Decision Making Lauden

7/16/2019 Decision Making Lauden

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10.1

AnalyticsBusiness

Improving Decision

Making and ManagingKnowledge

Video Cases:

Case 1 FreshDirect Uses Business Intelligence to Manage Its Online Grocery

Case 2 IBM and Cognos: Business Intelligence and Analytics for Improved Decision

Making

Instructional Videos:

Instructional Video 1 FreshDirect's Secret Sauce: Customer Data From the Website

Instructional Video 2 Oracle's Mobile Business Intelligence App


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10.2

Student Learning Objectives

Essentials of Management Information SystemsChapter 10 Improving Decision Making and Managing Knowledge

What are the different types of decisions, andhow does the decision-making process work?

How do business intelligence and businessanalytics support decision making?

How do information systems help people working

individually and in groups make decisions moreeffectively?


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10.3

Student Learning Objectives

What are the business benefits of usingintelligent techniques in decision making andknowledge management?

What types of systems are used for enterprise-wide knowledge management, and how do theyprovide value for businesses?



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What to Sell? What Price to Charge? Ask the Data.

Problem: Retailerssuch as 1-800-Flowers and DuaneReade need to

determine whatproducts will sellbest, at what prices,and at differentlocations

Solution: Businessanalytics software toanalyze patterns insales data



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1-800-Flowers uses SAS Inc. analytics software torecord and analyze online buyer profiles toimprove targeting, determine specials, and plan

sales and marketing. Analytics software can createpricing profiles buyer profiles for different regions,locales, even times of day

Demonstrates the use of business intelligence and

analysis systems to improve sales and profits

Illustrates how information systems improvedecision making




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Decision Making and Information Systems

Business Value of Improved Decision Making

Possible to measure value of improved decisionmaking

Decisions made at all levels of the firm

Some are common, routine, and numerous.

Although value of improving any single decision

may be small, improving hundreds of thousands ofsmall decisions adds up to large annual value for

the business



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Business Value of Improved Decision Making

Decision Maker Number/ year

Value ofdecision

Annual valueto firm

Allocate support to most

valuable customers.

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

Predict call center dailydemand.

Call Centermanagement

4 150,000 600,000

Decide parts inventory leveldaily.

Inventory manager 365 5,000 1,825,000

Identify competitive bidsfrom major suppliers. Senior management1 2,000,000 2,000,000

Schedule production to fillorders.

Manufacturingmanager

150 10,000 1,500,000



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Types of Decisions


Unstructured

Decision maker must provide judgment to solve problem

Novel, important, non routine

No well-understood or agreed-upon procedure for makingthem

Structured

Repetitive and routine

Involve definite procedure for handling them so do not have tobe treated as new

Semi structured

Only part of problem has clear-cut answer provided by

accepted procedure



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Figure 10-1

Senior managers,

middle managers,operationalmanagers, andemployees havedifferent types ofdecisions andinformation

requirements.

Information Requirements of Key Decision-MakingGroups in a Firm




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The Decision-Making Process


1. Intelligence

Discovering, identifying, and understanding the problems

occurring in the organizationwhy is there a problem, where,

what effects it is having on the firm

2. Design

Identifying and exploring various solutions

3. Choice

Choosing among solution alternatives

4. Implementation

Making chosen alternative work and monitoring how well

solution is working



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Figure 10-2

The decision-makingprocess can be brokendown into four stages.

Stages in Decision Making



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High velocity decision-making

Humans eliminated

Trading programs at electronic stock exchanges

Quality of decisions, decision making

Accuracy

Comprehensiveness or Completeness

Fairness Speed (efficiency)

Coherence or Consistant

Due process



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Business intelligence (BI) is a set of theories,methodologies, processes, architectures, and

technologies that transform raw data into meaningful and

useful information for business purposes.

BI can handle large amounts of information to help

identify and develop new opportunities. Making use of

new opportunities and implementing an effective strategy

can provide a competitive market advantage and long-term stability.

Business intelligence (BI)


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The Business Intelligence Environment

Business Intelligence in the Enterprise

Six elements in business intelligenceenvironment

1. Data from business environment

2. Business intelligence infrastructure

3. Business analytics toolset

4. Managerial users and methods5. Delivery platform: MSS, DSS, ESS

6. User interface



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Figure 10-3

Businessintelligence andanalytics requiresa strong databasefoundation, a setof analytic tools,and an involvedmanagement teamthat can ask

intelligentquestions andanalyze data.

Business Intelligence and Analytics for Decision Support




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Business Intelligence and Analytics Capabilities

Production reports

Predefined, based on industry standards

Parameterized reports E.g. pivot tables (A data summarization tool)

Dashboards/scorecards- To see trends

Ad-hoc query/search/report creation Drill-down

Forecasts, scenarios, models

What-if scenario analysis, statistical analysis




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In data processing, a pivot table is a datasummarization tool found in data visualization

programs such as spreadsheets orbusiness

intelligence software. Among other functions, apivot-table can automatically sort, count, total or

give the average of the data stored in one table

or spreadsheet.

It displays the results in a second table (called

a "pivot table") showing the summarized data.

Pivot Table
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Examples of Business Intelligence Pre-Defined Reports


Business Functional Area Production Reports

Sales Sales forecasts, sales team performance, cross selling, sales cycle times

Service/Call Center Customer satisfaction, service cost, resolution rates, churn rates

Marketing Campaign effectiveness, loyalty and attrition, market basket analysis

Procurement and Support Direct and indirect spending, off-contract purchases, supplier

performance

Supply Chain Backlog, fulfillment status, order cycle time, bill of materials analysis

Financials General ledger, accounts receivable and payable, cash flow, profitability

Human Resources Employee productivity, compensation, workforce demographics,

retention



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Predictive analytics

Predictive analytics encompasses avariety of techniques

from statistics, modeling, machine

learning, and data mining that analyzecurrent and historical facts to

make predictions about future, or

otherwise unknown, events.
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Predictive Analytics

Use statistical analytics and other techniques

Extracts information from data and uses it to

predict future trends and behavior patterns

Predicting responses to direct marketing campaigns

Identifying best potential customers for credit cards

Identify at-risk customers

Predict how customers will respond to price changes and

new services

Accuracies range from 65 to 90%




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Data Visualization, Visual Analytics, and GIS

Data visualization, visual analytics tools

Rich graphs, charts, dashboards, maps

Help users see patterns and relationships inlarge amounts of data

GISgeographic information systems

Visualization of data related to geographicdistribution

E.g., GIS to help government calculate

response times to emergencies




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Figure 10-4

Casual users areconsumers of BIoutput, whileintense powerusers are theproducers ofreports, newanalyses,models, and

forecasts.

Business Intelligence Users




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Support for Semi-Structured Decisions

Decision-support systems (DSS)

BI delivery platform forsuper-users who want to create own

reports, use more sophisticated analytics and models

What-if analysis

Sensitivity analysis

Backward sensitivity analysis

Pivot tables: spreadsheet function for multidimensionalanalysis

Intensive modeling techniques




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Figure 10-5

This table displays the results of a sensitivity analysis of the effect ofchanging the sales price of a necktie and the cost per unit on the productsbreak-even point. It answers the question, W h at happens to the break-evenpoint i fthe sales price and the cost to make each unit increase ordecrease?

Sensitivity Analysis




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Figure 10-6

In this pivot table, we

are able to examinewhere an online trainingcompanys customerscome from in terms ofregion and advertisingsource.

A Pivot Table That Examines Customer RegionalDistribution and Advertising Source




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Decision Support for Senior Management

Executive support systems

Balanced scorecard method

Leading methodology for understanding informationmost needed by executives

Focuses on measurable outcomes

Measures four dimensions of firm performance

Financial Business process

Customer

Learning and growth




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Figure 10-7

The Balanced Scorecard Framework


In the balanced score-card framework, thefirms strategic

objectives areoperationalized alongfour dimensions:financial, businessprocess, customer,and learning and

growth. Eachdimension ismeasured usingseveral KPIs.



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Business performance management (BPM)

Management methodology

Based on firms strategies

E.g., differentiation, low-cost producer, market sharegrowth, scope of operation

Translates strategies into operational targets

Uses set of KPI (key performance indicators) to measureprogress toward targets

ESS combine internal data with external

Financial data, news, etc.

Drill-down capabilities




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Interactive Session: PeopleColgate-Palmolive Keeps Managers Smiling with Executive Dashboards

Read the Interactive Session and then discuss thefollowing questions:

Describe the different types of business intelligence users atColgate-Palmolive.

Describe the people issues that were affecting Colgates ability

to use business intelligence.

What people, organization, and technology factors had to beaddressed in providing business intelligence capabilities foreach type of user?

What kind of decisions does Colgates new business intelligencecapability support? Give three examples. What is their potentialbusiness impact?




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Group Decision-Support Systems (GDSS)

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

Used in conference rooms with special hardware andsoftware for collecting, ranking, storing ideas anddecisions

Promotes a collaborative atmosphere by guaranteeingcontributors anonymity

Supports increased meeting sizes with increasedproductivity

Software follows structured methods for organizing andevaluating ideas



E i l f M I f i S


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Intelligent techniques for enhancing decision making

Many based on artificial intelligence (AI)

Computer-based systems (hardware and software) thatattempt to emulate human behavior and thought

patterns

Include:

Expert systems

Case-based reasoning

Fuzzy logic Neural networks

Genetic algorithms

Intelligent agents

Intelligent Systems for Decision Support


E ti l f M t I f ti S t


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Expert systems

Model human knowledge as a set of rules that are

collectively called the knowledge base

From 200 to 10,000 rules, depending on complexity

The systems inference engine searches through the rulesand 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 advance





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Figure 10-8

An expert system contains a setof rules to be followed whenused. The rules areinterconnected; the number ofoutcomes is known in advanceand is limited; there are multiplepaths to the same outcome; andthe system can consider multiple

rules at a single time. The rulesillustrated are for a simple credit-granting expert system.

Rules in an Expert System





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Case-based reasoning

Knowledge and past experiences of human specialistsare represented as cases and stored in a database for

later retrieval

System searches for stored cases with problemcharacteristics similar to new one, finds closest fit, andapplies solutions of old case to new case

Successful and unsuccessful applications are tagged andlinked in database

Used in medical diagnostic systems, customer support





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Figure 10-9

Case-based reasoningrepresents knowledge as

a database of past casesand their solutions. Thesystem uses a six-stepprocess to generatesolutions to newproblems encountered bythe user.

How Case-Based Reasoning Works





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Fuzzy logic

Rule-based technology that represents imprecision incategories (e.g., cold versus cool) by creating rules

that use approximate or subjective values Describes a particular phenomenon or process

linguistically and then represents that description in asmall number of flexible rules

Provides solutions to problems requiring expertise that isdifficult to represent in the form of IF-THEN rules

E.g., Sendai, Japan subway system uses fuzzy logiccontrols to accelerate so smoothly that standingpassengers need not hold on



Essentials of Management Information Systems


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Figure 10-10

The membership functions for the input called temperature are in the logic of thethermostat to control the room temperature. Membership functions help translatelinguistic expressions, such as warm, into numbers that the computer canmanipulate


Fuzzy Logic for Temperature Control




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Neural networks

Use hardware and software that parallel the processingpatterns of a biological brain.

Learn patterns from large quantities of data by searchingfor relationships, building models, and correcting over andover again the models own mistakes

Humans train the network by feeding it data for which the

inputs produce a known set of outputs or conclusions

Machine learning

Useful for solving complex, poorly understood problems forwhich large amounts of data have been collected





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Figure 10-11 A neural network uses rules it learns from patterns in data to construct ahidden layer of logic. The hidden layer then processes inputs, classifying thembased on the experience of the model. In this example, the neural network hasbeen trained to distinguish between valid and fraudulent credit card purchases.


How a Neural Network Works




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Genetic algorithms

Find the optimal solution for a specific problem byexamining very large number of alternative solutions forthat problem

Based on techniques inspired by evolutionary biology:inheritance, mutation, selection, and so on

Work by representing a solution as a string of 0s and 1s,

then searching randomly generated strings of binarydigits to identify best possible solution

Used to solve complex problems that are very dynamicand complex, involving hundreds or thousands ofvariables or formulas





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Figure 10-12

This example illustrates an initial population ofchromosomes, each representing a different solution. Thegenetic algorithm uses an iterative process to refine the initial solutions so that the better ones, those withthe higher fitness, are more likely to emerge as the best solution.


The Components of a Genetic Algorithm




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Intelligent agents

Programs that work in the background without directhuman intervention to carry out specific, repetitive, andpredictable tasks for user, business process, orsoftware application

Shopping bots

Procter & Gamble (P&G) programmed group of

semiautonomous agents to emulate behavior ofsupply-chain components, such as trucks, productionfacilities, distributors, and retail stores and createdsimulations to determine how to make supply chainmore efficient





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Figure 10-13

Intelligentagents arehelping Procter& Gamble

shorten thereplenishmentcycles forproducts, suchas a box ofTide.

Intelligent Agents in P&Gs Supply Chain Network





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Interactive Session: TechnologyIBMs Watson: Can Computers Replace Humans?

Read the Interactive Session and then discuss thefollowing questions:

How powerful is Watson? Describe its technology. Whydoes it require so much powerful hardware?

How intelligent is Watson? What can it do? What cantit do?

What kinds of problems is Watson able to solve?

Do you think Watson will be as useful in other disciplinesas IBM hopes? Will it be beneficial to everyone? Explainyour answer.





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Systems for Managing Knowledge

Knowledge management

Business processes developed for creating,storing, transferring, and applying knowledge

Increases the ability of organization to learnfrom environment and to incorporate knowledgeinto business processes and decision making

Knowing how to do things effectively andefficiently in ways that other organizationscannot duplicate is major source of profit andcompetitive advantage




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Three kinds of knowledge

Structured: structured text documents

Semistructured: e-mail, voice mail, digital pictures, etc.

Tacit knowledge (unstructured): knowledge residing in headsof employees, rarely written down

Enterprise-wide knowledge management systems

Deal with all three types of knowledge

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

Enterprise-Wide Knowledge Management Systems





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Enterprise content management systems

Capabilities for knowledge capture, storage

Repositories for documents and best practices

Capabilities for collecting and organizing

semistructured knowledge such as e-mail

Classification schemes Key problem in managing knowledge

Each knowledge object must be tagged for retrieval

Enterprise-Wide Knowledge Management Systems





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Figure 10-14

An enterprise content managementsystem has capabilities for classifying,organizing, and managing structuredand semistructured knowledge andmaking it available throughout theenterprise.

An Enterprise Content Management System





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Digital asset management systems

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

Knowledge network systems (expertise locationand management systems)

Provide online directory of corporate experts in well-

defined knowledge domains

Use communication technologies to make it easy foremployees to find appropriate expert in firm





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Figure 10-15

A knowledge network maintainsa database of firm experts, aswell as accepted solutions toknown problems, and then

facilitates the communicationbetween employees looking forknowledge and experts whohave that knowledge. Solutionscreated in this communicationare then added to a database ofsolutions in the form of

frequently asked questions(FAQs), best practices, or otherdocuments.

An Enterprise Knowledge Network System





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Collaboration tools

Social bookmarking: allow users to save theirbookmarks publicly and tag with keywords

Folksonomies

Learning management systems (LMS)

Provide tools for management, delivery,

tracking, and assessment of various types ofemployee learning and training





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Knowledge Work Systems (KWS)


Specialized systems for knowledge workers

Requirements of knowledge work systems:

Specialized tools Powerful graphics, analytical tools, and

communications and document management

Computing power to handle sophisticated

graphics or complex calculations Access to external databases

User-friendly interfaces

sse t a s o a age e t o at o Syste sChapter 10 Improving Decision Making and Managing Knowledge



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Figure 10-16

Knowledge work systemsrequire strong links toexternal knowledge basesin addition to specializedhardware and software.

Requirements of Knowledge Work Systems

g yChapter 10 Improving Decision Making and Managing Knowledge




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Knowledge Work Systems (KWS)


Examples of knowledge worksystems:

Computer-aided design (CAD) systems

Virtual reality (VR) systems

Virtual Reality Modeling Language (VRML)

Augmented reality (AR) systems

Investment workstations

g yChapter 10 Improving Decision Making and Managing Knowledge

Decision Making Lauden

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