Methodology for Selecting Optimal Knowledge Acquisition Through Analytic Hierarchy Process and Environment Parameters Impact.

Abstract

In a global economy characterised by increasingly dynamic markets and technologies, the primary importance of intangible resources like knowledge is growing dramatically, especially for small and medium-sized enterprises (SME). Therefore, many companies are trying to support changes by configuring their production systems towards mass customisation. This evolving paradigm shift from mass production to mass customisation brings about complex product lifecycles that require continuous re-engineering/configuration of modern manufacturing systems. Rapid manufacturing companies' changes result in adjusting and updating the existing knowledge base to maintain their competitive advantage. Within companies, different tacit and explicit knowledge are available, relating to resources, processes and components. This data is usually not digitised, and therefore the main challenge for small and medium-sized enterprises is how to automate the knowledge acquisition process, choosing the best tools for knowledge preservation. Starting from the analysis of models presented in the literature, we defined a methodology to support selecting the optimal acquisition of knowledge and preservation in any phase of production systems. In an environment where business uncertainty is the norm, developing knowledge acquisition capabilities is increasingly important. The paper's main contribution is the AHP-PIE methodology, which provides a helpful guideline as a structured and logical means of ranking KA methods for evaluating appropriate tools for a small manufacturing industry organisation.