Managing the Knowledge Supply Chain to Support Innovation

Transcription

1 International Conference on Competitive Manufacturing Managing the Knowledge Supply Chain to Support Innovation N. D. Du Preez 1, L. Louw 1 1 Laboratory for Enterprise Engineering, Department of Industrial Engineering, Stellenbosch University, South Africa Abstract Sharing appropriate knowledge throughout the product- and service value chains fuels innovative design. As this knowledge itself has an enormous supply chain and results in the rapid proliferation of information and derived knowledge, one of the challenges in manufacturing research today is how to deal efficiently with this knowledge supply chain. This paper describes the landscape and characteristics of the Knowledge Supply Chain, and Integrated Knowledge Networks (IKN) [1] as a means of enhancing its efficiency. In spite of the bottlenecks and limitations, the pragmatic use of IKN fosters the rapid exchange of applicable knowledge that supports innovative designs. Consequently, the Global Competitiveness Centre in Engineering and the Enterprise Engineering group uses the following concepts in its practice: Formalised networks with flexible frameworks Proactively built repositories for public domain knowledge Information structured around life-cycle and knowledge matrix roadmaps The context of information as the common denominator The integration of knowledge over different innovation projects using a three-dimensional coordinate system KM part of every working day to foster the growth of community maturity. Keywords Knowledge Networks, Knowledge Value Chain, Innovation, Design Life Cycle, Reference Architecture 1 DEFINITIONS The application of Knowledge Networks in innovation is increasingly important. Vassiliadis [2] says: We use the term Knowledge Networking to signify a number of people, resources and relationships among them, who are assembled in order to capture, transfer and create knowledge for the purpose of creating value. An Integrated Knowledge Network includes all domains, communities, and relationships of trust required to foster sustainable innovation that will continue to promote the competitiveness of its users. However, it is also an evolving dynamic network and should be managed as such. 2 INTRODUCTION As the general rate of innovation in both the public and private domain is continually expedited, there is increased pressure on enterprises to innovate faster than the competition. Innovation is fundamentally an evolutionary process of tacit and explicit knowledge exchange, as indicated in Figure 1 below. 2.1 Innovation Knowledge Evolution During any innovation project an interrelated tacit and explicit knowledge development evolves until the final project objectives are reached or the project timeline is reached. This process is depicted in Figure 2 and is in line with the knowledge creation process of Nonaka and Takeuchi [3]. Figure 1 - Interrelated tacit and explicit knowledge cycles.

2 Complex knowledge exchange between different communities in different domains (as indicated in Figure 3 - Knowledge work processes as a knowledge spiral [4]) facilitates the innovation process. To support this knowledge spiral for innovation, a knowledge framework should be structured in a dynamic way that also provides for multiple knowledge views, so that both the epistemological and ontological dimensions of knowledge work are fully supported. Figure 2 - Interrelated knowledge life cycle. Figure 3 - Knowledge work processes as a knowledge spiral. Advancement in Information and Communications Technology and abundant connectivity, together with concerted efforts by governments, industry and academia to foster and integrate the full innovation value chain, all contribute to the current complexity of Knowledge Work. The enterprise must understand the innovation landscape and related drivers in order to position it and to grow internal maturity in the innovation capability. This positioning can become a significant discriminating competitive factor in global competition. Innovative product design, for example, can be supported by managing explicit knowledge in an integrated knowledge network [5]. Some imperatives are clear: Pre-competitive and competitive collaboration provide discriminating advantages to mature enterprises [6]. There needs to be increased collaboration not only between the major role-players (government, academia, users and industry) but also between domains [7]. The proliferation of integrated knowledge networks in different formats substantiates the fact that formalised knowledge networking is acknowledged as a discriminating competitive advantage [8]. Integrating and linking participation with different role-players in such networks remains a challenge [9]. A formal Enterprise-wide Innovation Management system that utilises advanced knowledge management concepts and also builds on life-cycle oriented project roadmaps is a logical development and one that supports expedited innovation projects [10]. 3 THE MACRO INNOVATION PICTURE The frequency with which inventions and subsequent innovations are deployed is increasing. Individual enterprises thus need to speed up their own innovation efforts to keep up with the competition. To maintain competitiveness, enterprises need to exploit and concurrently manage the contributions of four important constituents (government, academia, users and industry) in an integrated and focused manner. Figure 4 below depicts long-term trends as well as the contributions of universities, government and industry. It also refers to expected future trends. This diagram presents the macro picture of how innovation impacts on competitiveness. Obviously, this will also impact the planning of any engineering project for innovation and the allocation of resources and how such projects are deployed. It is important to distinguish between competitive and precompetitive activities and the relevant contribution of the primary role-players in these constituents. This roadmap of technological revolutions depicts the relevant contributions of three primary roleplayers (government, academia and industry). An important new category of innovation is defined by Dismukes [11] - that of radical innovation. He claims that if it is possible to recognise patterns for successful radical innovation then by adhering to those patterns it is possible to select the best inventions and fast track their successful implementation. In so doing, he claims that the next technological revolution can be expedited.

3 Figure 4 - Technological Revolution Roadmap. 4 THE MICRO INNOVATION PICTURE There are a number of reasons why industry generally and enterprises specifically are under pressure to innovate more rapidly [12]. Figure 5 [13] below depicts the challenges and pressures of industry (and thus also enterprises) to innovate. Figure 5 - Change, Complexity & Competition. This required (rapid) rate of innovation and technological developments make it difficult for enterprises to succeed without collaborating with others. Invariably, innovation entails a competitive part, which is significantly supported by the results of pre-competitive research. The innovation implementation life cycle entails the introduction of a new concept, the implementation and successful commercialisation of that concept, and the operation of that concept eventually culminating in its use. Understanding the knowledge supply chain will facilitate rapid innovation in the material supply chain, and the two supply chains are thus also interrelated. This is discussed in section 5. 5 THE MATERIAL SUPPLY CHAIN AND KNOWLEDGE SUPPLY CHAIN Innovation is the successful implementation of a new product, process, service or technology in such a way that sustainable execution of the enterprise mission, either to make money, to provide a service or to give sustainability, is fulfilled. Figure 6 below, from the NGM report [14], indicates the basic components of two supply chains: a material supply chain and a knowledge supply chain. The material supply chain is applicable to the product development innovation process and show how engineering, manufacturing and customer value are linked in the process of creating a product from concept through to customer utilisation. Engineering, manufacturing and the customer are involved in the continuous flow of related information

4 and knowledge about the product, its manufacturing and its utilisation. A similar supply chain could be depicted for services and logistics. The knowledge supply chain is equivalent to a knowledge generation value chain. It indicates how discovering new knowledge, making the knowledge transferable (from tacit to explicit), transferring that knowledge through documentation and from person to person, and finally applying that knowledge, all support the material supply chain. Since 1997, the abundant proliferation of information and knowledge in both the public and private domains, as well as the increased rate of demand for new innovation, has significantly challenged this knowledge supply chain. An analysis of the nature of the current knowledge supply chain reveals a number of interesting observations: The pre-competitive and competitive domains are interrelated. Knowledge is created and utilised in a spectrum that spans the pre-competitive public domain across the competitive private domain and through to the public and private user domains. This knowledge is linked through abundant connectivity, as well as an increase in ICT maturity. Knowledge is created by teams executing specific projects. Most knowledge can be contextualised by the life cycle of the project in which it was created. Projects can be linked to an innovation space consisting of three life cycles: product, enterprise and technology. Public domain science and technology roadmap projects have proliferated, consolidating considerable teamwork into concise roadmap reports. Currently, more than 500 examples are available. Projects address planning horizons that range from as long as 50 years, in the case of science and technology roadmaps, to two weeks or less, in the cases of a rapid deployment projects. Resources consumed by projects similarly span a wide range. A large public domain project like the Next Generation Manufacturing project may represent 500 man-years of work, whilst a rapid deployment project may represent one manmonth of work. The stakeholders in the different projects are also diverse. They could be government and academia, employees and consultants of specific enterprises working on competitive projects, or users and clients organising themselves in user groups and social networks to ensure that they are fully informed as to what is available on the market. Figure 6 - The Material and Knowledge supply chains that support sustainable innovation.

5 Project sizes are similarly diverse and may range from two to as many as 5000 persons. It is possible to categorise the composition of the knowledge supply chain in a number of ways. Figure 7 depicts the components of the knowledge supply chain as defined by the authors. Formalised integrated knowledge networks are increasingly deployed to facilitate the integration of the knowledge supply chain. 6 DOMAINS AND ROLE-PLAYERS OF THE KNOWLEDGE SUPPLY CHAIN Figure 7 dissects the knowledge supply chain into different knowledge domains, different corresponding supply chain outputs as well as different role-players. In many cases the different role-players are also organised in more or less formal Integrated Knowledge Networks. Some aspects are clear when analysing this diagram: Public and private domain information result in an abundance of knowledge. This implies an extensive risk of information overload The innovation process that must support the material supply chain is much too complex to be addressed by a single team in a single project thus dividing to conquer is an imperative. Thus a multiple team approach with proactive knowledge creation, evaluation, filtering and deployment is advised. The extensive interaction between public domain activities and private domain development work is an imperative. Such a hierarchy of interrelated teamwork can be devised, and can facilitate the rate at which innovation is deployed. 7 COLLABORATION AS AN IMPERATIVE The Knowledge Management Magazine [6] reflects on collaboration: As research and analysis of the market show, collaborative working is going from strength to strength as companies embrace the benefits and value of organisational networks. A recent Knowledge Management survey found that twothirds of respondents have communities of practice in place, and cite enhanced innovation, collaboration and learning, and reduced levels of rework as key business drivers. Yet only 11% of these companies can point to tangible success in realising these benefits. Our case studies from Caterpillar and ChevronTexaco offer two shining examples of companies that have integrated knowledge networks into their everyday working practices. Indeed, as Reed Stuedemann says, Caterpillar has found an ROI of 200% for internally focused communities, and over 700% for the external. You can t get more tangible than that. It is clear that significant benefits are to be gained for those companies that successfully formalise knowledge networks. Continents, regions and governments are all participating and contributing to the drive to foster innovation. The European Union Framework programmes are examples of regional collaboration. Governments engage in similar schemes. Figure 7 - The components of the knowledge supply chain.

6 The Canadian government provides an excellent example of positioning for innovation. Their position is aptly summarised by the following statements, contextualising the importance of innovation as viewed by the Conference Board of Canada [9]. Note how collaboration is highlighted as an important aspect of innovation: Collaboration can improve Canada's innovation performance. Collaborating firms are more likely to introduce breakthrough innovations. There is room for more collaboration, especially among smaller firms, universities and government labs. The key findings of this report are: Globalization, knowledge, and information technologies are spearheading an increase in the importance of collaboration in technological innovation between firms and between industry and public sector organizations. Countries are placing emphasis on developing networks and collaborative linkages in order to enhance the knowledge diffusion power of their national systems of innovation. Collaboration to develop new or significantly improved products or processes is associated with superior innovation performance. Firms that collaborate are more likely to draw a higher share of revenue from sales of new products. Firms that collaborate are significantly more likely to introduce breakthrough (world first) innovations. We believe that Canada is still far removed from the stage when enhancing collaborations will do more harm than good. There is room for improvement, and we should strive to encourage more collaboration. 8 KNOWLEDGE NETWORKS FOR IMPROVED MANAGEMENT OF THE KNOWLEDGE SUPPLY CHAIN The most valuable and most up-to-date knowledge in an organisation is the collective knowledge that is contained in the heads of the individuals (tacit knowledge) combined with captured (explicit) knowledge. Due to new personal learning, experiences, insights and ideas, this tacit knowledge is continually being updated. By enabling individuals to better communicate and collaborate within a team, across teams and across entire organisations and inter-organisations, even more significant new knowledge, insights and ideas will be created, transferred, shared, absorbed and leveraged at a much faster rate, thus promoting innovation rates. 8.1 Overview of Knowledge Networks Seufert et al.[15] define a Knowledge Network as a number of people, resources and relationships among them, who are assembled in order to accumulate and use knowledge primarily by means of knowledge creation and transfer processes, for the purpose of creating value. Concerning the development of knowledge networks, they distinguish between emergent and intentional ones. Intentional knowledge networks are networks that are built up from scratch, whereas emergent knowledge networks already exist but have to be cultivated in order to perform well. A related concept to that of a knowledge network is the concept of a community of practice (COP). Cassi [16] provides the following definition for COP: These communities could be defined as a group of individuals engaged on a recognised subset of applied research questions, with accepted attitudes, behaviours relating to the communication of research findings. The different reasons for collaborating with different partners are tabulated in Figure 8, from which it is clear that a wide range of stakeholders and benefits are realised through collaboration. Figure 8 Different reasons for collaboration with different partners.

7 Knowledge Networks can therefore be seen as the organizational environment within which knowledge processes take place in order to achieve innovation. 8.2 Role-players in knowledge networks The following are relevant components of knowledge networks according to Vassiliadis et al.[2]: A Project Team/Task Force represents a group of people having a specific issue or a problem to solve in order to achieve a desired goal. A community of interest is for example a platform on the internet, where a group of people in a loose confederation share common interest in, and information about their interest. A community of practice is a group of people who are to a large extent involved in similar work in a common craft. Members. Actors or members in a social network can be persons, groups, but also collectives of organizations, communities or even societies. Members of knowledge network take different roles. From an organizational perspective, customers, suppliers, competitors or government organizations as well as employees have distinct functions within a network. Relationships. In order to create connectivity of the members through interactivity in a network we have to examine closely the relationships. Knowledge. As stated earlier knowledge can be of different kinds: it can be tacit or explicit, social or individual. The main role-players as defined by the authors are universities, S&T Institutions, government bodies, single enterprises, competitors, suppliers and the market as is depicted in Figure 9. These are organised in different communities, that when integrated, constitutes a knowledge network. This diagram implies some high-level integration realities. 8.3 Key characteristics of a network The Global Innovations Forum [17] lists a number of characteristics impacting on the performance of such a network. These are: reach, relationships, resources, roles, technology, flows, performance, and openness. Figure 9 - The GCC view of Components of an IKN.

8 Vassiliadis et al. [2] provide a table (Table 1) with the characteristics of members, relationships and knowledge. Seufert et al. [15] define the framework of knowledge networks as comprising actors, individuals, groups, organizations and relationships between actors which can be categorized by form, content and intensity; resources which may be used by actors within their relationships, and institutional properties, including structural and cultural dimensions such as control mechanisms, standard operating-procedures, norms and rules, communication patterns, etc.. This is depicted in Figure 10. Table 1 Examples of knowledge network characteristics. This is depicted in the diagram (Figure 10) below: Figure 10 - Framework Knowledge Networks - a micro perspective [15].

9 9 ALIGNING THE KNOWLEDGE NETWORK TO SUPPORT HIERARCHICAL DEPLOYMENT OF INNOVATION PROJECTS It is clear that the challenges of rapidly deploying innovation processes require more than just incidental integration and knowledge alignment. Competitive Industry has responded by formalising knowledge networks that could address specific integration requirements. Vassiliadis et al. [2] state that knowledge network characteristics differ according to the business goal they are mainly dedicated to. They continue saying that it is important to stress that a deeper understanding of the business strategy of a knowledge driven company is necessary in order to develop and nurture an appropriate knowledge network. It is clear that structure and communication protocol are not the only important aspects for successfully utilising an IKN in enterprise-wide Innovation Management. The ability to proactively manage the knowledge work within all subsets of the network (components) as well as to rapidly integrate it in the bigger whole is one of the main challenges of an IKN. Efficient planning for innovation projects over different levels of knowledge aggregation is therefore essential. 9.1 Resolution of integrated planning Another challenge is that of resolution management in deployment of the innovation life cycle. Table 2 depicts examples of different levels of knowledge aggregation together with the various role-players involved. As some of these exist in the public domain, it is possible to proactively pre-process some of the knowledge resources into repositories in anticipation of later use in innovation projects. From Table 2 it is clear that: A hierarchy of planning for innovation ranges from a literally global level right down to a single person s role and mandate for a specific task within a single innovation project. Although it is impossible to address all the different layers of planning for each and every innovation project, it is imperative to have readily available as much information as is possible before the start of an innovation project. Table 2 - Aggregation levels of different role-players and roadmaps.

10 It is worth mentioning two areas of support that make relevant information more readily available: An integrated and formalised Knowledge Network that provides access to the relevant information that is available in the external public domain as well as within the social networks of the semi-public domain. Enterprise-wide innovation management systems that are able to support and integrate different innovation activities within an enterprise or group of enterprises. Figure 11 below depicts the different integration components and how different levels are interlinked, covering the full range from national or global projects through to specific projects and tasks. Public and private domain activities are connected by the diamond shaped Rendezvous points which indicate the anticipated readiness of aspects like technology, market and product strategy. Alignment of the R&D activities of an individual enterprise and the allocation of the required resources are achieved and synchronised with perceived market readiness from the user domain activities and project results. 9.2 The PADMLC cycle of innovation activities The activities involved in the innovation life cycle consist of two separate but interrelated parts. These are: a Plan, Allocate and Deploy (PAD) sub-cycle, which is supported by a Measuring and Learning and Capturing (MLC) sub-cycle. The two sub-cycles of the life cycle happen concurrently, are interrelated and are also executed on different levels of resolution. They nevertheless form an integral part of the successful innovation and design processes. Figure 12 to 14 below depict the sub-cycles and relevant interrelationships. All innovation activities normally start with top-down planning, followed by an allocation of resources to execute or deploy the plan on a specific level of detail. Figure 11 - Interrelationships of different levels of aggregation in the innovation cycle.

11 This cycle may result in different outputs, depending on the level of detail and the stage within the innovation life cycle. Figure 12 - Hierarchical planning allocation and deployment (PAD) sub-cycle. Planning, allocation and deployment are closely followed by a measure, learn and capture sub-cycle that ensures that the enterprise meets its objectives, that lessons from the activity of planning and deployment are indeed learned, and that the experience is captured in company memory, preferably in an enterprise-wide innovation management system. The PADMLC cycles are executed on different levels of detail during the innovation process and need to be coordinated. The diagram below depicts the hierarchical interdependence of different projects within the innovation process. 10 A CASE STUDY The Enterprise Engineering group of the Global Competitiveness Centre (GCC) in the Engineering faculty at Stellenbosch University compiled a framework for an Integrated Knowledge Network (IKN). The deployment of this IKN facilitates innovation research (spanning different private and public domains and including more than 200 projects, 200 users and in excess of documents) is briefly discussed below. Some of the elements of the IKN are briefly covered The project as the common denominator Innovations are executed in projects with associated project goals, common team members, associated expertise and experience, different formal and informal communities and their specialist equipment. The innovation project context is thus captured by the associated project parameters for the interactive tacit and explicit knowledge exchange. Knowledge progression is tracked by each innovation project life cycle. For the purpose of this knowledge network, the innovation project life cycle is considered the smallest common denominator for managing associated knowledge. Such projects are normally subsets of larger design life cycles (enterprise, product or technology life cycles). See Figure 15. Figure 13 - Controlling elements added for the MLC sub-cycle. Figure 14 - Learning and capturing elements added. Figure 15 - Coordinate system to navigate interand intra- enterprise innovation projects Components contributing to innovation To be effective in supporting innovation, Integrated Knowledge Networks must encompass the following interrelated components. The first component is people organised into different communities that interact with different formal and informal trust relationships and contracts that allow different collaborative arrangements to share innovation experience.

12 The second component comprises competencies and experience of the people organised in formal organisational structures such as institutes, research units and departments at universities that have access to various resources like laboratories, networks and technologies, making use of the said tacit, latent and explicit knowledge that resides in the different communities. The third component is different role-players who are participating in the public domain, private domain and the user s domain to exploit precompetitive, competitive and user domain knowledge in innovation of products, processes, enterprises and technologies. These components are represented in Figure 7; dissecting the knowledge supply chain into different knowledge domains, different corresponding supply chain outputs and different role-players. In many cases the different role-players are also organised into smaller less formal Knowledge Networks Networking different components contributing to innovation The largest single community networked for sharing explicit knowledge are all users of the Internet. This network is, however, not agile or focussed enough to facilitate the rapid innovation required, and also lacks the tacit knowledge exchange capacity. At the other end of the spectrum is the community consisting of the team members of a specific innovation project. In between are a range of different communities that are focussing on innovation. They span these two extremes. Two examples in the EU are the Networks of Excellence and Integrated Projects of the Sixth Framework Programme. The Integrated Knowledge Network should provide for access to different communities. Participation in the Virtual Research Laboratory for a Knowledge Community in Production (VRL-kCIP at is an example of such access Structuring an integrated knowledge network to support innovation Ontologies are used to describe and depict the relationships between entities within a knowledge network. Such relationships are not static but vary over time, with different projects and as objectives of communities are modified. However, it is necessary to decide on some of the parameters of an integrated network in order to start the collaboration and knowledge sharing. A conceptual framework is used to model and modify applicable relationships as the network evolves [18] Project types: Internal and external The GCC constituents are divided into internal and external projects. Internal projects are those for which we have the primary responsibility. External projects are the responsibility of our external collaborators Project categories As project life cycles are important drivers for contextualising knowledge, project categories are used. These include undergraduate, masters and PhD projects as well as those of industrial partners Existing knowledge repositories For completed projects, knowledge categorisation and indexing of project documentation is used. Knowledge matrixes are used for navigation. Conceptual frameworks are used to model and maintain the relationships and entities, such as different communities of expertise, domains of knowledge and available resources. Extensive evaluation of a number of professional society publications in fields such as manufacturing engineering is also carried out and the results updated regularly Collaborative Enterprise-wide Innovation platform The IKN is operated within an Enterprise-wide Innovation Management Platform. Generic project roadmaps are available for each project team so that they can then configure their individual project roadmaps to suit their specific requirements. Configured security and access control, notification of document activities, as well as progress checklists facilitate the collaborative creation, refinement and reuse of knowledge Integrated Component view of case study Several scalable and configurable components form part of the IKN and they provide selective context access to more that 200 individual projects, more than documents and about 50 generic life cycle roadmaps. In addition, the IKN has about 140 internal and 60 external registered users. Based on the analysis in Figure 9, the following components constitute the IKN. (This list is indicative only and not exhaustive.): 1. Project life cycle (Primary Building Block) Common Objective(s) 1.2. Team members 1.3. Configured roadmap(s) 1.4. Documents in Context of Project Life Cycle 2. Within a 3D solution space 2.1. Based on Bodies Of Knowledge and Best Practise and generic Reference Architectures Product design life cycle Enterprise design life cycle Technology development life cycle 2.2. Innovation program instances 2.3. Concurrent Project Roadmaps with context information linked to a common research programme

13 3. Other repositories 3.1. Publications of Professional Institutes IEEE South African Institute of Industrial Engineers CIRP (College International pour la Recherche en Productique) etc Proceedings of specific conferences COMA etc. 4. Specialist networks and Focus Groups (EU And OTHERS) 4.1. Networks of Excellence VRL-kCiP 4.2. Integrated Projects 5. Public Domain 5.1. Selection of Technology Roadmaps and Foresight Studies Regional level Country Industry Supply Chain etc Conventional electronic Library access Books Dissertations Electronic Journals etc. 6. Other 1 7. Other 2 8. Broader Internet Access 8.1. Google 8.2. etc. SUMMARY Innovation and competitiveness rely on structured knowledge management. Innovative designs are dependent on collaboration that spans both the pre-competitive and competitive domains, and that spans global, industry, regional and enterprise hierarchies [19]. This paper reflected on the knowledge supply chain that supports product, process, enterprise and technology innovations. By defining pre-competitive knowledge repositories, aligning with different communities of interest, deploying trust relationships, proactively managing explicit and tacit knowledge, an enterprise can position itself for innovation in mature way, and thereby beat the competition. Limitation regarding user maturity, systems functionality and human interaction and trust relationships are still some of the most limiting factors. These bottlenecks are addressed in the research programme of our group and related research communities. 11 REFERENCES [1] Lodge, G.C. and Walton, R.E., 1989, The American corporation and its new relationships, California Management Review, 31:9-24. [2] Vassiliadis, S., Wicki, Y., Seufert, A., Back, A., von Krogh, G., Knowledge Networks: Linking Knowledge Management to Business Strategy, Research Center, KnowledgeSource, University of St. Gallen, (Dec 06) [3] Nonaka, I. and Takeuchi, H., 1995, The Knowledge-Creating Company, Oxford University Press, Oxford. [4] Seufert, A., von Krogh, G., Back, A., Towards Knowledge Networking, KnowledgeSource, University of St. Gallen, [5] Rozenfeld, H., 2002, An Architecture for Shared Management of Explicit Knowledge Applied to Product Development Processes, Annals of the CIRP, 51/1: pp [6] Knowledge Management Magazine Archive, 7/5, Inside Knowledge, me.7/issue.5/. (Dec 06) [7] Organisation for Economic Co-operation and Development, 1996, The Knowledge-Based Economy, OECD, Paris. [8] Creech, H. and Willard, T., 2001, Strategic Intentions: Managing knowledge networks for sustainable development, International Institute for Sustainable Development. [9] Conference Board of Canada, 2000, Collaborating for Innovation, 2nd Annual Innovation Report. [10] Du Preez, N.; Perry; N. Candlot; A., Bernard, A., Uys; W., Louw L., 2005, Customised highvalue document generation, Annals of the CIRP, 54/1: [11] Dismukes, J.P., 2005, Information Accelerated Radical Innovation from Principles to an Operational Methodology, The Industrial Geographer, 3/1: [12] Thannhuber, M., Tseng, M. M., Bullinger,.H, An Autopoietic Approach for Building Knowledge Management Systems in Manufacturing Enterprises, Annals of the CIRP, 50/1: pp

14 [13] Commonwealth of Australia, 2001, Technology Planning for Business Competitiveness, A Guide to Developing Technology Roadmaps, ABS Data. [14] NGM Group, 1997, Knowledge Supply Chains A Next-Generation Manufacturing Imperative, Agility Forum. [15] Seufert, A., Von Krogh, G., and Bach, A., 1999, Towards Knowledge Networking, Journal of Knowledge Management, 3: [16] Cassi, L., 2003, Information, knowledge and social networks: Is a new buzzword coming up? Paper at the DRUID PhD Conference, Aalborg, Denmark, January [17] Global Innovations Forum, 2001, Social Networks in the World of Abundant Connectivity, Project Year, 2001: SR-764. [18] Uys, W., 2007, The Implementation of a Conceptual Framework Based Approach for the Improved Viewing and Utilisation of Organisational Information, COMA 07 proceedings [tbp]. [19] Heebyung Koh, Sungdo Ha, Taesoo Kim, Hyung-Min Rho, Soo Hong Lee, Design Knowledge Management with Reconstructible Structure, Annals of the CIRP 52/1: pp BIOGRAPHY Niek du Preez obtained his PhD degree in Industrial Engineering from the University of Stellenbosch. He is a Professor in Enterprise Engineering at Stellenbosch University, South Africa. Niek is the founder of the Global Competitiveness Centre in Engineering and is currently the CEO of Indutech, an Enterprisewide Innovation Management company. Dr Louis Louw obtained a PhD in engineering at the University of Stellenbosch. He is currently the Research Manager at Indutech and a part time lecturer at the Department of Industrial Engineering at Stellenbosch University.