Article Preview
TopIntroduction
The worldwide fashion industry is one of the most competitive sectors of the global economy where a vast number of manufacturers compete without geographic boundaries, producing countless product varieties at an extremely fast pace, and for smaller and smaller profit margins. In such an environment, efficiency has long been a must, making the use of process improvement methods essential at all levels, from production operations to supply chain management. Lean manufacturing is one of the most popular methods for continuous improvement across all stages of a production operation, focusing on minimizing wastes in production time and effort, and maximizing productivity such as production throughput and product quality. However, one of the biggest challenges facing lean success is sustaining the performance improvements over the long run (Rymaszewska, 2014) due to a variety of problems. Some of these problems are well known, such as cultural resistance to change (Oudhuis & Olsson, 2015) and the lack of management commitment to improvement initiatives (Salge, 2008), but other problems are unpredictable and are due to the complexity of large-scale manufacturing systems. Such problems are best understood with the use of systems thinking and system dynamics (SD) modeling, especially when the purpose is to identify the problem and its root cause rather than to patch up the visible negative impacts (Meadows, 2008). Therefore, the use of SD modeling to guide lean initiatives and ensure their long-term sustainment presents promising potential.
To date, the use of SD to model manufacturing operations or lean implementations has had little attention in the literature. In this work, SD modeling is used to guide an actual lean implementation for reducing work-in-process (WIP) at a children’s apparel manufacturer in a developing country. The manufacturer experiences internal inefficiencies due to management and production problems, and external challenges in the form of fluctuating demand. Therefore, the manufacturer stands to benefit from the application of appropriate lean techniques. Furthermore, the use of SD modeling to test the impact of lean improvements on the performance of the system has the potential to capture undesirable feedback effects over the long-term. The SD model can serve as a decision support tool that helps mitigate undesirable impacts, and therefore SD modeling can ensure the long-term sustainability of the lean initiative. This is why an SD model of the production process is developed in this study in order to simulate the impacts of complementary lean techniques on system performance over the long-term.
What differentiates this study from previous work is in using SD modeling for assessing the performance of an actual lean implementation effort involving a variety of complementary lean tools in a real-world context, whereas previous studies have modeled mostly hypothetical manufacturing operations in order to investigate the impact of a single lean technique. In addition, the developing country context of this study provides an opportunity to identify significant challenges facing lean sustainment over the long-term because in this context resources are typically much more limited, management much less committed and work methods much less organized than in the industrialized world.
The rest of this paper is structured as follows: the following section presents an overview of the recent literature on system dynamics modeling of actual and hypothetical lean manufacturing implementations. This is followed by the methodology section detailing the research approach used in the study. After the methodology section is a description of the current state of production operations using value-stream mapping and system dynamics modeling, and the section after that describes the implementation of lean techniques in the future state with an assessment of their impacts on production efficiency using system dynamics modeling. The results are then reported and discussed in terms of reduced WIP and improved productivity. Concluding remarks and proposed directions for future research are reported in the final section.