Chapter 11: Managing Knowledge

Dr. Andrew P. Ciganek, Ph.D.

Important Dimensions of Knowledge

• Knowledge is a firm asset– Intangible

– Creation of knowledge from data, information, requires organizational resources

– As it is shared, experiences network effects

• Knowledge has different forms– May be explicit (documented) or tacit (in minds)

– Know-how, craft, skill

– How (and when) to follow procedure

– Knowing why things happen (causality)

Important Dimensions of Knowledge

• Knowledge has a location– Cognitive event

– Both social and individual

– “Sticky” (hard to move), situated (enmeshed in firm’s culture), contextual (works only in certain situations)

• Knowledge is situational– Conditional: Know when to apply procedure

– Contextual: Know circumstances to use certain tool

The Knowledge Management Landscape

• To transform information into knowledge, firm must expend resources to discover patterns, rules, and contexts where knowledge works

• Wisdom: Collective and individual experience of applying knowledge to solve problems – Involves where, when, and how to apply knowledge

– Knowing how to do things effectively and efficiently in ways other organizations cannot duplicate

• A primary source of profit and competitive advantage that cannot be purchased easily by competitors

• e.g., Having a unique build-to-order production system

Knowledge Management

• Set of business processes developed to create, store, transfer, and apply knowledge

• Knowledge management value chain– Each stage adds value to raw data and information

as they are transformed into usable knowledge1. Knowledge acquisition

2. Knowledge storage

3. Knowledge dissemination

4. Knowledge application

KM Value Chain

• Knowledge acquisition– Document tacit and explicit knowledge– Creating knowledge– Tracking data from TPS and external sources

• Knowledge storage– Management must

• Support development of knowledge storage systems• Encourage development of corporate-wide schemas

for indexing documents• Reward employees for taking time to update and store

documents properly

KM Value Chain

• Knowledge dissemination– Training programs, informal networks, and shared

management experience help managers focus attention on important knowledge and information

• Knowledge application – To provide ROI, knowledge must become systematic

part of management decision making and become situated in decision-support systems

• New business practices, New products and services, New markets

3 Major Types of KM Systems

• Enterprise-wide knowledge management systems– General-purpose firm-wide efforts to collect, store,

distribute, and apply digital content and knowledge

• Knowledge work systems (KWS)– Specialized systems built for engineers, scientists,

other knowledge workers charged with discovering and creating new knowledge

• Intelligent techniques – Diverse group of techniques such as data mining

used for various goals: discovering knowledge, distilling knowledge, discovering optimal solutions

Enterprise-wide KM Systems

• Main categories of systems for dealing with different kinds of knowledge– Structured knowledge systems

• Reports, presentations, formal rules

– Semistructured knowledge systems• E-mail, voice mail, memos, brochures, digital pictures,

bulletin boards, other unstructured documents• 80% of an organization’s business content is

semistructured or unstructured

– Knowledge networks• Network (tacit) knowledge – expertise of individuals

Enterprise Knowledge Network System

Knowledge Work System Examples

• CAD: Automates creation and revision of engineering or architectural designs using computers and sophisticated graphics software

• Virtual reality systems: Software and special hardware to simulate real-life environments– e.g., 3-D medical modeling for surgeons– VRML (Virtual reality modeling language):

Specifications for interactive, 3-D modeling on Web that can organize multiple media types

Intelligent Techniques

• Capture individual and collective knowledge, extend knowledge base– To capture tacit knowledge: Expert systems, case-based

reasoning, fuzzy logic

– Knowledge discovery: Neural networks and data mining

– Generating solutions: Genetic algorithms

– Automating tasks: Intelligent agents

• Artificial intelligence (AI) technology– Computer-based systems that emulate human behavior

– Able to learn languages, accomplish physical tasks, etc.

Expert Systems

• Capture tacit knowledge in very specific and limited domain of human expertise– Capture knowledge of skilled employees in form of

set of rules in software system that can be used by others in organization

– Typically perform limited tasks that may take a few minutes or hours

• e.g., Diagnose software problem, determining whether to grant credit for loan, admission to graduate school

Rules in an Expert System

Case-based Reasoning (CBR)

• Descriptions of past experiences of human specialists, represented as cases, stored in knowledge base– System searches for stored cases with problem characteristics

similar to new one, finds closest fit, applies solutions of old case to new case

– Successful, unsuccessful applications are grouped with case

• Stores organizational intelligence: Knowledge base is continuously expanded and refined by users

• CBR found in– Medical diagnostic systems

– Customer support

How Case-Based Reasoning Works

Fuzzy Logic Systems

• Rule-based technology that represents imprecision used in linguistic categories (e.g., “cold”, “cool”) that represent range of values

• Describe a particular phenomenon or process linguistically and then represent that description in a small number of flexible rules

• Provides solutions to problems requiring expertise that is difficult to represent with crisp IF-THEN rules– Autofocus devices in cameras

– Systems to detect possible medical fraud

– Sendai’s subway system use of fuzzy logic controls to accelerate smoothly

How a Neural Network Works

• Uses rules “learned” from patterns in data to construct a hidden layer of logic, which processes (classifies) inputs based on the model experience

Genetic Algorithms

• Useful for finding optimal solution for specific problem by examining very large number of possible solutions for that problem– Conceptually based on process of evolution

• Search among solution variables by changing and reorganizing component parts using processes such as reproduction, mutation, and natural selection

• Used in optimization of business problems (minimization of costs, efficient scheduling, etc.) in which hundreds or thousands of variables exist

Hybrid AI Systems

• Genetic algorithms, fuzzy logic, neural networks, and expert systems integrated into single application to take advantage of their best features– e.g., Matsushita “neurofuzzy” washing machine that

combines fuzzy logic with neural networks

Intelligent Agents

• Work in background to carry out specific, repetitive, and predictable tasks for individual user, business process, or software application

• Use limited built-in or learned knowledge base to accomplish tasks or make decisions on user’s behalf– e.g., Deleting junk e-mail, finding cheapest airfare, Microsoft

Office software wizards

• Agent-based modeling applications: Model behavior of consumers, stock markets, and supply chains and to predict spread of epidemics – Procter & Gamble used agent-based modeling to improve

coordination among supply-chain members

Intelligent Agents in P&G’s Supply Chain Network