DisneyHint: Lean and coaching-based employee suggestion system for the human challenges of Industry 4.0

Juliana Salvadorinho, Paulo Pintor, Tiago Bastos, Leonor Teixeira


Purpose: This paper presents the concept of an Employee Suggestion System (ESS) that integrates a strategy originated from Neuro-Linguistic Programming with application in Coaching (Disney Strategy) to face the human challenges of Industry 4.0.

Design/methodology/approach: A four-phase methodology was followed, starting with a systematic literature review of the ESS to obtain a theoretical perspective about this concept and its characteristics. Subsequently, 30 interviews were carried out to recognize the ESSs of three partner companies and, as well as to perceive the receptivity of the new concept of ESS. Finally, the concept of the system was modelled, prototyped and tested and combines the Japanese (Kaizen Teian) and American ESS (Kaizen Teian adapted to the western industry) approaches.

Findings: Given the existing systems in organizations, the platform presented brings more maturity to the suggestions made (through the Disney strategy applied in Coaching), greater visibility of their status and evaluation, and greater promotion of workforce engagement (through the promotion of voice behaviour). At the same time, it supports the collection of tacit ideas from employees, preserving organizational knowledge and, therefore, a source of competitive advantage.

Originality/value: This paper presents a digital tool with Lean origins, which includes Coaching principles, essential in empowering the workforce (through the voice behaviour) and preserving organizational knowledge. It is a platform built in a way adapted to today's Lean shop floor and intends to prove itself as a resource to promote happy, engaged and committed employees.


Employee suggestion system, Disney strategy, Industry 4.0, Voice behaviour, employee engagement

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DOI: https://doi.org/10.3926/jiem.6314

Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2024

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience