DSS Technologies in Business, management homework help

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Based on your understandings from the readings this week, what are some of the technologies and applications used within your current organization, or one with which you are familiar? How are they used?

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Decision Support and Business Intelligence Systems Chapter 1: Decision Support Systems and Business Intelligence Learning Objectives     1-2 Understand today's turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploiting opportunities) Understand the need for computerized support of managerial decision making Understand an early framework for managerial decision making Learn the conceptual foundations of the decision support systems (DSS) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Learning Objectives – cont.     1-3 Describe the business intelligence (BI) methodology and concepts and relate them to DSS Describe the concept of work systems and its relationship to decision support List the major tools of computerized decision support Understand the major issues in implementing computerized support systems Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Opening Vignette: “Norfolk Southern Uses BI for Decision Support to Reach Success”  Company background  Problem  Proposed solution  Results  Answer and discuss the case questions 1-4 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Changing Business Environment   Companies are moving aggressively to computerized support of their operations => Business Intelligence Business Pressures–Responses–Support Model    1-5 Business pressures result of today's competitive business climate Responses to counter the pressures Support to better facilitate the process Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Business Pressures–Responses– Support Model 1-6 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall The Business Environment  The environment in which organizations operate today is becoming more and more complex, creating:     Business environment factors:  1-7 opportunities, and problems Example: globalization markets, consumer demands, technology, and societal… Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Business Environment Factors FACTOR Markets Consumer demand Technology Societal 1-8 DESCRIPTION Strong competition Expanding global markets Blooming electronic markets on the Internet Innovative marketing methods Opportunities for outsourcing with IT support Need for real-time, on-demand transactions Desire for customization Desire for quality, diversity of products, and speed of delivery Customers getting powerful and less loyal More innovations, new products, and new services Increasing obsolescence rate Increasing information overload Social networking, Web 2.0 and beyond Growing government regulations and deregulation Workforce more diversified, older, and composed of more women Prime concerns of homeland security and terrorist attacks Necessity of Sarbanes-Oxley Act and other reporting-related legislation Increasing social responsibility of companies Greater emphasis on sustainability Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Organizational Responses   Be Reactive, Anticipative, Adaptive, and Proactive Managers may take actions, such as        1-9 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 …cont…> Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Managers actions, continued         1-10 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Closing the Strategy Gap  1-11 One of the major objectives of computerized decision support is to facilitate closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Managerial Decision Making  Management is a process by which organizational goals are achieved by using resources      1-12 Inputs: resources Output: attainment of goals Measure of success: outputs / inputs Management  Decision Making Decision making: selecting the best solution from two or more alternatives Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Mintzberg's 10 Managerial Roles Interpersonal 1. Figurehead 2. Leader 3. Liaison Informational 4. Monitor 5. Disseminator 6. Spokesperson 1-13 Decisional 7. Entrepreneur 8. Disturbance handler 9. Resource allocator 10. Negotiator Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Making Process  Managers usually make decisions by following a four-step process (a.k.a. the scientific approach) 1. 2. 3. 4. 1-14 Define the problem (or opportunity) Construct a model that describes the realworld problem Identify possible solutions to the modeled problem and evaluate the solutions Compare, choose, and recommend a potential solution to the problem Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision making is difficult, because    1-15 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Why Use Computerized DSS  Computerized DSS can facilitate decision via:        1-16 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A Decision Support Framework (by Gory and Scott-Morten, 1971) 1-17 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A Decision Support Framework – cont.  Degree of Structuredness (Simon, 1977)  Decision are classified as     Types of Control (Anthony, 1965)    1-18 Highly structured (a.k.a. programmed) Semi-structured Highly unstructured (i.e., non-programmed) Strategic planning (top-level, long-range) Management control (tactical planning) Operational control Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Simon’s Decision-Making Process 1-19 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 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   1-20 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Management Science Approach   Also referred to as Operation Research In solving problems, managers should follow the five-step MS approach 1. 2. 3. 4. 5. 1-21 Define the problem Classify the problem into a standard category (*) Construct a model that describes the real-world problem Identify possible solutions to the modeled problem and evaluate the solutions Compare, choose, and recommend a potential solution to the problem Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Automated Decision Making     A relatively new approach to supporting decision making Applies to highly structures decisions Automated decision systems (ADS) (or decision automation systems) An ADS is a rule-based system that provides a solution to a repetitive managerial problem in a specific area  1-22 e.g., simple-loan approval system Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Automated Decision Making  ADS initially appeared in the airline industry called revenue (or yield) management (or revenue optimization) systems    1-23 dynamically price tickets based on actual demand Today, many service industries use similar pricing models ADS are driven by business rules! Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Computer Support for Unstructured Decisions      1-24 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Computer Support for Semi-structured Problems    1-25 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Automated Decision-Making Framework 1-26 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Concept of Decision Support Systems Classical Definitions of DSS   1-27 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS as an Umbrella Term  The term DSS can be used as an umbrella term to describe any computerized system that supports decision making in an organization  1-28 E.g., an organization wide knowledge management system; a decision support system specific to an organizational function (marketing, finance, accounting, manufacturing, planning, SCM, etc.) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS as a Specific Application   In a narrow sense DSS refers to a process for building customized applications for unstructured or semistructured problems Components of the DSS Architecture   1-29 Data, Model, Knowledge/Intelligence, User, Interface (API and/or user interface) DSS often is created by putting together loosely coupled instances of these components Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall High-Level Architecture of a DSS 1-30 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Types of DSS  Two major types:    Evolution of DSS into Business Intelligence   1-31 Model-oriented DSS Data-oriented DSS Use of DSS moved from specialist to managers, and then whomever, whenever, wherever Enabling tools like OLAP, data warehousing, data mining, intelligent systems, delivered via Web technology have collectively led to the term “business intelligence” (BI) and “business analytics” Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Business Intelligence (BI)     1-32 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A Brief History of BI   The term BI was coined by the Gartner Group in the mid-1990s However, the concept is much older      1-33 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall The Evolution of BI Capabilities 1-34 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall The Architecture of BI  A BI system has four major components     1-35 a data warehouse, with its source data business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse; business performance management (BPM) for monitoring and analyzing performance a user interface (e.g., dashboard) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A High-Level Architecture of BI 1-36 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Components in a BI Architecture     1-37 The data warehouse is a large repository of well-organized historical data Business analytics are the tools that allow transformation of data into information and knowledge Business performance management (BPM) allows monitoring, measuring, and comparing key performance indicators User interface (e.g., dashboards) allows access and easy manipulation of other BI components Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Styles of BI  MicroStrategy, Corp. distinguishes five styles of BI and offers tools for each 1. 2. 3. 4. 5. 1-38 report delivery and alerting enterprise reporting (using dashboards and scorecards) cube analysis (also known as slice-anddice analysis) ad-hoc queries statistics and data mining Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall The Benefits of BI   The ability to provide accurate information when needed, including a real-time view of the corporate performance and its parts A survey by Thompson (2004)      1-39 Faster, more accurate reporting (81%) Improved decision making (78%) Improved customer service (56%) Increased revenue (49%) See Table 1.3 for a list of BI analytic applications, the business questions they answer and the business value they bring Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall The DSS–BI Connection    1-40 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall The DSS–BI Connection – cont.    1-41 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) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall The DSS–BI Connection – cont.  Although some people equate DSS with BI, these systems are not, at present, the same      1-42 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A Work System View of Decision Support (Alter, 2004)    1-43 drop the word “systems” from DSS focus on “decision support” “use of any plausible computerized or noncomputerized means for improving decision making in a particular repetitive or nonrepetitive business situation in a particular organization” 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Elements of a Work System 1. 2. 3. 4. 1-44 Business process. Variations in the process rationale, sequence of steps, or methods used for performing particular steps Participants. Better training, better skills, higher levels of commitment, or better real-time or delayed feedback Information. Better information quality, information availability, or information presentation Technology. Better data storage and retrieval, models, algorithms, statistical or graphical capabilities, or computer interaction --> Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Elements of a Work System – cont. 5. 6. 7. 8. 9. 1-45 Product and services. Better ways to evaluate potential decisions Customers. Better ways to involve customers in the decision process and to obtain greater clarity about their needs Infrastructure. More effective use of shared infrastructure, which might lead to improvements Environment. Better methods for incorporating concerns from the surrounding environment Strategy. A fundamentally different operational strategy for the work system Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Major Tool Categories for MSS TOOL CATEGORY TOOLS AND THEIR ACRONYMS Data management 1-46 Databases and database management system (DBMS) Extraction, transformation, and load (ETL) systems Data warehouses (DW), real-time DW, and data marts Reporting status tracking Online analytical processing (OLAP) Executive information systems (EIS) Visualization Geographical information systems (GIS) Dashboards, Information portals Multidimensional presentations Business analytics Optimization, Web analytics Data mining, Web mining, and text mining Strategy and performance Business performance management (BPM)/ management Corporate performance management (CPM) Business activity management (BAM) Dashboards and Scorecards Communication and Group decision support systems (GDSS) collaboration Group support systems (GSS) Collaborative information portals and systems Social networking Web 2.0, Expert locating systems Knowledge management Knowledge management systems (KMS) Intelligent systems Expert systems (ES) Artificial neural networks (ANN) Fuzzy logic, Genetic algorithms, Intelligent agents Enterprise systems Enterprise resource planning (ERP), Customer Relationship Management (CRM), and Supply-Chain Management (SCM) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Source: Table 1.4 Hybrid (Integrated) Support Systems    1-47 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Hybrid (Integrated) Support Systems  Type of integration     1-48 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall End of the Chapter  1-49 Questions / Comments… Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 1-50 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Support and Business Intelligence Systems (9th Ed., Prentice Hall) Chapter 2: Decision Making, Systems, Modeling, and Support Learning Objectives    Understand the conceptual foundations of decision making Understand the need for and the nature of models in decision making Understand Simon's four phases of decision making:     2-2 intelligence, design, choice, and implementation Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Learning Objectives     2-3 Recognize the concepts of rationality and bounded rationality and how they relate to decision making Differentiate between the concepts of making a choice and establishing a principle of choice Learn how DSS provide support for decision making in practice Understand the systems approach Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Opening Vignette: “Decision Modeling at HP Using Spreadsheets”  Company background  Problem  Proposed solution  Results  Answer and discuss the case questions 2-4 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Support Systems (DSS) Dissecting DSS into its main concepts Building successful DSS requires a through understanding of these concepts 2-5 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Characteristics of Decision Making        2-6 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Characteristics of Decision Making  Better decisions    Fast decision may be detrimental Areas suffering most from fast decisions       2-7 Tradeoff: accuracy versus speed personnel/human resources (27%) budgeting/finance (24%) organizational structuring (22%) quality/productivity (20%) IT selection and installation (17%) process improvement (17%) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 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  2-8 What should be done? When? Where? Why? How? By whom? Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Making and Problem Solving  A problem occurs when a system      2-9 does not meet its established goals does not yield the predicted results, or does not work as planned Problem is the difference between the desired and actual outcome Problem solving also involves identification of new opportunities Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Making and Problem Solving   Are problem solving and decision making different? Or, are they the same thing? Consider 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  2-10 This book: decision making  problem solving Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making Disciplines    2-11 Behavioral: anthropology, law, philosophy, political science, psychology, social psychology, and sociology Scientific: computer science, decision analysis, economics, engineering, the hard sciences (e.g., biology, chemistry, physics), management science/operations research, mathematics, and statistics Each discipline has its own set of assumptions and each contributes a unique, valid view of how people make decisions Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Style  The manner by which decision makers think and react to problems     When making decisions, people…   2-12 perceive a problem cognitive response values and beliefs follow different steps/sequence give different emphasis, time allotment, and priority to each steps Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Style   Personality temperament tests are often used to determine decision styles There are many such tests      2-13 Meyers/Briggs, True Colors (Birkman), Keirsey Temperament Theory, … Various tests measure somewhat different aspects of personality They cannot be equated! Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Style  Decision-making styles     A successful computerized system should fit the decision style and the decision situation  2-14 Heuristic versus Analytic Autocratic versus Democratic Consultative (with individuals or groups) Should be flexible and adaptable to different users (individuals vs. groups) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Makers  Small organizations    Medium-to-large organizations      2-15 Individuals Conflicting objectives Groups Different styles, backgrounds, expectations Conflicting objectives Consensus is often difficult to reach Help: Computer support, GSS, … Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Model      2-16 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Types of Models  Models can be classified based on their degree of abstraction Degree of abstraction Less More 2-17  Iconic models (scale models)  Analog models  Mental Models  Mathematical (quantitative) models Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall The Benefits of Models         2-18 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Phases of Decision-Making Process  Humans consciously or sub consciously follow a systematic decision-making process - Simon (1977) 1) 2) 3) 4) 5) 2-19 Intelligence Design Choice Implementation (?) Monitoring (a part of intelligence?) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Simon’s Decision-Making Process 2-20 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: Intelligence Phase     Scan the environment, either intermittently or continuously Identify problem situations or opportunities Monitor the results of the implementation Problem is the difference between what people desire (or expect) and what is actually occurring   2-21 Symptom versus Problem Timely identification of opportunities is as important as identification of problems Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: Intelligence Phase  Potential issues in data/information collection and estimation        2-22 Lack of data Cost of data collection Inaccurate and/or imprecise data Data estimation is often subjective Data may be insecure Key data may be qualitative Data change over time (time-dependence) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: Intelligence Phase  Problem Classification   Problem Decomposition     2-23 Classification of problems according to the degree of structuredness Often solving the simpler subproblems may help in solving a complex problem Information/data can improve the structuredness of a problem situation Problem Ownership A Formal Outcome of intelligence phase: Problem Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Statement 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 Modeling: conceptualizing a problem and abstracting it into a quantitative and/or qualitative form (i.e., using symbols/variables)    2-24 Abstraction: making assumptions for simplification Tradeoff (cost/benefit): more or less abstraction Modeling: both an art and a science Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Design Phase  Selection of a Principle of Choice     It is a criterion that describes the acceptability of a solution approach Reflection of decision-making objective(s) In a model, it is the result variable Choosing and validating against    2-25 High-risk versus low-risk Optimize versus satisfice Criterion is not a constraint Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Design Phase  Normative models (= optimization)   the chosen alternative is demonstrably the best of all possible alternatives Assumptions of rational decision makers    2-26 Humans are economic beings whose objective is to maximize the attainment of goals For a decision-making situation, all alternative courses of action and consequences are known Decision makers have an order or preference that enables them to rank the desirability of all consequences Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Design Phase  Heuristic models (= suboptimization)     2-27 the chosen alternative is the best of only a subset of possible alternatives Often, it is not feasible to optimize realistic (size/complexity) problems Suboptimization may also help relax unrealistic assumptions in models Help reach a good enough solution faster Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Design Phase  Descriptive models     2-28 describe things as they are or as they are believed to be (mathematically based) They do not provide a solution but information that may lead to a solution Simulation - most common descriptive modeling method (mathematical depiction of systems in a computer environment) Allows experimentation with the descriptive model of a system Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Design Phase  Good Enough, or Satisficing “something less than the best”  A form of suboptimization  Seeking to achieving a desired level of performance as opposed to the “best”  Benefit: time saving  2-29 Simon’s idea of bounded rationality Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Design Phase  Developing (Generating) Alternatives     Measuring/ranking the outcomes  2-30 In optimization models (such as linear programming), the alternatives may be generated automatically In most MSS situations, however, it is necessary to generate alternatives manually Use of GSS helps generating alternatives Using the principle of choice Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Design Phase  Risk    Scenario (what-if case)   2-31 Lack of precise knowledge (uncertainty) Risk can be measured with probability A statement of assumptions about the operating environment (variables) of a particular system at a given time Possible scenarios: best, worst, most likely, average (and custom intervals) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Choice Phase   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)    2-32 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! Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Choice Phase  Search approaches      Additional activities    2-33 Analytic techniques (solving with a formula) Algorithms (step-by-step procedures) Heuristics (rule of thumb) Blind search (truly random search) Sensitivity analysis What-if analysis Goal seeking Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision-Making: The Implementation Phase “Nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things.” - The Prince, Machiavelli 1500s    2-34 Solution to a problem = Change Change management? Implementation: putting a recommended solution to work Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall How Decisions Are Supported 2-35 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall How Decisions Are Supported  Support for the Intelligence Phase      2-36 Enabling continuous scanning of external and internal information sources to identify problems and/or opportunities Resources/technologies: Web; ES, OLAP, data warehousing, data/text/Web mining, EIS/Dashboards, KMS, GSS, GIS,… Business activity monitoring (BAM) Business process management (BPM) Product life-cycle management (PLM) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall How Decisions Are Supported  Support for the Design Phase   Enabling generating alternative courses of action, determining the criteria for choice Generating alternatives    2-37 Structured/simple problems: standard and/or special models Unstructured/complex problems: human experts, ES, KMS, brainstorming/GSS, OLAP, data/text mining A good “criteria for choice” is critical! Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall How Decisions Are Supported  Support for the Choice Phase    Enabling selection of the best alternative given a complex constraint structure Use sensitivity analyses, what-if analyses, goal seeking Resources    2-38 KMS CRM, ERP, and SCM Simulation and other descriptive models Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall How Decisions Are Supported  Support for the Implementation Phase    Enabling implementation/deployment of the selected solution to the system Decision communication, explanation and justification to reduce resistance to change Resources    2-39 Corporate portals, Web 2.0/Wikis Brainstorming/GSS KMS , ES Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall New Technologies to Support Decision Making       2-40 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall End of the Chapter  2-41 Questions / Comments… Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 2-42 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Decision Support and Business Intelligence Systems (9th Ed., Prentice Hall) Chapter 3: Decision Support Systems Concepts, Methodologies, and Technologies: An Overview Learning Objectives       3-2 Understand possible decision support system (DSS) configurations Understand the key differences and similarities between DSS and BI systems Describe DSS characteristics and capabilities Understand the essential definition of DSS Understand important DSS classifications Understand DSS components and how they integrate Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Learning Objectives       3-3 Describe the components and structure of each DSS component Explain Internet impacts on DSS (and vice versa) Explain the unique role of the user in DSS versus management information systems Describe DSS hardware and software platforms Become familiar with a DSS development language Understand current DSS issues Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Opening Vignette: “Decision Support System Cures for Health Care”  Company background  Problem  Proposed solution  Results  Answer and discuss the case questions 3-4 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Opening Vignette: “Decision Support System Cures for Health Care” - Projected Vacancy Rate versus Desired Vacancy Rate 3-5 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Opening Vignette: - Projected Vacancy Rate vs. Desired Vacancy Rate "What-if" scenario with 6 additional RN recruiters 3-6 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Opening Vignette: - Demanded Hours versus Total Actual Hours versus Total Actual Hours with New Hires 3-7 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Configurations  Many configurations exist; based on    management-decision situation specific technologies used for support DSS have three basic components Data 2. Model 3. User interface 4. (+ optional) Knowledge 1. 3-8 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Configurations  Each component    Typical types:   3-9 has several variations; are typically deployed online Managed by a commercial of custom software Model-oriented DSS Data-oriented DSS Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Description  An early definition of DSS     3-10 A system intended to support managerial decision makers in semistructured and unstructured decision situations meant to be adjuncts to decision makers (extending their capabilities but not replacing their judgment) aimed at decisions that required judgment or at decisions that could not be completely supported by algorithms would be computer based; operate interactively; and would have graphical output capabilities… Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Description  A DSS is typically built to support the solution of a certain problem (or to evaluate a specific opportunity). This is a key difference between DSS and BI applications     3-11 BI systems monitor situations and identify problems and/or opportunities, using variety of analytic methods The user generally must identify whether a particular situation warrants attention Reporting/data warehouse plays a major role in BI DSS often has its own database and models Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Description  DSS is an approach (or methodology) for supporting decision making       3-12 uses an interactive, flexible, adaptable computerbased information system (CBIS) developed (by end user) for supporting the solution to a specific nonstructured management problem uses data, model and knowledge along with a friendly (often graphical; Web-based) user interface incorporate the decision maker's own insights supports all phases of decision making can be used by a single user or by many people Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A Web-Based DSS Architecture 3-13 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Characteristics and Capabilities  DSS is not quite synonymous with BI     3-14 DSS are generally built to solve a specific problem and include their own database(s) BI applications focus on reporting and identifying problems by scanning data stored in data warehouses Both systems generally include analytical tools (BI called business analytics systems) Although some may run locally as a spreadsheet, both DSS and BI uses Web Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Characteristics and Capabilities 3-15 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Characteristics and Capabilities    3-16 Business analytics implies the use of models and data to improve an organization's performance and/or competitive posture Web analytics implies using business analytics on real-time Web information to assist in decision making; often related to e-Commerce Predictive analytics describes the business analytics method of forecasting problems and opportunities rather than simply reporting them as they occur Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Classifications  Other DSS Categories     3-17 Institutional and ad-hoc DSS Personal, group, and organizational support Individual support system versus group support system (GSS) Custom-made systems versus ready-made systems Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Classifications  Holsapple and Whinston's Classification 1. 2. 3. 4. 5. 6. 3-18 The text-oriented DSS The database-oriented DSS. The spreadsheet-oriented DSS The solver-oriented DSS The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications) The compound DSS Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Classifications  Alter's Output Classification Orientation Category Type of Operation Data Access data items File drawer systems Data analysis systems Ad hoc analysis of data files 3-19 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Classifications  Holsapple and Whinston's Classification 1. 2. 3. 4. 5. 6. 3-20 The text-oriented DSS The database-oriented DSS The spreadsheet-oriented DSS The solver-oriented DSS The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications) The compound DSS Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Components of DSS 3-21 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Components of DSS  Data Management Subsystem     Model Management Subsystem    Model base management system (MBMS) User Interface Subsystem Knowledgebase Management Subsystem  3-22 Includes the database that contains the data Database management system (DBMS) Can be connected to a data warehouse Organizational knowledge base Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 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 - See Table 3.2 for a complete list... 3-23 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Components and Web Impacts  Impacts of Web to DSS      DSS impact on Web   3-24 Data management via Web servers Easy access to variety of models, tools Consistent user interface (browsers) Deployment to PDAs, cell phones, etc. … Intelligent e-Business/e-Commerce Better management of Web resources and security, … (see Table 3.3 for more…) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Components Data Management Subsystem     3-25 DSS database DBMS Data directory Query facility Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Database Management Subsystem Key Data Issues  Data quality   Data integration      3-26 “Garbage in/garbage out" (GIGO) “Creating a single version of the truth” Scalability Data Security Timeliness Completeness, … Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 10 Key Ingredients of Data (Information) Quality Management 1. 2. 3. 4. 5. 3-27 Data quality is a business problem, not only a systems problem Focus on information about customers and suppliers, not just data Focus on all components of data: definition, content, and presentation Implement data/information quality management processes, not just software to handle them Measure data accuracy as well as validity Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 10 Key Ingredients of Data (Information) Quality Management 6. 7. 8. 9. 10. 3-28 Measure real costs (not just the percentage) of poor quality data/information Emphasize process improvement/preventive maintenance, not just data cleansing Improve processes (and hence data quality) at the source Educate managers about the impacts of poor data quality and how to improve it Actively transform the culture to one that values data quality Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Components Model Management Subsystem      3-29 Model base MBMS Modeling language Model directory Model execution, integration, and command processor Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Components Model Management Subsystem   Model base (= database ?) Model Types       3-30 Strategic models Tactical models Operational models Analytic models Model building blocks Modeling tools Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Components Model Management Subsystem  The four (4) functions 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 1.   3-31 Model directory Model execution, integration and command Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Components User Interface (Dialog) Subsystem  Interface   Application interface User Interface   DSS User Interface   Portal Graphical icons    3-32 Graphical User Interface (GUI) Dashboard Color coding Interfacing with PDAs, cell phones, etc. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Components Knowledgebase Management System   Incorporation of intelligence and expertise Knowledge components:        3-33 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 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Components Future/current DSS Developments  Hardware enhancements   Software/hardware advancements   3-34 Smaller, faster, cheaper, … data warehousing, data mining, OLAP, Web technologies, integration and dissemination technologies (XML, Web services, SOA, grid computing, cloud computing, …) Integration of AI -> smart systems Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS User  One faced with a decision that an MSS is designed to support   The users differ greatly from each other    3-35 Manager, decision maker, problem solver, … Different organizational positions they occupy; cognitive preferences/abilities; the ways of arriving at a decision (i.e., decision styles) User = Individual versus Group Managers versus Staff Specialists [staff assistants, expert tool users, business (system) analysts, facilitators (in a GSS)] Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall DSS Hardware     Typically, MSS run on standard hardware Can be composed of mainframe computers with legacy DBMS, workstations, personal computers, or client/server systems Nowadays, usually implemented as a distributed/integrated, loosely-coupled Web-based systems Can be acquired from   3-36 A single vendor Many vendors (best-of-breed) Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com)  Generating Assumptions 3-37 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com)  Creating a new model 3-38 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-39 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-40 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-41 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-42 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-43 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-44 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-45 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-46 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-47 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-48 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-49 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall A DSS Modeling Language Planners Lab (plannerslab.com) 3-50 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall End of the Chapter  3-51 Questions / Comments… Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 3-52 Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
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DSS technologies in business

Decision support system is one of the technologies used in running the organization. The
technology has been in use of over a decade now helping the organization in achieving its
objectives. This is because it has been helping the different decision makers in the organization
to compile different information. With this information, they are in a position to make effective
decisions on the matter which concern the organization. The decision support system comes with
a different system which helps in securing and providing the necessary information for the
organization.

The communication-driven DSS ensures that the organization has enough information
which is crucial in decision making. This system is worked by more than one person in...


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