IiDiiImproving Decision Making and ManagingMaking and ...is.cba.edu.kw/240/Handouts/ch10.pdfDSS database may be a small database residing on a PC or a large data warehouse. Essentials

10Chapter

I i D i iI i D i iI i D i iI i D i iImproving Decision Improving Decision Making and ManagingMaking and Managing

Improving Decision Improving Decision Making and ManagingMaking and ManagingMaking and Managing Making and Managing

KnowledgeKnowledgeMaking and Managing Making and Managing

KnowledgeKnowledge

10.1 © 2007 by Prentice Hall

Essentials of Business Information SystemsEssentials of Business Information SystemsChapter 10 Improving Decision Making and Managing KnowledgeChapter 10 Improving Decision Making and Managing Knowledge

STUDENT LEARNING OBJECTIVES

Wh t th diff t t f d i i d• What are the different types of decisions, and how does the decision-making process work?

• How do information systems help people working individually and in groups make decisions more ff ti l ?effectively?

• What are the business benefits of usingWhat are the business benefits of using intelligent techniques in decision making and knowledge management?



STUDENT OBJECTIVES

Wh t t f t d f t i• What types of systems are used for enterprise-wide knowledge management, and how do they provide value for businesses?p

• What are the major types of knowledge work t d h d th id l fsystems, and how do they provide value for

firms?



Eastern Mountain Sports Forges a Trail to Better Decisions

• Problem: Dated and clumsy information systems, unnecessary labor, poor inventory decisionsdecisions.

• Solutions: Deploy a business intelligenceintelligence system to more efficiently collect and communicate


and communicate important data.



• WebFOCUS and iWay middleware from Information Builders extracts key data andInformation Builders extracts key data and displays it through dashboards accessible via the Web.

• Demonstrates IT’s role in revamping outdated information systems.

• Illustrates digital technology’s role in improving decision making.



Decision Making and Information Systems

Business Value of Improved Decision MakingBusiness Value of Improved Decision Makingus ess a ue o p o ed ec s o a gus ess a ue o p o ed ec s o a g

• Possible to measure value of improved decision making

• Decisions made at all levels of the firm

• Some are common routine and numerous• Some are common, routine, and numerous

• Although value of improving any single decision may be small improving hundreds ofmay be small, improving hundreds of thousands of “small” decisions adds up to large annual value for the business




Business Value of Improved Decision MakingBusiness Value of Improved Decision Makingus ess a ue o p o ed ec s o a gus ess a ue o p o ed ec s o a gDecision Maker Number

/ yearValue of decision

Annual value to firm

Allocate support to most Accounts manager 12 $100 000 $1 200 000Allocate support to most valuable customers

Accounts manager 12 $100,000 $1,200,000

Predict call center daily demand

Call Center management

4 150,000 600,000g

Decide parts inventory level daily

Inventory manager 365 5,000 1,825,000

Identify competitive bids Senior management 1 2,000,000 2,000,000from major suppliers

Schedule production to fill orders

Manufacturing manager

150 10,000 1,500,000

All t l b t l t P d ti fl 100 4 000 400 000


Allocate labor to complete job

Production floor manager

100 4,000 400,000


Types of DecisionsTypes of Decisions


• Unstructured• Decision maker must provide judgment to solve problem• Novel important nonroutine• Novel, important, nonroutine• No well-understood or agreed-on procedure for making

them• Structured

• Repetitive and routine• Involve definite procedure for handling them so do notInvolve definite procedure for handling them so do not

have to be treated as new• Semistructured


• Only part of problem has clear-cut answer provided by accepted procedure


Information Requirements of Key DecisionInformation Requirements of Key Decision--Making Making


o at o equ e e ts o ey ec s oo at o equ e e ts o ey ec s o a ga gGroups in a FirmGroups in a Firm

S i iddl ti l


Figure 10-1

Senior managers, middle managers, operational managers, and employees have different types of decisions and information requirements.


The DecisionThe Decision--Making ProcessMaking Process


e ec s oe ec s o a g ocessa g ocess1. Intelligence

• Discovering, identifying, and understanding the problems occurring in the organization—why is there a problem, where, what effects it is having on the firm

2. Designg• Identifying and exploring various solutions

3. ChoiceC• Choosing among solution alternatives

4. Implementation• Making chosen alternative work and monitoring how well


Making chosen alternative work and monitoring how well solution is working



Stages in Decision MakingStages in Decision MakingStages ec s o a gStages ec s o a g


Figure 10-2The decision-making process can be broken down into four stages.


Quality Dimensions of DecisionsQuality Dimensions of Decisions


Qua ty e s o s o ec s o sQua ty e s o s o ec s o s• Accuracy

• Decision reflects reality• Decision reflects reality

• ComprehensivenessD i i fl t f ll id ti f th• Decision reflects a full consideration of the facts and circumstances

• Fairness• Fairness• Decision faithfully reflects the concerns and

interests of affected parties


interests of affected parties


Quality Dimensions of DecisionsQuality Dimensions of Decisions


Qua ty e s o s o ec s o sQua ty e s o s o ec s o s• Speed (efficiency)

• Decision making is efficient with respect to time g pand other resources

• Coherence• Decision reflects rational process that can be

explained to others and made understandable

• Due process• Decision is the result of a known process and


can be appealed to a higher authority


Systems and Technologies for Supporting DecisionsSystems and Technologies for Supporting Decisions


Syste s a d ec o og es o Suppo t g ec s o sSyste s a d ec o og es o Suppo t g ec s o s

• Management information systems (MIS)

• Decision-support systems (DSS)

• Executive support systems (ESS)• Executive support systems (ESS)

• Group-decision support systems (GDSS)

• Intelligent techniques



Management Information Systems (MIS)Management Information Systems (MIS)

Systems for Decision Support

a age e t o at o Syste s ( S)a age e t o at o Syste s ( S)

• Help managers monitor and control a business by providing information on the firm’s performance

• Typically produce fixed, regularly scheduled t b d d t f TPSreports based on data from TPS

• E.g. summary of monthly or annual sales for each of the major sales territories of aeach of the major sales territories of a company.

• Exception reports: Highlighting exceptional


Exception reports: Highlighting exceptional conditions only


DecisionDecision--Support Systems (DSS)Support Systems (DSS)


ec s oec s o Suppo t Syste s ( SS)Suppo t Syste s ( SS)

• Support semistructured and unstructured problem analysis

• Earliest DSS were model-driven

• “What-if” analysis: Model is developed variousWhat if analysis: Model is developed, various input factors are changed, and the output changes are measured

• Data-driven DSS

• Use OLAP and data mining to analyze large


pools of data in major corporate systems



Interactive Session: PeopleInteractive Session: Peoplete act e Sess o eop ete act e Sess o eop eToo Many Bumped Fliers: Why?Too Many Bumped Fliers: Why?

• Read the Interactive Session and then discuss the following questions:following questions:• Is the decision support system being used by airlines to

overbook flights working well? Answer from the perspective of th i li d f th ti f tthe airlines and from the perspective of customers.

• What is the impact on the airlines if they are bumping too many passengers?

• What are the inputs, processes, and outputs of this DSS?

• What people, organization, and technology factors are responsible for excessive bumping problems?


p p g p

• How much of this is a “people” problem? Explain your answer.


Components of DSSComponents of DSS


Co po e ts o SSCo po e ts o SS• DSS database: Collection of current or historical

data from a number of applications or groups

• DSS software system

• Software tools that are used for data analysisSoftware tools that are used for data analysis

• OLAP tools

• Data mining tools• Data mining tools

• Mathematical and analytical models


• DSS user interface



Overview of a Decision Support SystemOverview of a Decision Support SystemO e e o a ec s o Suppo t SysteO e e o a ec s o Suppo t Syste

Figure 10-3The main components of the DSS are the DSS database, the DSS software system, and the user interface. The


DSS database may be a small database residing on a PC or a large data warehouse.



• Models: Abstract representation that illustrates the• Models: Abstract representation that illustrates the components or relationships of a phenomenon

• Statistical modeling helps establish relationshipsg p p• E.g. relating product sales to differences in age, income,

or other factors

• Optimization models, forecasting models• Sensitivity analysis models

• Ask “what-if” questions repeatedly to determine the impact on outcomes of changes in one or more factors

• E g What happens if we raise product price by 5 percent


• E.g. What happens if we raise product price by 5 percent



Sensitivity AnalysisSensitivity AnalysisSe s t ty a ys sSe s t ty a ys s

This table displays the results of a sensitivity analysis of the effect of changing the sales price of a necktie and the cost per unit on the product’s break-even point. It answers the question, “What happens to the break even point if the sales price and the cost to make each unit increase or decrease?”


Figure 10-4

to the break-even point if the sales price and the cost to make each unit increase or decrease?


Using Spreadsheet Tables to Support DecisionUsing Spreadsheet Tables to Support Decision--MakingMaking


Us g Sp eads eet ab es to Suppo t ec s oUs g Sp eads eet ab es to Suppo t ec s o a ga g• Spreadsheet tables can answer multiple

dimensions of questionsq• Time of day and average purchase• Payment type and average purchase• Payment type, region, and source

• Pivot table• Displays two or more dimensions of data in a

convenient format


convenient format



Sample List of Transactions for Online Management Sample List of Transactions for Online Management Sa p e st o a sact o s o O e a age e tSa p e st o a sact o s o O e a age e tTraining Inc. on October 28, 2007Training Inc. on October 28, 2007

Figure 10-5This list shows a portion of the order


ptransactions for Online Management Training Inc. on October 28, 2007.



A Pivot Table That Examines the Regional A Pivot Table That Examines the Regional ot ab e at a es t e eg o aot ab e at a es t e eg o aDistribution of CustomersDistribution of Customers

Figure 10-6This pivot table was created using Excel 2007 to quickly produce a


table showing the relationship between region and number of customers.



A Pivot Table That Examines Customer Regional A Pivot Table That Examines Customer Regional ot ab e at a es Custo e eg o aot ab e at a es Custo e eg o aDistribution and Advertising SourceDistribution and Advertising Source

Figure 10-7Figure 10 7In this pivot table, we can examine where customers come from in terms of region and advertising source. It appears nearly 30 percent of the customers respond to e


customers respond to e-mail campaigns, and there are some regional variations.


Data Visualization and Geographic Information Systems (GIS)Data Visualization and Geographic Information Systems (GIS)


ata sua at o a d Geog ap c o at o Syste s (G S)ata sua at o a d Geog ap c o at o Syste s (G S)• Data visualization tools:

• Present data in graphical form to help users seePresent data in graphical form to help users see patterns and relationships in large quantities of data

• Geographic information systems (GIS): • Use data visualization technology to analyze and

di l d t i th f f di iti ddisplay data in the form of digitized maps• Support decisions that require knowledge about

the geographic distribution of people or other


the geographic distribution of people or other resources



South Carolina used aSouth Carolina used a GIS-based program called HAZUS to estimate and map the regional damage and lossesdamage and losses resulting from an earthquake of a given location and intensity. HAZUS estimates theHAZUS estimates the degree and geographic extent of earthquake damage across the state based on inputs ofbased on inputs of building use, type, and construction materials. The GIS helps the state plan for natural hazards


plan for natural hazards mitigation and response.


WebWeb--Based Customer DecisionBased Customer Decision--Support Systems (CDSS)Support Systems (CDSS)


ebeb ased Custo e ec s oased Custo e ec s o Suppo t Syste s (C SS)Suppo t Syste s (C SS)

• Support customers in the decision-making process• Include: Search engines, intelligent agents, online

catalogs, Web directories, newsgroups, e-mail, etc.

• Many firms have customer Web sites where all the information, models, or other analytical tools for evaluating alternatives are concentrated in one glocation

• E.g. T. Rowe Price online tools, guides for college l i ti t l i t t l i t


planning, retirement planning, estate planning, etc.


Executive Support Systems (ESS)Executive Support Systems (ESS)


Executive Support Systems (ESS)Executive Support Systems (ESS)• Bring together data from many different internal and

external sources, often through a portal

• Digital dashboard: Gives senior executives a picture of the overall performance of an organization

• Drill down capability: Enables an executive to zoom in on details or zoom out for a broader view

• Used to monitor organizational performance, track activities of competitors, identify changing market conditions spot problems identify opportunities


conditions, spot problems, identify opportunities, and forecast trends


Group DecisionGroup Decision--Support Systems (GDSS)Support Systems (GDSS)


G oup ec s oG oup ec s o Suppo t Syste s (G SS)Suppo t Syste s (G SS)

• Interactive, computer-based systems that facilitate solving of unstructured problems by set of decision makers

• Used in conference rooms with special hardware and software for collecting, ranking, storing ideas and decisions

P t ll b ti t h b• Promote a collaborative atmosphere by guaranteeing contributors’ anonymity

• Support increased meeting sizes with increased


• Support increased meeting sizes with increased productivity


• Intelligent techniques for enhancing decision making

Intelligent Systems for Decision Support

Intelligent techniques for enhancing decision making• Many based on artificial intelligence (AI)

• Computer-based systems (hardware and software) that tt t t l t h b h i d th htattempt to emulate human behavior and thought

patterns• Include:

• Expert systems• Case-based reasoning• Fuzzy logic• Fuzzy logic• Neural networks• Genetic algorithms


• Intelligent agents


• Expert systems


Expert systems• Model human knowledge as a set of rules that are

collectively called the knowledge base

• 200 – 10,000 rules, depending on complexity

• The system’s inference engine searches through theThe system s inference engine searches through the rules and “fires” those rules that are triggered by facts gathered and entered by the user

• Useful for dealing with problems of classification in which there are relatively few alternative outcomes and in which these possible outcomes are all known in ad ance


advance


Rules in an Expert SystemRules in an Expert System


u es a pe t Systeu es a pe t Syste

Figure 10-8An expert system contains a set of rules to be followed when used. The rules are interconnected; the number of outcomes is known in advance and is limited; there are multiple paths to the same outcome;

d th t id lti l


and the system can consider multiple rules at a single time. The rules illustrated are for a simple credit-granting expert system.


C b d i


• Case-based reasoning• Knowledge and past experiences of human specialists

are represented as cases and stored in a database forare represented as cases and stored in a database for later retrieval

• System searches for stored cases with problem y pcharacteristics similar to new one, finds closest fit, and applies solutions of old case to new case.

Successful and unsuccessful applications are tagged and• Successful and unsuccessful applications are tagged and linked in database

• Used in medical diagnostic systems, customer support


g y , pp


How CaseHow Case--Based Reasoning WorksBased Reasoning Works


o Caseo Case ased easo g o sased easo g o s

Figure 10-9Figure 10-9Case-based reasoning represents knowledge as a database of past cases and their solutions. The system uses a six-step process


to generate solutions to new problems encountered by the user.


F l i


• Fuzzy logic• Rule-based technology that represents imprecision in

categories (e g “cold” vs “cool”) by creating rules thatcategories (e.g. cold vs. cool ) by creating rules that use approximate or subjective values

• Describes a particular phenomenon or process li i ti ll d th t th t d i ti ilinguistically and then represents that description in a small number of flexible rules

• Provides solutions to problems requiring expertise that is p q g pdifficult to represent in the form of crisp IF-THEN rules

• E.g. Sendai, Japan subway system uses fuzzy logic controls to accelerate so smoothly that standing


controls to accelerate so smoothly that standing passengers need not hold on



Fuzzy Logic for Temperature ControlFuzzy Logic for Temperature Controlu y og c o e pe atu e Co t ou y og c o e pe atu e Co t o

The membership functions for the input called temperature are in the logic of the thermostat to control the room temperature.


Figure 10-10

The membership functions for the input called temperature are in the logic of the thermostat to control the room temperature.Membership functions help translate linguistic expressions, such as warm, into numbers that the computer can manipulate


N l t k


• Neural networks• Use hardware and software that parallel the processing

patterns of a biological brainpatterns of a biological brain

• “Learn” patterns from large quantities of data by searching for relationships, building models, and g p gcorrecting over and over again the model’s own mistakes

• Humans may “train” the network by feeding it data for which the inputs produce a known set of outputs orwhich the inputs produce a known set of outputs or conclusions.

• Useful for solving complex, poorly understood problems


g p , p y pfor which large amounts of data have been collected



How a Neural Network WorksHow a Neural Network Works

A neural network uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs classifying them based on the experience of the model In this example the


Figure 10-11

layer then processes inputs, classifying them based on the experience of the model. In this example, the neural network has been trained to distinguish between valid and fraudulent credit card purchases.


• Genetic algorithms


Genetic algorithms• Find the optimal solution for a specific problem by

examining very large number of alternative solutions for that problem.

• Based on techniques inspired by evolutionary biology: inheritance mutation selection etcinheritance, mutation, selection, etc.

• Work by representing a solution as a string of 0s and 1s, then searching randomly generated strings of binary g y g g ydigits to identify best possible solution

• Used to solve complex problems that are very dynamic and comple in ol ing h ndreds or tho sands of


and complex, involving hundreds or thousands of variables or formulas



The Components of a Genetic AlgorithmThe Components of a Genetic Algorithmp gp g

This example illustrates an initial population of “chromosomes,” each representing a different solution The genetic algorithm uses an iterative process to refine the initial solutions so that the


Figure 10-12

solution. The genetic algorithm uses an iterative process to refine the initial solutions so that the better ones, those with the higher fitness, are more likely to emerge as the best solution.


• Intelligent agents


Intelligent agents• Programs that work in the background without direct

human intervention to carry out specific, repetitive, and predictable tasks for user, business process, or software application

• Shopping bots• Shopping bots

• Procter & Gamble (P&G) programmed group of semiautonomous agents to emulate behavior of gsupply-chain components, such as trucks, production facilities, distributors, and retail stores and created simulations to determine how to make supply chain


more efficient


Intelligent Agents in P&G’s Supply Chain NetworkIntelligent Agents in P&G’s Supply Chain Network


te ge t ge ts &G s Supp y C a et ote ge t ge ts &G s Supp y C a et o


Figure 10-13Intelligent agents are helping Procter & Gamble shorten the replenishment cycles for products, such as a box of Tide.


Systems for Managing Knowledge

• Knowledge management:Knowledge management: • Business processes developed for creating,

storing, transferring, and applying knowledgestoring, transferring, and applying knowledge

• Increases the ability of organization to learn from environment and to incorporate knowledgefrom environment and to incorporate knowledge into business processes and decision making

• Knowing how to do things effectively and g g yefficiently in ways that other organizations cannot duplicate is major source of profit and competitive advantage


competitive advantage


EnterpriseEnterprise--Wide Knowledge Management SystemsWide Knowledge Management Systems


• Three kinds of knowledge• Structured: Structured text documents (reports,

presentations)• Semistructured: E-mail, voice mail, digital pictures, bulletin-

board postings• Tacit knowledge (unstructured): Knowledge residing in

heads of employees, rarely written down

Enterprise wide knowledge management systems• Enterprise-wide knowledge management systems• Deal with all three types of knowledge• General-purpose, firm-wide systems that collect, store,


General purpose, firm wide systems that collect, store, distribute, and apply digital content and knowledge




• Enterprise content management systems• Capabilities for knowledge capture, storageCapabilities for knowledge capture, storage• Repositories for documents and best practices• Capabilities for collecting and organizing• Capabilities for collecting and organizing

semistructured knowledge such as e-mail

• Classification schemesClassification schemes• Key problem in managing knowledge• Each knowledge object must be tagged for


• Each knowledge object must be tagged for retrieval


An Enterprise Content Management SystemAn Enterprise Content Management System


te p se Co te t a age e t Systete p se Co te t a age e t Syste

An enterprise content management system has


Figure 10-14

p g ycapabilities for classifying, organizing, and managing structured and Semistructured knowledge and making it available throughout the enterprise.


EnterpriseEnterprise Wide Knowledge Management SystemsWide Knowledge Management Systems


• Digital asset management systems


• Manage unstructured digital data like photographs, graphic images, video, audio

• Knowledge network systems (Expertise location• Knowledge network systems (Expertise location and management systems)

• Provide online directory of corporate experts in well-• Provide online directory of corporate experts in well-defined knowledge domains

• Use communication technologies to make it easy for


employees to find appropriate expert in firm


An Enterprise Knowledge Network SystemAn Enterprise Knowledge Network System


te p se o edge et o Systete p se o edge et o Syste

Figure 10-15Figure 10 15A knowledge network maintains a database of firm experts, as well as accepted solutions to known problems, and then facilitates the communication between employees looking for knowledge and experts who have that knowledge. Solutions


p gcreated in this communication are then added to a database of solutions in the form of frequently asked questions (FAQs), best practices, or other documents.




• Collaboration toolsBl

pp g g yg g y

• Blogs• Wikis• Social bookmarking

• Learning management systems (LMS)Learning management systems (LMS)• Provide tools for management, delivery,

tracking, and assessment of various


tracking, and assessment of various types of employee learning and training


Interactive Session: OrganizationsInteractive Session: Organizations


te act e Sess o O ga at o ste act e Sess o O ga at o sManaging With Web 2.0Managing With Web 2.0

• Read the Interactive Session and then discuss the following questions:following questions:• How do Web 2.0 tools help companies manage knowledge,

coordinate work, and enhance decision making?

• What business problems do blogs, wikis, and other social networking tools help solve?

• Describe how a company such as Wal-Mart or Procter & Gamble would benefit from using Web 2.0 tools internally.

• What challenges do companies face in spreading the use of Web 2.0? What issues should managers be concerned with?



Knowledge Work Systems (KWS)Knowledge Work Systems (KWS)


o edge o Syste s ( S)o edge o Syste s ( S)• Requirements of knowledge work systems

• Specialized toolsSpecialized tools• Powerful graphics, analytical tools, and

communications and document management• Computing power to handle

sophisticated graphics or complex l l icalculations

• Access to external databases


• User-friendly interfaces


Requirements of Knowledge Work SystemsRequirements of Knowledge Work Systems


equ e e ts o o edge o Syste sequ e e ts o o edge o Syste s

Figure 10-16Knowledge work systems require strong links to external


knowledge bases in addition to specialized hardware and software.


Knowledge Work Systems (KWS)Knowledge Work Systems (KWS)


o edge o Syste s ( S)o edge o Syste s ( S)

• Examples of knowledge work systems• Computer-aided design (CAD) systems

• Virtual reality systems• Virtual reality systems

• Virtual Reality Modeling Language (VRML)(VRML)

• Investment workstations


Documents

IiDiiImproving Decision Making and ManagingMaking and ...is.cba.edu.kw/240/Handouts/ch10.pdfDSS database may be a small database residing on a PC or a large data warehouse. Essentials