CHAPTER 9 Knowledge Management.

CHAPTER 9 Knowledge Management

Introduction What do we mean by knowledge? Class Discussion
Drucker (1994): “The knowledge society will be more competitive than anything that we have seen so far.” Why? With knowledge being universally accessible there will be no reason for por performance. Cyert (1991): “The most crucial variable in economic development is Knowledge.”

Introduction Leonard-Barton (1995): “Organizations that are successful innovators are those that build and manage knowledge effectively through activities as developing shared problem-solving skill, experimentation, integrating knowledge across functional boundaries, and importing expertise from external sources.”

Knowledge Management Ancient Collaboration at the organizational level
Could revolutionize collaboration and computing

Opening Vignette: Knowledge Management Gives Mitre a Sharper Edge
Mitre - knowledge management system (KMS) to leverage organizational knowledge effectively throughout the organization Internal marketing during development Supported at the highest level Provided an important application Organizational culture shift was critical Saved $54.91 million / invested $7.19 million

Knowledge Management Leverages intellectual assets
Delivers appropriate solutions to anyone, anywhere Good managers have always done this Ancient concept

DSS Insights- GEM: A DSS for Workload-Planning Decisions
Overview: . GEM a large stevedoring company . Schedules developed a week ahead . Each ship is expected to arrive within days . Unexpected conditions cause the schedule to be re- written

System Description: * means very important variable Ships . ETA . Cargo Information . Ship’s workload per location . DWT . Permitted berths . Maximum number of elevators . ETD*

Berth . Equipment information . Availability of equipment . Maximum permitted length . Maximum permitted draught . Maximum permitted DWT

Other characteristics .The planner can override the system .Each ship has a max number of elevators which can be set by the planner System operation .Run planning scenario with no penalties .Study results .If there are ships in an unfavorable position (ETD) - manipulate penalties to improve ships position .Repeat until satisfactory Class discussion!!!!!!

Knowledge Management Helps organizations Identify Select Organize
Disseminate Transfer Important information and expertise within the organizational memory in an unstructured manner

Knowledge As a form of capital, must be exchangeable among persons, and must be able to grow Intellectual Capital- as the competence of an individual and the commitment of the individual to the organization’s goals (competence * commitment)

Knowledge Management Requires a major transformation in organizational culture to create a desire to share

Knowledge Information that is contextual, relevant, and actionable
Knowledge is INFORMATION IN ACTION Higher than data and information

Knowledge Types Advantaged knowledge Base knowledge Trivial knowledge
Explicit knowledge Tacit knowledge

Knowledge Types Advantaged Knowledge- Knowledge that provides competitive advantage Base Knowledge- Knowledge that is integral to an organization, providing it with short-term solutions (i.e. best practices) Trivial knowledge- knowledge that has no impact on the organization

Explicit Knowledge Objective, rational, technical Easily documented
Easily transferred / taught / learned

Tacit Knowledge Subjective, cognitive, experiential learning
Hard to document Hard to transfer / teach / learn Involves a lot of human interpretation

Data, Information and Knowledge

Knowledge Has Extraordinary leverage and increasing returns
Fragmentation, leakage, and the need to refresh Uncertain value Uncertain value sharing

Organizational Learning and Organizational Memory
Group memory Learning The learning organization Organizational memory Organizational learning Organizational culture

Organizational Memory
Individual wells Information well Culture well Transformation well Structural well Ecology well

Organizational Learning Focuses
Knowledge source Product-process focus Documentation mode Dissemination mode Learning focus Value chain focus Skill development focus

Organizational Culture
Culture is a pattern of shared basic assumptions Most important aspect of KM success Why don’t people share knowledge?

Knowledge Management (KM)
A process of elicitation, transformation, and diffusion of knowledge throughout an enterprise so that it can be shared and thus REUSED Helps organizations find, select, organize, disseminate, and transfer important information and expertise Transforms data / information into actionable knowledge to be used effectively anywhere in the organization by anyone

How Core Competency is Linked to Explicit and Tacit Knowledge

KM Objectives Create knowledge repositories Improve knowledge access
Enhance the knowledge environment Manage knowledge as an asset

KMS Manage Knowledge creation through learning
Knowledge capture and explication Knowledge sharing and communication through collaboration Knowledge access Knowledge use and reuse Knowledge archiving

Knowledge Repository Not a database Not a knowledge base (like for ES)
A collection of internal and external knowledge

Knowledge Repository Types
External Structured internal knowledge Informal internal knowledge

KM Activities Externalization Internalization Intermediation Cognition

KM Features Create a knowledge culture Capture knowledge
Generate knowledge Explicate (and digitize) knowledge Share and reuse knowledge Renew knowledge

Cyclic Model of KM Create knowledge Capture knowledge Refine knowledge
Store knowledge Manage knowledge Disseminate knowledge

Cyclic Model of KM Capture Knowledge Create Knowledge Refine Knowledge
Disseminate Knowledge Store Knowledge Manage Knowledge

KM Examples Mitre Dow Chemical Company Xerox Chrysler Monsanto Chevron
Buckman Laboratories KPMG Ernst & Young Arthur Andersen Andersen Consulting

Why Adopt KM Cost savings Better performance Demonstrated success
Share Best Practices Competitive advantage

Chief Knowledge Officer (CKO)
Maximize firm’s knowledge assets Design and implement KM strategies Effectively exchange knowledge assets Promote system use

KM Development Need a knowledge strategy Identify knowledge assets

KM Development Identify the problem Prepare for change Create the team
Map out the knowledge Create a feedback mechanism Define the building blocks Integrate existing information systems

Strategies for Successful KM Implementation
Establish a KM methodology Designate a pointperson Empower knowledge workers Manage customer-centric knowledge Manage core competencies

More Strategies Foster collaboration and innovation
Learn from best practices Extend knowledge sourcing Interconnect communities of expertise (communities of practice) Report the measured value of knowledge assets

KM Methods, Technologies, and Tools
or messaging Document management Search engines Enterprise information portal Data warehouse Groupware Workflow management Web-based training Others

How to KM Integrate the technologies to manage knowledge effectively

KM Tool Categories Information architecture Technical architecture
Application architecture

KM Software Knowware still developing but… DecisionSuite Wincite
DataWare KnowledgeX Knowledge Share

KM Success Economic performance
Technical and organizational infrastructure Standard, flexible knowledge structure Knowledge-friendly culture Clear purpose and language Change in motivational practices Multiple channels for knowledge transfer Worthwhile level of process orientation Nontrivial motivational encouragement Senior management support

Measuring Success Balanced Scorecard Skandia Navigator
Economic Value Added Inclusive Valuation Methodology

KM Failure Causes Unclear definition of knowledge
Overemphasis on knowledge stock, not flow Belief that knowledge exists outside people’s heads Not recognizing the importance of managing knowledge Failure to manage tacit knowledge

More Failure Causes Failure to disentangle knowledge from its uses
Downplaying reason and thinking Focusing on the past and present, not the future Failure to recognize the importance of experimentation Substituting technology contact for human interface Overemphasis on measuring knowledge, not its outcomes

KM and AI Can use AI in KM Can use KM in AI
Data mining can create knowledge

Electronic Document Management
A KM for documents Everyone is on the same page Documents are up to date Simple example: corporate phonebook Lower costs Better performance

The Knowledge-Based View of Decision Making
Accepting Messages: see next slide A decision maker (human-being) can accept stimuli from the environment The stimuli are messages that carry knowledge (information) Some messages have a direct and immediate impact on the decisions being manufactured Other messages can be: . discarded . passed along to others and or other places . stored for future use

Issuing Messages The decision maker can issue messages to the surroundings: . other people . documents/storage vessels The message may also be the Manufactured Decision

Assimilating Knowledge Figure 4.2, page 99. Once the Decision maker has established the meaning of an incoming message it can be assimilated with the DM’s knowledge store When new knowledge is assimilated, it alters the knowledge store: . just be added . cause existing knowledge to be altered, discarded, or marked as being obsolete . It may cause fundamental alterations of the knowledge store

Recognizing the Need for a Decision May be very obvious: . Highly structured . Happens frequently May be take many repetitions of the event/stimuli to initiate action, thus it is: . unstructured . novel . by observing conditions (economic, political, mechanical) we may come to recognize that: - a problem exits - a solution is required

Manufacturing a Decision: The manufacturing process produces new knowledge from knowledge. The sources of raw materials is the decision maker’s storehouse of knowledge (experience, facts, rules, etc). Knowledge is extracted on an as needed basis and manipulated by Cognitive abilities to produce solutions for the flow of problems that constitutes the KNOWLEDGE Manufacturing Process. The solution that is the product of the process is the NEW KNOWLEDGE.

Defining Knowledge Three Views of Knowledge
· Knowledge Representations: If a system has and can use a representation of something then the system itself can also be said to have KNOWLEDGE. The textbook can be a representation of knowledge if it can be read. Representation is pattern of Symbols => an abstraction It embodies knowledge:

Defining Knowledge à A useable representation of something
à From a DSS point of view we must be concerned with the computer memory and how it processes knowledge represents knowledge Clean data and defined objects are required

· Knowledge States A set of states ranging from raw data to decisions
Six states of Knowledge: data Information structured information insight judgment decision: The highest state

Defining Knowledge One state of knowledge can be used to generate different states of knowledge DSS help in: Acquiring knowledge deriving knowledge

Knowledge Production · The result of a productivity activity (i.e. LEARNING) involving acquisition and/or derivation The flows and stock of knowledge Figure 4.3, page 106 Stocks are the inventories of knowledge The flows are the messages that tell the stock to do something

Knowledge Sources The decision makers store house of Knowledge: Internal & External The DM can be active or Passive about acquiring knowledge Active: Message can be emitted to invoke a response Passive the DM observes without invoking reactions

Knowledge Sources The Decision to Acquire/ Derive Knowledge
General a mixture of acquiring and derivation of knowledge Acquiring knowledge may tax: cognitive abilities, time, economic limits There are tradeoffs DSS tens to promote greater reliance on internal production of knowledge

Knowledge Sources Reliability of Knowledge:
Do we get the same knowledge from internal and external sources? If there are multiple external sources- to they yield the same result DSS · Without a DSS it may be infeasible to produce the it internally Use the DSS in parallel with the knowledge acquisition to check the source reliability

Knowledge Sources Knowledge Qualities DSS
accurately retaining knowledge flagging inconsistencies analyzing uncertainties tracking multiple sets of knowledge

Knowledge Sources Utility of Knowledge: Usefulness
Knowledge can be useful to different people To a history professor knowledge about particle physics is probably not useful Figure 4.4 DSS: Present what is relevant to a specific DM Provide high quality knowledge

Knowledge Management Techniques
Text Management Forms Management Business Graphics Solver Management Rule Management Database Management Report Generation Spreadsheet Analysis Programming Message Management

Knowledge Management Reasons for Understanding Knowledge Management
Position or integrate Knowledge into a decision Extend the role of supporting participants “from mere production to the processing, storage, retrieval, dissemination, utilization and general management of knowledge.” Facilitate and develop a philosophy and methodologies for handling knowledge Shift the role of supporting participants “from producing certainty and complete knowledge to structuring ignorance and managing uncertainty Lohuizen & Kochen, 1986 in Holsapple & Whinston, 1996, page 112

Knowledge Management Five types of Knowledge Practical 2. Intellectual 3. Pastime 4. Spiritual 5. Unwanted

Knowledge Management Three Primary Types of Knowledge 1. Descriptive: Includes descriptions of past, present, future, and hypothetical situations. DATA and INFORMATION- To Know What 2. Procedural Knowledge: The how to do; Step-by-Step 3. Reasoning Knowledge: To know Why- Approaches to problem solving

Knowledge Management Three Secondary Types of Knowledge Linguistic: Vocabulary and grammar, body language, meaning of gestures Assimilation Knowledge: The basis for controlling changes to the knowledge store. A filtering mechanism Presentation Knowledge: The basis for packaging outgoing responses

Knowledge Management The Decision Maker possess knowledge
The DSS has processing abilities that can supplement the Decision Maker

The Cognitive Basis for Knowledge
Declarative Knowledge factual information that is static in nature it is usually describable to us history- events, facts flexible- it can be reorganized to suite our purposes Knowing That

Cognitive- Knowledge Some knowledge can be encoded in a declarative format which can later be transformed into a procedural format as we become familiar with the information. Examples: Reading Windows for Dummies Reading a Golf technique book then truing on the PC/ Play golf

Cognitive Knowledge Attention: the concentration and focusing of mental activity Paying attention seems to accentuate, or enhance, sensory input that has been focused on

The Cognitive Basis for Knowledge
Procedural Knowledge the underlying skillful actions we possess it is dynamic it is not very describable the acquisition of a skill involves making and detecting errors (skiing, bike riding, ballet) with additional experience we improve Knowing How

Information Processing
Sensory system: where specific aspects of the environment are detected and organized- into cognitive code The code is passed into memory Memory Working memory: a workbench for cognitive codes (short term memory) Permanent memory: long term storage of declarative and procedural knowledge

Memory External Input Sensory Register Short-term Storage
Long-term Storage

Sensory Register Where our feature detection and pattern recognition process produce a cognitive code that can be stored for a short time. The Sensory register does not depend on resource allocation- we do not have to pay attention to incoming stimuli in order to have this cognitive code created.

Sensory Register It must have a large storage capacity
It is modality specific: has difference storages for audio, visual The code in storage Decays over time Resources must be allocated to transfer the code to STM or LTM

Short-term Memory Limited capacity (RAM)
Storage is organized by sensory component: acoustic, verbal, linguistic Storage duration of unrehearsed material is about 30 seconds Material that is not elaborated or transferred decays.

Long-term Memory To go from STM to LTM requires rehearsal Rehearsal:
procedures that maintain the vitality of the code STS code will last indefinitely if it is occasionally refreshed by rehearsal. Rehearsal duplicates and augments the code for long-term storage (associations/links are created),

The Architecture of Knowledge Repositories --- "Pipeline"
Knowledge Views Knowledge Repository S Repository * Content U * Content * Packaging/Format U P * Structure * Accessing/Distribution S P E L R I S E R S Storage Aquisition Refinement Distributed Presented Retrieval Process Platform Technology Infrastructure Organizational Infrastructure

The Architecture of Interactive Knowledge Repositories --- "Virtual"
Knowledge Views Knowledge Repository * Content Repository * Packaging/Format * Content * Accessing/ * Structure Distribution Storage Aquisition Refinement Distributed Presented Retrieval Discussion Participants

Knowledge Engineering and Acquisition

Taxonomy of Knowledge Types
Primary Descriptive: data, information, descriptions of past, present, and future situations Procedural: how to do something Reasoning: codes of conduct, regulations, policies, diagnostic rules Secondary Linguistic: vocabulary, grammar, knowledge of gestures Assimilative: permissible contents, retention cycles, relevancy filters Presentation: modes of communication, graphing, messaging, inverse of Linguistic knowledge

Conceptual Model of Knowledge Engineering Process
Validation and Verification Feedback of Performance and Knowledge Knowledge Elicitation Tools Knowledge Structuring Knowledge Modeling PERSON Expert performance of some task in some domain COMPUTER Emulation of expert performance of some task in some domain Psychology of person Personal construct psychology of person as an anticipatory system Ontology of computer knowledge representation and operationalizing an anticipatory system Psychological model of skilled performance Representation of skill in terms of conceptual structures Computational model of skilled performance Representation of skill in terms of logical structures Required Expertise Transfer Elicitation Feedback Unification of psychological and computational representation

Knowledge Acquisition Dimensions
Strategic KE-driven Expert-driven Machine-driven Interviews Protocol analysis Repertory Grid Tactical

Five-Stage General Process of Knowledge Acquisition
Identification Identify Problem Characteristics Conceptualization Find Concepts To Represent Knowledge Formalization Design Structure to Organize Knowledge Implementation Formulate Rules, Frames, etc., to Embody Knowledge Testing Validate Rules that Organize Knowledge Refinements Redesigns Reformulations Requirements Concepts Structure Rules

Basic Pre-Interview Checklist
Decide what you need to know. Ask yourself why this information is needed. Determine that an interview is the best method for obtaining this information. Determine the appropriate degree of structure for the interview. Consider the method in which the answers to your questions will be coded and analyzed.

Necessary Task Conditions for Successful Concurrent Protocols
The sample of cases employed must be highly representative of the task under study. Each task must have a clearly defined conclusion or point of completion. The task must be able to be completed in one protocol session. All data must be presented to the expert in a familiar form. A test case should be given to the expert prior to the collection of protocols so that he or she may become familiar and comfortable with the verbalization process.

Typical Repertory Grid Structure
Constructs Element 1 Element 2 Element 3 Distinction 1Constraint 1,1Constraint 1,2Constraint 1,3 Distinction 2Constraint 2,1Constraint 2,2Constraint 2,3 Distinction 3Constraint 3,1Constraint 3,2Constraint 3,3 Distinction 4Constraint 4,1Constraint 4,2Constraint 4,3 Distinction 5Constraint 5,1Constraint 5,2Constraint 5,3 Individual Concept

Knowledge Base Validation Measures and Techniques
Accuracy: How well does the system reflect reality. How correct is the knowledge in the knowledge base. Adaptability: Possibilities for future development or changes. Adequacy: The portion of the necessary knowledge that is included in the knowledge base. Appeal: How well the knowledge base matches intuition and stimulates thought and practicability Breadth: How well is the domain covered. Depth: The degree of the detailed knowledge. Face Validity: How credible is the knowledge. Generality: Capability of a knowledge base to be used with a broad range of similar problems. Precision: Capability of the system to replicate particular system parameters. Consistency of advice and coverage of variables in the knowledge base. Realism: Accounting for the relevant variables and relations. Similarity to reality. Reliability: The frequency of system predictions that are correct. Robustness: Sensitivity of conclusions to model structure. Sensitivity: The impact of changes in the knowledge base on the quality of outputs. Technical/Operational: Goodness of the assumptions, context, constraints, and conditions. Turing test: Ability of a human evaluator to identify if a given conclusion is made by a real expert or a computer. Usefulness: How adequate the knowledge is (in terms of parameters and relationships) for solving correctly. Validity: The capability of the knowledge base for producing empirically correct predictions.

KM – The Future Not a fad Impact is immense
Research on organizational culture How to do each step Are they the right steps?

Knowledge Management The definition is clear The concepts are clear
The challenges are Clear Surmountable The benefits are clear (and can be huge) The tools and technologies are viable

Knowledge Management Key Issues
Organizational culture Executive sponsorship Measuring success The future: Comprehensive standardized KM packages

Knowledge Mangement “The wise see knowledge and action as one” (Bhagvad Gita) Intelligent organizations recognize that knowledge is an asset, perhaps the only one that grows over time, and when harnessed effectively can sustain the ability to continuously compete and innovate.

CHAPTER 9 Knowledge Management.

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