An experience feedback process for learning from collaboration experiences

• Published in: Computers in industry 2022-10, Vol.141, p.103693, Article 103693
• Main Authors: Melendez, Sofia, Sima, Xingyu, Coudert, Thierry, Geneste, Laurent, Valroger, Aymeric de
• Format: Article
• Language: English
• Subjects: Collaboration; Engineering Sciences; Experience feedback; Industrial processes; Learning; Performance assessment
• ISSN: 0166-3615; 1872-6194
• DOI: 10.1016/j.compind.2022.103693

In the fast evolving context of industry 4.0, companies or organizations must continuously evolve and improve. To maintain their business efficiency, they join industrial networks in which they have to collaborate. In more recent context of industry 5.0, humans are placed at the heart of the industrial processes by developing human-centric and society-centric approach. In such a context, human collaboration experiences are numerous and constitute meaningful pieces of knowledge which can be reused within a human-centric approach. This needs to (i) formalize and capitalize collaboration experiences, (ii) enable actors to assess collaboration and, (iii) develop reusing mechanisms. This article proposes an experience feedback approach where collaboration experiences are formalized and directly assessed by actors who have collaborated. Assessment grids are proposed to guide the human evaluations. From the individual evaluations, aggregation mechanisms are proposed to compute the collaboration performance of organizations. Finally, a reusing mechanism allows to learn from prior experiences, identifying the best organizations to make collaborating for a new industrial process. •a human-centric experience feedback method to learn from prior collaborations.•A collaboration experience model to record human collaborations in processes.•A method to assess individual collaboration experiences and reuse them.•A retrieve method to assess and select organizations for new collaborations.