Digital Ecosystems For Knowledge Management in Systems Engineering Proceedings of IEEE/DEST 2010 Paola Di Maio DMEM University of Strathclyde
CONTENTS Overall Aim Introduction Definitions Background Contribution Knowledge in SYSENG Some Distinctions Knowledge Networks Knowledge Ecosystems Conclusion
OVERALL AIM To introduce the rationale for positioning our research in Knowledge Management, specifically Knowledge Reuse and Learning in Systems Engineering, in the context of broader ‘collective intelligence’ scenarios and emerging knowledge ecosystems. It presents observations from the exploratory phase of our work, discusses motivating questions, and justifies the choice of the proposed future direction for this research.
<ul><li>SOME DEFINITIONS AND ABBREVIATIONS </li></ul><ul><li>Knowledge (K) </li></ul><ul><li>Knowledge Reuse and Learning (KR, KRL) </li></ul><ul><li>Systems Engineering (SE) </li></ul><ul><li>Knowledge Management (KM) </li></ul><ul><li>Knowledge intensive organisations (KIOs) </li></ul><ul><li>The paper provides background to the notions of ‘knowledge’ and ‘knowledge reuse’, and introduces knowledge networks and knowledge ecosystems in relation to collective intelligence and systems engineering. </li></ul>
Introduction and Goals Early stages of an empirical enquiry in the context of a broader three year investigation of converging research fields in relation to our theme Knowledge Reuse and Learning for Networked Organisations in Systems Engineering [KRLNO/SE] It introduces the underlying questions, and presents at least it part the rationale and justification for the direction of the proposed research. The main goal of this paper is therefore to provide a preliminary overview of our approach, and present some of the initial questions that motivate the inquiry so far
Background Dichotomy: a gap between the theory and the practice. The focus of this enquiry spans across various aspects of the research space: from evaluating the shared understanding of what is knowledge (for the purpose of communicating systems engineering knowledge, for example) to ensuring there is a common agreed understanding (roughly corresponding to ‘shared knowledge’) of what is systems engineering in the first place. Limited or no shared understanding of the difference between knowledge and information No metric in use to verify if and how knowledge outputted by research for example is actually reused in practice, or not. No Knowledge Management practices, which include Knowledge Sharing (KS) and Knowledge Reuse (KR) are actually in use, or documented to be in use, in systems engineering, in the UK.
Contribution This paper makes the case for the adoption for digital ecosystems and knowledge networks to increase knowledge flow in systems engineering This research is the first to identify, to our knowledge, the lack of Knowledge Management literature and practice in the Systems Engineering discipline and various gaps that follow, discussed in more detail in the following sections.   In addition it is the first research to position knowledge management and knowledge networks and collective intelligence in relation to systems engineering
KNOWLEDGE IN SYSENG The value of Knowledge as an essential organisational asset is demonstrated [1, 2] Many different approaches set knowledge apart from information . In our work we take the view that Knowledge (K) results from the combination of various types of information (which in turn is a combination of documented facts) considered the ‘relevant’ context. Knowledge in this paper refers to an optimal combination of data, information and their corresponding contextual interrelations, that can only be captured by human intellectual, cognitive and experiential socio-technical spher e, and that constitute the basis for understanding and skill building.
WE DISTINGUISH Explicit/tacit knowledge  whereby former is declared, and the latter is implicit – we note however that the notion of explicit/tacit recently is drifting toward the preferred interpretation more like codified/non codified . Ongoing advances in knowledge networks research demonstrate that ‘tacit knowledge cannot be shared’ as false.  Internal/External Knowledge , . The difference as to whether knowledge is internal or external to the organisation is critical in at least two aspects : a) whether the resources are accessible/findable to everyone or only internally within an organisation b) if knowledge exchanges take place between internal and external sources, this may impact the organisational structure, processes and knowledge requirements.
WE ALSO DISTINGUISH BETWEEN: Shared/not shared knowledge (various). This is not only a fundamental distinction, but a critical one at the heart of many issues pertaining to KM. In order to be ‘reused’ it is assumed that knowledge must be shared. What constitutes ‘shared knowledge’ is a matter of codification (a protocol, a standard). Tacit knowledge can be shared without being explicitly codified, although it must be formulated/expressed/represented at some level. Recent research also addresses the distinction between shared/not shared K as dependent on whether knowledge is perceived as private or public.  Source: Valdis Krebs
KNOWLEDGE NETWORKS Research demonstrates that knowledge sharing in contemporary, connected organisations, happens through 'knowledge networks': "Due to increasing dynamics of the global environment, organizations are forced to integrate changing demands and to adapt or develop new competencies to be able to stay flexible and dynamic/dynamic adaptations is a requirement". Knowledge networks are social and information clusters that depend on, or can be mapped to, knowledge flows in relation to a specific knowledge requirement. Knowledge networks can reduce the effort of sharing knowledge in systems engineering, making what is known as ‘tacit knowledge sharing’ not only possible, but a matter of course
From our perspective, a knowledge network is at least two things: 1) PEOPLE A network of individuals or groups who are considered either to be repositories of knowledge or to be conduits of knowledge (for example, can point to where knowledge can be located) 2) INFORMATION SYSTEMS A hybrid, open socio-technical system of people, environments, infrastructure and information that represents the best repository or conduit to knowledge. In the age of pervasive internet connectivity, knowledge networks are becoming more and more ‘virtual’, and deployed among remote distributed agents via electronic means of communications (email, voice over IP, video over IP). The uptake of ‘social networks’ for knowledge sharing in professional practices has become the norm .
MORE ABOUT SOCIAL NETWORKS Expanding one’s social network, is equivalent to expanding one’s knowledge assets, this is true in systems engineering like in any other discipline. Part of the rationale for this approach is provided by Social Network Analysis techniques , which study knowledge transfer from the perspective of individuals in relation to his or her relationships with other people and the respective context and environment the actor is embedded in. ‘ Knowledge’ (especially the tacit and non codified portions of knowledge) is exchanged through informal and social communication opportunities, which are valuable and ‘sought after’ mechanisms of bridging the distinction between explicit and non-explicit knowledge.
<ul><li>Case study in the automotive and machine building industry  demonstrates the evolution of a social network in the IT department to support information sharing and problem-oriented advice, which represents an opportunity to enhance the flexibility and dynamics of organizational capabilities. </li></ul><ul><li>A typical knowledge discovery process, especially in the absence of explicit, shared knowledge such as accessible 'knowledge maps', relies on ‘people’ and the informal, fragmentary exchanges of proverbial 'who knows what' and 'who knows who'. </li></ul><ul><li>In contemporary (and future) distributed organisational scenarios, these social informal, fragmentary patterns supporting knowledge exchange will not depend on physical proximity but will happen via open access to various digital forms of knowledge sharing environments via 'social knowledge networks'. </li></ul><ul><li>Open social networks, such as Linkedin, Twitter and Facebook support targeted knowledge sharing via artefacts such as ‘groups’, lists, and all sort of gadgetry. </li></ul>
Knowledge Ecosystems for Engineering The knowledge sharing benefits for engineering are known and demonstrated . One of the benefits of social networks is that they widen the interaction with actors and agents from different disciplinary perspectives, the lack of interdisciplinary interaction at certain stages of engineering process is identified as the cause of ‘stagnation in innovation’. The internet and the web, as a dynamic, 'live' collective entity is the most meaningful observatory and experimentation ground for studying and advancing the state of the art of knowledge sharing in systems engineering, as well as in every other field. Knowledge ecosystems, open shared environments where knowledge bounces off unconstrained and spontaneously between actors and groups , emerge from the combination of various knowledge networks.
Natural VS Artificial Ecosystems Natural = many different types of systems interacting freely, (for example an open ocean which interacts constantly with atmosphere, water sources and responds to the environmental stimuli) OPEN BOUNDARIES, OPEN INTERACTIONS NECESSARY TO BALANCE Artificial = a closed environment, such as an aquarium, where most of the dynamics of open ecosystems are replicated, but its biological equilibrium is maintained by controlling its states via balancing the input of certain components (such as oxygen for example). BALANCE IS MAINTAINED BY ARTIFICIALLY REGULATING EQUILIBRIUM
A similar metaphor can be applied to distinguish knowledge networks (which can be either open or closed) from knowledge ecosystems (which in order to qualify for the term, would need to be 'open', else they should be called artificial ecosystems). The openness of a social ecosystem is here provisionally defined as a general ‘good practice’ measured by the propensity to share access and knowledge resources, widely research in the many approaches of the ‘participatory paradigm. 
CONCLUSION In our work we pursue digital ecosystems and knowledge networks as the way forward to increase knowledge flow in systems engineering This paper provides the justification for the proposed approach for research ahead in this direction It also identifies the lack of knowledge management literature in relation to systems engineering, and identifies the knowledge gap
PAOLA DI MAIO - SysEng, BA, MSc (PhD) DMEM, University of Strathclyde, Room 106 75 Montrose Street Glasgow G1 1XJ Desk: 0044 141 5494308 paola.dimaio AT gmail, OR paola.dimaio at strath.ac.uk