تجزیه و تحلیل کمی روابط بین انواع محصول و استراتژی های زنجیره تامین

Supply chain management has been given so much attention that various technologies and concepts have been applied to improving and optimising supply chain performance. However, failures in supply chain management are still not uncommon in today's industries. One important reason is the failure to sort different products into categories related to appropriate supply chain management strategies. Qualitative analyses on the characteristics of products and their impacts on supply chain performance have been reported in the literature. Whereas quantitative analysis of matching products to supply chain strategies has so far not been sufficient to assist decision-making significantly in supply chain management. This paper is focused on a quantitative analysis to match types of products to supply chains based on a mathematical model. Using a multiple objective optimisation model, a sensitivity analysis has been conducted to detect variance of performance in relation to three typical supply chain strategies (manufacturing to order, manufacturing from stocks and manufacturing to stocks), and based on different product characteristics (value-adding and demand uncertainty). The model is particularly designed for evaluating performance of supply chain strategies with the product characteristics mentioned above. The analytic results disclosed some quantitative relationships between the performance of the supply chain strategies and product attributes, which could provide assistance to decision making on operational and strategic supply chain management.

Despite the attention given to new technologies and to concepts for improving supply chain (SC) efficiency, ‘the performance of many SC has never been worse’ [1]. In Fisher's [1] research, the reason for this phenomenon is attributed to a mismatch between product types and SC strategies. In the research, a number of cases were analysed to indicate reasons for success and failure in supply chain management (SCM). As a result of the research, Fisher proposed a conceptual model for matching product types to SCM strategies as illustrated in Fig. 1. As seen in the conceptual mode, products are classified into two categories, functional products (with small demand variance and low profit margin) and innovative products (with uncertain demand, high variety, and high profit). Then, SC strategies are classified into a physically efficient process to ‘supply predictable demand efficiently at the lowest possible cost’, and a market-responsive process to provide quickly response to unpredictable demands. Fisher's conclusion provides a significant framework for establishing suitable SCM strategies under particular operational environment.In practical supply chain operations, companies are facing SCM problems which concern more concrete production planning and quantitative operational decisions than merely qualitative strategies and product type identifications. Therefore, more explicit quantitative criteria and tools for SC strategy designs would provide further benefits to SCM. The research in this paper is focused on investigating the impacts of such operational parameters as value adding capacity, demand uncertainty and material cost on the performance of manufacturing strategies by quantitatively modelling the operational process of companies in supply chains. The objective of the research is to explore the influence of significant operational parameters (which reflect influencing product attributes proposed by Fisher's research [1]) on SC efficiency based on different strategies. The research shows the impact of the quantitative product characteristics on the operational performance of three manufacturing strategies (manufacturing to order, manufacturing from stocks and manufacturing to stocks) based on sensitivity analysis of a multiple objective optimisation model. The quantitative model can be a tentative decision support tool to optimise efficient or responsive SC strategies under particular operational environments.

As seen in Table 3, the performance of the three strategies varies against the analysed factors. When demand uncertainties of materials and finished products are at lower levels, the physical responsive strategy (MTS) always performs better than the other two strategies and the physical efficient process is the last choice. However, as the demand uncertainties increase, the performance of MTO and MFS surpasses the performance of MTS process. As seen in Fig. 4, the influence of value-adding capacity on the ranking of the performance of the three strategies is not significant when demand uncertainties are at lower levels (e.g., Uf=Us=0.01, 0.05, 0.1). However, the ranking of the performance of the three strategies is highly dependent on value-adding capacity when demand uncertainties are at a high level (e.g., Uf=Us=0.3) as seen in Fig. 4. At such a demand uncertainty level, performance levels of the three strategies are very close to each other at all value-adding capacity levels. This implies that, at some demand uncertainty levels, it is flexible to employ different strategies for pursuing high performance (which is evaluated by multiple criteria). But, this does not mean that the strategies play similar roles in operations as they may contribute to the multiple objectives differently. It has been noticed that, when demand uncertainty is high and value-adding capacity is low (e.g. Uf, Us>0.3 and VC<0.90), MTO performs much better than the others as the strategy tries to avoid any wastes in stocks at the expense of longer lead times. This result implies the most suitable situations for the physical efficient strategy to be employed and is somewhat different from Fisher's conclusion (physical efficient process is suitable to functional products with low profit and low demand uncertainty). The results in Table 3 also disclose the different influence from materials demand uncertainty and finished product demand uncertainty on the performance. As seen in the table, when the demand uncertainty of finished products (Uf) is at the higher levels and the demand uncertainty of materials (Um) is at the lower levels (e.g., Uf=0.3, 0.5 and Us=0.01, 0.05, 0.1), the market responsive process (MFS) performs much better than MTO, although the physical responsive strategy (MTS) performs similarly when value-adding capacity is high (e.g., VC>0.95). The finding is consistent with the conclusion of Fisher's [1] qualitative research (market responsive process is suitable to novelty product with unpredictable demand and high profit margin). On the other hand, higher levels of materials demand uncertainty usually lead to the physical efficient process (MTO) performing better than the physical responsive process (MTS). This may be explained by the reason that, in situations of high uncertain materials demand, the material stocks are probably ineffective and the non-stock strategy with acceptable lead times may surpass the MTS strategies. But, as the performance has been evaluated by multiple criteria, the MTS strategy can still be the best choice if the gain of the MTS process from lead time reduction can offset the loss from wastes in stocks. This may explain the higher performance of MTS than MTO in the situations of high value-adding capacity (possibly leading to acceptable profit) and high materials demand uncertainty as shown in Table 3 (Nos. 6 and 7). Through the results of such analysis, it can be seen that none of the three typical SC strategies performs the best all the time. The operational environment significantly influences their roles in SCM. From the figures, quantitative comparative advantages of the strategies under different conditions can be perceived more easily. By quantitative modelling of the three commonly used strategies, the impacts from different operational conditions are analysed. The results enrich the Fisher's concept [1] by quantitatively comparing the three strategies against variations in key factors. Besides market responsive process and physical efficient process mentioned in Fisher's [1] research, manufacturing to stocks process (named physical responsive process in this research) is analysed as well. Some of the results in this research have provided quantitative evidence to Fisher's [1] qualitative conclusion (i.e., market responsive process matching novelty products). But, some other quantitative results are different from Fisher's [1] conclusion to some extent (i.e. physical efficient process matching functional products). This may result from difference in definitions of the process. Further research on more operational factors such as supplier lead times and delays, etc., which are given in this research and influence the SC performance would be beneficial to such SC strategy design problems. This research provides a quantitative view of interactions between some key operational parameters and the performance of different SC strategies. The approach shows prospects of being a tool to investigate operational conditions quantitatively in the design of appropriate SC strategies.