Managing Knowledge Acquisition Application of Knowledge to improve

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 – 7 -11 銷售品項 » 因應商圈特性 – Wisdom • Collective and individual experience of applying knowledge – Where, When, How – 7 -11 展店策略 » 同一條街有好幾家、好幾條街找不到一家

2016 Election

Info: Clinton Win

– 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 • 圖 11. 1

• 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 • Figure 11. 2

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 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)

• Figure 11. 4

• 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 • Du. Pont 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 Figure 11 -5

– 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

• Figure 11 -8

• 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

Figure 11 -9

• 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 You. Tube 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)

Figure 11. 10

– Examples • GE Jet Turbine Aircraft Engine – Each design change requires changes in up to 100 variables • i 2 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 Figure 11 -11

Interactive session (Minicase) – Technology • Firewire surfboards lights up with CAD • P. 460 – Organization • Alabssami’s job is not feasible without IT • P. 469