supporting decision making

Supporting Decision Making

Chapter 11

Information Systems Management In Practice 7E

McNurlin & Sprague

PowerPoints prepared by Michael MatthewVisiting Lecturer, GACC, Macquarie University – Sydney Australia

©2006 Barbara C. McNurlin. Published by Pearson Education.

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Part IV: Systems for Supporting Knowledge-Based Work• This part consists of three chapters that discuss

supporting three kinds of work – decision making, collaboration, and knowledge work

• As shown in the book’s framework figure, we distinguish between procedure-based and knowledge-based information-handling activities

• The two previous chapters, in Part III, dealt mainly with building systems for procedure-based work

• This part focuses on supporting knowledge-based activities: the systems that support people in performing information-handling activities to solve problems, work together, and share expertise


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Part IV: Systems for Supporting Knowledge-Based Work cont.

• Chapter 11 discusses supporting decision making by first presenting five underlying technologies and some examples of their use:– Decision Support Systems (DSS)– Data Mining– Executive Information Systems (EIS), and – Expert Systems (ES)– Agent-based Modelling

• The chapter then discusses the fascinating subject of the real-time enterprise, which has a goal of gaining competitive edge by learning of an event as soon as possible and then responding to that event quickly, if necessary


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Part IV: Systems for Supporting Knowledge-Based Work cont.• Chapter 12 deals with supporting collaboration by

– First describing various kinds of groups– Different systems that support their collaboration, and – Finally how executives might think about managing

virtual organizations – an increasing phenomenon these days

• Chapter 13 discusses supporting knowledge work by presenting a model for thinking about how to manage knowledge– It ends with a discussion of computer ethics and

intellectual capital issues in this Internet era


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Chapter 11• This lecture / chapter discusses technologies for

supporting decision making:– Decision Support Systems (DSS) – Data Mining– Executive Information Systems (EIS), and – Expert Systems– Agent-based Modelling

• It then discusses IT issues related to creating the real-time enterprise

• Case examples include: a problem-solving scenario, Ore-Ida Foods, a major services company, Harrah’s Entertainment, Xerox Corporation, General Electric, American Express, Delta Air Lines, a real-time interaction on a website, and Western Digital


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Introduction

• Most computer systems support decision making because all software programs involve automating decision steps that people would take

• Decision making is a process that involves a variety of activities, most of which handle information

• A wide variety of computer-based tools and approaches can be used to confront the problem at hand and work through its solution


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A PROBLEM-SOLVING SCENARIOCase Example – Supporting Decision Making

• Using an executive information system, (EIS) to compare budget to actual sales

• Discover a sale shortfall in one region

• Searches for the cause of the shortfall

• But couldn’t find an answer


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A PROBLEM-SOLVING SCENARIOCase Example – Supporting Decision Making cont.Investigate – several possible causes• Economic Conditions – through the EIS & the Web accesses:

– Wire services– Bank economic newsletters– Current business and economic publications

• Competitive Analysis – through the same sources investigates whether competitors:– Have introduced a new product– Have launched an effective ad campaign

• Written Sales Report – browses the reports– “Concept based” text retrieval system makes this easier

• A Data Mining Analysis– Looking for any previously unknown relationships


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A PROBLEM-SOLVING SCENARIOCase Example – Supporting Decision Making cont.

• Then accesses a marketing DSS – includes a set of models to analyze sales patterns by:– Product– Sales representative– Major customer

Result – no clear problems revealed.Action – hold a meeting, in an electronic meeting room

supported by group DSS (GDSS) software

• This scenario illustrates:– The wide variety of activities involved in problem solving,

and– The wide variety of technologies that can be used to assist

decision makers and problem solvers


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Technologies that Support Decision Making

• The purpose of tractors, engines, machines etc. = to enhance humans’ physical capabilities

• The purpose of computers has been to enhance our mental capabilities

• Hence, a major use of IT is to relieve humans of some decision making or help us make more informed decisions


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Technologies that Support Decision Making Decision Support Systems

• Systems that support, not replace, managers in their decision-making activities

• Decision modeling, decision theory, and decision analysis, attempt to make models from which the ‘best decision’ can be derived, by computation

• DSS are defined as: Computer-based systems– That help decision makers– Confront ill-structured problems– Through direct interaction– With data and analysis models

• Wide range of technologies can be used to assist decision makers and problem solvers


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Decision Support Systems The Architecture for DSSs

• Figure 11-1 shows the relationship between the three components of the DDM model

• Software system in the middle of the figure consists of:– The database management system (DBMS)– The model base management system (MBMS)– The dialog generation and management system

(DGMS)


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Decision Support Systems The Architecture for DSSs cont.

The Dialog Component• The DSS contains a dialog component to link the user to the system• Was ‘mouse’ (Mac) now = browser interface

The Data Component• Data sources – as the importance of DSS has grown, it has become

increasingly critical for the DSS to use all the important data sources within and outside the organization

• Data warehousing• Data mining

– Much of the work on the data component of DSS has taken the form of activities in this area

The Model Component• Models provide the analysis capabilities for a DSS

– Using a mathematical representation of the problem, algorithmic processes are employed to generate information to support decision making


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Decision Support Systems Types of DSS

• The size and complexity of DSS range from large complex systems that have many of the attributes of major applications down to simple ad hoc analyses that might be called end user computing tasks

• Institutional DSSs tend to be fairly well defined– They are based on pre=defined data sources

• Heavily internal with perhaps some external data

– Use well established models in a prescheduled way

• Quick-hit DSSs are developed quickly to help a manager make either a one-time decision or a recurring one

– Can be every bit as useful for a small or large company– Most today = Excel spreadsheets (and not ‘called’ DSS)


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ORE-IDA FOODSCase Example – Institutional DSS

• Frozen food division of H.J. Heinz

• Marketing DSS must support 3 main tasks in the decision making process:

1. Data retrieval – helps managers find answers to the question, “what has happened?”

2. Market analysis – addresses the question, “Why did it happen?”3. Modeling – helps managers get answers to, “What will happen

if…?”

• Modeling for projection purposes, offers the greatest potential value of marketing management

• For successful use – line managers must take over the ownership of the models and be responsible for keeping them up-to-date


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A MAJOR SERVICES COMPANYCase Example – “Quick Hit” DSS – Short Analysis Programs

• Considering – new employee benefit program: an employee stock ownership plan (ESOP).

• Wanted a study made to determine the possible impact of the ESOP on the company and to answer such questions as:– How many shares of company stock will be needed in 10,20

and 30yrs to support the ESOP?– What level of growth will be needed to meet these stock

requirements?

• The information systems manager wrote a program to perform the calculations & printed the results

• Results = showed the impact of the ESOP over a 30yr period– Surprising results


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Technologies that Support Decision Making Data Mining

• A promising use of data warehouses is to let the computer uncover unknown correlations by searching for interesting patterns, anomalies, or clusters of data that people are unaware exist

• Called data mining, its purpose is to give people new insights into data

• Also covered in Chapter 7

• Most frequent type of data mined = customer data


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HARRAH’S ENTERTAINMENTCase Example – Data Mining (Customer)

• To better know its customers, Harrah’s encourages them to sign up for its frequent-gambler card, Total Rewards

• Harrah’s mined its Total Rewards database to uncover patterns and clusters of customers

• It has created 90 demographic clusters, each of which is sent different direct mail offers – encouraging them to visit other Harrah’s casinos– Profit and loss for each customer calculating the likely

‘return’ for every ‘investment’ it makes in that customer


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HARRAH’S ENTERTAINMENTCase Example – Data Mining (Customer) cont.

• Much of its $3.7B in revenues (and 80% of its profits) comes from its slot machines and electronic gaming-machine players– Found = locals who played often

• It was not the ‘high rollers’ who were the most profitable

• Within the first two years of operation of Total Rewards, revenue from customers who visited more than one Harrah’s casino increased by $100 million

©2006 Barbara C. McNurlin. Published by Pearson Education. 11-22

Technologies that Support Decision Making Executive Information Systems (EIS)

• As the name implies EISs are for use by executives

• They have been used for the following purposes:

1. Gauge company performance: sales, production, earnings, budgets, and forecasts

2. Scan the environmental: for news on government regulations, competition, financial and economics developments, and scientific subjects


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Technologies that Support Decision Making Executive Information Systems (EIS) cont.

• EIS can be viewed as a DSS that:1. Provides access to summary performance data2. Uses graphics to display and visualize the data in an

easy-to-use fashion, and 3. Has a minimum of analysis for modeling beyond the

capability to “drill down” in summary data to examine components

• In many companies, the EIS is called a dashboard and may look like a dashboard of a car


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XEROX CORPORATIONCase Example – Executive Information System

• The EIS at Xerox began small and evolved to the point where even skeptical users became avid supporters

• Its objective was to improve communications and planning, such as giving executives pre-meeting documents

• It was also used in strategic planning and resulted in better plans, especially across divisions


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Executive Information Systems (EIS) Pitfalls in EIS Development

1. Lack of executive support: executives must provide the funding, but are the principal users and supply the needed continuity

2. Undefined system objectives: the technology, the convenience, and the power of EIS are impressive, but the underlying objectives and business values of an EIS must be carefully thought through

3. Poorly defined information requirements: EIS typically need non - traditional information sources - judgments, opinion, external text-based documents - in addition to traditional financial and operating data


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4. Inadequate support staff: support staff must:– Have technical competence– Understand the business, and – Have the ability to relate to the varied responsibilities and work patterns

of executives

5. Poorly planned evolution: highly competent system professionals using the wrong development process will fail with EIS

• EIS are not developed, delivered, and then maintained– They should evolve over a period of time under the leadership of a team

that includes:• The executive sponsor• The operating sponsor• Executive users• The EIS support staff manager, and • The IS technical staff

Executive Information Systems (EIS) Pitfalls in EIS Development cont.


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Executive Information Systems (EIS)Why Install an EIS?

• Attack a critical business need: EIS can be viewed as an aid to dealing with important needs that involve the future health of the organization

• A strong personal desire by the executive: The executive sponsoring the project may– Want to get information faster than he/she is now getting it, or – Have a quicker access to a broader range of information, or – Have the ability to select and display only desired information and

to probe for supporting detail, or – To see information in graphical form


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Executive Information Systems (EIS)

A Weak Reason to Install an EIS

• “The thing to do”: An EIS is seen as something that modern management must have, in order to be current in management practices

• The rationale given is that the EIS will increase executive performance and reduce time that is wasted looking for information and by such things as telephone tag


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Executive Information Systems (EIS) What Should the EIS Do?

• A Status Access System: Filter, extract, and compress a broad range of up-to-date internal and external information

– It should call attention to variances from plan.

– It should also monitor and highlight the critical success factors of the individual executive user

– EIS is a structured reporting system for executive management, providing the executive with the data and information of choice and desired form


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GENERAL ELECTRICCase Example – Executive Information System

• Most senior GE executives have a real-time view of their portion of GE via an executive dashboard– Each dashboard compares expected goals (sales, response

times, etc) with actual, alerting the executive when gaps of a certain magnitude appear

• GE’s goal is to gain better visibility into all its operations in real time and give employees a way to monitor corporate operations quickly and easily

• The system is based on complex enterprise software that interlinks existing systems

• GE’s actions are also moving its partners and business ecosystem closer to real-time operation


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Technologies that Support Decision Making Expert Systems

• A real-world use of artificial intelligence (AI)– AI is a group of technologies that attempts to mimic our senses

and emulate certain aspects of human behavior such as reasoning and communication

– Promising for 40 years +. Now = finally living up to promise

• An expert system is an automated type of analysis or problem-solving model that deals with a problem the way an “expert” does

– Note: Expert Systems are not new LISP Prolog

– Languages in the ’70s


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Technologies that Support Decision Making Expert Systems cont.

• The process involves consulting a base of knowledge or expertise to reason out an answer based on the characteristics of the problem

• Like DSSs, they have:– A user interface– An inference engine, and – Stored expertise (in the form of a knowledge base)

• The inference engine is that portion of the software that contains the reasoning methods used to search the knowledge base and solve the problem


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Expert SystemsKnowledge Representation

• Knowledge can be represented in a number of ways:1. One is as cases; case-based reasoning expert systems

using this approach draw inferences by comparing a current problem (or case) to hundreds or thousands of similar past cases

2. A second form is neural networks, which store knowledge as nodes in a network and are more intelligent than the other forms of knowledge representation because they can learn

3. Third, knowledge can be stored as rules (the most common form of knowledge representation), which are obtained from experts drawing on their own expertise, experience, common sense, ways of doing business, regulations, and laws


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AMERICAN EXPRESSCase Example – Expert System• One of the first commercially successful ESs and a

fundamental part of the company’s everyday credit card operation

• Authorizer’s Assistant is an expert system that approves credit at the point of sale

• It has over 2,600 rules and supports all AmEx card products around the world

• Authorizes credit by looking at:– Whether cardholders are creditworthy– Whether they have been paying their bills– Whether a purchase is within their normal spending patterns

• It also assesses whether the request for credit could be a potential fraud


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AMERICAN EXPRESSCase Example – Expert System cont.

• The most difficult credit-authorization decisions are still referred to people

• Avoids ‘sensitive’ transactions– Restaurants– Airline queues

• The rules were generated by interviewing authorizers with various levels of expertise – comparing good decisions to poor decisions

• The system can be adapted quickly to meet changing business requirements


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Expert SystemsDegree of Expertise

1. As an assistant, the lowest level of expertise, the expert system can help a person perform routine analysis and point out those portions of the work where the expertise of the human is required

2. As a colleague, the second level of expertise, the system and the human can “talk over” the problem until a “joint decision” has been reached

3. As an expert, the highest level of expertise, the system gives answers that the user accepts, perhaps without question


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Agent –Based Modelling

• A simulation technology for studying emergent behaviour (e.g. traffic jam) that emerges from the decisions of a large number of distinct individuals (drivers)– Simulation contains computer generated agents, each

making decisions typical of the decisions an individual would make in the real world

• Trying to understand the mysteries of why businesses, markets, consumers, and other complex systems behave as they do

• Some examples:– Nasdaq; Change its tick size– Retailer = redesign its incentive program– Southwest Airlines = revamp its cargo operations– Company changing its recruiting practices


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Technologies that Support Decision Making Conclusion

• This section has discussed five seemingly competing technologies that support decision making

• In reality they often overlap and combine

• The next section demonstrates how these decision support technologies and other technologies are being mixed and matched to form the foundation for the real-time enterprise


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Toward the Real-Time Enterprise

• Through IT, organizations have been able to see the status of operations more and more toward real time

• The Internet is giving companies a way to disseminate closer-to-real-time information about events

• The essence of the phrase real-time enterprise is that organizations can know how they are doing at the moment, rather than have to wait days, weeks, or months for analysis results


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Toward the Real-Time Enterprise cont.

• It is occurring on a whole host of fronts, including:– Enterprise nervous systems

• To coordinate company operations

– Straight-through processing• To reduce distortion in supply chains

– Real-time CRM• To automate decision making relating to

customers, and

– Communicating objects• To gain real-time data about the physical world


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Toward the Real-Time Enterprise Enterprise Nervous Systems These are the technical means to a real-time

enterprise They are:

– Message based - because sending messages is efficient and effective in dispersing information among parties simultaneously

– Event driven - when an event occurs, it is recorded and made available

– Use a publish and subscribe approach - the event is “published” to an electronic address and any system, person, or device authorized to see that information can “subscribe” to that address’s information feed, and

– Use common data formats - data formats from disparate systems are reduced to common denominators that can be understood by other systems and hence shared


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DELTA AIRLINESCase Example – Enterprise Nervous Systems • Delta has built an enterprise nervous system to manage its gate

operations by incorporating the disparate systems the airline had in the late 1990s

• Information about each flight is managed by the system, in real time, and everyone who needs to know about a change can get the data

• The system uses a publish-and-subscribe approach using enterprise application integration (EAI) products, whereby the messaging middleware allows disparate applications to share data

• When an event occurs, it ripples to everyone

• Delta is now expanding those ripples out to their partners who serve their passengers, such as caterers and security companies


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Toward the Real-Time EnterpriseStraight-Through Processing • The notion of a real-time enterprise has

generated two “buzzwords”

• One is zero latency, which means reacting quickly to new information (with no wait time)

• The second is straight-through processing, which means that transaction data are entered just once in a process or a supply chain (like at Delta)

• The goal is to reduce lags and latency in supply chains


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Toward the Real-Time EnterpriseReal-Time CRM

Another view of a real-time response might occur between a company and a potential customer- Perhaps via a customer call center or a

Website


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A REAL-TIME INTERACTION ON A WEB SITE

Case Example – Real-Time CRM

• E.piphany CRM software example• A potential guest visits the Website of a hotel

chain, checking for a hotel in Orlando– The real-time CRM system initiates requests to create

a profile of the customer • All past interactions with that customer• Past billing information• Past purchasing history

• Using this information, it makes real-time offers to the Website visitor, and the visitor’s responses are recorded and taken into account for future Website visitors


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Toward the Real-Time EnterpriseCommunicating Objects These are sensors and tags that provide

information about the physical world via real-time data

A communicating object can tell you:– What it is attached to– Where it is located– Where it belongs, and – A lot more information about itself

• It is a radio frequency identification device (RFID), also called “smart tags”– Based on WW2 technology


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Toward the Real-Time EnterpriseCommunicating Objects cont.

In Singapore, cars carry smart tags, and drivers are charged variable prices for where they drive in the city and when– The prices are set to encourage or discourage

driving at different places at different times– Also proposed for Sydney’s new toll ways

• It’s an example of real-time traffic control

• Smart tags will transform industries because they will talk to one another (object-to-object communication), changing how work is handled


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Toward the Real-Time EnterpriseVigilant Information Systems

• The premise of the real-time enterprise is not only that it can capture data in real time, but that it has the means to act on that data quickly

• US Air Force pilot = bet he could win any dogfight– Never lost a bet, even to superior aircraft– Called his theory OODA

• Observe where his challenger’s plane is• Orient himself and size up his own vulnerabilities and

opportunities• Decide which manoeuvre to take• Act to perform it before the challenger could go through

the same four steps


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WESTERN DIGITALCase Example: Vigilant Information Systems (OODA)

• PC disk manufacturer used OODA type of thinking to move itself closer to operating in real time with a sense-and-respond culture that aims to operate faster than its competitors

• Built what they call a Vigilant Information System (VIS) which they define as a system that is “alertly watchful”– Complex and builds on the firm’s legacy systems– Essentially has four layers – Figure 11-5


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WESTERN DIGITALCase Example: Vigilant Information Systems (OODA) cont.

• VIS had to be complemented by appropriate business processes

– To operate inside it competitors OODA loops– Three new company policies were drafted

1. Company’s strategic goals must be translated into time based objectives and aligned across the company

2. KPIs must be captured in real time and be comparable3. Collaborative decision making to co-ordinate actions company-wide

– Shop-Floor OODA loop– Factory OODA loop– Corporate OODA loop

• Benefits of the VIS– Quickened all 3 OODA loops and helped to link decisions across

them– Corporate performance improved measurably

• Margins doubled since introduction 3 years ago– Sense and response culture where Western digital learns and

adapts quickly in a coordinated fashion


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Toward the Real-Time EnterpriseThe Dark Side of Real Time • What are the drawbacks of real-time activities?

– Object-to-object communication could compromise privacy, since knowing the exact location of a company truck every minute of the day and night can be construed as invading the driver’s privacy That’s a political issue, not a technical issue, and many CEOs

are going to face this question in the future

– Also, in the era of speed, a situation can become very bad very fast, so people must be constantly watching for signals that something negative is likely to happen

• Need for circuit breakers? e.g. NYSE


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Conclusion• Use of IT to support decision making covers a

broad swath of territory

• Some technologies aim to alert people to anomalies, discontinuities, and shortfalls

• Others aim to make decisions, either as recommendations to people or to act on behalf of people

• Handing over decisions to systems has its pros and cons, thus their actions need to be monitored


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Conclusion cont.

• CIOs need to alert their management team of potential social and economic effects of computer-based decision making because errant computer-based decisions have devastated corporate reputations and cost a lot of money

• With vendors pushing toward the real-time enterprise, this is a use of computers that should give pause to explore the ramifications

supporting decision making

Documents

supporting decision

supporting knowledge

decision making chapter

building systems

knowledgebased activities

information systems

decision making cont

knowledgebased work