Assessing the Sustainment of a Lean Implementation Using System Dynamics Modeling

IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of System Dynamics Applications Pub Date : 2019-10-01 DOI:10.4018/ijsda.2019100102
Marc Haddad, Rami Otayek
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引用次数: 9

Abstract

The adoption of the lean approach has yet to extend to the majority of manufacturers in developing countries where traditional work practices are dominant and cultural resistance to change is high. This research consists of a case study about lean implementation at a clothing manufacturer in a developing country. Production wastes are identified and appropriate lean techniques, namely Total Productive Maintenance, Kanban and Supermarket Pull, are identified to eliminate or reduce them. The potential impacts on the manufacturing system are first assessed using a system dynamics model. The modeling results showed a “getting worse before getting better” behavior as work-in-process increased in the short-term, before a net reduction of 34% on average was achieved over the first 3 months. This result was replicated by a similar trend in the actual lean implementation on the factory floor, showing the usefulness of SD modeling for supporting the sustainability of lean interventions where short-term drawbacks can be deceptive when compared to the long-term benefits of lean.
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使用系统动力学建模评估精益实施的可持续性
在发展中国家,传统的工作方式占主导地位,对变革的文化阻力很大,因此精益方法的采用尚未推广到大多数制造商。本研究以发展中国家某服装生产企业实施精益生产为案例进行研究。确定生产浪费,并确定适当的精益技术,即全面生产维护,看板和超市拉动式,以消除或减少生产浪费。首先使用系统动力学模型评估对制造系统的潜在影响。建模结果显示,随着在制品在短期内的增加,在前3个月平均净减少34%之前,出现了“先变坏后变好”的行为。这一结果在工厂车间的实际精益实施中也出现了类似的趋势,显示了SD模型在支持精益干预的可持续性方面的有用性,在这种情况下,与精益的长期效益相比,短期缺点可能具有欺骗性。
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来源期刊
International Journal of System Dynamics Applications
International Journal of System Dynamics Applications COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
自引率
38.90%
发文量
26
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