Notes

Outline

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PARTNERS

Objectives How effectively construction teams’ knowledge is captured and applied Where IT has facilitated this capture and application What lessons are suggested regarding improvements in construction processes for KM and OL What future applications of IT tools would support construction projects’ KM and OL

Project Stages Assessment of current state of KM and OL in the construction industry Activity and information modelling of geotechnical investigations Development of a contractor knowledge management system Development of a consultant extraction and classification tool Survey of knowledge management practice in leading construction firms

Stage One Identification of potential IT KM tools Evaluation of relevant KM research Interviews with construction executives to assess the state of KM

Knowledge Management & IT Support

Stage One Findings Use of IT for KM constrained by companies’ culture To be supportive, IT support has to be flexible, easy to use, and cost effective Key area for ensuring optimum knowledge input into construction is briefing – both of designers and constructors

Stage Two Goal: To combine an understanding of WHAT information organisations use with an understanding of HOW they use it Plan: Deliver an exemplar based on a realistic scenario. The domain selected was geotechnical site investigation.  Result: Developed a model of the activities that take place in Geotechnical Site Investigation – and linked it to relevant data.

Choosing an Example We looked for an example that was: complex enough to allow sensible evaluation of the approach of interest to people in the domain  We required: support from domain experts access to real project data in the selected area

Why Geotechnical Site Investigation? Relevant to many stages of the construction process From early site investigation to final reclamation Used for different purposes Risk analysis, costing, foundation design, environmental audit, long term settlement Available support Ove Arup and Partners provided access to domain expertise and real project data examples in support of the work

Demonstration Example - 1 Permits the user to look at a specification of activities for the domain of interest Highlights the inputs and outputs, as well as the controls and resources associated with the activities Links to information specific to a given project  e.g.  results of a bore hole investigation  reading of a particular piezometer  Links to generic information e.g. glossary which explains terminology or standards

Demonstration Example - 2  Delivered via the web  Requires Internet Explorer or Netscape browser only  Presents information in a familiar style  Tables, spreadsheets, links to domain tools  Builds on domain and support standards Can be delivered using non-proprietary tools which indicate that it need not be expensive to deploy Example domain standard: AGS format Example support standards: XML, HTML, VHG

Describing Domain Activities  Diagrammatic Presentation (IDEF0)

Knowledge Links

Stage Two Findings Linking the activity diagram to both generic and project data opens up many new avenues Project audit, training, project or process management Intelligent knowledge management  The underlying computer science approach offers flexibility and genericity  Based on formal models  Based on standards and readily available public-domain tools

Stage Three Development of a generic theory-based approach for a KMS Development of a web-based KMS for LNG projects

Knowledge Transfer Methods Dixon’s Model

A Generic Approach for A Knowledge Management System

Development Process for LNG Knowledge System

Structure of LNG Knowledge System

Stage Three Findings Dixon model needs adapting Routine/Frequency and Tacit/Explicit are very useful The structure of knowledge area is key

Stage Four Development of a knowledge extraction and classification tool Investigating the ability and limitation of IT to create knowledge systems without pre-defined ontologies

Classification of Information using EXTRACTOR Software EXTRACTOR generates keywords and provides contents classification The tool creates automatic links within the classification system EXTRACTOR builds a tree structure of subsets starting with the high-scoring keywords

The EXTRACTOR Data Structure

Knowledge Extraction & Classification Tool

Stage Four Findings Substantial human input is required to build knowledge systems IT represents 10% of the effort needed to build knowledge system Extractor software is more suitable for SMEs

Stage Five Survey of KM practice in construction Nine leading construction organisations were interviewed (semi-structured interview)

Stage Five Findings Current state of KM in construction is generally at a rudimentary level The web is seen as a major IT KM opportunity: particularly the capabilities for general search and finding relevant people

Lessons Learned – A     How effectively construction teams’ knowledge is captured? Type of knowledge (tacit/ explicit), structure of knowledge domain, and the timing of knowledge collection The limitations imposed by supply chain configuration / form of procurement and culture

Lessons Learned – B     How could IT facilitate construction projects’ KM and OL? Particularly effective tools – the web, activity and information model driven tools, and search engines The nature, type and size of the construction organisation influence the choice and use of IT tools

Lessons Learned – C     Are there lessons on process improvements? Process influences the efficiency of knowledge collection and value of use People play a leading part in creating and maintaining an effective knowledge management system Culture is critical

Lessons Learned – D     What are the suitable IT tools to support construction KM and OL? KLICON found that the most effective tools would seem to be web-based tools, supported integrally by search engines, databases and modelling tools

Inter-relationships: People, Knowledge & IT

Summary Human involvement and organisational culture are vital in the creation of KMS Stages 2, 3 and 4 lead to the observation that the structuring of the knowledge area influences the nature of the IT support “Process” is critical for knowledge capture and dissemination

Further Research UMIST PROBOL HK PII on KM AZ (Duff & Elhag) Manchester Process or Project Execution Simulation Execution environment for Manufacturing Engineering

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