Making organizational knowledge more Accessible, Quality, & Currency Managing Knowledge Making organizational knowledge more Accessible, Quality, & Currency

Canadian Tire Five interrelated companies 57,000 employees 1,200 stores Independently owned and operated Spread across Canada Need efficient and effective ways to communicate with workforce and dealers

Dealer portal & employee information intranet Dealer portal Central source for Merchandise setup info Alerts Best practices Products ordering Problem solution Save money by reducing daily and weekly mailings Easy access info for dealers

Reduce the time required to find info Employee intranet TIREnet Catalogued more than 30,000 documents Search technology Easier to keep document current Reduce the time required to find info

11. 1 The knowledge management landscape 11 11.1 The knowledge management landscape 11.2 Enterprise-wide knowledge management systems 11.3 Knowledge work systems 11.4 Intelligent techniques

The knowledge management landscape Communicating & sharing knowledge Knowledge management Collaboration Production & distribution Information Knowledge Companies’ value depend on its ability to create and manage knowledge

Important dimensions of knowledge Data Events or transactions captured Information Organized data into categories of understanding Monthly, regional, store-based reports

Knowledge Discover patterns, rules, and contexts where the knowledge works Wisdom Collective and individual experience of applying knowledge Where, When, How

Tacit knowledge Explicit knowledge Knowledge is Knowledge resides in the mind of employees Explicit knowledge Knowledge has been documented Emails Voice mails Graphics Knowledge is Situational & contextual

Organizational learning and Knowledge management The ability to reflect and adjust from learning Create new business process Change of patterns of management decision

The knowledge management value chain

Knowledge acquisition Corporate repositories Documents, reports, presentations, best practices Unstructured documents Online expert networks Enable employee to find “experts” Knowledge work stations Discovering patterns in corporate data

Knowledge storage System for employees to retrieve and use knowledge Encourage the development of corporate-wide schemas for indexing documents Reward employees for taking time to update and store documents properly

Knowledge Dissemination Portal Email Instant message Wikis Social networks Search engines Collaboration technologies

Knowledge application Build knowledge into Decision makings systems Decision support systems Business processes Enterprise systems ERP SCM CRM

Building organizational and management capital: Collaboration, community of practice, & office environments Communities of Practice Professionals and employees Similar work-related activities and interests Reduce the learning curve for new employees Spawning ground for new ideas

Types of knowledge management systems

11. 1 The knowledge management landscape 11 11.1 The knowledge management landscape 11.2 Enterprise-wide knowledge management systems 11.3 Knowledge work systems 11.4 Intelligent techniques

Three kinds of knowledge Structured text documents Reports, presentations Semi-structured Emails, digital pictures, graphs Tacit knowledge Reside in the heads of employees

Enterprise content management systems Capabilities for knowledge Capture Storage Retrieval Distribution Preservation Enable users to access external sources of info Create a portal for easy access

Fig 11-3, An Enterprise Content Management System

Leading vendors Open Text Corporation EMC (Documentum) IBM Oracle

Taxonomy Classification scheme Organize information into meaningful categories

Knowledge network systems Expertise location and management systems Online directory of corporate experts Best practices knowledge base FAQ repository

Collaboration tools and Learning management systems Web technology to foster collaboration and information exchanges Portal Emails Chat, instant message Blog, wikis

Learning management systems Social bookmarking Users save their bookmarks Tag bookmarks Tags can be shared or searched Delicious, Digg Learning management systems Track and manage employee’s learning Whirlpool corporation Training program for 3,500 salepeople

11. 1 The knowledge management landscape 11 11.1 The knowledge management landscape 11.2 Enterprise-wide knowledge management systems 11.3 Knowledge work systems 11.4 Intelligent techniques

Specialized systems for knowledge worker to create new knowledge Knowledge workers Researchers Designers Architects Scientists Engineers

Requirements of knowledge work systems Substantial computing power for graphics, complex calculations Powerful graphics and analytical tools Communications and document management Access to external databases User-friendly interfaces Optimized for tasks to be performed (design engineering, financial analysis)

Examples of knowledge work systems Computer-aided design (CAD) Traditional A Mold A Prototype CAD Designs can be easily tested and changed Virtual reality systems Boeing CO. 787 Dreamliner mechanics’ training

Virtual reality for the web Augmented reality Enhance a direct or indirect view of a physical real-world environment Virtual reality for the web Virtual reality modeling language DuPont Chemical VRML for a virtual walkthrough of a plant

11. 1 The knowledge management landscape 11 11.1 The knowledge management landscape 11.2 Enterprise-wide knowledge management systems 11.3 Knowledge work systems 11.4 Intelligent techniques

Tools to capture individual and collective knowledge Capture tacit knowledge Expert systems Case-based reasoning Fuzzy logic Discovering knowledge Neural networks Data mining

Generating solutions to problems Automate routine tasks Genetic algorithm Automate routine tasks Intelligent agent

Artificial intelligence (AI) To emulate human behavior Watson Won Jeopardy

Capturing knowledge: expert systems Specific and limited domain of human expertise Compare to human experts, ES lack the breadth of knowledge the understanding of fundamental principles Diagnosis a m/c Grant credit of a loan

Rules in an Expert system

Knowledge base Inference engine 200 to many thousands of rules Forward chaining Begin with the info entered by the users Search the rule base Arrive a solution Backward chaining Start with a hypothesis Asking the user questions Until hypothesis is confirmed or disproved

Examples of successful expert systems Con-Way transportation Automate and optimized planning of overnight shipping route 50,000 shipments of heavy freight each night across 25 states Dispatcher tweak the routing plan provide by the expert system

Organizational intelligence: case-based reasoning Cases Descriptions of past experiences of human specialists Systems Search the stored cases Find the closest fit and applied the solution EX: diagnostic systems in medicine

Fuzzy logic systems Human tend to categorize things imprecisely Each categories represent a range of values Use rules for making decisions that may have many shades of meaning

Applications Sendai subway system Auto focus of cameras Use fuzzy logic control to accelerate so smoothly that standing passengers need not hold on. Auto focus of cameras

Neural network Solving complex, poorly understood problems Large amount of data have been collected Parallel the processing patterns of the biological or human brain Learn the correct solution by examples

Applications Screening patients for disease Visa international Detect credit card fraud

Genetic algorithm Finding the optimal solution for a specific problem Dynamic and complex Involve hundreds or thousands of variables or formulas Large number of possible solutions exists Inspired by evolutionary biology Inheritance, mutation, selection, crossover (recombination)

Examples GE Jet Turbine Aircraft Engine i2 technology Each design change requires changes in up to 100 variables i2 technology Supply chain management software Optimize production-scheduling models Customer orders Material Manufacturing capability Delivery dates …

Hybrid AI systems Intelligent agent Neurofuzzy washing machines Software programs that work in the background Without human intervention To carry out specific, repetitive, and predictable tasks

INTELLIGENT AGENTS IN P&G’S SUPPLY CHAIN NETWORK

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