The Expectations and Practicalityof Knowledge Management

in Industry

Colin Piddingtoncolin@tanet.org.ukTANET Association

Who am I

• BAE systems 1962 to 1994• CSC 1994 to 2004• MD for Cimmedia 2004 to 2010– Contract with Salford University 2006 - 2010

• Director of TANet 2004 to date • Associate of Control 2K manufacturing SME

KM the pathway• Look at the beginning through to today from an Industrial

perspective – Not an OWL computer expert• We will start with expert systems • Look at projects using knowledge data bases• Discuss the experiences of each and the limitations with aspects

of– Data Collection– Human Interaction– Collaborative Working

• We will end with a summary of work to be done and give some insight to the next steps that are being taken by the industrial working group of the I-VLab

Expert Systems

• 1989 Investigations into expert systems for glue and airmotor selection

• These were based on data sheets transferred to data bases to serve the application

• Tests worked successfully with good results• BUT – the range covered was limited– AND the cost of populating the data bases exceeded the

efficiency gains.• Left a legacy of distrust in industry as expectations

could not be met

Fashion businessebusiness garment selection

• EU research project• This initiative was prompted by the

emergence of the business internet.• Needed to pass design requirements quickly

across the potential supply chains to optimise the selection

• Worked to a degree but need to develop a common set of words - Glossary - difficult

Commercial package deploymentof I2 software for component

selection in electronics

• A different business model• Software was free• Commonisation of the data from various

suppliers was sold as a service

Enterprise bus deploymentfor PDM to ERP

• Enterprise bus technology – single end application adaptors – cuts change costs

• Uses PDM Schema approach for information translation• Allows for multiple glossaries with a rule based mapping

capability

• Still requires a lot of consultation to establish mapping rules• Maintenance in line with business change is still a problem• Solutions still operate in a limited domain

KM and shop floor management• Flexquar - Enterprise organisation• Principle – to give the shop floor manager the ability to

impact his knowledge to give tactical decision making and schedules

• Has access to computer data at his workplace controller• Allows him to input rules to capture his knowledge – it

is his decision• By changing the rule set the organisation can change

easily to represent the enhanced responsibilities of the individual

Organisational Management Position

demand report

delegation Feedback collection

Manager Window to Digital world

Automation Layer

Execution of rulesExecution of rules

Input receiving and display to Manager

Distribution of commands

Interop – NoE KMAP

• The need was to develop a competence map of the Enterprise Interoperable domain to see who was doing what, collaborating with whom and where the gaps were in research across Europe

• Multiple views• People• Organisation• Papers – digital library

• Needed a common ontology– No existing standards

KMap

• Provided a unique opportunity as there was no defined Interoperability domain

• 3 Phases were determined– To create a list of terms– To create a Glossary– To determine a taxonomical relationship between

Glossary terms• To create a set of analysis tools

KMap Process

• Search the current papers in the depository for common used terms

• Web based Expert voting to reach agreement• Using the list of terms search the internet for

meanings of terms• Web based Expert voting to reach agreement• Expert inputs to form groups• Web based Expert voting to reach agreement• Reclassification of papers in the depository

KMap competencies

• A competence MAP of the Interop NoE partners was created using Protégé as an initial starting point – this was to be replaced at a later date to better reflect the Taxonomy

• All partners were requested in input both the personal and corporate involvement on projects and papers written

KMap

• Analysis tools were then used to identify the gaps and the concentrations of research and people and institutional relationships.

• Pictorial views were used to clearly identify these relationships

Collaborative working & KM

• A multi dimensional problem• Need to understand the relationship between

organisations, processes, skill silos, tacit and explicit knowledge.

• Have to understand both formal and informal structures

Identification of Collaboration SpacesProduct Life cycle – Birth to death –Concept to recycling

Project management process - Workflow

Decisional gate

Decisional gate

Decisional gate

Decisional gate

Functional organisation

Project organisation

Discipline

Application

Information store

API

Discipline

Project lead

Discipline

Application

Information store

API

Discipline

Project lead

Discipline

Application

Information store

API

Discipline

Project lead

A Collaborative Work SpaceDecisional Gates

Design optimisationRisk analysis – next stage start

Problem resolution

Represents the design, engineering, procurement,

contracts etc

Agrees,.approach to problem solving,

Information SharingLead is supported

by Discipline expertsDiscipline related Automated toolsCaptive data base

When data is required to support problem resolution

it is via the vendors API

Information based on need to know

transferred mapped across discipline

Same for many disciplinesEach contribute to a different level

depending on maturity

Collaborative Domain – Formal

Experience

• Difficult to motivate people to input data• Data inputs had differing granularity• Domain was in constant evolution• Maintaining the taxonomy was hard to justify• People asked why we could not take inputs from

institutional sites for their data (duplication of effort)• The creation of the KMap was a major step forward

from previous experiences and gave renewed hope for KM futures

Other EU projects

• STASIS - Completed 2009

• ADVENTURE - http://www.fp7-adventure.eu

• Both these projects used the basis of agreed ontology's and enterprise bus approaches to provide common order exchanges to support dynamic supply chains(STASIS) and provide feedback information (ADVENTURE)

• These are federated approaches where the users ‘buy into’ a common ontology

• Problems as always is the maintenance of information in line with the changing global business and IT evolution

Overall Observations

• Knowledge is a moving feast• Some knowledge will be inaccurate• Maintenance of data is a major problem• 50% of effort to create the projects is data

collection• Justification is still difficult to encourage

implementations in Industry• Specific implementations are possible e.g.

Security, personnel with specific objectives

Next Steps -FLEXINET• Intelligent Systems Configuration Services for

FLEXIble Dynamic Global Production NETworks

Basic premise - reference ontologies• Any multi-system design or configuration method needs a

common base of concepts from which to build and to share knowledge

• Text based concepts are not good enough – see next slide• Formal languages like OWL are NOT sufficiently expressive

to represent the complexities of manufacturing• Common Logic based formalisms provide both a semantic

capability and also an inference capability for compliance checking

Conclusions

• The problem domain is increasing faster than the science capability

• Knowledge is not static • Data can be proved wrong in the light of

experience• The costs of data collection must be an

integral part of the user processes to avoid unjustifiable costs

Thank You for listening

Can we Meet the Challenge?• With multi-skills in collaborative working the

human application of experience is critical assisted by technology

• Reconciliation of multiple skill taxonomies is a steep hill to climb

• The world is increasingly GLOBAL – language and its use and customs play a part