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This paper will discuss how cooperative agent-based systems, deployed with social skills and embodied automation features, can be used to interact with the operators in order to facilitate sharing of tacit knowledge and its later conversion into explicit knowledge. The proposal is to combine social software robots (softbots) with industrial collaborative robots (co-bots) to create a digital apprentice for experienced operators in humanrobot collaboration workstations. This is to address the problem within industry that experienced operators have difficulties in explaining how they perform their tasks and later, how to turn this procedural knowledge (knowhow) into instructions to be shared among other operators. By using social softbots and co-bots, as cooperative agents with embodied automation features, we think we can facilitate the 'externalization' of procedural knowledge in human-robot interaction(s). This enabled by the capabilities of social cooperative agents with embodied automation features of continuously learning by looking over the shoulder of the operators, and documenting and collaborating with them in a non-intrusive way as they perform their daily tasks.

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This paper aims at presenting an overview of how the manufacturing industry formulates business transformation and knowledge strategies, to find gaps in Industry 4.0 concepts' impacts on the workforce. The results indicate that the industry is still focusing on the digital transformation era that was adopted at the end of the 20th century, and how to adopt computing technologies to work more efficiently in existing business processes. The approach of this article is to adopt a methodology of three areas; (a) Human resource processes, (b) Industry 4.0 pillars and (c) Process knowledge, where links between these three generate opportunities to address for further research.

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