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The Manufacturing line (ML) design plays a critical and valuable role in today’s manufacturing system. Developing and realizing an efficient ML is the main task of ML designers, which consist of firstly designing a new ML and then modifying the current manufacturing line when the given production volume exceeds the current line capacity.
Over the years, there have been many supporting tools developed to help designers to better assess the efficiency of their designs. “Design for X” (DFX) is one of the best-known tools. The ‘X’ in DFX represents any parameters of design considerations occurring throughout the product life cycle, such as quality, manufacturing, production, or environment (Jeffrey et al., 2004). DFX method can be presented in a variety of forms such as a procedure, a set of guidelines on a paper, or a computer program. Each form will be used to optimize the performance of a specific type of analysis in cost, manufacturability, or performance to support the designers in making decisions. “Design for Manufacturing” (DFM) and “Design for Assembly” (DFA) are two of the most common and popular DFX tools (Jeffrey et al., 2004). Several papers about DFA and DFM can be found in the literature such as Bralla (1986), Anderson (1990), Yu (1993), and Boothroyd et al. (2002), which provide detailed discussion on manufacturability and design. Others such as Ehresman (1992), Parmer & Laney (1993), Singh (1996), and Fagade & Kazmer (1998) focus on design guidelines. Other literature mostly focuses on the determinants of ML design’s performance. These studies examine several factors including: takt time (Duanmu & Taaffe, 2007), breakdowns (Elleuch et al., 2007), bottlenecks (Roser et al., 2001), equipment replacement (Sullivan et al., 2002), e-commerce application (Beck et al., 2005) and information and communication technologies (ICTs) (Grant & Yeo, 2019).
Besides, another approach to design manufacturing system is simulation. Nguyen & Takakuwa (2008) introduced a framework in manufacturing line designs in Japanese manufacturing plants to show the contribution of a simulation study in the design process. Nguyen (2009) combined a lean production model and simulation investigation to support ML designs in dealing with the ML modification tasks. Schniederjans and Hoffman (1999), Masmoudi (2006), and Grimard and Marvel (2005) also used simulation as a method to size the ML design.
However, the previous analyses mainly focused DFX tools and provided some specific approaches in practice without modeling and building the optimal decision-making models for manufacturing design. Therefore, this is the gap in best practice and research related to decision-making in manufacturing design.