Introducing Lean practices through simulation: A case study in an Italian SME. Issue 2 (3rd April 2023)

• Record Type: Journal Article
• Authors: Frecassetti, Stefano; Kassem, Bassel; Kundu, Kaustav; Ferrazzi, Matteo; Portioli-Staudacher, Alberto
• Is Part Of: Quality management journal. Volume 30:Issue 2(2023)
• Page Start: 90
• Page End: 104
• Publication Date: 2023-04-03
• Subjects: Industry 4.0 -- Lean -- simulation -- small and medium-sized enterprises -- SMEs; Total quality management -- Periodicals; Personnel management -- Periodicals; Personnel management; Total quality management; Electronic journals; Periodicals; 658.56205 Q1 M2 J
• DOI: 10.1080/10686967.2023.2171326
• Languages: English
• ISSNs: 1068-6967

Abstract

Abstract: Lean is a management paradigm that focuses on respect for people and promises improvement in a company's performance across sectors and sizes. SMEs, in particular, are a point of interest, considering that their malleability in bureaucracy allows for a more significant margin of continuous improvement application. Several organizational theories have gained advantages from technological innovation, and Lean is no exception. The relationships between Lean and new technologies have been studied throughout the years and gained particular interest in the years when Lean started to be integrated with Industry 4.0 (I4.0) to enhance firms' performance. This article would like to take that discussion from a different point of view, that of I4.0's impact on proving the importance of implementing Lean in manufacturing companies. Through a mixed-method approach of case study and Simulation, this article will answer the identified research question: "How do Lean tools and their integration impact SMEs' process performance?" by showing the importance of implementing Lean tools in a non-Lean company. The results show that Simulation can identify the Lean tools that benefit the company's performance most. Managers could benefit from this study by highlighting which tool or combination to invest in according to the impact on operational performance.