Information Systems for Managerial Decision Support

1. Introduction • Information, Decisions, and Management • Decision Support Technologies • OLAP and DSS • DSS Applications in Corporate Functional Management • Practitioners of Management Science Information Systems for Managerial Decision Support

2. Achieving Success with Analytics Optimization Modeling Predictive Modeling What is the best that could happen? Descriptive Modeling $ROI Ad Hoc Reports and OLAP What will happen? Standard Reports RawData Why did it happen? What happened? Intelligence Information Data Decision Support Decision Guidance

3. Introduction • Management • A process by which organizational goals are achieved through the use of resources • Resources: Inputs • Goal Attainment: Output • Measuring Success: Productivity = Outputs / Inputs

4. Introduction cont. • Management is decision making • The manager is a decision maker • Now fast changing, complex environment • Factors affecting decision making • Technology/Information/Computers • Structural Complexity/Competition • International Markets/Political Stability • Consumerism/Changes, Fluctuations

5. Information, Decisions, and Management • Information • Type of information required is directly related to the level of management and the amount of structure in the decision situation • Levels of managerial decision-making • Strategic Management • Tactical Management • Operational Management

6. Strategic Management • Monitor the strategic performance of the organization and its overall direction in the political, economic, and competitive business environment • Unstructured Decisions • Not possible to specify in advance most of the decision procedures to follow • Decision maker must provide judgement, evaluation and insights to a novel, important and nonroutine-type decision • Require more summarized, ad hoc, unscheduled reports, forecasts, and external intelligence to support their more unstructured planning and policy-making responsibilities

7. TacticalManagement • Allocate resources and monitor the performance of their organizational subunits, including departments, divisions, process teams, and other workgroups • Semistructured Decisions • Some decision procedures can be prespecified, but not enough to lead to a definite recommended decision • Only part of the decision has a clear-cut answer provided by an accepted procedure • Require information from both the operational level and the strategic level to support their semistructured decision making responsibilities

8. Operational Management • Direct the use of resources and the performance of tasks according to procedures and established budgets and schedules • Structured Decisions • The procedures to follow when a decision is needed can be specified in advance • Involves a repetitive and routine-type decision where there is a definite procedure to follow • Require more prespecified internal reports emphasizing detailed current and historical data comparisons that support day-to-day operations

9. Decision Support Technologies • Management Information Systems (MIS) • Decision Support Systems (DSS) • Enterprise (Executive) Information Systems (EIS) • Enterprise Resource Planning (ERP) and Supply-Chain Management (SCM) • Knowledge Management Systems • Expert Systems (ES) • Artificial Neural Networks (ANN)

10. OLAP • Online Analytical Processing (OLAP) • A capability of management, decision support, and executive information systems that enables managers and analysts to interactively examine and manipulate large amounts of detailed and consolidated data from many perspectives • Basic analytical operations include • Consolidation: aggregation of data • Drill-Down: display detail data • Slicing & Dicing: produce different views from database

11. Decision Support Systems • Computer-based information systems that provide interactive information support to managers during the decision-making process • DSS use: • Analytical models • Specialized databases • Decision makers’ own insights and judgements • Interactive, computer-based modeling processes to support the making of semistructured and unstructured decisions by individual managers • Data mining analysis of large pools of data to find patterns and rules that can be used to guide decision making and predict future behavior

12. Decision Support Systems • Using a DSS involves four basic types of modeling activities: • What-if Analysisan end user makes changes to variables, or relationships among variables, and observes the resulting changes in the value of other variables • Sensitivitiy Analysisa special case of what-if analysis—the value of only one variable is changed repeatedly, and the resulting changes on other variables are observed. • Goal Seeking Analysissets a target value for a variable and then repeatedly changes other variables until the target value is achieved • Optimization Analysisthe goal is to find the optimum value for one or more target variables, given certain constraints

13. DSS Applications According to a recent survey, computer-based DSS are widely applied in both profit making and non-profit organizations. In corporate functional management fields, production and operations management contain the largest number of application articles, followed by management information systems, marketing, finance, strategic management and multifunctional areas. The following website list some of the important application examples from the survey. http://cstl-hcb.semo.edu/eom/ORINSIHT.HTM

14. Management Science A field of study that uses computers, statistics, and mathematics to analyze and solve business problems The Problem Solving Process Formulate & Implement Model Identify Problem Analyze Model Test Results Implement Solution unsatisfactory results Computer Model: A set of mathematical relationships and logical assumptions implemented in a computer as an abstract representation of a real-world object or phenomenon

15. A Generic Mathematical Model Y = f(X1, X2,…,Xk) Where: Y = dependent variable (a bottom line performance measure) Xi = independent variables (inputs having an impact on Y) f(.) = function defining the relationship between the Xi and Y

16. Categories of Mathematical Models Model Independent OR/MS Category Form of f(.) Variables Techniques Prescriptive known, known or under LP, Networks, IP, well-defined decision maker’s CPM, EOQ, NLP, control GP, MOLP Predictive unknown, known or under Regression Analysis, ill-defined decision maker’s Time Series Analysis, control Discriminant Analysis Descriptive known, unknown or Simulation, PERT, well-defined uncertain Queueing, Inventory Models