Continuous Knowledge and Information Capture and Use
Continuous improvement in service support depends crucially upon the implementation of effective KM systems within a dynamic learning environment. Firms operating within market sectors such as defence, health, education and prisons have the opportunity to capture operational knowledge through in-service evaluation and to feed-forward this knowledge into new design projects. However, the design and implementation of such systems are faced with extensive challenges due to the discontinuities between diverse communities at different stages of the total product life cycle. The precise nature of the challenges depends upon the adopted approach to KM. From the perspective of the codification strategy, the problem relates to how information and knowledge concerning the product can be captured during design and manufacture and used during the product's life. Furthermore, knowledge of the performance of the product in service must also be captured to enable the management, upgrading and improvement of the product and to feed into new designs.
In contrast to the above, the personalisation strategy would give less emphasis to 'information capture and re-use' and more emphasis to the dynamic capabilities of organisations to learn in response to changing circumstances. This alternative perspective challenges the assumed dichotomy between 'knowing' and 'doing', preferring to conceptualise decision-making as an arena where knowledge and action coalesce continuously to re-shape the ongoing context. From this perspective, decision-support models are facilitative devices that support organisational learning within a social context (cf. Antonacopoulou and Papamichail, (2004)). The resultant actions directly influence the structural context within which future decisions are made and provide the basis for renewed learning. However, decision-support models must be informed by factual performance information and relevant engineering expertise relating to the feasibility of proposed technical solutions. Moreover, the realities of bounded rationality (cf. Simon, 1960) limit the capacity of decision-makers to determine which information should be 'captured' and re-introduced into decision-making arenas. Not all information is of equal value, and some lessons are more important than others. Here lies the crux of the problem that is re-played continuously throughout the project life cycle.
An issue that bridges the two approaches is the importance of a broader contextual understanding. Information capture in the design process depends upon identification and representation of the context in which the design has been created, both in terms of the design process that has been applied, and of the rationale behind the designers' decisions. The complex interaction between decision-making and structural context will be important throughout the research programme, which will also explore the particular features of the business context for firms engaged in very long-term provision of complex product/service packages – including similarities and differences between sectors and how these impact upon engineering information and knowledge management strategies, practices, and systems – and the changing application contexts for particular products.
