1. The Business Pressures-Responses-Support Model- Three components:
    • business pressures that result from today's business climate
    • responses (actions taken) by companies to counter the pressures (or to take advantage of the opportunities available in the envir.)
    • computerized support that facilitates the monitoring of the envir. and enhances the response actions taken by orgs.
  2. Business Environment Factors
    • markets,
    • consumer demands,
    • technology,
    • societal
  3. Organizational Responses
    • Be Reactive, Anticipative, Adaptive, and Proactive
    • Managers may take actions, such as:
    • - Employ strategic planning
    • - Use new and innovative business models
    • - Restructure business processes
    • - Participate in business alliances
    • - Improve corporate information systems
    • - Improve partnership relationships
    • - Encourage innovation and creativity
    • - Improve customer service and relationships
    • - Move to electronic commerce (e-commerce)
    • - Move to make-to-order production and on-demand manufacturing and services
    • - Use new IT to improve communication, data access (discovery of information), and collaboration
    • - Respond quickly to competitors' actions (e.g., in pricing, promotions, new products and services)
    • - Automate many tasks of white-collar employees
    • - Automate certain decision processes
    • - Improve decision making by employing analytics
  4. Mintzberg's 10 Managerial Roles
    • Interpersonal
    • 1. Figurehead
    • 2. Leader
    • 3. Liasio
    • Informational
    • 4. Monitor
    • 5. Disseminator
    • 6. Spokesperson
    • Decisional
    • 7. Entrepreneur
    • 8. Disturbance handler
    • 9. Resource allocator
    • 10. Negotiator
  5. Four-step process(the scientific approach) managers usually make decisions
    • 1. Define the problem (or opportunity)
    • 2. Construct a model that describes the real-world problem
    • 3. Identify possible solutions to the modeled problem and evaluate the solutions
    • 4. Compare, choose, and recommend a potential solution to the problem
  6. Decision making is difficult, because
    • -Technology, information systems, advanced search engines, and globalization result in more and more alternatives from which to choose
    • -Government regulations and the need for compliance, political instability and terrorism, competition, and changing consumer demands produce more uncertainty, making it more difficult to predict consequences and the future
    • -Other factors are the need to make rapid decisions, the frequent and unpredictable changes that make trial-and-error learning difficult, and the potential costs of making mistakes
  7. Why Use Computerized DSS -Computerized DSS can facilitate decision via:
    • - Speedy computations
    • - Improved communication and collaboration
    • - Increased productivity of group members
    • - Improved data management
    • - Overcoming cognitive limits
    • - Quality support; agility support
    • - Using Web; anywhere, anytime support
  8. A Decision Support Framework (by Gory and Scott-Morten, 1971)
    • operational control, managerial control, strategic planning
    • structured 1. 2. 3.
    • Semistructured 4. 5. 6.
    • Unstructured 7. 8. 9.
  9. Degree of Structuredness (Simon, 1977): -Decision are classified as
    • - Highly structured (a.k.a. programmed)
    • Semi-structured
    • Highly unstructured (i.e., non-programmed)
  10. Types of Control (Anthony, 1965)
    • - Strategic planning (top-level, long-range)
    • - Management control (tactical planning)
    • - Operational control
  11. Computer Support for Structured Decisions
    • - Structured problems: encountered repeatedly, have a high level of structure
    • - It is possible to abstract, analyze, and classify them into specific categories
    • - e.g., make-or-buy decisions, capital budgeting, resource allocation, distribution, procurement, and inventory control
    • - For each category a solution approach is developed => Management Science
  12. Simon’s Decision-Making Process: Steps of Decision Support
    • Intelligence
    • Design
    • Choice
    • Implementation
  13. The five-step Management Science/Operation Research approach managers follow to solve problem
    • 1. Define the problem
    • 2. Classify the problem into a standard category (*)
    • 3. Construct a model that describes the real-world problem
    • 4. Identify possible solutions to the modeled problem and evaluate the solutions
    • 5. Compare, choose, and recommend a potential solution to the problem
  14. Automated Decision Making (ADS) (or decision automation systems)
    • A relatively new approach to supporting decision making
    • Applies to highly structured decisions
    • Is a rule-based system that provides a solution to a repetitive managerial problem in a specific area
    • ADS are driven by business rules!
  15. Computer Support for Unstructured Decisions
    • - Unstructured problems can be only partially supported by standard computerized quantitative methods
    • - They often require customized solutions
    • - They benefit from data and information
    • - Intuition and judgment may play a role
    • - Computerized communication and collaboration technologies along with knowledge management is often used
  16. Computer Support for Semi-structured Problems
    • - Solving semi-structured problems may involve a combination of standard solution procedures and human judgment
    • - MS handles the structured parts while DSS deals with the unstructured parts
    • - With proper data and information, a range of alternative solutions, along with their potential impacts
  17. Decision Support Systems
    • Interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems" - Gorry and Scott-Morton, 1971
    • -Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems - Keen and Scott-Morton, 1978
    • The term DSS can be used as an umbrella term to describe any computerized system that supports decision making in an organization
    • -As a Specific App.: In a narrow sense DSS refers to a process for building customized applications for unstructured or semi-structured problems
  18. Components of the DSS Architecture
    • Data, Model, Knowledge/Intelligence,
    • User Interface (API and/or user interface)
    • -DSS often is created by putting together loosely coupled instances of these components
  19. Two major types of DSS:
    • Model-oriented DSS
    • Data-oriented DSS
  20. Model-oriented DSS
    -which quantitative models are used to generate a recommended solution to a problem
  21. Data-oriented DSS
    -which support ad hoc reporting and queries
  22. Business Intelligence (BI)
    • -Is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies
    • Like DSS, BI a content-free expression, so it means different things to different people
    • BI's major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis
    • BI helps transform data, to information (and knowledge), to decisions and finally to action
  23. A Brief History of BI
    • The term BI was coined by the Gartner Group in the mid-1990s
    • However, the concept is much older
    • 1970s - MIS reporting - static/periodic reports
    • 1980s - Executive Information Systems (EIS)
    • 1990s - OLAP, dynamic, multidimensional, ad-hoc reporting -> coining of the term “BI”
    • 2005+ Inclusion of AI and Data/Text Mining capabilities; Web-based Portals/Dashboards
    • 2010s - yet to be seen
  24. BI system four major components:
    • -A data warehouse, with its source data
    • -Business analytics,
    • -Business performance management (BPM) for monitoring and analyzing performance
    • -A user interface (e.g., dashboard)
  25. MicroStrategy, Corp. distinguishes five styles of BI and offers tools for each:
    • Report delivery and alerting
    • Enterprise reporting (using dashboards and scorecards)
    • Cube analysis (also known as slice-and-dice analysis)
    • Ad-hoc queries
    • Statistics and data mining
  26. The DSS–BI Connection
    • First, their architectures are very similar because BI evolved from DSS
    • Second, DSS directly support specific decision making, while BI provides accurate and timely information, and indirectly support decision making
    • Third, BI has an executive and strategy orientation, especially in its BPM and dashboard components, while DSS, in contrast, is oriented toward analysts
    • Fourth, most BI systems are constructed with commercially available tools and components, while DSS is often built from scratch
    • Fifth, DSS methodologies and even some tools were developed mostly in the academic world, while BI methodologies and tools were developed mostly by software companies
    • Sixth, many of the tools that BI uses are also considered DSS tools (e.g., data mining and predictive analysis are core tools in both)
    • Although some people equate DSS with BI, these systems are not, at present, the same
    • -some people believe that DSS is a part of BI—one of its analytical tools
    • -others think that BI is a special case of DSS that deals mostly with reporting, communication, and collaboration (a form of data-oriented DSS)
    • -BI is a result of a continuous revolution and, as such, DSS is one of BI's original elements
    • -In this book, we separate DSS from BI
    • MSS = BI and/or DSS
  27. Work system:
    A system in which human participants and/or machines perform a business process, using information, technology, and other resources, to produce products and/or services for internal or external customers
  28. Elements of a Work System
    • 1. Business process. Variations in the process rationale, sequence of steps, or methods used for performing particular steps
    • 2. Participants. Better training, better skills, higher levels of commitment, or better real-time or delayed feedback
    • 3. Information. Better information quality, information availability, or information presentation
    • 4. Technology. Better data storage and retrieval, models, algorithms, statistical or graphical capabilities, or computer interaction
    • 5. Product and services. Better ways to evaluate potential decisions
    • 6. Customers. Better ways to involve customers in the decision process and to obtain greater clarity about their needs
    • 7. Infrastructure. More effective use of shared infrastructure, which might lead to improvements
    • 8. Environment. Better methods for incorporating concerns from the surrounding environment
    • 9. Strategy. A fundamentally different operational strategy for the work system
  29. Major Tool Categories for MSS
    • Data mgmt
    • Reporting status tracking
    • Visualization
    • Business analytics
    • Strategy and performance mgmt
    • Communication and collaboration
    • Social networking Knowledge mgmt
    • Intelligent systems
    • Enterprise systems
  30. Hybrid (Integrated) Support Systems
    • -The objective of computerized decision support, regardless of its name or nature, is to assist management in solving managerial or organizational problems (and assess opportunities and strategies) faster and better than possible without computers
    • -Every type of tool has certain capabilities and limitations. By integrating several tools, we can improve decision support because one tool can provide advantages where another is weak
    • -The trend is therefore towards developing hybrid (integrated) support system
  31. Hybrid (Integrated) Support Systems: Type of integration
    • -Use each tool independently to solve different aspects of the problem
    • -Use several loosely integrated tools. This mainly involves transferring data from one tool to another for further processing
    • -Use several tightly integrated tools. From the user's standpoint, the tool appears as a unified system
    • -In addition to performing different tasks in the problem-solving process, tools can support each other
  32. Characteristics of Decision Making
    • -Groupthink
    • -Evaluating what-if scenarios
    • -Experimentation with a real system!
    • -Changes in the decision-making environment may occur continuously
    • -Time pressure on the decision maker
    • -Analyzing a problem takes time/money
    • -Insufficient or too much information
    • -Better decisions
    • -Tradeoff: accuracy versus speed
    • -Fast decision may be detrimental
  33. Decision Making
    • -A process of choosing among two or more alternative courses of action for the purpose of attaining a goal(s)
    • -Managerial decision making is synonymous with the entire management process - Simon (1977)
    • -e.g., Planning
    • -What should be done? When? Where? Why? How? By whom?
  34. Phases of the decision process
    • Phase (1) Intelligence
    • Phase (2) Design
    • Phase (3) Choice, and
    • Phase (4) Implementation
    • -(1)-(4): problem solving; (3): decision making
    • -(1)-(3): decision making; (4): problem solving
  35. Decision-making styles
    • --the manner by which decision makers think and react to problems
    • -Heuristic versus Analytic
    • -Autocratic versus Democratic
    • -Consultative (with individuals or groups)
  36. Decision Makers
    • -Small organizations
    • -Individuals
    • -Conflicting objectives
    • -Medium-to-large organizations
    • -Groups
    • -Different styles, backgrounds, expectations
    • -Conflicting objectives
    • -Consensus is often difficult to reach
    • -Help: Computer support, GSS, ...
  37. Model
    • -A significant part of many DSS and BI systems
    • -A model is a simplified representation (or abstraction) of reality
    • -Often, reality is too complex to describe
    • -Much of the complexity is actually irrelevant in solving a specific problem
    • -Models can represent systems/problems at various degrees of abstraction
  38. Models can be classified based on their degree of abstraction; type of models:
    • -Iconic models (scale models)
    • -Analog models
    • -Mathematical (quantitative) models- a system of symbols and expressions that represent a real solution
    • -Iconic models (scale models)
  39. -Analog models
    – such as organization chart, behaves like the real system but does not look like it; an abstract, symbolic model of a system that behaves like the system but looks different
  40. -Mental Models
    – descriptive, such as cognitive map; the mechanisms or images through which a human mind performs sense-making in decision making
  41. -Iconic models (scale models)
    – physical replica like a bridge, usually on a different scale from the original; a scaled physical replica
  42. The Benefits of Models
    • -Ease of manipulation
    • -Compression of time
    • -Lower cost of analysis on models
    • -Cost of making mistakes on experiments
    • -Inclusion of risk/uncertainty
    • -Evaluation of many alternatives
    • -Reinforce learning and training
    • -Web is source and a destination for it
  43. Phases of Decision-Making Process
    • -Humans consciously or sub consciously follow a systematic decision-making process - Simon (1977)
    • 1. Intelligence
    • 2. Design
    • 3. Choice
    • 4. Implementation
    • 5. (?) Monitoring (a part of intelligence?)
  44. Decision-Making: Intelligence Phase: 4 steps
    • - the initial phase of problem definition in decision making
    • - the phase involves searching for conditions that call for decisions
    • -1. Problem Identification
    • -2. Problem Classification
    • -Classification of problems according to the degree of structuredness
    • -3. Problem Decomposition
    • -Often solving the simpler subproblems may help in solving a complex problem
    • -Information/data can improve the structuredness of a problem situation
    • -4. Problem Ownership
    • -Outcome of intelligence phase: A Formal Problem Statement
  45. Decision-Making: The Design Phase
    • -Finding/developing and analyzing possible courses of actions
    • -A model of the decision-making problem is constructed, tested, and validated
    • - the second decision-making phase, which involves finding possible alternatives in decision making and assessing their contributions
    • - the phase involves inventing, developing, and analyzing possible alternative courses of action (solutions)
  46. Decision-Making: The Choice Phase
    • - the third phase in decision making, in which alternative is selected
    • - this phase involves selecting a course of action from among those available
    • -The actual decision and the commitment to follow a certain course of action are made here
    • -The boundary between the design and choice is often unclear (partially overlapping phases)
    • ---Generate alternatives while performing evaluations
    • -Includes the search, evaluation, and recommendation of an appropriate solution to the model
    • -Solving the model versus solving the problem!
  47. -Search approaches in Decision-Making: The Choice Phase
    • ---Analytic techniques (solving with a formula)
    • ---Algorithms (step-by-step procedures)
    • ---Heuristics (rule of thumb)
    • ---Blind search (truly random search)
  48. -Additional activities to Decision-Making: The Choice Phase
    • ---Sensitivity analysis
    • ---What-if analysis
    • ---Goal seeking
  49. Decision-Making: The Implementation Phase
    • -Implementation: putting a recommended solution to work
    • - the fourth decision-making phase, involving actually putting a recommended solution to work
    • - the phase involves adapting the selected course of action to the decision situation (i.e. problem solving or opportunity exploiting)
  50. New Technologies to Support Decision Making
    • -Web-based systems
    • -m-Commerce
    • -PDA, Cell phones, Tablet PCs
    • -GSS with visual/immersive presence
    • -RFID and other wireless technologies
    • -Faster computers, better algorithms, to process “huge” amounts of heterogeneous/distributed data
  51. DSS have three basic components
    • 1. Data
    • 2. Model
    • 3. User interface
    • 4. (+ optional) Knowledge
  52. Holsapple and Whinston's Classification
    • 1. The text-oriented DSS
    • 2. The database-oriented DSS.
    • 3. The spreadsheet-oriented DSS
    • 4. The solver-oriented DSS
    • 5. The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications)
    • 6. The compound DSS
  53. Alter's Output Classification
    • Based on the "degree of action implication of system outputs" or the extent to which system outputs can directly support (or determine) the decision
    • Data
    • -File drawer systems
    • --access data items
    • -data analysis systems
    • --ad hoc analysis of data files
    • Data or models
    • -analysis information systems
    • --ad hoc analysis involving multiple databases and small models
    • Models
    • -accounting models
    • --standard calculations that estimate future results on the basis of accounting definitions
    • -optimization models
    • --calculating an optimal solution to a combinatorial problem
  54. Components of DSS
    • -Data Management Subsystem
    • --Includes the database that contains the data
    • --Database management system (DBMS)
    • --Can be connected to a data warehouse
    • -Model Management Subsystem
    • --Model base management system (MBMS)
    • -User Interface Subsystem
    • -Knowledgebase Management Subsystem
    • --Organizational knowledge base
  55. Overall Capabilities of DSS
    • -Easy access to data/models/knowledge
    • -Proper management of organizational experiences and knowledge
    • -Easy to use, adaptive and flexible GUI
    • -Timely, correct, concise, consistent support for decision making
    • -Support for all who needs it, where and when he/she needs it
    • -more...
  56. DSS Components and Web Impacts
    • -Impacts of Web to DSS
    • --Data management via Web servers
    • --Easy access to variety of models, tools
    • --Consistent user interface (browsers)
    • --Deployment to PDAs, cell phones, etc. …
    • -DSS impact on Web
    • --Intelligent e-Business/e-Commerce
    • --Better management of Web resources and security,
    • --… (see Table 3.3 for more…)
  57. DSS Components: Data Management Subsystem
    • -DSS database
    • -DBMS
    • -Data directory
    • -Query facility
  58. Database Management Subsystem Key Data Issues
    • -Data quality
    • -“Garbage in/garbage out" (GIGO)
    • -Data integration
    • -“Creating a single version of the truth”
    • -Scalability
    • -Data Security
    • -Timeliness
    • -Completeness, …
  59. 10 Key Ingredients of Data (Information) Quality Management
    • 1. Data quality is a business problem, not only a systems problem
    • 2. Focus on information about customers and suppliers, not just data
    • 3. Focus on all components of data: definition, content, and presentation
    • 4. Implement data/information quality management processes, not just software to handle them
    • 5. Measure data accuracy as well as validity
    • 6. Measure real costs (not just the percentage) of poor quality data/information
    • 7. Emphasize process improvement/preventive maintenance, not just data cleansing
    • 8. Improve processes (and hence data quality) at the source
    • 9. Educate managers about the impacts of poor data quality and how to improve it
    • 10. Actively transform the culture to one that values data quality
  60. DSS Components: Model Management Subsystem
    • -Model base
    • -MBMS
    • -Modeling language
    • -Model directory
    • -Model execution, integration, and command processor
    • Slide30
    • -Model base (= database ?)
    • -Analytic models
    • -Model building blocks
    • -Modeling tools
    • -Model directory
    • -Model execution, integration and command
  61. Model Management Subsystem: The four (4) functions
    • 1. Model creation, using programming languages, DSS tools and/or subroutines, and other building blocks
    • 2. Generation of new routines and reports
    • 3. Model updating and changing
    • 4. Model data manipulation
  62. Model Management Subsystem: Model Types
    • -Strategic models
    • -Tactical models
    • -Operational models
  63. DSS Components: User Interface (Dialog) Subsystem
    • -Interface
    • --Application interface
    • --User Interface
    • ---Graphical User Interface (GUI)
    • -DSS User Interface
    • --Portal
    • ---Graphical icons
    • --Dashboard
    • --Color coding
    • -Interfacing with PDAs, cell phones, etc.
    • Slide33
  64. DSS Components: Knowledgebase Management System
    • -Incorporation of intelligence and expertise
    • -Knowledge components:
    • --Expert systems,
    • --Knowledge management systems,
    • --Neural networks,
    • --Intelligent agents,
    • --Fuzzy logic,
    • --Case-based reasoning systems, and so on
    • -Often used to better manage the other DSS components
  65. DSS Components: Future/current DSS Developments
    • -Hardware enhancements
    • --Smaller, faster, cheaper, …
    • -Software/hardware advancements
    • --data warehousing, data mining, OLAP, Web technologies, integration and dissemination technologies (XML, Web services, SOA, grid computing, cloud computing, …)
    • -Integration of AI -> smart systems
  66. Modeling and Analysis Topics
    • -Modeling for MSS (a critical component)
    • -Static and dynamic models
    • -Treating certainty, uncertainty, and risk
    • -Influence diagrams
    • -MSS modeling in spreadsheets
    • -Decision analysis of a few alternatives (with decision tables and decision trees)
    • -Optimization via mathematical programming
    • -Heuristic programming
    • -Simulation
    • -Model base management
  67. Major Modeling Issues
    • -Problem identification and environmental analysis (information collection)
    • -Variable identification
    • -Influence diagrams, cognitive maps
    • -Forecasting/predicting
    • -More information leads to better prediction
    • -Multiple models: A MSS can include several models, each of which represents a different part of the decision-making problem
    • -Model management
  68. Categories of models
    • Optimization of problems with few alternatives
    • Optimization via algorithm
    • Optimization via an analytic formula
    • Simulation
    • Heuristics
    • Predictive and other models
  69. Static Analysis
    • -Single snapshot of the situation
    • -Single interval
    • -Steady state
  70. Dynamic Analysis
    • -Dynamic models
    • -Evaluate scenarios that change over time
    • -Time dependent
    • -Represents trends and patterns over time
    • -More realistic: Extends static models
  71. -Certainty Models
    • -Assume complete knowledge
    • -All potential outcomes are known
    • -May yield optimal solution
  72. Uncertainty
    • -Several outcomes for each decision
    • -Probability of each outcome is unknown
    • -Knowledge would lead to less uncertainty
  73. Risk analysis (probabilistic decision making)
    • -Probability of each of several outcomes occurring
    • -Level of uncertainty => Risk (expected value)
  74. Influence Diagrams
    • -Graphical representations of a model “Model of a model”
    • -A tool for visual communication
    • -Some influence diagram packages create and solve the mathematical model
    • -Framework for expressing MSS model relationships
    • --Rectangle = a decision variable
    • --Circle = uncontrollable or intermediate variable
    • --Oval = result (outcome) variable: intermediate or final
    • -Variables are connected with arrows à indicates the direction of influence (relationship)
  75. Decision tables from slide
    • -Multiple criteria decision analysis
    • -Features include decision variables (alternatives), uncontrollable variables, result variables
    • One goal: maximize the yield after one year
  76. Decision trees from slide
    • -Graphical representation of relationships
    • -Multiple criteria approach
    • -Demonstrates complex relationships
    • -Cumbersome, if many alternatives exists
  77. Mathematical Programming from slide
    -A family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal
  78. Optimal solution: The best possible solution to a modeled problem
    -Linear programming (LP): A mathematical model for the optimal solution of resource allocation problems. All the relationships are linear
  79. Linear Programming Steps
    • 1. Identify the …
    • -Decision variables
    • -Objective function
    • -Objective function coefficients
    • -Constraints
    • --Capacities / Demands
    • 2. Represent the model
    • -LINDO: Write mathematical formulation
    • -EXCEL: Input data into specific cells in Excel
    • 3. Run the model and observe the results
  80. Sensitivity
    • -Assesses impact of change in inputs on outputs
    • -Eliminates or reduces variables
    • -Can be automatic or trial and error
  81. What-if
    -Assesses solutions based on changes in variables or assumptions (scenario analysis)
  82. Goal seeking
    • -Backwards approach, starts with goal
    • -Determines values of inputs needed to achieve goal
    • -Example is break-even point determination
  83. Heuristic Programming from slide
    • -Cuts the search space
    • -Gets satisfactory solutions more quickly and less expensively
    • -Finds good enough feasible solutions to very complex problems
    • -Heuristics can be
    • -Quantitative
    • -Qualitative (in ES)
  84. Traveling Salesman Problem
    -A traveling salesman must visit customers in several cities, visiting each city only once, across the country. Goal: Find the shortest possible route
  85. When to Use Heuristics
    • -Inexact or limited input data
    • -Complex reality
    • -Reliable, exact algorithm not available
    • -Computation time excessive
    • -For making quick decisions
  86. Limitations of Heuristics
    -Cannot guarantee an optimal solution
  87. Modern Heuristic Methods
    • -Tabu search
    • -Intelligent search algorithm
    • -Genetic algorithms
    • -Survival of the fittest
    • -Simulated annealing
    • -Analogy to Thermodynamics
  88. Simulation from slide
    • -Technique for conducting experiments with a computer on a comprehensive model of the behavior of a system
    • -Frequently used in DSS tools
  89. Major Characteristics of Simulation
    • -Imitates reality and capture its richness
    • -Technique for conducting experiments
    • -Descriptive, not normative tool
    • -Often to “solve” very complex problems
    • --Simulation is normally used only when a problem is too complex to be treated using numerical optimization techniques
  90. Advantages of Simulation
    • -The theory is fairly straightforward
    • -Great deal of time compression
    • -Experiment with different alternatives
    • -The model reflects manager’s perspective
    • -Can handle wide variety of problem types
    • -Can include the real complexities of problems
    • -Produces important performance measures
    • -Often it is the only DSS modeling tool for non-structured problems
  91. Limitations of Simulation
    • -Cannot guarantee an optimal solution
    • -Slow and costly construction process
    • -Cannot transfer solutions and inferences to solve other problems (problem specific)
    • -So easy to explain/sell to managers, may lead overlooking analytical solutions
    • -Software may require special skills
  92. Simulation Methodology -Steps:
    • -Model real system and conduct repetitive experiments.
    • 1. Define problem
    • 2. Construct simulation model
    • 3. Test and validate model
    • 4. Design experiments
    • 5. Conduct experiments
    • 6. Evaluate results
    • 7. Implement solution
  93. Stochastic vs. Deterministic Simulation
    • -In stochastic simulations: We use distributions (Discrete or Continuous probability distributions)
    • Stochastic model- has one or more stochastic element; the system having stochastic element is generally not solved analytically and moreover there are several cases for which it is difficult to build an intuitive perspective
    • --used in a situation wherein the cause and effect relationship is stochastically or randomly
    • Deterministic model- has no stochastic elements and the entire input and output relation of the model is conclusively determined and included dynamic model and static model
    • --is used in a situation wherein the result is established straightforwardly from a series of conditions
  94. Time-dependent vs. Time-independent Simulation
    • -Time independent stochastic simulation via Monte Carlo technique (X = A + B)
    • -Time-independent- a situation in which it is not important to know exactly when the even occurred
    • -Time-dependent-a situation in which it is important to know the precise time of arrival (to know whether the customer will have to wait)
  95. Discrete event vs. Continuous simulation
    • -Discrete distributions- involve a situation with a limited number of events (or variables) that can take on only a finite number of values
    • -Continuous distributions- situations with unlimited numbers of possible events that follow density functions, such as the normal distribution
  96. Steady State vs. Transient Simulation
    • Steady state: with no temporal variations, often used for diagnostic applications
    • Transient state: non-steady state; not designed to external time constraints nor finite computation
  97. Simulation Implementation
    • -Visual simulation: the graphical display of computerized results, which may include animation, is one of the successful developments in computer-human interaction and problem solving
    • -Object-oriented simulation: such as SIMPROCESS or Unified Modeling Language (UML); provides a structured, computer-supported way of doing mathematical and equation-based modeling.
  98. -Visual simulation:
    -the graphical display of computerized results, which may include animation, is one of the successful developments in computer-human interaction and problem solving
  99. -Object-oriented simulation:
    -such as SIMPROCESS or Unified Modeling Language (UML); provides a structured, computer-supported way of doing mathematical and equation-based modeling.
  100. Visual interactive modeling (VIM) Also called
    • -Visual interactive problem solving
    • -Visual interactive modeling
    • -Visual interactive simulation
    • -Uses computer graphics to present the impact of different management decisions
    • -Often integrated with GIS
    • -Users perform sensitivity analysis
    • -Static or a dynamic (animation) systems
  101. Model Base Management
    • -MBMS: capabilities similar to that of DBMS
    • -But, there are no comprehensive model base management packages
    • -Each organization uses models somewhat differently
    • -There are many model classes
    • -Within each class there are different solution approaches
    • -Relations MBMS
    • -Object-oriented MBMS
  102. Why Data Mining?
    • -More intense competition at the global scale
    • -Recognition of the value in data sources
    • -Availability of quality data on customers, vendors, transactions, Web, etc.
    • -Consolidation and integration of data repositories into data warehouses
    • -The exponential increase in data processing and storage capabilities; and decrease in cost
    • -Movement toward conversion of information resources into nonphysical form
  103. Definition of Data Mining
    • -The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases. - Fayyad et al., (1996)
    • -Keywords in this definition: Process, nontrivial, valid, novel, potentially useful, understandable.
    • -Data mining: a misnomer?
    • -Other names: knowledge extraction, pattern analysis, knowledge discovery, information harvesting, pattern searching, data dredging,…
  104. Data Mining at the Intersection of Many Disciplines Slide 8
    • -not a new discipline, but rather a new definition for the use of many disciplines
    • -data mining is tightly positioned at the intersection of many disciplines, including statistics, artificial intelligence, machine learning, mgmt science, info sys, and databases
  105. Data Mining Characteristics/Objectives
    • -Source of data for DM is often a consolidated data warehouse (not always!)
    • -DM environment is usually a client-server or a Web-based information systems architecture
    • -Data is the most critical ingredient for DM which may include soft/unstructured data
    • -The miner is often an end user
    • -Striking it rich requires creative thinking
    • -Data mining tools’ capabilities and ease of use are essential (Web, Parallel processing, etc.)
  106. Data in Data Mining
    • Data: a collection of facts usually obtained as the result of experiences, observations, or experiments
    • -Data may consist of numbers, words, images, …
    • -Data: lowest level of abstraction (from which information and knowledge are derived)
    • Slide 10
    • Examples
    • -Categorical: Male, Female
    • -Ordinal: Freshmen, Sophmore, Junior, Senior
  107. Data Mining Applications
    • Customer Relationship Management
    • Banking and Other Financial
    • Retailing and Logistics
    • Manufacturing and Maintenance
    • Brokerage and Securities Trading
    • Insurance
  108. Customer Relationship Management
    • Data Mining Applications
    • -Maximize return on marketing campaigns
    • -Improve customer retention (churn analysis)
    • -Maximize customer value (cross-, up-selling)
    • -Identify and treat most valued customers
  109. Banking and Other Financial
    • Data Mining Applications
    • -Automate the loan application process
    • -Detecting fraudulent transactions
    • -Maximize customer value (cross-, up-selling)
    • -Optimizing cash reserves with forecasting
  110. Retailing and Logistics
    • Data Mining Applications
    • -Optimize inventory levels at different locations
    • -Improve the store layout and sales promotions
    • -Optimize logistics by predicting seasonal effects
    • -Minimize losses due to limited shelf life
  111. Manufacturing and Maintenance
    • Data Mining Applications
    • -Predict/prevent machinery failures
    • -Identify anomalies in production systems to optimize the use manufacturing capacity
    • -Discover novel patterns to improve product quality
  112. Brokerage and Securities Trading
    • Data Mining Applications
    • -Predict changes on certain bond prices
    • -Forecast the direction of stock fluctuations
    • -Assess the effect of events on market movements
    • -Identify and prevent fraudulent activities in trading
  113. Insurance
    • Data Mining Applications
    • -Forecast claim costs for better business planning
    • -Determine optimal rate plans
    • -Optimize marketing to specific customers
    • -Identify and prevent fraudulent claim activities
  114. Data Mining Process
    • -A manifestation of best practices
    • -A systematic way to conduct DM projects
    • -Different groups has different versions
    • -Most common standard processes:
    • -CRISP-DM (Cross-Industry Standard Process for Data Mining)
    • -SEMMA (Sample, Explore, Modify, Model, and Assess)
    • -KDD (Knowledge Discovery in Databases)
  115. Data Mining Process Slide 16
    • -CRISP-DM (Cross-Industry Standard Process for Data Mining)
    • -SEMMA (Sample, Explore, Modify, Model, and Assess)
    • -KDD (Knowledge Discovery in Databases)
  116. CRISP-DM abbreviate to
    (Cross-Industry Standard Process for Data Mining)
  117. Data Mining Process: CRISP-DM Slide17
    • (Cross-Industry Standard Process for Data Mining)
    • - a cross-industry standardized process of conducting data mining projects, which is a sequence of six steps that starts with a good understanding of the business and the need for the data mining project (i.e. the application domain) and ends with the deployment of the solution that satisfied the specific business need
  118. Step of CRISP-DM
    • Step 1: Business Understanding
    • Step 2: Data Understanding
    • Step 3: Data Preparation (!)
    • Step 4: Model Building
    • Step 5: Testing and Evaluation
    • Step 6: Deployment
    • -The process is highly repetitive and experimental (DM: art versus science?)
  119. Data Preparation – A Critical DM TaskSlide19
    • -Data Preprocessing Steps
    • --Real-world data --> data consolidation --> Data cleaning --> data transformation --> data reduction --> well-formed data
  120. Data Mining Process: SEMMA slide 20
    • (Sample, Explore, Modify, Model, and Assess)
    • -an alternative process for data mining projects proposed by the SAS Institute. The acronym SEMMA stands for sample, explore, modify, model, and assess
    • -SEMMA makes it easy to apply exploratory statistical and visualization techniques, select and transform the most significant predictive variables, model the variables to predict outcomes, and confirm a mode's accuracy.
    • -SEMMA is driven by a highly iterative experimentation cycle
  121. Different between CRISP-DM and SEMMA
    • -CRISP-DM- takes a more comprehensive approach-including understanding of the business and the relevant data- to data mining projects
    • -SEMMA implicitly assumes that the data mining project's goals and objectives along with the appropriate data sources have been identified and understood
  122. SEMMA abbreviate to or steps:
    • Sample- generate a representative sample of the data
    • Explore- visualization and basic description of the data
    • Modify- select variables, transform variable representations
    • Model- use variety of statistical and machine learning models
    • Access - evaluate the accuracy and usefulness of the models
  123. Data Mining Methods: Classification
    • -Most frequently used DM method
    • -Part of the machine-learning family
    • -Employ supervised learning
    • -Learn from past data, classify new data
    • -The output variable is categorical (nominal or ordinal) in nature
    • -Classification versus regression?
    • -Classification versus clustering?
  124. Assessment Methods for Classification
    • -Predictive accuracy
    • --Hit rate
    • -Speed
    • --Model building; predicting
    • -Robustness
    • -Scalability
    • -Interpretability
    • --Transparency, explainability
  125. Accuracy of Classification Models Slide23
    • -In classification problems, the primary source for accuracy estimation is the confusion matrix
    • --Confusion matrix (aka a classification matrix or a contingency table) Slide23
    • ---Can be used to estimate its future prediction accuracy, which could imply the level of confidence one should have in the classifier's output in the prediction system
    • ---can be used for choosing a classifier from a given set (identifying the "best" classification model among the many trained)
  126. Estimation Methodologies for Classification
    • -Simple split (or holdout or test sample estimation)
    • -Split the data into 2 mutually exclusive sets training (~70%) and testing (30%)
    • Slide24
    • -For ANN, the data is split into three sub-sets (training [~60%], validation [~20%], testing [~20%])
    • -k-Fold Cross Validation (rotation estimation)
    • -Split the data into k mutually exclusive subsets
    • -Use each subset as testing while using the rest of the subsets as training
    • -Repeat the experimentation for k times
    • -Aggregate the test results for true estimation of prediction accuracy training
  127. Classification Techniques
    • -Decision tree analysis
    • -Statistical analysis
    • -Neural networks
    • -Support vector machines
    • -Case-based reasoning
    • -Bayesian classifiers
    • -Genetic algorithms
    • -Rough sets
  128. Decision Trees from slide
    • -Employs the divide and conquer method
    • -Recursively divides a training set until each division consists of examples from one class
    • -1. Create a root node and assign all of the training data to it
    • -2. Select the best splitting attribute
    • -3. Add a branch to the root node for each value of the split. Split the data into mutually exclusive subsets along the lines of the specific split
    • -4. Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached
  129. DT algorithms mainly differ on
    • -Splitting criteria
    • --Which variable to split first?
    • --What values to use to split?
    • --How many splits to form for each node?
    • -Stopping criteria
    • --When to stop building the tree
    • -Pruning (generalization method)
    • --Pre-pruning versus post-pruning
  130. Most popular DT algorithms include
    • -ID3,
    • C4.5, C5;
    • CART;
    • CHAID;
    • M5
  131. Alternative splitting criteria
    • -Gini index determines the purity of a specific class as a result of a decision to branch along a particular attribute/value
    • --Used in CART
    • -Information gain uses entropy to measure the extent of uncertainty or randomness of a particular attribute/value split
    • --Used in ID3, C4.5, C5
    • -Chi-square statistics (used in CHAID)
  132. Cluster Analysis for Data Mining
    • -Used for automatic identification of natural groupings of things
    • -Part of the machine-learning family
    • -Employ unsupervised learning
    • -Learns the clusters of things from past data, then assigns new instances
    • -There is not an output variable
    • -Also known as segmentation
  133. Clustering results may be used to
    • -Identify natural groupings of customers
    • -Identify rules for assigning new cases to classes for targeting/diagnostic purposes
    • -Provide characterization, definition, labeling of populations
    • -Decrease the size and complexity of problems for other data mining methods
    • -Identify outliers in a specific domain (e.g., rare-event detection)
  134. Analysis methods
    • -Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on
    • -Neural networks (adaptive resonance theory [ART], self-organizing map [SOM])
    • -Fuzzy logic (e.g., fuzzy c-means algorithm)
    • -Genetic algorithms
  135. Divisive versus Agglomerative methods
    • -with Divisive classes- all items start in one cluster and are broken apart
    • -with agglomerative classes- all items start in individual clusters, and the clusters are joined together
  136. How many clusters?
    • -There is not a “truly optimal” way to calculate it
    • -Heuristics are often used
    • --Look at the sparseness of clusters
    • --Number of clusters = (n/2)1/2 (n: no of data points)
  137. Most cluster analysis methods involve the use of a distance measure to calculate the closeness between pairs of items-Euclidian versus Manhattan (rectilinear) distance
    • --Euclidian distance- the ordinary distance between two points that one would measures with a ruler
    • --Manhattan (rectilinear/ taxicab) distance- between two points
  138. k-Means Clustering Algorithm
    • -k : pre-determined number of clusters
    • -the algorithm assigns each data point (customer, event, object, etc.) to the cluster whose center is the nearest; the center (aka centroid) is calculated as the average of all the points in the cluster, that is, its coordinates are the arithmetic mean for each dimension separately over all the points in the cluster
    • -Algorithm (Step 0: determine value of k)
    • Step 1: Randomly generate k random points as initial cluster centers
    • Step 2: Assign each point to the nearest cluster center
    • Step 3: Re-compute the new cluster centers
    • Repetition step: Repeat steps 3 and 4 until some convergence criterion is met (usually that the assignment of points to clusters becomes stable)
    • Cluster Analysis for Data Mining - k-Means Clustering Algorithm Slide35
  139. Association Rule Mining
    • -A very popular DM method in business
    • -Finds interesting relationships (affinities) between variables (items or events)
    • -Part of machine learning family
    • -Employs unsupervised learning
    • -There is no output variable
    • -Also known as market basket analysis
    • -Often used as an example to describe DM to ordinary people, such as the famous “relationship between diapers and beers!”
  140. Data Mining Software
    • Commercial
    • -SPSS - PASW (formerly Clementine)
    • -SAS - Enterprise Miner
    • -IBM - Intelligent Miner
    • -StatSoft – Statistical Data Miner
    • -… many more
    • Free and/or Open Source
    • -Weka
    • -RapidMiner
  141. Ad hoc DSS 82
    - a DSS that deals with specific problems that are usually neither anticipated nor recurring
  142. Algorithm 58
    - a step-by-step search in which improvement made at every step until the best solution is found
  143. Analog model 44
    - an abstract, symbolic model of a system that behaves like the system but looks different
  144. Analytic hierarchy process (AHP) 165
    - a modeling structure for representing multicriteria (multiple goals, multiple objectives) problems - with sets of criteria and alternatives (choices)- commonly found in business environments
  145. Analytical model 97
    - mathematical models into which data are loaded for analysis
  146. Analytical techniques 58
    - methods that use mathematical formulas to derive an optimal solution directly or to predict a certain result, mainly in solving structured problems
  147. Apriori algorithm 227
    - the most commonly used algorithm to discover association rules by recursively identifying frequent item sets
  148. Area under the ROC curve 219
    - a graphical assessment technique for binary classification models where the true positive rate is plotted on the Y-axis and false positive rate is plotted on the X-axis
  149. Automated decision system (ADS) 13/32
    • - aka decision automation system (DAS)- is a rule-based system that provides a solution, usually in one functional area (e.g. finance, manufacturing) to a specific repetitive managerial problem, usually in one industry (e.g. to approve or not to approve a request for a loan, to determine the price of an item in a store).
    • A business rule-based system that uses intelligence to recommend solutions to repetitive decisions (such as pricing)
  150. Bootingstrapping 219
    - a sampling technique where a fixed number of instances from the original data is sampled (with replacement) for training and the rest of the dataset is used for testing
  151. Business (or system) analyst 107
    - an individual whose job is to analyze business processes and the support they receive (or need) from information technology
  152. Business analytics (BA) 78
    • - the application of models directly to business data. Business analytics involve using DSS tools, especially models, in assisting decision makers. It is essentially OLAP/DSS
    • -a collection of tools for manipulating, mining, and analyzing the data in the data warehouse;
  153. Business intelligence (BI) 19/38
    • - a conceptual framework for decision support. It combines architecture, databases (or data warehouses), analytical tools, and applications.
    • Business intelligence (BI) 86
  154. Business performance mgmt (BPM) (or corporate performance mgmt (CPM]) 22
    - an advanced performance measurement and analysis approach that embraces planning and strategy
  155. Categorical data 198
    - data that represent the labels of multiple classes used to divide a variable into specific groups
  156. Certainty 148
    - a condition under which it is assumed that future values are known for sure and only one result is associated with an action
  157. Choice phase 46
    • - the third phase in decision making, in which alternative is selected
    • - this phase involves selecting a course of action from among those available
  158. Classification 201
    - supervised induction used to analyze the historical data stored in a database and to automatically generate a model that can predict future behavior
  159. Clustering 202
    - partitioning a database into segments in which the members of a segment share similar qualities
  160. Cognitive limits 10
    - the limitations of the human mind related to processing information
  161. Complexity 172
    - a measure of how difficult a problem is in terms of its formulation for optimization, its required optimization effort, or its stochastic nature
  162. Confidence 226
    - in association rules, the conditional probability of finding the RHS of the rules present in a list of transactions where the LHS of the rule already exists
  163. CRISP-DM 207
    - a cross-industry standardized process of conducting data mining projects, which is a sequence of six steps that starts with a good understanding of the business and the need for the data mining project (i.e. the application domain) and ends with the deployment of the solution that satisfied the specific business need
  164. Dashboard 22
    -a visual presentation of critical data for executives to view. It allows executives to see hot spots in seconds and explore the situation
  165. Data mining 21
    • - a process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases
    • Data mining 196
  166. Data warehouse 86 (DW)
    - a physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format
  167. Database 90
    - a collection of files that are viewed as a single storage concept. The data are then available to a wide ranges of users
  168. Database mgmt system (DBMS) 86
    - software for establishing, updating, and querying (e.g. managing) a database
  169. Decision analysis 161
    - methods for determining the solution to a problem, typically when it is inappropriate to use iterative algorithms
  170. Decision making 42
    - the action of selecting among alternatives
  171. Decision style 42
    - the manner in which a decision maker thinks and reacts to problems. It includes perceptions, cognitive responses, values, and beliefs
  172. Decision support system (DSS) 16
    - a conceptual framework for a process of supporting managerial decision making, usually by modeling problems and employing quantitative models for solution analysis
  173. Decision table 162
    - conveniently organized information and knowledge in a systematic, tabular manner, often prepared for further analysis
  174. Decision tree 163
    • - a graphical presentation of a sequence of interrelated decisions to be made under assumed risk. This technique classifies specific entities into particular classes based upon the features of the entities; a root followed by internal nodes, each node (including root) is labeled with a question, and arcs associated with each node cover all possible reponses
    • Decision tree 220
  175. Decision variable 50
    • - a variable in a model that can be changed and manipulated by the decision make. Decision variables correspond to the decisions to be made, such as quantity to produce, amounts of resources to allocate, etc.
    • Decision variable 146
  176. Descriptive model 54
    -a model that describes things as they are
  177. Design phase 46
    • - the second decision-making phase, which involves finding possible alternatives in decision making and assessing their contributions
    • - the phase involves inventing, developing, and analyzing possible alternative courses of action (solutions)
  178. Directory 93
    - a catalog of all the data in a database or all the models in a model base
  179. Discovery-driven data mining 204
    - a form of data mining that finds patterns, associations, and relationships among data in order to uncover facts that were previously unknown or not even contemplated by an organization
  180. Distance measure 225
    - a method used to calculate the closeness between pairs of items in most cluster analysis methods. Popular distance measure include Euclidian distance ( the ordinary distance between two points that one would measure with a ruler) and Manhattan distance (also called the rectilinear distance, or taxicab distance, between two points).
  181. DSS application 75
    - a DSS program built for a specific purpose (e.g. a scheduling system for a specific company).
  182. Dynamic models 143
    - models whose input data are changed over time (e.g. a 5-year profit or loss projection).
  183. Effectiveness 42
    - the degree of goal attainment. Doing the right things
  184. Efficiency 42
    - the ration of output to input. Appropriate use of resources. Doing things right
  185. Entropy 222
    - a metric that measures the extent of uncertainty or randomness in a data set. If all the data in a subset belong to just one class, then there is no uncertainty or randomness in that data set, and therefore the entropy is zero
  186. Environmental scanning and analysis 141
    - a process that involves conducting a search for and an analysis of information in external databases and flows of information
  187. Expert tool user 107
    - a person who is skilled in the application of one or more types of specialized problem-solving tools
  188. Extraction 92
    - the process of capturing data from several sources, synthesizing them, summarizing them, determining which of them are relevant, and organizing them, resulting in their effective integration
  189. Facilitator (in a GSS) 107
    - a person who plans, organizes, and electronically controls a group in a collaborative computing environment
  190. Forecasting 142
    - predicting the future
  191. Gini index 221
    - a metric that is used in economics to measure the diversity of the population. The same concept can be used to determine the purity of a specific class as a result of a decision to branch along a particular attribute/variable
  192. Goal seeking 159
    - asking a computer what values certain variables must have in order to attain desired goals
  193. Graphical user interface (GUI) 101
    - an interactive, user-friendly interface in which, by using icons and similar objects, the user can control communication with a computer
  194. Group support system (GSS) 84
    - information systems, specifically DSS, that support the collaborative work of groups
  195. Heuristic programming 170
    - the use of heuristics in problem solving
  196. Heuristics 170
    - informal, judgmental knowledge of an application area that constitutes the rules of good judgement in the field. Heuristics also encompasses the knowledge of how to solve problems efficiently and effectively, how to plan steps in solving a complex problem, how to improve performance, and so forth.
  197. Hybrid (integrated support system 27
    - different but integrated computer support system used together in one decision-making situation
  198. Hypothesis-drive data mining 204
    - a form of data mining that begins with a proposition by the user, who then seeks to validate the truthfulness of the proposition
  199. Iconic model 44
    - a scaled physical replica
  200. Implementation phase 59
    • - the fourth decision-making phase, involving actually putting a recommended solution to work
    • - the phase involves adapting the selected course of action to the decision situation (i.e. problem solving or opportunity exploiting)
  201. Influence diagram 145
    - a diagram that shows the various types of variables in a problem (e.g. decision, independent, result) and how they are related to each other
  202. Information gain 221
    - the splitting mechanism used in ID3 (a popular decision-tree algorithm)
  203. Institutional DSS 82
    - a DSS that is a permanent fixture in an organization and has continuing financial support. It deals with decisions of a recurring nature
  204. Intelligent phase 46
    • - the initial phase of problem definition in decision making
    • - the phase involves searching for conditions that call for decisions
  205. Intermediary 107
    - a person who uses a computer to fulfill requests made by other people (e.g. a financial analyst who uses a computer to answer questions for top mgmt)
  206. Intermediate result variable 147
    - a variable that contains the values of intermediate outcomes in mathematical models
  207. Interval data 198
    - variables that can be measured on interval scales
  208. K-fold cross-validation 218
    - a popular accuracy assessment technique for prediction models where the complete dataset is randomly split into k mutually exclusive subsets of approximately equal size. The classification model is trained and tested k times. Each time it is trained on all but one fold and then tested on the remaining single fold. The cross-validation estimate of the overall accuracy of a model is calculated by simply averaging the k individual accuracy measures
  209. Knowledge discovery in databases (KDD) 215
    - a machine-learning process that performs rule induction or a related procedure to establish knowledge from large databases
  210. Linear programming (LP) 152
    - a mathematical model for the optical solution of resource allocation problems. All the relationships among the variables in this type of model are linear
  211. Link analysis 203
    - the linkage among many objects of interest is discovered automatically, such as the link between Web pages and referential relationships among groups of academic publication authors.
  212. Mgmt science (MS) (or operations research [OR]) 13
    - the application of a scientific approach and mathematical models to the anlysis and solution of managerial decision situations (e.g. problems, opportunities)
  213. Mgmt support system (MSS) 24
    - a system that applies any type of decision support tool or technique to managerial decision making
  214. Mathematical (quantitative) model 140
    - a system of symbols and expressions that represent a real solution
  215. Mathematical programming 153
    - an optimization technique for the allocation of resources, subject to constraints
  216. Mental model 46
    - the mechanisms or images through which a human mind performs sense-making in decision making
  217. Microsoft Enterprise Consortium 229-
    Serves as the worldwide source for access to Microsoft's SQL Server 2008 software suite for academic purpose- teaching and research. The consortium has been established to enable universities around the world to access enterprise technology without having to maintain the necessary hardware and software on their own campus. The consortium provides a wide range of business intelligence development tools (e.g. data mining, cube building, business reporting) as well as a number of large, realistic datasets from Sam's Club, Dillard's, and Tyson Foods.
  218. Microsoft SQL Server 229
    A suite of business intelligence capabilities where data and the models are stored in the same relational database environment, making model mgmt a considerably easier task
  219. Model base 96
    - a collection of preprogrammed quantitative models (e.g. statistical, financial, optimization) organized as a single unit
  220. Model base mgmt system (MBMS) 86
    • - software for establishing, updating, combining, and so on (e.g. managing) a DSS model base
    • Model base mgmt system (MBMS) 179
  221. Model building blocks 97
    - preprogrammed software elements that can be used to build computerized models. For example, a random-number generator can be employed in the construction of a simulation model
  222. Multidimensional analysis (modeling) 145
    - a modeling method that involves data analysis in several dimensions
  223. Multiple goals 164
    - refers to a decision situation in which alternatives are evaluated with several, sometimes conflicting, goals.
  224. Nominal data
    - a type of data that contains measurements of simple codes assigned to objects as labels, which are not measurements. For example, the variable marital status can be generally categorized as (1) single, (2) married, and (3) divorced
  225. Normative model 51
    - a model that prescribes how a system should operate
  226. Numeric data 198
    - a type of data that represent the numeric values of specific variables. Examples of numerically valued variables include age, number of children, total household income (in U.S. dollars), travel distance (in miles), and temperature ( in Fahrenheit degrees)
  227. Object 101
    - a person, place, or thing about which information is collected, processed, or stored
  228. Object-oriented model base mgmt system (OOMBMS) 180
    - an MBMS constructed in an object-oriented environment
  229. Operational knowledge base 86-
    An organization's knowledge repository
  230. Optimal solution 153-
    a best possible solution to a modeled problem
  231. Optimization 51
    - the process of identifying the best possible solution to a problem
  232. Ordinal data 198
    - data that contains codes assigned to objects or events as labels that also represent the rank order among them. For example, the variable credit score can be generally categorized as (1) low, (2), medium, and (3) high.
  233. Prediction 201
    - the act or telling about the future
  234. Predictive analytics 78
    - use of tools that help determine the probable future outcome for an event or the likelihood of a situation occurring. These tools also identify relationships and patterns
  235. Principle of choice 51
    - the criterion for making a choice among alternatives
  236. Problem ownership 50
    - the jurisdiction (authority) to solve a problem
  237. Problem solving 42
    - a process in which one starts from an initial state and proceeds to search through a problem space to identify a desired goal
  238. Quantitative software package 179
    - a preprogrammed (some times called ready-made) model or optimization system. These packages sometimes serve as building blocks for other quantitative models
  239. Query facility 93
    - the (database) mechanism that accepts requests for data, access them, manipulates them, and queries them
  240. RapidMiner 229
    - a popular, open-source, free-of-charge data mining software suite that employs a graphically enhanced user interface, a rather large number of algorithms, and a variety of data visualization features
  241. Ration data 198
    - continuous data where both differences and ratios are interpretable. The distinguishing feature of a ratio scale is the possession of a nonarbitrary zero value
  242. Regression 216
    - a data mining method for real-world prediction problems where the predicted values (i.e. the output variable or dependent variable) are numeric (e.g predicting the termperature for tomorrow as 68 degree F)
  243. Relational model base mgmt system (RMBMS) 180
    - a relational approach (as in relational databases) to the design and development of a model base mgmt system
  244. Result (outcome) variable 146
    - a variable that expresses the result of a decision (e.g. one concerning profit), usually one of the goals of a decision-making problem
  245. Risk 148
    - a probabilistic or stochastic decision situation
  246. Risk analysis 149
    - a decision-making method that analyzes the risk (based on assumed known probabilities) associated with different alternatives
  247. SAS Enterprise Miner 230
    - a comprehensive, and commercial data mining software tool developed by SAS Institute
  248. Satisficing 55
    - a process by which one seeks a solution that will satisfy a set of constraints. In contrast to optimization, which seeks the best possible solution, satisficing simply seeks a solution that will work well enough
  249. Scenario 57
    - a statement of assumptions and configurations concerning the operating environment of a particular system at a particular time
  250. Semistructured problem 12
    Fall between structured and unstructured problems, having some structured elements and some unstructured elements such problems as trading bonds, setting marketing budgets for consumer products, and performing capital acquisition analysis
  251. SEMMA
    - an alternative process for data mining projects proposed by the SAS Institute. The acronym SEMMA stands for sample, explore, modify, model, and assess
  252. Sensitivity analysis 58
    • - a study of the effect of a change in one or more input variables on a proposed solution
    • Sensitivity analysis 158
  253. Sequence mining 203-
    a pattern discovery method where relationships among the things are examined in terms of their order of occurrence to identify associations over time
  254. Simulation 54
    • - an imitation of reality in computers
    • Simulation 171
  255. Simple split 217
    - data is partitioned into two mutually exclusive subsets called a training set and a test set (or boldout set). It is common to designate two-thirds of the data as the training set and the remaining one-third as the test set
  256. SPSS PASW Modeler 230
    - a very popular, commercially available, comprehensive data, text, and Web mining software suite developed by SPSS (formerly Clementine)
  257. Staff assistant 107 -
    an individual who acts as an assistant to a manager
  258. Static models 143 -
    models that describe a single interval of a situation
  259. Strategic models 96 - models that represent problems for the strategic level (i.e. executive level) of mgmt
  260. Structure problem 12 -
    All phases are structured. The procedures for obtaining the best (or at least a good enough) solution are known. Whether the problem involves finding an appropriate inventory level or choosing an optimal investment strategy, the objectives are clearly defined. Common objectives are cost minimization and profit maximization
  261. Suboptimiation 53 -
    an optimization-based procedure that does not consider all the alternatives for or impacts on an organization
  262. Support 226-
    the measure of how often products and/or services appear together in the same transaction; that is, the proportion of transactions in the dataset that contain all of the products and/or services mentioned in a specific rule
  263. Tactical models 97-
    models that represent problems for the tactical level (i.e. midlevel) of mgmt
  264. Uncertainty 148 -
    in expert systems, a value that cannot be determined during a consultation. Many expert systems can accommodate uncertainty; that is, they allow the user to indicate whether he or she does not know the answer
  265. Uncontrollable variable or parameter 146 -
    a factor that affects the result of a decision but is not under the control of the decision maker. These variables can be internal (e.g. related to technology or to policies) or external (e.g. related to legal issues or to climiate)
  266. Unstructured problem 12
    One in which none of the Simon's four phases is structured
  267. User interface 100 -
    the component of a computer system that allows bidirectional communication between the system and its user
  268. User inter face mgmt system (UIMS) 100 -
    the DSS component that handles all interaction between users and the system
  269. Visual interactive simulation (VIS) 177 and Visual interactive modeling (VIM) -
    a simulation approach used in the decision-making process that shows graphical animation in which systems and processes are presented dynamically to the decision maker. It enables visualization of the results of different potential actions
  270. Weka 228 -
    a popular, free-of-charge, open-source suite of machine-learning software written in Java, developed at the University of Waikato
  271. What-if analysis 58 -
    • a process that involves asking a computer what the effect of changing some of the input data or parameters would be
    • What-if analysis 159
  272. Work system 25 -
    a system in which humans and/or machines perform a business process, using resources to produce products or services for internal or external customers.
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