Enabling the twin transitions: Digital technologies support environmental sustainability through lean principles
2023 (English)In: Sustainable Production and Consumption, ISSN 2352-5509, Vol. 38, p. 13-27Article in journal (Refereed) Published
Abstract [en]
Manufacturing companies seek innovative approaches to achieve successful Green and Digital transitions, where adopting lean production is one alternative. However, further investigation is required to formulate the strategy with practical inputs and identify what digital technologies could be applied with which lean principles for environmental benefits. Therefore, this study first conducted a case study in three companies to collect practice-based data. A complementary literature review was then carried out, investigating the existing frameworks and complementing practices of digitalized lean implementations and the resulting environmental impact. Consequently, the Internet of Things and related connection-level technologies were identified as the key facilitators in lean implementations, specifically in visualization, communication, and poka-yoke, leading to environmental benefits. Furthermore, a framework of DIgitalization Supports Environmental sustainability through Lean principles (DISEL) was proposed to help manufacturing companies identify the opportunities of digitalizing lean principles for Environmental sustainability, thus enabling the twin transitions and being resilient. © 2023 The Authors
Place, publisher, year, edition, pages
Elsevier B.V. , 2023. Vol. 38, p. 13-27
Keywords [en]
Digital technologies, Digitalization, Environmental sustainability, Industry 4.0, Lean production, Production system, Resilience, Twin transitions, Agile manufacturing systems, Environmental impact, Environmental technology, Sustainable development, Environmental benefits, Lean implementation, Lean principles, Manufacturing companies, Twin transition
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:ri:diva-64313DOI: 10.1016/j.spc.2023.03.020Scopus ID: 2-s2.0-85151404805OAI: oai:DiVA.org:ri-64313DiVA, id: diva2:1755319
Note
Funding details: 2022-01704; Funding details: European Commission, EC, 101058384; Funding details: Chalmers Tekniska Högskola; Funding text 1: This research was conducted with AoA Production at Chalmers University of Technology and RISE Excellence in Production Research (XPRES). It was partly funded by Vinnova's Production 2030 program in the project Scarce II and PLENUM (2022-01704) and partly by the EU in the project RE4DY (101058384). The support is greatly appreciated.; Funding text 2: This research was conducted with AoA Production at Chalmers University of Technology and RISE Excellence in Production Research (XPRES). It was partly funded by Vinnova 's Production 2030 program in the project Scarce II and PLENUM ( 2022-01704 ) and partly by the EU in the project RE4DY ( 101058384 ). The support is greatly appreciated.
2023-05-082023-05-082023-05-08Bibliographically approved