Chapter 12 - Chapter 12: Enhancing Decision Making Part 1:...

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Unformatted text preview: Chapter 12: Enhancing Decision Making Part 1: Introduc:on to Decision Making Learning Objec:ves •  Describe different types of decisions and the decision‐ making process •  Assess how informa:on systems support the ac:vi:es of managers and management decision making •  Demonstrate how decision‐support systems (DSS) differ from MIS and how they provide value to the business •  Demonstrate how execu:ve support systems (ESS) help senior managers make beLer decisions •  Evaluate the role of informa:on systems in helping people working in a group make decisions more efficiently •  Discuss the piNalls we as humans face when making decisions, to hopefully avoid them in the future Produced by Brian D. Janz, Ph.D. 1 Decision Making and Informa:on Systems •  Business value of improved decision making •  Types of decisions: –  Improving hundreds of thousands of “small” decisions adds up to large annual value for the business –  Structured: Repe::ve and rou:ne; involve definite procedure for handling so they do not have to be treated each :me as new –  Semistructured: Only part of problem has clear‐cut answer provided by accepted procedure –  Unstructured: Decision maker must provide judgment, evalua:on, and insight to solve problem Decision Making and Informa:on Systems •  Opera:onal managers, rank and file employees •  Middle managers: –  Make more structured decisions –  e.g., does customer meet criteria for credit? •  Senior managers: –  Make more structured decisions but these may include unstructured components –  e.g., why is order fulfillment report showing decline in Minneapolis? –  Make many unstructured decisions, –  e.g., should we enter a new market? Produced by Brian D. Janz, Ph.D. 2 Simon’s Four Stage Decision Making Model Decision Making and Informa:on Systems •  Informa:on systems can only assist in some of the roles played by managers •  Classical model of management –  Five func:ons of management: Planning, organizing, coordina:ng, deciding, and controlling •  More contemporary behavioral models –  Actual behavior of managers appears to be less systema:c, more informal, less reflec:ve, more reac:ve, and less well organized than in classical model –  Mintzberg’s behavioral model of managers defines 10 managerial roles falling into 3 categories Produced by Brian D. Janz, Ph.D. 3 Mintzberg’s 10 Managerial Roles •  Interpersonal roles: –  Figurehead –  Leader –  Liaison •  Informa:onal roles: –  Nerve center –  Disseminator –  Spokesperson •  Decisional roles: –  Entrepreneur –  Disturbance handler –  Resource allocator –  Nego:ator Decision Making and Informa:on Systems •  Three main reasons why investments in informa:on technology do not always produce posi:ve results –  Informa:on quality •  High‐quality decisions require high‐quality informa:on –  Management filters •  Managers have selec:ve aLen:on and have variety of biases that reject informa:on that does not conform to prior concep:ons •  Strong forces within organiza:ons resist making decisions calling for major change –  Organiza:onal culture Produced by Brian D. Janz, Ph.D. 4 Systems for Decision Support •  Management informa:on systems (MIS) •  Decision support systems (DSS) •  Execu:ve support systems (ESS) •  Group decision support systems (GDSS) Chapter 12: Enhancing Decision Making Part 2: Decision Support Technologies Produced by Brian D. Janz, Ph.D. 5 Management Informa:on Systems (MIS) •  Help managers monitor and control business by providing informa:on on firm’s performance and address structured problems •  Typically produce fixed, regularly scheduled reports based on data from TPS •  Example: California Pizza Kitchen MIS –  e.g., excep:on reports: Highligh:ng excep:onal condi:ons, such as sales quotas below an:cipated level –  For each restaurant, compares amount of ingredients used per ordered menu item to predefined por:on measurements and iden:fies restaurants with out‐of‐line por:ons Decision‐Support Systems (DSS) •  Support unstructured and semistructured decisions •  Model‐driven DSS –  Earliest DSS were heavily model‐driven –  e.g., voyage‐es:ma:ng DSS (Chapter 2) •  Data‐driven DSS –  Some contemporary DSS are data‐driven –  Use OLAP and data mining to analyze large pools of data –  e.g., business intelligence applica:ons (Chapter 6) Produced by Brian D. Janz, Ph.D. 6 Components of a DSS •  Database – Used for query and analysis – Current or historical data from number of applica:ons or groups – May be small database or large data warehouse •  User interface – Ofen a web interface •  Sofware system – With models, data mining, other analy:cal tools Decision Support Models •  Model: – Abstract representa:on that illustrates components or rela:onships of phenomenon; may be physical, mathema:cal, or verbal model – Sta:s:cal models – Op:miza:on models – Forecas:ng models – Sensi:vity analysis models Produced by Brian D. Janz, Ph.D. 7 Sensi:vity Analysis Systems for Decision Support •  Using spreadsheet pivot tables to support decision making •  Where do most customers come from? •  Where are average purchases higher? •  What :me of day do people buy? •  What kinds of ads work best? –  Records of online transac:ons can be analyzed using Excel –  Pivot table: •  Categorizes and summarizes data very quickly •  Displays two or more dimensions of data in a convenient format Produced by Brian D. Janz, Ph.D. 8 Sample List of Transac:ons for Online Management Training A Pivot Table that Determines Regional Distribu:on of Customers Produced by Brian D. Janz, Ph.D. 9 Pivot Table that Examines Customer Regional Distribu:on and Adver:sing Source Systems for Decision Support •  Data visualiza:on tools: –  Help users see paLerns and rela:onships in large amounts of data that would be difficult to discern if data were presented as tradi:onal lists of text •  Geographic informa:on systems (GIS): –  Category of DSS that use data visualiza:on technology to analyze and display data in form of digi:zed maps –  Used for decisions that require knowledge about geographic distribu:on of people or other resources, e.g.: •  Helping local governments calculate emergency response :mes to natural disasters •  Help retail chains iden:fy profitable new store loca:ons Produced by Brian D. Janz, Ph.D. 10 GIS‐Based Decision Support Web‐based Customer Decision‐Support Systems (CDSS) •  Support decision‐making process for exis:ng or poten:al customers •  Use web informa:on resources and capabili:es for interac:vity and personaliza:on to help users select products and services –  e.g., search engines, intelligent agents, online catalogs, web directories, newsgroup discussions, other tools •  Automobile companies that use CDSS to allow Web site visitors to configure desired car •  Financial services companies with Web‐based asset‐ management tools for customers Produced by Brian D. Janz, Ph.D. 11 Group Decision Support Systems (GDSS) •  Interac:ve system to facilitate solu:on of unstructured problems by group of decision makers •  Hardware – computer and networking hardware, overhead projectors, display screens •  GDSS sofware collects, documents, ranks, edits and stores par:cipant ideas, responses •  May require facilitator and staff •  Enables increasing mee:ng size and increasing produc:vity •  Promotes collabora:ve atmosphere, guaranteeing anonymity •  Follow structured methods for organizing and evalua:ng ideas and preserving mee:ng results Execu:ve Support Systems (ESS) •  Designed to help execu:ves focus on important performance indicators •  Balanced scorecard method: –  Measures outcomes on four dimensions: •  Financial •  Business process •  Customer •  Learning & growth –  Key performance indicators (KPIs) measure each dimension •  In developing an ESS, first concern is for senior execu:ves and consultants to develop scorecard and then to automate flow of informa:on for each KPI Produced by Brian D. Janz, Ph.D. 12 Role of ESS in the Firm •  Used by both execu:ves and subordinates: –  “Everyone’s Support System” •  Drill‐down capability: Ability to move from summary informa:on to finer levels of detail •  Integrate data from different func:onal systems for a firm‐wide view •  Incorporate external data, e.g. stock market news, compe::ve informa:on, industry trends, legisla:ve ac:on •  Include tools for modeling and analysis –  Primarily for status, comparison informa:on about performance Business Value of Execu:ve Support Systems •  Enables execu:ve to review more data in less :me with greater clarity than paper‐based systems –  Needed ac:ons iden:fied and carried out earlier •  Improves management performance •  Increases upper management’s span of control •  Increases execu:ves’ ability to monitor ac:vi:es of lower units repor:ng to them –  Also enables decision making to be decentralized and take place at lower opera:ng levels Produced by Brian D. Janz, Ph.D. 13 Part 3: Avoiding the PiNalls of Human Decision Making Decision Making: Experts vs. Novices •  Experts can beLer see the big picture – Forests vs. trees •  Experts possess knowledge vs. counter‐ knowledge – The importance of knowing context •  Experts know their limita:ons – Focusing on our limita:ons can help us avoid problems Produced by Brian D. Janz, Ph.D. 14 Humans: Limited “Informa:on Processors” •  Nobel Laureate Herb Simon (with a liLle help from Jim March) says that we display “bounded ra:onality.” •  Human memory – Short‐term vs. long‐term memory •  The “Magical Number 7” – Plus or minus 2… •  Chunking – Experts can handle large chunks Humans: “Naïve Sta:s:cians” •  Insensi:vity to sample sizes – Anecdotes vs. popula:ons •  Sta:s:cal vs. prac:cal significance •  Misconcep:ons of chance – Assuming dependence where this none – Feeling overconfident in groups – Coincidences and degrees of separa:on •  Ignoring regression to the mean – Too much credit for excep:onal events Produced by Brian D. Janz, Ph.D. 15 More Naïve Sta:s:cs: The Erroneous Search for Causality •  The Japanese eat very liLle fat and suffer fewer heart aLacks than the Bri:sh or Americans •  The French eat a lot of fat and also suffer fewer heart aLacks than the Bri:sh or Americans •  The Japanese drink very liLle red wine and suffer fewer heart aLacks than the Bri:sh or Americans •  The Italians drink a lot of red wine and also suffer fewer heart aLacks than the Bri:sh or Americans Obvious Conclusion: Eat & drink what you like. It's speaking English that kills you. Produced by Brian D. Janz, Ph.D. 16 Mistaking Correla:on for Causality •  In order for X to cause Y: – Correla:on: X and Y must be correlated •  Much more difficult when :me lags exist – Precedence: X must occur before Y – Eliminate other plausible explana:ons: A, B, and C can’t cause Y Humans: Limited Informa:on Processors (con:nued) •  Cogni:ve Heuris:cs: “Rules of thumb” or shortcuts that we unconsciously employ •  Lead to biased decisions •  Lead to less‐than‐op:mal decisions Produced by Brian D. Janz, Ph.D. 17 Common Heuris:cs and Biases •  The “Availability” heuris:c – Familiarity – Ease of recall •  Other availability issues: – Recency effects – Primacy effects – Confirmatory bias Other Common Heuris:cs and Biases •  The “Representa:veness” heuris:c – Insensi:vity to base rates – Insensi:vity to sample sizes – The “framing” a.k.a. “anchoring and adjustment” problem Produced by Brian D. Janz, Ph.D. 18 The BoLom Line •  Understanding the types of decisions we are being asked to make can help us make beLer decisions •  Knowing our limita:ons as human decision makers can help us avoid decision piNalls •  Decision support technologies can help us make beLer decisions Produced by Brian D. Janz, Ph.D. 19 ...
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This note was uploaded on 02/24/2011 for the course MIS 7650 taught by Professor Janz during the Spring '11 term at U. Memphis.

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