Laudon MIS 9th Edition Chapter 12

12.1 © 2006 by Prentice Hall

12Chapter

Managing Knowledge Managing Knowledge in the Digital Firmin the Digital Firm

Managing Knowledge Managing Knowledge in the Digital Firmin the Digital Firm


Important Dimensions of Knowledge Important Dimensions of Knowledge

• Knowledge: Concepts, experience, and insight that

provide a framework for creating, evaluating, and

using information. Can be tacit (undocumented) or

explicit (documented)

Management Information SystemsManagement Information SystemsChapter 12 Managing Knowledge in the Digital Firm Chapter 12 Managing Knowledge in the Digital Firm

THE KNOWLEDGE MANAGEMENT LANDSCAPE


Knowledge is a Firm Asset:

• Intangible asset

• Requires organizational resources

• Value increases as more people share it


• Wisdom: The collective and individual experience of

applying knowledge to the solution of problem;

knowing when, where, and how to apply knowledge


Important Dimensions of Knowledge (Continued)


• Organizational learning: Adjusting business

processes and patterns of decision making to

reflect knowledge gained through information and

experience gathered

Organizational Learning and Knowledge Management




• Knowledge acquisition

• Knowledge storage

• Knowledge dissemination

• Knowledge application

• Building organizational and management capital: collaboration, communities of practice, and office environments


The Knowledge Management Value Chain



The Knowledge Management Value Chain

Figure 12-2




Types of Knowledge Management Systems

Figure 12-3




Examples of Knowledge Work Systems Examples of Knowledge Work Systems

• Information system that automates the creation and revision of industrial and manufacturing designs using sophisticated graphics software

Computer-Aided Design (CAD):

• Interactive graphics software and hardware that create computer-generated simulations that emulate real-world activities or photorealistic simulations

Virtual Reality Systems:


KNOWLEDGE WORK SYSTEMS


• Powerful desktop computer for financial

specialists, which is optimized to access and

manipulate massive amounts of financial data

Investment Workstation:


KNOWLEDGE WORK SYSTEMS

Examples of Knowledge Work Systems (Continued)


INTELLIGENT TECHNIQUES

• Identification of underlying patterns, categories, and

behaviors in large data sets, using techniques such

as neural networks and data mining

Knowledge Discovery:

• Computer-based systems based on human behavior,

with the ability to learn languages, accomplish

physical tasks, use a perceptual apparatus, and

emulate human expertise and decision making

Artificial Intelligence (AI) technology:



Capturing Knowledge: Expert Systems Capturing Knowledge: Expert Systems

Expert system:• An intelligent technique for capturing tacit knowledge in

a very specific and limited domain of human expertise

Knowledge base: • Model of human knowledge that is used by expert

systems

• Series of 200-10,000 IF-THEN rules to form a rule base



AI shell: The programming environment of an expert system


Organizational IntelligenceOrganizational Intelligence

• Knowledge system that represents knowledge as a database of cases and solutions

• Searches for stored cases with problem characteristics similar to the new case and applies solutions of the old case to the new case

Case-Based Reasoning (CBR):




• Hardware or software that emulates the processing

patterns of the biological brain to discover patterns

and relationships in massive amounts of data

• Use large numbers of sensing and processing

nodes that interact with each other

Neural Networks Neural Networks



Neural Network:Neural Network:


• Uses rules it ‘learns” from patterns in data to

construct a hidden layer of logic that can be applied

to model new data

• Applications are found in medicine, science, and

business



Neural Networks (Continued)


• Adaptive computation that examines very large

number of solutions for a problem to find optimal

solution

• Programmed to “evolve” by changing and

reorganizing component parts using processes

such as reproduction, mutation, and natural

selection: worst solutions are discarded and better

ones survive to produce even better solutions

Genetic Algorithms Genetic Algorithms




• Integration of multiple AI technologies (genetic algorithms, fuzzy logic, neural networks) into a single application to take advantage of the best features of these technologies

Intelligent Agents:

• Software programs that work in the background without direct human intervention to carry out specific, repetitive, and predictable tasks for an individual user, business process, or software application



Hybrid AI system:


13Chapter

Enhancing Decision Making Enhancing Decision Making for the Digital Firm for the Digital Firm

Enhancing Decision Making Enhancing Decision Making for the Digital Firm for the Digital Firm


Systems and Technologies for Business Intelligence

DECISION MAKING AND DECISION-SUPPORT SYSTEMS

Figure 13-1

Management Information SystemsManagement Information SystemsChapter 13 Enhancing Decision Making for the Digital Firm Chapter 13 Enhancing Decision Making for the Digital Firm


Business Decision Making and the Decision-Making Process Business Decision Making and the Decision-Making Process

• Senior management

• Middle management and project teams

• Operational management and project teams

• Individual employees

Decision-Making Levels:





Information Requirements of Key Decision-Making Groups in a Firm

Figure 13-2



Unstructured decisions:

Types of Decisions



• Novel, non-routine decisions requiring judgment and insights

• Examples: Approve capital budget; decide corporate objectives


Structured decisions:



• Routine decisions with definite procedures

• Examples: Restock inventory; determine special offers to customers

Semistructured decisions:

• Only part of decision has clear-cut answers provided by accepted procedures

• Examples: Allocate resources to managers; develop a marketing plan

Types of Decisions (Continued)


• Management Information Systems (MIS)

• Decision-Support Systems (DSS)

• Executive Support Systems (ESS)

• Group Decision-Support Systems (GDSS)

Systems for Decision SupportSystems for Decision Support

There are four kinds of systems that support the different levels and types of decisions:




Stages in Decision Making

Figure 13-3




• Primarily address structured problems

• Provides typically fixed, scheduled reports based

on routine flows of data and assists in the general

control of the business

SYSTEMS FOR DECISION SUPPORT

The Difference between MIS and DSS The Difference between MIS and DSS


Management Information Systems:


• Support semistructured and unstructured problems

• Greater emphasis on models, assumptions, ad-hoc queries, display graphics

• Emphasizes change, flexibility, and a rapid response



Decision Support Systems:


Model-driven DSS:


Types of Decision-Support Systems Types of Decision-Support Systems


• Primarily stand-alone systems

• Use a strong theory or model to perform “what-if” and similar analyses


Data-driven DSS:



• Integrated with large pools of data in major enterprise systems and Web sites

• Support decision making by enabling user to extract useful information

• Data mining: Can obtain types of information such as associations, sequences, classifications,

clusters, and forecasts




• Statistical models

• Optimization models

• Forecasting models

• Sensitivity analysis (“what-if” models)

Model: An abstract representation that illustrates the components or relationships of a phenomenon


A DSS for Customer Analysis and Segmentation



Figure 13-6


• Group Decision-Support System (GDSS) is an

interactive computer-based system used to facilitate

the solution of unstructured problems by a set of

decision makers working together as a group.

GROUP DECISION-SUPPORT SYSTEMS

What Is a GDSS? What Is a GDSS?


Laudon MIS 9th Edition Chapter 12

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