Tacit knowledge elicitation process for industry 4.0

Abstract

Manufacturers migrate their processes to Industry 4.0, which includes new technologies for improving productivity and efficiency of operations. One of the issues is capturing, recreating, and documenting the tacit knowledge of the aging workers. However, there are no systematic procedures to incorporate this knowledge into Enterprise Resource Planning systems and maintain a competitive advantage. This paper describes a solution proposal for a tacit knowledge elicitation process for capturing operational best practices of experienced workers in industrial domains based on a mix of algorithmic techniques and a cooperative game. We use domain ontologies for Industry 4.0 and reasoning techniques to discover and integrate new facts from textual sources into an Operational Knowledge Graph. We describe a concepts formation iterative process in a role game played by human and virtual agents through socialization and externalization for knowledge graph refinement. Ethical and societal concerns are discussed as well.

Electronic knowledge books (eK-Books) as a medium to capitalise on and transfer scientific, engineering, operational, technological and craft knowledge

“…The loss of operational, craft and tacit knowledge is now always mitigated by recording this knowledge via internal reports, succession plans and storytelling, passing it on during on-site training, or formalising it by means of rules and model systems [10,11]. These approaches remain informal and rely on workers' communication skills and the availability of seniors and experts, and having inherent updating difficulties, for instance in rule-based systems.…”

“…Identifying future users also makes it possible to assess the level of general knowledge on which to base the corpus of knowledge to be contained in the eK-Book. The The process of knowledge acquisition and expert knowledge elicitation [10,41,42] involves gathering the prior knowledge necessary for the understanding of a field, a study. It requires identifying existing platforms, tools, projects, ontology, thesaurus, compendium and experts related to the study domain.…”

Harnessing Large Language Models for Cognitive Assistants in Factories

“…Technological probes have shown that supporting workers to think aloud can result in the creation of high-quality reports from which knowledge can be extracted [17]. Fenoglio et al [11] suggest a system for capturing explicit and tacit knowledge (through best practices) from experienced industrial workers, employing a role-playing game where a virtual agent interacts with human experts and knowledge engineers to iteratively extract and represent knowledge. Although the system can capture tacit knowledge, it requires human intervention.…”

“…Although the system can capture tacit knowledge, it requires human intervention. Fully capturing tacit knowledge (typically nonverbal and unexpressed) with a purely algorithmic approach is deemed impossible by Fenoglio et al [11]. Similarly, Soliman and Vanharanta [30] propose a knowledge creation and retention model using artificial intelligence, but no practical application has been reported in the literature.…”

Role of Circular Economy, Industry 4.0 and Supply Chain Management for Tribal Economy: A Systematic Review

“…Access to the internet via mobile cellular telephones, smart pre-paid metering for electricity, cheaper and renewable energy, solar-powered routers to equip rural libraries/schools with internet connectivity and so forth are all readily available technologies that could make a significant difference in providing basic necessities in developing nations. However, the existing literature has not yet identified the constraints that prevent the tribal groups from using Ind 4.0 to maintain sustainability in a closed-loop supply chain (Fenoglio et al, 2022;Katiki et al, 2021;Misra & Hiremath, 2012;Panda, 2017). Furthermore, additive manufacturing and IoT are the two technologies that can act as significant enablers between CE and Ind 4.0 (Lahrour & Brissaud, 2018;Lettori et al, 2020;Zhong et al, 2016).…”