The Expectations and Practicality of Knowledge Management in Industry

1. The Expectations and Practicalityof Knowledge Managementin Industry Colin Piddington colin@tanet.org.uk TANET Association

2. 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

3. 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

4. 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

5. 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

6. Commercial package deploymentof I2 software for componentselection in electronics A different business model Software was free Commonisation of the data from various suppliers was sold as a service

7. 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

8. 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

9. Organisational Management Position demand report Manager Window to Digital world delegation Feedback collection

10. Automation Layer Input receiving and display to Manager Execution of rules Execution of rules Distribution of commands

11. 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

12. 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

13. 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

14. KMap competencies A competence MAP of the InteropNoE 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

15. 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

16. 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

17. Decisional gate Decisional gate Decisional gate Decisional gate Discipline Project lead Discipline Project lead Discipline Discipline API API Application Application Information store Information store Identification of Collaboration Spaces Product Life cycle – Birth to death –Concept to recycling Project organisation Project management process - Workflow Collaborative Domain – Formal Discipline Project lead Functional organisation Discipline Decisional Gates Design optimisation Risk analysis – next stage start Problem resolution Represents the design, engineering, procurement, contracts etc A Collaborative Work Space Agrees,. approach to problem solving, Information Sharing Information based on need to know transferred mapped across discipline API Application Lead is supported by Discipline experts When data is required to support problem resolution it is via the vendors API Discipline related Automated tools Captive data base Same for many disciplines Each contribute to a different level depending on maturity Information store

18. 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

19. 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

20. 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

21. Next Steps -FLEXINET Intelligent Systems Configuration Services for FLEXIble Dynamic Global Production NETworks

22. 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

23. 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

24. 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