false statement Predicate logic Variable functions applied to components of statements © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Reasoning Programs Inference Engine Rule interpreter Algorithms
Directs search of knowledge base Forward chaining Data driven Start with information, draw conclusions Backward chaining Goal driven Start with expectations, seek supporting evidence Inference/goal tree Schematic view of inference process AND/OR/NOT nodes Answers why and how Rule interpreter © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Explanation Facility Justifier Types Makes system more understandable
Exposes shortcomings Explains situations that the user did not anticipate Satisfies user’s psychological and social needs Clarifies underlying assumptions Conducts sensitivity analysis Types Why How Journalism based Who, what, where, when, why, how Why not © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Generating Explanations
Static explanation Preinsertion of text Dynamic explanation Reconstruction by rule evaluation Tracing records or line of reasoning Justification based on empirical associations Strategic use of metaknowledge © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Uncertainty Widespread Important component Representation
Numeric scale 1 to 100 Graphical presentation Bars, pie charts Symbolic scales Very likely to very unlikely © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Uncertainty Probability Ratio Bayes Theory Dempster-Shafer
Degree of confidence in conclusion Chance of occurrence of event Bayes Theory Subjective probability for propositions Imprecise Combines values Dempster-Shafer Belief functions Creates boundaries for assignments of probabilities Assumes statistical independence © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Certainty Certainty factors Belief in event based on evidence
Belief and disbelief independent and not combinable Certainty factors may be combined into one rule Rules may be combined © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Expert System Development
Phases Project initialization Systems analysis and design Prototyping System development Implementation Postimplementation © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Project Initialization
Identify problems Determine functional requirements Evaluate solutions Verify and justify requirements Conduct feasibility study and cost-benefit analysis Determine management issues Select team Project approval © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Systems Analysis and Design
Create conceptual system design Determine development strategy In house, outsource, mixed Determine knowledge sources Obtain cooperation of experts Select development environment Expert system shells Programming languages Hybrids with tools General or domain specific shells Domain specific tools © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Prototyping Rapid production Demonstration prototype
Small system or part of system Iterative Each iteration tested by users Additional rules applied to later iterations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

System Development Development strategies formalized
Knowledge base developed Interfaces created System evaluated and improved © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Adoption strategies formulated System installed
Implementation Adoption strategies formulated System installed All parts of system must be fully documented and security mechanisms employed Field testing if it stands alone; otherwise, must be integrated User approval © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Postimplementation Operation of system Maintenance plans
Review, revision of rules Data integrity checks Linking to databases Upgrading and expansion Periodic evaluation and testing © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Internet Facilitates knowledge acquisition and distribution
Problems with use of informal knowledge Open knowledge source © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 12 Advanced Intelligent Systems
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 12 Advanced Intelligent Systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Understand second-generation intelligent systems.
Learning Objectives Understand second-generation intelligent systems. Learn the basic concepts and applications of case-based systems. Understand the uses of artificial neural networks. Examine the advantages and disadvantages of artificial neural networks. Learn about genetic algorithms. Examine the theories and applications of fuzzy knowledge. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Household Financial’s Vision Speeds Loan Approvals With Neural Networks Vignette
Loan product regulation varies in each state Develop an object-oriented loan approval system Neural network-based Fed risk, interest rate variables, customer data Estimates credit worthiness, potential for fraud Pattern recognition Integrates all loan approval phases Uses intelligent underwriting engine Reduced training time and administrative overhead Decreased managed basis efficiency ratio Upgradeable to web-based architecture © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Machine Learning Acquisition of knowledge through historical examples
Implicitly induces expert knowledge from history Different from the way that humans learn Implications of system success and failure unclear Manipulates of symbols instead of numbers © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Methods Supervised learning Unsupervised learning
Induce knowledge from known outcomes New cases used to modify existing theories Statistical methods Rule induction Case based and inference Neural computing Genetic algorithms leading to survival of fittest Unsupervised learning Determine knowledge from data with unknown outcomes Clustering data into similar groups © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Case Reasoning Inductive Case base used for decision-making
Effective when rule-based reasoning is not Case Primary knowledge element Ossified Paradigmatic Stories © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Process Features assigned as character indexes
Indexing rules identify input features Indexes used to retrieve similar cases from memory Episodic case memories Similarity metrics applied Old solution adjusted to fit new case Modification rules Solution tested If successful, assigned value and stored If failure, explain, repair, test Alter plan to fit situation Rules for permissible alterations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Case Reasoning Success Factors
Specific business objectives Knowledge should directly support end users Appropriate design Updatable Measurable metrics Acceptable ROI User accessible Expandable across enterprise © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Human Brain 50 to 150 billion neurons in brain
Neurons grouped into networks Axons send outputs to cells Received by dendrites, across synapses © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Neural Networks Attempts to mimic brain functions
Analogy, not accurate model Artificial neurons connected in network Organized by topologies Structure Three or more layers Input, intermediate (one or more hidden layers), output Receives modifiable signals © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Processing Processing elements are neurons
Allows for parallel processing Each input is single attribute Connection weight Adjustable mathematical value of input Summation function Weighted sum of input elements Internal stimulation Transfer function Relation between internal activation and output Sigmoid/transfer function Threshold value Outputs are problem solution © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Architecture Feedforward-backpropogation Associative memory system
Neurons link output in one layer to input in next No feedback Associative memory system Correlates input data with stored information May have incomplete inputs Detects similarities Recurrent structure Activities go through network multiple times to produce output © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Network Learning Learning algorithms Supervised Unsupervised
Connection weights derived from known cases Pattern recognition combined with weighting changes Back error propagation Easy implementation Multiple hidden layers Adjust learning rate and momentum Known patterns compared to output and allows for weight adjustment Established error tolerance Unsupervised Only stimuli shown to network Humans assign meanings and determine usefulness Adaptive resonance theory Kohonen self-organizing feature maps © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Development of Systems
Collect data The more, the better Separate data into training set to adjust weights Divide into test sets for network validation Select network topology Determine input, output, and hidden nodes, and hidden layers Select learning algorithm and connection weights Iterative training until network achieves preset error level Black box testing to verify inputs produce appropriate outputs Contains routine and problematic cases Implementation Integration with other systems User training Monitoring and feedback © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Genetic Algorithms Computer programs that apply processes of evolution
Viability of candidate solutions Self-organized Adaptable Fitness function Measured by objective obtained Iterative process Candidate solutions combine to produce generations Reproduction, crossover, mutation © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Genetic Algorithms Establish problem Generate initial set of solutions
Parameters Number of initial solutions, number of offspring, number of parents and offspring for each generation, mutation level, probability distribution of crossover point occurrence Generate initial set of solutions Compute fitness functions Total all fitness functions Compare each solution’s fitness function to total Apply crossover Apply random mutation Repeat until good enough solution or no improvement © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Fuzzy Logic Mathematical theory of fuzzy sets Imprecise thinking
Describes human perception Continuous logic Not 100% true or false, black or white Fuzzy neural networks Fuzzification Fuzzy logic applied to input and output used to create model Defuzzification Model converted back to original input, output scales Output becomes input for another intelligent system © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 13 Intelligent Systems Over the Internet
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 13 Intelligent Systems Over the Internet © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Understand intelligent systems operating across the Internet.
Learning Objectives Understand intelligent systems operating across the Internet. Examine the concept of intelligent agents. Learn intelligent agent applications. Explore the concept of Web-based semantic knowledge. Understand recommendation systems. Design recommendation systems. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Spartan Uses Intelligent Systems to Find the Right Person and Reduce Turnover Vignette
Supermarket chains experience over 100% turnover Employee replacement expensive Front-end positions critical in terms of customer relationships Spartan employed automated hiring system Analyze applicant profile Selects candidates from huge applicant pool Reduced turnover rate to 59% Increased operational efficiency Integrated with other systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Intelligent Systems Programs with tasks automated according to rules and inference mechanisms Web used as delivery platform May include semantic information Semantic Web Generally perform specific tasks Information agents Monitoring agents Recommendation agents © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Intelligent Agents Program that helps user perform routine tasks
Software agents, wizards, demons, bots Degree of independence or autonomy Three functions Perception of dynamic conditions Actions that affect environment Reasoning © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Intelligence Levels Wooldridge Lee
Reactivity to changes in environment Ability to choose response Capability of interaction with other agents Lee Level 0 Retrieve documents from URLs specified by user Level 1 User-initiated search for relevant pages Level 2 Maintain user profiles Notify users when relevant materials located Level 3 Learning and deductive reasoning component to assist user in expressing queries © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Components Owner Author Account Goals and metrics Subject Description
User name, parent process name, or master agent name Author Development owner, service, or master agent name Account Anchor to owner’s account Goals and metrics Determines task’s point of completion and value of results Subject Description Description of goal’s attributes Creation and Duration Request and response date Background information Intelligent subsystem Can provide several of the above characteristics © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Agents Can act on own or be empowered Can make some decisions
Can decide when to initiate actions Unscripted actions Designed to interact with other agents, programs, or humans Automates repetitive, narrowly defined tasks Continuously running process Must be believable Should be transparent Should work on a variety of machines May be capable of learning © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Successful Intelligent Agents
Decision support systems Employee empowerment for customer service Automation of routine tasks Search and retrieval of data Expert models Mundane personal activity © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Classifications Franklin and Graesser’s autonomous agents
Organization agents Task execution for processes or applications Personal agents Perform tasks for users Private or public agents Used by single user or many Software or intelligent agents Ability to learn © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Characteristics Agency Intelligence Mobility Mobile agents
Degree of measurable autonomy Ability to run asynchronously Intelligence Degree of reasoning and learned behavior Mobility Degree to which agents move through networks and transmit and receive data Mobile agents Nonmobile are two dimensional Mobile are three dimensional © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Web Based Software Agents
/Mailbot agents Softbots: Agents offering assistance with Web browsing Assistance with frequently asked questions Search engines Metasearch engines Network agents Monitor Diagnose problems Security Resource management © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

E-commerce Agents Identify needs Search for product Find best bargain
Negotiate price Arrangement of payment Arrange delivery After sales service Advertisement Payment support Fraud detection © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Other Agents Computer interfaces Agents to facilitate learning
Speech agents Intelligent tutoring Support for activities along supply chain Administrative office management Workflow, computer-telephone integration Web mining for information Monitoring for alerts Collaboration among agents Mobile commerce using WAP-based services © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Agents Agent types Data monitoring, data gathering, modeling, domain management, learning preferences Holsapple and Whinston Map types against Characteristics Homeostatic goals, persistence, reactivity Reference points Client, task,domain Hess Components data., modeling, user interface © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Multi-agent Systems Multiple software agents used to perform tasks
Multiple designers Agents work toward different goals Can cooperate or compete Distributed artificial intelligence Single designer Decomposes tasks into subtasks Distributed problem solving Single goal © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Semantic Web Content presentation Organization standard
Enables access to Web-based knowledge Allows Web-based collaboration and cooperation Technologies XML Scripting language employing user defined tags Web services XML-based technologies comprised of four layers Transport, XML messaging, service description, publication and integration © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Components of Semantic Web
Resource Description Framework data model Relate Uniform Resource Identifiers to each other Point to Web resources Language with defined semantics Standardized terminologies for knowledge domain Service logic establishes rules governing use Proof Trust © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Advantages and Limitations
Easy to understand Systems and modules easily integrated Saves development time and expense Allows for incremental and rapid development Updates automatically Resources reuse Limitations: Oversimplified graphical representation Needs additional tools Incorrect definitions Information may be incorrect or inconsistent Security © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Recommendation Systems
Personalized Collect and analyze each user’s information and needs Profile generation and maintenance Profiling method determination Initial profile generation Data processing for pattern recognition Feedback collection Analyze feedback and adapt Profile exploitation and recommendation Identify useful information Compare user profile to new items Locate similar users, create neighborhood, make prediction © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Recommendation Systems
Collaborative filtering Market segmentation used to predict preferences Compares individual to population in order to locate similar users Similarity index metrics Infer interests Predicts preferences based on weighted sums Content-based filtering Recommendations-based on similarities between products Attribute based Works with small base of data Neglects aesthetic aspects of products © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Management Issues Expense Security Systems integration and flexibility
Hardware and software requirements Agent accuracy Agent learning Invasion of privacy Competitive intelligence and industrial intelligence Other ethical issues Heightened expectations Systems acceptance © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 14 Electronic Commerce
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 14 Electronic Commerce © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Describe the concepts involved in electronic commerce. Understand auctions and portal mechanisms. Know the applications involved in e-commerce. Learn about electronic market research, eCRM, and online advertising. Define collaborative commerce and B2B applications. Understand e-government activities. Describe mobile commerce and pervasive computing. Learn e-commerce infrastructure and support services. Understand the ethical and legal issues involved in e-commerce. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

E-commerce Provides Decision Support to Hi-Life Corp. Vignette
Convenience store chain needs accurate stock count Overstocking expensive Understocking results in customer dissatisfaction Losses due to shrinkage Manual counts used data collection sheets Expensive, labor intensive Solution based on handheld computer Counts entered relayed immediately to headquarters Bar code scanner employed to shorten process, minimize errors Allows for real time product totals Dramatic reduction in labor involved Lower inventory levels and quicker response time © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

E-commerce Process of buying, selling, transferring, exchanging products, services, or information over computer networks Pure versus partial Based on degree of digitization Product Process Intermediary Pure requires all three components to be fully digitized Internet versus non-Internet Most are Internet based May be value-added networks or local area networks © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

E-commerce Transactions
Business-to-business (B2B) Business-to-consumer (B2C) Consumer-to-consumer (C2C) Consumer-to-business (C2B) Government-to-citizens (G2C) Collaborative commerce between partners Business to employees Intrabusiness/Intraorganizational commerce Mobile commerce (M-commerce) © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Scope of E-commerce Applications supported by infrastructure
Hardware Software Messaging, multimedia, interfaces, business services Networks communications Support areas People Legal and public policy and regulations Marketing and advertisements Support services ranging from payments to order delivery Business partnerships like joint ventures, e-marketplaces, affiliations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Advantages Advantages: Expands marketplace globally
Expands availability of resources Shortens marketing-distribution channels Decreases expenses Reduces inventory Aids small businesses in competing Enables specialized niches Quicker delivery of information Enables individuals to work from home Facilitates delivery of public services Allows for purchase of goods at lowered prices Enables customization, personalization Decreases costs to customers, while increasing their choices Allows for 24 hour shopping Makes electronic auctions possible Enables people to interact in electronic communities © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Limitations Limitations: Lack of universal standards
Insufficient bandwidth Software-development tools are still evolving Integration difficulties Need for special Web servers in addition to network servers Accessibility expensive Unresolved legal issues Lack of national and international governmental regulations Lack of mature methodologies to measure benefits and justify Customer resistance Security questions Insufficient number of buyers and sellers for profitable e-commerce operations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS and E-commerce DSS supports e-commerce
DSS allows for scheduling and transportation optimization Match buyers to sellers Improves market operations Conducts risk analysis Optimizes selection of transportation routes Assists in running B2C operations Data collection Business intelligence © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS and E-commerce E-commerce facilitates decision support
Efficient transfer of information Enhances decision-support process Data collection and storage E-commerce works with DSS Inventory management Produce strategic change in call center by integration of simulation decision support Marketing database applications and distribution systems Streaming financial reports Comparison shopping engines Data transfer and storage for BI analysis © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

E-commerce Mechanisms
Electronic auctions Competitive market mechanisms Forward auctions Sellers place offers and buyers make sequential bids Reverse auctions Sellers are invited to submit bids on product or service buyer wants Bartering Exchange of goods or services without money transactions Portals Information gateways Single point of access through Web browser © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Portals Commercial Publishing Personal Mobile
Offer content to broad audiences Routine Little personalization Publishing Based on specific interests Extensive search capabilities Personal Target specific filtered information Narrow content Personalized Mobile Accessible through mobile devices © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Portals Voice Corporate Audio interfaces Accessible through phones
Access to business information located both within and outside of organization Rich content Limited communities Organized focal point Suppliers Customers Employees Supervisors © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Business to Consumer Applications
E-tailing Storefronts General or specialized May be extensions of physical stores E-Malls Collection of stores under single Internet address Manufacturers may sell direct Retailers may act as intermediaries © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

DSS Support Channel conflict resolution through GDSS tools
DSS and GDSS can be used for conflict resolution on pricing, resource allocation, logistics services DSS can aid in order fulfillment and logistics of small quantities DSS models can foster strategies and determine viability Identification of appropriate revenue models Risk analysis with DSS modeling © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Online Service Industries
Electronic banking International banking Securities trading Online job market Travel Real estate © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Market Research E-commerce model of consumer behavior
Independent uncontrollable variables Personal characteristics Age, gender, demographics Environmental characteristics Social, cultural, available information, government regulations, legal constraints Intervening variables Vendor controlled Market stimuli E-commerce systems Physical environment, logistics support, customer services © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Market Research Decision making process Dependent variables
Influenced by independent and intervening variables Feeds into buyers’ decisions Dependent variables Buyers’ decisions © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Market Research Decision-making process Generic model
Identification of needs, information search, evaluation of alternatives, purchase and delivery, after-purchase evaluation Consumer decision support system model Support facilities from CDSS and Internet and Web produce framework for Web purchasing Online buyer decision support model Customer decision-making guided by Web purchasing models © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Discovering Customer Desires
Software agent search engines Intelligent agents Monitor site activity Searching and filtering agents for customers Comparison agents Electronic questionnaires Site tracking Cookies, Web bugs, spyware Collaborative filtering through inference of interest © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

E-commerce CRM During life cycle of product Tools available
Determine customer requirements Help customer acquire product or service Ongoing support Aid in disposal Tools available FAQs messaging Track status of order Personalization of Web pages and information at vendor’s site Chat rooms and communities Web-based call centers © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Online Advertising Media rich, dynamic, interactive Types Issues
Banners Pop-ups and pop-unders advertisements Electronic catalogs and brochures Advertisement postings in chatrooms, communities, and newsgroups Online classifieds Issues Spam Permission marketing Viral marketing Passive, mass market advertising Interactive advertising © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

B2B Applications Sell-side marketplaces Buy-side marketplaces
Private e-marketplaces operated by seller Electronic catalogs Forward auctions Buy-side marketplaces Reverse auction Third-party bidding marketplace or buyer’s Web site Procurement models Group purchasing Desktop purchasing © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

B2B Applications Electronic exchanges Types
E-marketplaces with many sellers and buyers Types Systematic sourcing by vertical distributors of direct materials Indirect materials sold on “as needed” basis with dynamic pricing Systematic sourcing for indirect materials at fixed pricing Spot sourcing of services on “as needed” basis © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Collaborative E-commerce
Non-sales based e-commerce transactions between organizations Electronic support of communication, information sharing, joint decision making Types Retailers/suppliers Vendor-managed inventories supplied to retailers Product design Collaborative manufacturing through outsourcing of components and subassemblies © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Collaborative E-commerce
Collaborative workflow management Planning and scheduling Design New product information Product-content management Order management Sourcing and procurement © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Intrabusiness E-commerce
B2E Intranet-enabled business between business and employees E-commerce between business units Organization units sell and buy materials and products from each other E-commerce between corporate employees Classified ads Sales force automation Empowerment of salespersons © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

E-government Use of Internet technologies and e-commerce to deliver information and services to citizens Gives citizens more access to information Allows for more feedback from citizens Facilitates fundamental changes in relationships between citizen and government Types Government-to-citizens (G2C) Electronic benefits transfer, payment of taxes Government-to-business (G2B) RFQs, RFBs, reverse auctions Government-to-government (G2G) Sharing of databases, information © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

E-learning Online delivery of information for educational or training purposes Benefits Eliminates barriers of time, distance, socioeconomic status Saves money, reduces travel time Increases access to experts Enables large numbers to take classes Provides on-demand, self-paced learning Limitations Special training for instructors and students Requires special equipment and support services Lack of face-to-face interaction © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Customer to Customer E-commerce
Buyers and sellers not businesses Types Auctions Classified ads Personal services Bartering © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Variants of E-Commerce
Mobile commerce E-commerce through use of mobile computing devices on wireless networks Advantages Mobility People can be reached at any time L-commerce Location-based mobile commerce Information pushed out to recipient based on their current location Pervasive computing Computations become part of the environment Embodied in things Based on intelligent systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

E-commerce Support Systems
Electronic payments Electronic checks Electronic credit cards Virtual credit cards Purchasing cards Electronic cash Stored value money cards Smart cards with microprocessors Person-to-person payments Payment of bills online © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Security in Electronic Payments
Authentication of all parties Protection of data from alteration or destruction during transmission Protection from buyer’s unjustified repudiation Privacy Customer safety Protection of information at seller’s end © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Order Fulfillment in Electronic Commerce
Provide customers with ordered goods Goods must be quickly packaged, shipped, and delivered Payment collection system must be in force Handle the return of unwanted or defective merchandise Customer relations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Legal and Ethical Issues
Fraud Seller’s and buyer’s Buyer protection Seller protection Unwarranted repudiation Intellectual property rights Domain names Privacy issues Cookies Web tracking Sales of lists Monitoring s and site visits Taxation Disintermediation Intellectual Property issues © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition
Chapter 15 Integration, Impacts, and the Future of Management-Support Systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Learning Objectives Learn the processes of systems integration for MSS. Understand the difficulties in integrating systems. Describe major models in integration. Define intelligent DSS. Understand concept of intelligent modelling. Know MSS integration with enterprise and Web systems. Describe impacts of MSS on organization. Learn the potential impact of MSS on individuals. Define societal impacts of MSS. Be cognizant of the ethical and legal issues of MSS. Understanding the digital divide. Describe Internet communities. Overview of future of MSS. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Systems Integration Functional integration Physical integration
Different applications provided as single system Across differing MSS or within MSS Solves repetitive problems Integration of MSS techniques to build specific MSS Physical integration Hardware, software, and communications integration Applications integration Data, applications, methods, and processes Develop level integration Integrate to increase capabilities Integrate to enhance intelligent tools © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Models Integration of expert systems and DSS
Expert systems attached to DSS ES 1: Database intelligent component ES 2: Intelligent agent for model base and management ES 3: System for improving user interface ES 4: Consultant to DSS ES 5: Consultant to users Usually, only one or two are attached Expert system as separate components Expert systems output as input to DSS DSS output as input to expert system Feedback Expert systems generation of alternatives to DSS Unified approach © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Models Tight integration due to shared interfaces and resources
Shared decision-making Expandable to other intelligent systems Can integrate EIS, DSS and expert systems Information from EIS is inputted into DSS DSS feedback to EIS Expert system used for interpretation, explanation © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Intelligent DSS Active DSS Intelligent component Symbiotic
Understands domain and provides explanations Helps formulate problems Relates problems to solver Interprets results Explains results and decisions © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Intelligent DSS Self-evolving DSS Problem management
Aware of how it is being used and adapts to needs of use Dynamic menus User interface Intelligent model-based management system Problem management Automate processes by dividing into smaller steps Specific architecture to support functional requirements © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Intelligent Modeling Intelligence added to allow input of expertise
Multiple models available Construction Simplify real world situation Less complex version of reality Use of models Some judgmental values Expert systems supply sensitivity analysis Expert systems provide result explanations, patterns, anomalies Most based on quantitative models © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Integration Increases functionality
Makes enterprise systems more user friendly Provides greater flexibility Saves money by integration various systems Enables easier integration of functional systems © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Integration ERP Supply chain systems Knowledge management systems
Integrates analytical capabilities Supply chain systems Enhance capabilities Optimize tools Knowledge management systems Communication, collaboration, storage DSS integration Intelligent systems integration Data mining tools with manufacturing systems DSS and learning systems Data mining with business modeling © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Issues Cost-benefit justifications Feasibility Architecture choices
Infrastructure Development process and tools Connectivity Web-based integration Data issues Legal issues and privacy New technology introduction, integration © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Impacts of MSS Organizational Culture Creation of new departments
Virtual teams Business process reengineering Business simulation tools Increased production Increased customer satisfaction Improvement in quality Supply chain management improvements Improved performance of managers and employees © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Impacts of MSS Individual Increased job satisfaction
Negative effect on individuality Dehumanization Job stress Lack of cooperation by expert © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Impacts of MSS Societal Positive effects Negative effects
Reduction or elimination of humans in hazardous positions Increased opportunities for disabled, home bound, and single parents Telecommuting Improved health care Improved quality of life Negative effects Computer fraud and embezzlement Identity theft Neglect of family Increased power due to increased centralization of organizations © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Legal and Privacy Issues
Antitrust Unfair competition Unreasonable personal intrusion Collection of information about individuals Ethics Personal values Intellectual property rights Copyright Trademarks Domain names Patents Computer abuse Electronic surveillance Use of proprietary databases Data integrity © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Impacts of Artificial Intelligence
Increased employment through newly created MSS-related jobs Massive unemployment through automation of processes Social implications Increased leisure time Government intervention with employment levels Increased wealth © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Internet Communities Groups of people with common interests
Interact through Internet Types Communities of transactions Facilitate buying and selling Communities of interest Based on specific topic Communities of relations Organized around life experiences Communities of fantasy Based on imaginary environments Game playing © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Digital Divide Growing gap between those who have and those who do not have access to technology Exists within and between countries © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Future of MSS MSS is becoming a Web-based technology
Combining and integration with business intelligence BI is being combined with a number of Web-based applications Intelligent systems are being employed in the war against terrorism Web-based advisory services are being developed More complex MSS applications are being developed Trend toward increasing intelligence of systems Pervasive computing MSS are being disseminated via ASPs Natural language based search engines Semantic web © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Future of MSS Voice technologies are being enriched through use of MSS
CRM improvement Improvement along supply chain through integration with ERP Expertise availability on Internet Initiation of formal knowledge-management programs More intelligent agents on Internet and other networks Greater use of wireless technologies Intelligent agents will roam the Internet, intranets, and extranets to monitor information and assist in decision-making Increase in groupware technologies for collaboration and communication DSS for e-commerce Decision-support tools for e-commerce will be expanded © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

Chapter 1 Management Support Systems: An Overview

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