Chapter 11 - Chapter 11: Managing Knowledge and...

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Unformatted text preview: Chapter 11: Managing Knowledge and Collabora5on Part 1: Introduc5on to Knowledge Management Learning Objec5ves •  Assess the role of knowledge management and knowledge management programs in business •  Describe the types of systems used for enterprise‐wide knowledge management and demonstrate how they provide value for organiza5ons •  Describe the major types of knowledge work systems and assess how they provide value for firms •  Evaluate the business benefits of using intelligent techniques for knowledge management Produced by Dr. Brian Janz 1 Knowledge in Organiza5ons •  Knowledge workers – Researchers, designers, architects, scien5sts, and engineers who create knowledge and informa5on for the organiza5on – Three key roles: •  Keeping organiza5on current in knowledge •  Serving as internal consultants regarding their areas of exper5se •  Ac5ng as change agents, evalua5ng, ini5a5ng, and promo5ng change projects The Knowledge Management Landscape •  To transform informa5on into knowledge, firm must expend addi5onal resources to discover paOerns, rules, and contexts where knowledge works •  Knowing how to do things effec5vely and efficiently in ways other organiza5ons cannot duplicate is primary source of profit and compe55ve advantage that cannot be purchased easily by compe5tors –  Example: Having a unique build‐to‐order produc5on system Produced by Dr. Brian Janz 2 The Knowledge Management Landscape •  Substan5al part of a firm’s stock market value is related to intangible assets: knowledge, brands, reputa5ons, and unique business processes •  Knowledge‐based projects can produce extraordinary ROI •  Sales of enterprise content management soTware for knowledge management expected to grow 15 percent annually through 2012 Important Dimensions of Knowledge •  Knowledge is a firm asset –  Intangible –  Crea5on of knowledge from data, informa5on, requires organiza5onal resources –  As it is shared, experiences network effects –  May be explicit (documented) or tacit (residing in minds) –  Know‐how, craT, skill –  How to follow procedure –  Knowing why things happen (causality) •  Knowledge has different forms Produced by Dr. Brian Janz 3 Important Dimensions of Knowledge •  Knowledge has a loca5on –  Cogni5ve asset or event –  Both social and individual –  “S5cky” (hard to move), situated (enmeshed in firm’s culture), contextual (works only in certain situa5ons) •  Knowledge is situa5onal –  Condi5onal: Knowing when to apply procedure –  Contextual: Knowing circumstances to use certain tools Knowledge in Organiza5ons •  Three major types of knowledge in enterprise – Structured documents •  Reports, presenta5ons •  Formal rules – Semi‐structured documents •  E‐mails, videos – Unstructured, tacit knowledge •  80% of an organiza5on’s business content is semi‐structured or unstructured Produced by Dr. Brian Janz 4 Knowledge Management: New Organiza5onal Roles and Responsibili5es •  Chief knowledge officer execu5ves •  Dedicated staff / knowledge managers •  Communi5es of prac5ce (COPs) –  Informal social networks of professionals and employees within and outside firm who have similar work‐related ac5vi5es and interests –  Ac5vi5es include educa5on, online newsleOers, sharing experiences and techniques –  Facilitate reuse of knowledge, discussion –  Reduce learning curves of newemployees The Knowledge Management Value Chain •  Knowledge management: Set of business processes developed in an organiza5on to create, store, transfer, and apply knowledge •  Knowledge management value chain: Each stage adds value to raw data and informa5on as they are transformed into usable knowledge –  Knowledge acquisi5on –  Knowledge storage –  Knowledge dissemina5on –  Knowledge applica5on Produced by Dr. Brian Janz 5 The Knowledge Management Value Chain: Knowledge Acquisi5on •  Documen5ng tacit and explicit knowledge – Storing documents, reports, presenta5ons, best prac5ces – Unstructured documents (e.g., e‐mails) – Developing online expert networks •  Tracking data from TPS and external sources The Knowledge Management Value Chain: Knowledge Storage •  Databases •  Document management systems •  Role of management: – Support development of planned knowledge storage systems – Encourage development of corporate‐wide schemas for indexing documents – Reward employees for taking 5me to update and store documents properly Produced by Dr. Brian Janz 6 The Knowledge Management Value Chain: Knowledge Dissemina5on •  Portals •  Push e‐mail reports •  Search engines •  Collabora5on tools •  A deluge of informa5on? – Training programs, informal networks, and shared management experience help managers focus aOen5on on important informa5on The Knowledge Management Value Chain: Knowledge Applica5on •  To provide return on investment, organiza5onal knowledge must become a systema5c part of management decision making •  Decision‐support systems can help here – New business prac5ces – New products and services – New markets Produced by Dr. Brian Janz 7 Chapter 11: Managing Knowledge and Collabora5on Part 2: Enterprise‐Wide Knowledge Management Systems and Knowledge Work Systems Knowledge Management Systems •  Three major types of knowledge management systems: –  Enterprise‐wide knowledge management systems –  Knowledge work systems (KWS) •  General‐purpose firm‐wide efforts to collect, store, distribute, and apply digital content and knowledge –  Intelligent techniques •  Specialized systems built for engineers, scien5sts, other knowledge workers charged with discovering and crea5ng new knowledge •  Diverse group of techniques such as data mining used for various goals: discovering knowledge, dis5lling knowledge, discovering op5mal solu5ons Produced by Dr. Brian Janz 8 Enterprise‐Wide Knowledge Management Systems •  Help capture, store, retrieve, distribute, preserve – Documents, reports, best prac5ces – Semistructured knowledge (e‐mails) •  Bring in external sources – News feeds, research •  Tools for communica5on and collabora5on Enterprise‐Wide Knowledge Management Systems •  Enterprise‐wide content management systems – Key problem – Developing taxonomy •  Knowledge objects must be tagged with categories for retrieval – Digital asset management systems •  Specialized content management systems for classifying, storing, managing unstructured digital data •  Photographs, graphics, video, audio Produced by Dr. Brian Janz 9 Enterprise‐Wide Knowledge Management Systems •  Knowledge network systems – Provide online directory of corporate experts in well‐defined knowledge domains – Use communica5on technologies to make it easy for employees to find appropriate expert in a company – May systema5ze solu5ons developed by experts and store them in knowledge database •  Best‐prac5ces •  Frequently asked ques5ons (FAQ) repository Enterprise‐Wide Knowledge Management Systems •  Major knowledge management system vendors include powerful portal and collabora5on technologies –  Portal technologies: Access to external informa5on •  News feeds, research •  Access to internal knowledge resources –  Collabora5on tools •  E‐mail •  Discussion groups •  Blogs •  Wikis •  Social bookmarking Produced by Dr. Brian Janz 10 Enterprise‐Wide Knowledge Management Systems •  Learning management systems –  Provide tools for management, delivery, tracking, and assessment of various types of employee learning and training –  Support mul5ple modes of learning •  CD‐ROM, Web‐based classes, online forums, live instruc5on, etc. –  Automates selec5on and administra5on of courses –  Assembles and delivers learning content –  Measures learning effec5veness Knowledge Work Systems •  Knowledge work systems –  Systems for knowledge workers to help create new knowledge and ensure that knowledge is properly integrated into business –  Substan5al compu5ng power for graphics, complex calcula5ons –  Powerful graphics, and analy5cal tools –  Communica5ons and document management capabili5es –  Access to external databases –  User‐friendly interfaces –  Op5mized for tasks to be performed (design engineering, financial analysis) •  Requirements of knowledge work systems Produced by Dr. Brian Janz 11 Knowledge Work Systems •  Examples of knowledge work systems –  CAD (computer‐aided design): Automates crea5on and revision of engineering or architectural designs, using computers and sophis5cated graphics soTware –  Virtual reality systems: SoTware and special hardware to simulate real‐life environments •  E.g. 3‐D medical modeling for surgeons •  VRML: Specifica5ons for interac5ve, 3D modeling over Internet –  Investment worksta5ons: Streamline investment process and consolidate internal, external data for brokers, traders, porkolio managers Chapter 11: Managing Knowledge and Collabora5on Part 3: Intelligent Techniques for Managing Knowledge Produced by Dr. Brian Janz 12 Intelligent Techniques •  Intelligent techniques: Used to capture individual and collec5ve knowledge and to extend knowledge base –  To capture tacit knowledge: Expert systems, case‐based reasoning, fuzzy logic –  Knowledge discovery: Neural networks and data mining –  Genera5ng solu5ons to complex problems: Gene5c algorithms –  Automa5ng tasks: Intelligent agents •  Ar5ficial intelligence (AI) technology: –  Computer‐based systems that emulate human behavior Intelligent Techniques: Expert Systems •  Capture tacit knowledge in very specific and limited domain of human exper5se •  Capture knowledge of skilled employees as set of rules in soTware system that can be used by others in organiza5on •  Typically perform limited tasks that may take a few minutes or hours, e.g.: – Diagnosing malfunc5oning machine – Determining whether to grant credit for loan Produced by Dr. Brian Janz 13 How Expert Systems Work – Knowledge base: Set of hundreds or thousands of rules – Inference engine: Strategy used to search knowledge base •  Forward chaining: Inference engine begins with informa5on entered by user and searches knowledge base to arrive at conclusion •  Backward chaining: Begins with hypothesis and asks user ques5ons un5l hypothesis is confirmed or disproved Successful Expert Systems •  Countrywide Funding Corpora5on in Pasadena, California, uses expert system to improve decisions about gran5ng loans •  Con‐Way Transporta5on built expert system to automate and op5mize planning of overnight shipment routes for na5onwide freight‐trucking business •  Most expert systems deal with problems of classifica5on –  Have rela5vely few alterna5ve outcomes –  Possible outcomes are known in advance •  Many expert systems require large, lengthy, and expensive development and maintenance efforts –  Hiring or training more experts may be less expensive Produced by Dr. Brian Janz 14 Intelligent Techniques: Case‐Based Reasoning (CBR) •  Descrip5ons of past experiences of human specialists, represented as cases, stored in knowledge base •  System searches for stored cases with problem characteris5cs similar to new one, finds closest fit, and applies solu5ons of old case to new case •  Successful and unsuccessful applica5ons are grouped with case •  Stores organiza5onal intelligence: Knowledge base is con5nuously expanded and refined by users •  CBR found in –  Medical diagnos5c systems –  Customer support How Case‐Based Reasoning Works Produced by Dr. Brian Janz 15 Intelligent Techniques: Fuzzy Logic Systems •  Rule‐based technology that represents imprecision used in linguis5c categories (e.g., “cold,” “cool”) that represent range of values •  Describe a par5cular phenomenon or process linguis5cally and then represent that descrip5on in a small number of flexible rules •  Provides solu5ons to problems requiring exper5se that is difficult to represent with IF‐THEN rules –  Autofocus in cameras –  Detec5ng possible medical fraud –  Sendai’s subway system use of fuzzy logic controls to accelerate smoothly Intelligent Techniques: Neural Networks •  Find paOerns and rela5onships in massive amounts of data that are too complicated for human to analyze •  “Learn” paOerns by searching for rela5onships, building models, and correc5ng over and over again model’s own mistakes •  Humans “train” network by feeding it data inputs for which outputs are known, to help neural network learn solu5on by example •  Used in medicine, science, and business for problems in paOern classifica5on, predic5on, financial analysis, and control and op5miza5on •  Machine learning: Related AI technology allowing computers to learn by extrac5ng informa5on using computa5on and sta5s5cal methods Produced by Dr. Brian Janz 16 How a Neural Network Works Intelligent Techniques: Gene5c Algorithms •  Useful for finding op5mal solu5on for specific problem by examining very large number of possible solu5ons for that problem •  Conceptually based on process of evolu5on –  Search among solu5on variables by changing and reorganizing component parts using processes such as inheritance, muta5on, and selec5on •  Used in op5miza5on problems (minimiza5on of costs, efficient scheduling, op5mal jet engine design) in which hundreds or thousands of variables exist •  Able to evaluate many solu5on alterna5ves quickly Produced by Dr. Brian Janz 17 Intelligent Techniques: Intelligent Agents •  Work in background to carry out specific, repe55ve, and predictable tasks for user, process, or soTware applica5on •  Use limited built‐in or learned knowledge base to accomplish tasks or make decisions on user’s behalf –  Dele5ng junk e‐mail –  Finding cheapest airfare •  Agent‐based modeling applica5ons: –  Systems of autonomous agents –  Model behavior of consumers, stock markets, and supply chains; used to predict spread of epidemics Produced by Dr. Brian Janz 18 ...
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