مشکل انتخاب تامین کننده وابسته متمرکز زنجیره تامین

Increasing globalization, diversity of the product range, and increasing customer awareness are making the market(s) highly competitive thereby forcing different supply chains to adapt to different stimuli on a continuous basis. It is also well recognized that overall supply chain focus should be given an overriding priority over the individual goals of the players, if one were to improve overall supply chain surplus. Among all the possible order winners, ‘cost’ and ‘responsiveness’ seem to be the most significant metrics based on which majority of the supply chains compete with each other. Supplier selection problem is one of the crucial problems that need to be addressed in configuring a supply chain that could have far reaching ramifications on the total supply chain costs and order winnability. Our model, that considers inventory costs and the supply chain ‘cycle time’ reduction costs, would aid a supply chain manager to make informed decisions with regard to supplier selection problem at any stage, dependent upon the priorities attached to supply chain costs and cycle time. Inventory related costs and responsiveness related costs are the two primary cost elements that are considered in this model. We are also making use of a novel dimensionless quantity called the ‘coefficient of inverse responsiveness’ that not only facilitates the introduction of responsiveness related costs into the model but also improves the scalability and simplifies the analysis and interpretation of the results. Based on the strategic model developed, we offer some very interesting managerial insights with respect to the effect of cost efficient operations and/or location and cost of volume related flexibility at a stage on alternate suppliers, which in turn affects the overall supply chain performance.

“Purchased products and services account for more than 60% of the average company’s total costs. For steel companies, it may go up to 75%; it is 90% in petrochemical industry, etc. Bringing down procurement costs can have a dramatic effect on the bottom line—a 5% cut can translate into a 30% jump in profits” (Degraeve and Roofhooft, 2001). As the emphasis shifts from vertical integration to horizontal interconnectivity in today’s competitive markets, supplier selection turns out to be one of the key issues that affect the product’s competitiveness. The reduction of the manufacturing depth leads to an increase of the proportion of the purchased parts and consequently increases the dependency on suppliers (Maron and Brückner, 1998). Kagnicioglu (2006) opines that supplier selection is a critical activity of purchasing management in a supply chain due to the key role of supplier’s performance on cost, quality, delivery and service in achieving the objectives of a supply chain. An efficient supplier management that begins with the identification of potential suppliers is of central importance for successful supply chain management (Lasch and Janker, 2005). Also, proper supplier selection significantly reduces the purchasing costs and improves corporate competitiveness (Ghodsypour and O’Brien, 2001). Lin (2009) opines that supplier selection for reducing supplier base is an important goal in supply chain management (SCM). Majority of the supplier selection literature focuses upon the selection of relevant performance metrics, supplier rating for a chosen set of performance metrics and optimization models. In today’s competitive markets, it is a known fact that it is the respective supply chains that are competing and not the individual business entities. Lack of supply chain focus, planning horizon, inclusion of subjective performance metrics, multiple goals vs. unitary goal, consideration of interrelationships among the performance metrics chosen for supplier evaluation are some of the key issues that distinguish our model from the models presented in existing literature. Under this category the emphasis is primarily on delineating different performance metrics for supplier selection not the supplier evaluation itself. The number of performance metrics that one could consider to aid in supplier selection is not only large but also depends on the context (strategic, operational, etc.), type of the product, nature of the markets, etc. Among the possible order winners cost and responsiveness turn out to be more crucial than others. In the context of our model, responsiveness is the ability of the supply chain to respond quickly to changing customer needs, preferences, options, etc. in terms of supply chain cycle time, emphasis being on volume related flexibility. Majority of the existing models are cost focused and do not address the responsiveness aspect in an explicit fashion. Also interrelationships between cost and responsiveness are not sufficiently explored. Both of these issues are addressed in our model. Another major difference is that the model works on the strategic perspective with the aim of developing managerial insights that would aid supplier selection at a particular stage in a supply chain. We are modeling the supplier selection problem as a supply chain configuration problem in the sense that we are assuming that product design and supply chain topology are already fixed and there are competing suppliers at a stage who differ only in terms of cost and responsiveness. A typical configuration for a supply chain consists of defining components of the system, assigning values to characteristic parameters of each component and setting operation policies for governing the interrelationships among these components (Truong and Azadivar, 2005). There are primarily four drivers of cost in a supply chain, namely, infrastructure, inventories, transportation and information (Chopra and Meindl, 2004). Since we are assuming that the necessary network topology is already in place, it obviates the necessity to include infrastructure related cost elements and transportation related aspects explicitly into our model. However, these issues are addressed in an indirect fashion in our model. For example, cost added at a stage can be considered to be a function of fixed costs associated with infrastructure such as location, buildings, machinery, etc. Even though we are developing the model assuming that all the stages are involved in manufacturing, a stage purely dealing with transportation could be easily accommodated. We are also assuming information symmetry at all the stages and leave information asymmetry related issues for future research. That leaves us with inventory as our primary cost driver. We are considering both cycle stocks (in-process inventory) and safety stocks in our model. For a stochastic service model (Graves and Willems, 2003, Simchi-Levi and Zhao, 2005, Lee and Billington, 1993 and Ettl et al., 2000), which we have adopted in our model, we assume that the increase in cost at a stage depends on the opportunities that exist for resource flexibility. We model it as a continuous function of a novel dimensionless parameter called the ‘coefficient of inverse responsiveness’ (CIR) that also enhances the scalability of the model, with the focus being to develop managerial insights with regard to supplier selection at a stage. With the introduction of CIR, research gaps in terms of addressing the interrelationship between the costs and the responsiveness and the scalability limitation are addressed. Also, lack of explicit consideration of the processing time variability is one of the key issues in the existing literature. We have included both the demand variability and the processing time variability in our model thereby mimicking the reality as closely as possible. The rest of this manuscript is organized as follows. In Section 2 we present the relevant literature review. Section 3 deals with the development of the overall cost expression for the supply chain. Section 4 offers managerial insights in regards to the supplier selection problem in a serial supply chain. Section 5 offers an illustrative example involving the selection of a wiring harness supplier for an OEM facility. Finally, conclusions and limitations are offered in Section 6.

In present day competitive markets with shorter product life-cycles there is a need to reduce the costs and the supply chain cycle time. In this paper, primarily we offer certain managerial insights with regard to supplier selection problem in a supply chain by considering inventory costs and responsiveness related costs assuming that the structural decisions are already made with respect to the supply chain network. The most important aspect of this paper is that in addition to the traditional cost criterion, we have incorporated supply chain responsiveness related parameters into the model, which allows us to monitor the supply chain performance with respect to these two critical order winners. We make use of a new parameter called coefficient of inverse responsiveness (CIR) to model response related costs at a stage, which also enhances the scalability of the model. In an optimization context, the developed cost function for the ‘building block’ could be extended for any type of supply chain network to aid in supplier selection. So as to make the model more tractable we had to make certain simplifying assumptions and following are some of the limitations related to those assumptions. We did not consider order splitting in our model, which is a common phenomenon in many purchasing decision. We plan on considering this aspect in our future research. We also did not consider buyer collaboration, which is not uncommon while making purchasing decisions. It would an interesting extension, if this aspect is included. We did not take into account any qualitative factors such as quality, suppliers’ reputation, staying power/financial stability, cultural match, etc. in our model. An integrated framework that takes into account some of these factors would add more value to the model. A useful extension of the model is to account for non-stationary demands and to consider products with seasonal demands. Another limitation is that volume discounts and quantity discounts typically offered by suppliers are not taken into account in our model. Introducing contracts that take into account such discounts with financial ramifications will also be a very fertile area to pursue, which will make our model mimic the reality more closely. So as to make our research realistic, it would make more sense to consider capacity constraints at certain stages. Product mix related flexibility is crucial which is not addressed in our model. Addition of this feature would truly make the research more in tune with the reality. Finally, we really would like to see this model used in more real-world settings so that insights presented in our model could be further validated.