A methodology for selecting optimal knowledge acquisition through analytic hierarchy process and environment parameters impact

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 change results by adjusting and updating their 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 and choose the best tools for knowledge preservation. Starting from the analysis of models presented in the literature, we defined a methodology that optimally supports knowledge acquisition and preservation in any phase of production systems. Moreover, in any environment where business uncertainty is the norm, developing knowledge acquisition capabilities is more critical. This main paper contribution is the AHP-PIE methodology, which provides a helpful guideline as a structured and logical means of ranking knowledge acquisition methods for evaluating appropriate tools for a small manufacturing industry/organisation. The practical example is provided in a sequential order using manually operated assembly and maintenance operations. The result showed that verbal report is the best tool for knowledge acquisition for these engineering practices.