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© 2009 Pearson Education, Inc. Publishing as Prentice Hall Supporting Information- Centric Decision Making Chapter 12 Information Systems Management in.

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Presentation on theme: "© 2009 Pearson Education, Inc. Publishing as Prentice Hall Supporting Information- Centric Decision Making Chapter 12 Information Systems Management in."— Presentation transcript:

1 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Supporting Information- Centric Decision Making Chapter 12 Information Systems Management in Practice 8 th Edition

2 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 12-2 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 Procedure-based versus knowledge- based information-handling activities Part III dealt with procedural-based work Part IV focuses on knowledge-based work

3 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 12-3 Framework For IS Management

4 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 12-4 Chapter 12 Introduction Technologies-supported decision making Building timely business intelligence Decision support systems Data mining Executive information systems Expert systems Agent-based modeling

5 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 12-5 Chapter 12 cont’d Toward the real-time enterprise Enterprise nervous systems Straight-through processing Real-time CRM Communicating objects Vigilant information systems Requisites for successful real-time management Conclusion

6 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 12-6 Introduction Decision making is a process that involves a variety of activities, most of which handle information Most computer systems support decision making by automating decision processes A wide variety of computer-based tools and approaches can be used to solve problems.

7 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 12-7 A Problem-Solving Scenario Case Example: Supporting decision making 1. Use of executive information systems (EIS) to compare budget to actual sales 2. Discovery of a sale shortfall in one region 3. Analysis of possible cause(s) of the shortfall Economic conditions, competitive analysis, data mining, sales reports Sales pattern via marketing DSS Brainstorming session via GDSS 4. No discernable singular cause 5. Solution: Multimedia sales campaign

8 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 12-8 Technologies-Supported Decision Making

9 © 2009 Pearson Education, Inc. Publishing as Prentice Hall 12-9 Building Timely Business Intelligence Business intelligence (BI) is a broad set of concepts, methods and technologies to improve context- sensitive business decisions Gather, filter and analyze large quantities of data from various sources Sense-making is central to BI Ability to be aware and assess situations that seem important to the organization Awareness: Inductive process (data-driven) Assessment: Fitting observed data into a pre-determined model

10 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Decision Support Systems Computer-based systems that help decision makers confront ill-structured problems through direct interaction with data and analysis models. Architecture for DSS Dialog-Data Model (DDM) Ad hoc information requests Specific data query

11 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Components of a Decision Support System

12 © 2009 Pearson Education, Inc. Publishing as Prentice Hall ORE-IDA Foods Case Example: Institutional DSS Frozen food division of H.J. Heinz Marketing DSS must support three main tasks in decision making process: 1. Data retrieval “What has happened?” 2. Marketing analysis (70% of DSS function) “Why did it happen?” 3. Modeling “What will happen if…?” Modeling for projection purposes offers greatest potential value of marketing management

13 © 2009 Pearson Education, Inc. Publishing as Prentice Hall A Major Services Company Case Example: ‘Quick Hit’ DSS - short analysis programs Employee stock ownership plan (ESOP) Determine possible impact of the ESOP on the company and answer questions including How many company shares needed in years? Level of growth needed to meet stock requirements? IS manager wrote a program to perform calculations Program produced impact projections of ESOP over 30- year period (surprising results) DSS program subsequently expanded to other employee benefit programs

14 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Data Mining Use of computers to uncover unknown correlations from a large data set Classes Clusters Associations Sequential patterns Data mining gives people insights into data Customer data

15 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Harrah’s Entertainment Case Example: Data Mining (Customer) Total Rewards Program Mined customer data to create 90 demographic clusters for different direct mail offers Calculates the ROI on each customer Found that 80% of profits from slot machine and electronic game machine players rather than ‘high rollers’ Within first two years of program, revenue from repeat customers increased by $100 million

16 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Executive Information Systems EIS an “executive summary” form of DSS Used to gauge company performance, address a critical business need and scan the environment 1. Provides access to summary performance data 2. Uses graphics to display and visualize the data in a user-friendly fashion 3. Has a minimum of analysis for modeling capability beyond that for examining summary data

17 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Xerox Corporation Case Example: Executive Information Systems Objective for EIS at Xerox was to improve communications and strategic planning Quick access to related information at the right time Executive meetings More efficient and better planning, especially across divisions Explore relationships between plans and activities at several divisions Xerox corporate chief of staff was executive sponsor of EIS development

18 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Executive Information Systems cont’d Pitfalls for EIS development 1. Lack of executive support 2. Undefined system objectives 3. Poorly defined information requirements 4. Inadequate support staff 5. Poorly planned evolution (expansion of EIS)

19 © 2009 Pearson Education, Inc. Publishing as Prentice Hall General Electric Case Example: Executive Information Systems Most senior GE executives have a real-time view of their portion of GE via “dashboard” GE’s goal is to gain visibility into all its operations in real time and give managers a way to monitor operations quickly and easily EIS based on complex enterprise software that interlinks existing systems GE’s actions are also moving its partners and business ecosystem closer to real-time operations

20 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Expert Systems Expert systems are a real-world use of artificial intelligence (AI) AI mimics human cognition and communication to analyze a situation or solve a problem e.g. MIT’s Commonsense Computing project Expert system components User interface Inference engine Reasoning methods Knowledge base

21 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Expert Systems cont’d Knowledge representation Cases Knowledge from hundreds or thousands of cases to draw inferences from Neural networks Knowledge stored as nodes in a network (adaptive learning) Rules Knowledge obtained from human experts drawing on own expertise, experience, common sense, regulations, laws and regulations

22 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Neural Networks

23 © 2009 Pearson Education, Inc. Publishing as Prentice Hall American Express Case Example: Expert System Authorizer’s Assistant one of most successful commercial uses of expert system Approves all AmEx credit card transactions and assesses for fraud based on over 2600 rules Credit worth of card holders Bill payment Purchases within normal spending pattern Rules derived from authorizers with various levels of expertise Customer sensitive (to avoid customer embarrassment) Can be changed to meet changing business demands

24 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Agent-Based Modeling A simulation technology for studying emergent behavior (from large number of individuals) Simulation contains “software agents” making decisions to understand behavior of markets and other complex systems Nasdaq Example Performed simulation to investigate effect of switch in tick size from fixed eighths (.125) to decimals Found increase in buy-ask price spread instead of initially predicted decrease because of the reduction in market’s ability to do price discovery

25 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Toward Real-Time Enterprise This section builds on the five different types of decision support technologies and demonstrates how they can be mixed and matched to form the foundation for the real- time enterprise

26 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Toward the Real-Time Enterprise IT, especially the Internet, is giving companies a way to know how they are doing “at the moment” and disseminate the closer-to-real-time information about events Occurring on a whole host of fronts including Enterprise nervous systems Coordinate company operations Straight-through processing Reduce distortion in supply chains Communicating objects Gain real-time data about the physical world

27 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Enterprise Nervous Systems A kind of network that connects people, applications and devices (buzz phrase?) Message-based Messages are efficient and effective for dispersing information Event driven Events are recorded and made available Publish and subscribe approach Events are published to electronic address, which can be subscribed to as an information feed Common data formats

28 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Delta Airlines Case Example: Enterprise Nervous Systems Delta integrated existing disparate systems to build an enterprise nervous system to manage gate operations Information about each flight is managed in real- time by the system System uses a publish-and-subscribe approach using messaging middleware Delta is now expanding system out to their partners who serve their passengers

29 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Straight-Through Processing Real-time information Zero latency Quick reaction to new information Straight-through processing means transaction data are entered just once in a process, especially a supply chain Goal is to reduce bullwhip effect from process lags and latency

30 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Real-Time CRM Another view of real-time response might occur between a company and a potential customer (touch points) Customer call Web site

31 © 2009 Pearson Education, Inc. Publishing as Prentice Hall A Real-Time Interaction On a Web Site An illustration of how real-time CRM works A potential guest visits the Web site of a hotel chain The real-time CRM system initiates requests to create profile of customer  Past interactions with the customer  Past billing information  Past purchasing history Using this information, it makes real-time offers to the visitor, and visitor’s responses are recorded and taken into account for Web site visits

32 © 2009 Pearson Education, Inc. Publishing as Prentice Hall A Real-Time Interaction On a Web Site cont’d

33 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Communicating Objects These are “smart” sensors and tags that provide information about the physical world via real-time data radio frequency identification device (RFID) pet micro-chips (satellite GPS), product tags A tag can be passive (read-only) or active (send out signals) Carries far more information than bar codes Item code, price and history

34 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Communicating Objects cont’d Example: Real-time electronic road pricing (ERP) system in Singapore to control traffic congestion Cars have smart card devices attached to their windscreens Smart cards are debited (wirelessly) when cars pass through gantries in certain areas of the city Variable pricing dependent on when and where you drive

35 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Vigilant Information Systems The premise of a real-time enterprise is not only having the ability to capture data in real time, but also acting on that data quickly US Air Force pilot’s OODA framework Never lost a dog-fight even to superior aircraft! Observe where his challenger’s plane is Orient himself and size up his own vulnerabilities and opportunities Decide which maneuver to take Act to perform before the challenger through the same four steps

36 © 2009 Pearson Education, Inc. Publishing as Prentice Hall OODA Loop

37 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Western Digital Case Example: Vigilant Information Systems PC disk manufacturer used OODA type of thinking to move itself closer to operating in real-time with a sense-and-respond culture for competitive advantage Built “alertly watchful” vigilant information system (VIS) Complex and builds on the firm’s legacy systems Essentially four layers

38 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Western Digital’s Vigilant Information Systems

39 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Western Digital cont’d Changed business processes to complement VIS to give Western Digital a way to operate inside competitors’ OODA loops Established new company policies Translate strategic goals to time-based objectives Capture real-time key performance indicators (KPIs) Collaborate decision making and coordinate actions Three levels of OODA loops to maximize VIS “alerts” Shop-floor, Factory, Corporate Benefits of VIS Quickened all OODA loops and helped link decisions across them, which ultimately led to significant increase in firm performance

40 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Requisites for Successful Real-Time Management Real-time data and real-time performance metrics Focus on high value-added data Identify key activities and performance indicators that are needed in real time Technology readiness Substantial computing resources Integrated and seamless system that is capable of selecting, filtering and compiling data to send them in real time to designated users on demand.

41 © 2009 Pearson Education, Inc. Publishing as Prentice Hall Conclusion Use of IT to support decision making covers a variety of functions including Alert, recommendation or decision making itself Computer-supported decision making needs to be monitored IS managers must comprehend the potentials and limitations of these technologies

42 © 2009 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 © 2009 Pearson Education, Inc. Copyright © 2009 Pearson Education, Inc. Publishing as Prentice Hall


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