Managing Knowledge
Acquisition & Application
of Knowledge
to improve business
• Drug discovery process
– Pharmaceutical companies and medical
researchers
– Constantly look for new drug
– Better treatments for serious illnesses
• Disease-fighting drugs
– Attacking disease-causing protein
• stop its harmful interaction with molecules
• Traditional drug discovery process
• trial & error methods
– Huge library of potential compounds
– Mix and match common building blocks
– Robots
• Drop chemical into diseases
• Check if “hit” occurs
• Structure-based design
– Determine the shapes of disease-causing protein
– Find a customer molecule to bind
• Computers help evaluate
– Structure and Properties of molecules
• Likely to bind to that target
– Search database of chemical structures
• Identify promising candidates
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
• Knowledge resides in the mind of employees
– Explicit knowledge
• Knowledge has been documented
– Emails
– Voice mails
– Graphics
– Knowledge is
• Situational & contextual
– e.g. Inventory Control
• Make-to-order
– JIT
• Make-to-stock
– Batch
• Smartphone vs. automobile industry
– 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
11.2 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
– Information of interest
– Web technology to foster collaboration and
information exchanges
•
•
•
•
Portal
Emails
Chat, instant message
Blog, wikis
– 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 salespeople
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
• Exercise independent judgment
– Keep the organization current in knowledge
– Serve as internal consultants
– Acting as change agents
• 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
– Augmented reality
• Enhance a direct or indirect view of a physical realworld 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
11.2 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
• 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
• 200 to many thousands of rules
– Inference engine
• 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
• 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
Discovery of Pluto
– 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
– Google’s project to identify cats in photos
• An array of 16,000 processor
• One billion connections
• 10 million YouTube videos
• 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
– 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
– Neurofuzzy washing machines
• Intelligent agent
– 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
Interactive session
(Minicase)
– Technology
• Firewire surfboards lights up with CAD
• P. 460
– Organization
• Alabssami’s job is not feasible without IT
• P. 469