Author:
Abideen Ahmed Zainul,Mohamad Fazeeda Binti,Fernando Yudi
Abstract
Purpose
Recently, much research about the effects of simulation on an organization’s operational performance and efficiency has been carried out. But still, there is a need for lean-based simulations rather than just modelling the system. However, these studies still lack systematization. Consequently, the purpose of this paper is to systematize the literature on lean simulations in the area of production and operations management.
Design/methodology/approach
A systematic analysis was performed on 93 articles extracted from the Clarivate Analytics Web of Science (WoS) Core Collection database. The analysis was limited to only articles that come under the “Engineering industrial,” “Engineering Manufacturing” and “Operations Research and Management” subject category in WoS Core Collection database. Later, a bibliometric analysis was integrated with the literature review for deeper literature categorization.
Findings
A bibliometric analysis displayed the relevance of selected literature (keywords, authors and sources). The systematic literature review helped to clarify each clusters’ content and purpose.
Research limitations/implications
This study portrays the gaps in the lean simulation integration methods and techniques through a detailed systematic and bibliometric review.
Practical implications
Lean managers can get various insights and ideas on implementing lean and simulation integration methods in real-time process improvement scenarios.
Social implications
This study demonstrates the best future recommendations for lean based modelling for both consumers and organizations that shall address the social and economic issues.
Originality/value
To the best of the authors’ knowledge, minimal attention has been paid to systematizing the literature on lean simulation in industrial, manufacturing and operations management. This study contributes to close this gap.
Subject
Management Science and Operations Research,Strategy and Management,General Decision Sciences
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