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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|9883||2012||14 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 139, Issue 1, September 2012, Pages 288–301
The increasing frequency of new product introductions force today's companies to continuously upgrade their production capacities. The frequent revision of production capacities and the capacity loss during the reconfiguration period increase the importance of ramp up duration in evaluating capacity investments. This paper aims to explore how a firm should optimally allocate its capacity investments among dedicated manufacturing systems (DMSs), flexible manufacturing systems (FMSs) and reconfigurable manufacturing systems (RMSs) considering the capacity evolution in ramp up period. The proposed model addresses a firm making multiple products for which demand is deterministic and has a specific life cycle. Furthermore, the duration of reconfiguration period is modeled as a function of the amount of capacity change.
Manufacturing paradigm has shown rapid changes in the past decades due to an aggressive market competition on a global scale. Manufacturing system paradigms have evolved from mass production (dedicated lines), which focuses on the reduction of product cost; to lean manufacturing, which improves product quality while decreasing product costs; and then to flexible manufacturing systems (FMSs), which address changes in work orders, production schedules, part programs, and tooling for production of a part family. FMS is able to make a variety of products on the same system but the disadvantages of a flexible manufacturing system include a high initial investment, fixed hardware, and fixed but programmable software. These disadvantages cause a major problem for manufacturers who want to adapt to the new technologies. Mehrabi et al. (2000) review available manufacturing techniques; their key drivers and enablers; and their impacts, achievements, and limitations. According to the survey conducted by the authors, around 73% of manufacturers are looking for a system that could accommodate an incremental increase to their existing production system’s capacity rather than the extra functionality delivered by an FMS. To cope with this limitation, a new manufacturing system technology must provide minimum lead time for launching and integrating new technologies while having the capability to upgrade quickly to new functionality. A reconfigurable manufacturing system (RMS) is defined as a comprehensive system which provides exact required functionality and capacity. RMS has better scalability than dedicated and flexible manufacturing systems. The key rule for RMS is to meet the uncertainties of the open system architecture at the machine, shop floor and system levels. In other words, RMS promises to have a modular structure (software and hardware) that allows for the ease of reconfiguration as a strategy to adapt to market demands. Moreover, the modular structure of RMS enables the system to integrate/remove new software/hardware modules without affecting the rest of the system. Thus, RMS possesses the advantages of both DMS and FMS and occupies a middle ground between them in terms of quantity and variety. RMS could be a solution to industries in search of a system that is more adaptable to changes in terms of capacity and gradual changes in functionality. Although reconfigurable manufacturing system is a new paradigm in manufacturing systems, it might not be the best option and solution for all industries and manufacturers. There may be two main reasons for this. First, technology selection depends on different factors such as the scalability characteristics of the manufacturing system, product life cycles, market behavior, frequency of new products to market, and the cost of acquiring new capacity, as well as market economy and political situations. For instance, due to monopoly and lack of competitors, some markets such as chemical industries might benefit more from the dedicated manufacturing systems. In contrast, in markets, such as the electronic industry, where the frequency of new products is high, Flexible Systems might be more advantageous. Furthermore, when either excess capacity or shortage of product is vital for a firm from the strategic point of view, the characteristics of the manufacturing systems must be evaluated in the selection of a system. Secondly, agility is the premise of RMS in responding to unexpected market changes quickly. Therefore, ramp up time and reconfiguration period are important characteristics to assess the responsiveness of RMS. In other words, reconfiguration period is the major factor in assessing the agility of RMS and its capability to capture the market demand. Therefore, while selecting the manufacturing system alternatives, companies should consider the impact of reconfiguration and the relevant RMS cost structure. In this paper, we develop a decision model based on dedicated, flexible, and reconfigurable manufacturing characteristics to explain how product life cycle and frequency of new product introductions could affect the selection of manufacturing systems. The ramp up time and reconfiguration period of RMS is incorporated in the model as a function of the amount of added or removed capacity. Thus, through an analysis of parameters such as excess capacity cost, shortage cost, reconfiguration speed, we examine how the capacity portfolio of manufacturing systems is selected. The paper outline is as follows: Section 2 reviews the relevant literature. In Section 3, the problem statement is presented along with the assumptions and characteristics of each manufacturing system. In Section 4, we represent the characteristics of dedicated, flexible, and reconfigurable manufacturing systems by a mathematical model. In Section 5, we solve the model to understand which manufacturing system is desirable under what condition(s). Numerical results are discussed in Section 6. Conclusion and future research directions are presented in Section 7.
نتیجه گیری انگلیسی
In this paper, we aimed to analyze the impact of the scalability and RMS ramp up behavior on the optimal portfolio selection of manufacturing systems. We aimed to observe how manufacturing system types are selected in different product life cycle scenarios. We proposed a mix integer programming model that considers all dedicated, flexible, and reconfigurable manufacturing systems and differentiates them by their characteristics. The model considers multiple product families and takes into account reconfigurable capacity scalability lead time. One of the contributions of our proposed model is the improved modeling of the ramp up pattern and scalability of RMS by considering the reconfiguration period changes. Thorough this model, the reconfiguration time is determined as a function of the added/removed capacity and this period can be presented in a linear or nonlinear fashion. Moreover, we distinguished each system’s scalability according to its specification. Finally, including the shortage cost and the excess cost in the model provides a better option for manufacturers to come up with a capacity selection according to their attitude towards risk. The results show that RMS should have a low module to base cost ratio in order to justify the reconfigurations. Otherwise, instantaneous capacity purchase is substituted for reconfiguration. On the other hand, if reconfiguration does not occur in a short time, RMS would behave similar to DMS or FMS. Due to the lack of fast response in RMS, the frequency of reconfiguration in RMS decreases remarkably. In the proposed model, the uncertainty of product introduction time to market and the variability of demand at each period have not been considered. This phenomenon could affect the purchase of new capacity or selecting a new manufacturing system accordingly. Moreover, the excess holding cost or shortage cost of a product is a function of the product life cycle stage which could be incorporated into the analysis. As well, considering stochastic product demand would enable companies to analyze the impact of market uncertainty and determine how RMS reconfiguration speed profile could lower the risk of both excess capacity and demand loss. Finally, since we assume that the timing of future product introduction is known regardless of the market behavior and competitor’s activity in the market, the risk associated with new product introductions is not incorporated in the proposed model. Future research study will aim at incorporating these issues into the capacity selection.