Economic and Environmental Implications of Sourcing Decisions in Supply Networks

Transcription

1 Proceedings of the 2016 Industrial and Systems Engineering Research Conference H. Yang, Z. Kong, and MD Sarder, eds. Economic and Environmental Implications of Sourcing Decisions in Supply Networks Abstract ID: 259 Dinçer Konur, Christina Telles, Hadi Farhangi Engineering Management and Systems Engineering Missouri University of Science and Technology, Rolla, MO, USA Abstract In this paper, we study a three-echelon supply chain consisting of suppliers, distribution centers, and retailers with environmental considerations. In particular, we investigate how sourcing strategies at the retailer level affect the chain s economic as well as environmental performance (measured by CO 2 emissions). First, we mathematically formulate the distribution decisions among the different echelons of the chain with a given sourcing strategy. Then, we demonstrate the effects of the sourcing decisions through a numerical study. The results of the numerical study show that as the number of potential supply sources for the retailers, i.e., the number of sourcing distribution centers, increases, the environmental performance of the best economic distribution policy might improve or worsen. Similarly, as the sourcing decisions change, the economic performance of the best environmental distribution policy might improve or worsen. As companies are becoming increasingly concerned with sustainability of their operations, the model we present in this paper can assist in devising not only cost-effective but also environmentally efficient distribution systems. Keywords Supply chain management, sustainability, distribution, sourcing 1. Introduction and Literature Review Traditionally, supply chain network design models have primarily focused on minimizing operating costs and improving efficiency of freight transportation. However, one of today s most important problems is climate change, and its impact on the environment and society. Transportation and logistics activities generate an estimated 20-25% of the global CO 2 emissions [1]. Green supply chain management has emerged as a concept to mitigate the environmental effects of supply chain activities, and improved environmental performance give a competitive advantage to firms. Therefore, more firms have recently started changing business practices and/or operational policies to achieve potential reductions in emissions [2]. As supply chains have become increasingly globalized, the distances between the nodes of the distribution networks have increased. Larger distances traveled by products lead to increased transportation emissions on distribution networks, which have created an urgent need to design eco-friendly supply chain networks [3]. These improved network designs may be beneficial beyond a company s environmental impact as they can also positively impact a company s profitability [4]. Therefore, there is a growing body of literature on environmentally efficient supply chain network design models. In particular, related previous research has looked at the choices in green supply chain network design at the strategic level in terms of the location of the facilities such as production plants, distribution centers, and retailers, and the allocation of shipments among these facilities [3, 5]. Iakovou et al. [6] present an analytical model that includes an investigation of the cost and environmental impact of choosing the location of a production facility relative to the

2 markets it serves. Harris et al. [7] consider both logistics costs and CO 2 emissions in their supply chain optimization problem. They account for the variation in number (the structure of the supply chains) of depots as well as differing freight utilization ratios. Marjani et al. [8] propose a mixed integer non-linear prograing model for a green supply chain with forward and reverse flows which minimizes traditional supply chain costs, including costs incurred from CO 2 emissions. Li et al. [9] propose a bi-objective (profits maximization and emissions minimization) mathematical prograing model to find distribution center locations, considering both the cost of transportation as well as the emissions created during transportation and production. Wang et al. [10] develop a multi-objective optimization system for supply chain network design. Their model captures the trade-off between cost (encompassing setup, environmental protection fixed investments, transportation and handling costs) and environmental impact (strictly measured as CO 2 emissions). They modify their model as a linear program and then solve to obtain a Pareto frontier. Paksoy et al. [11], considering the green impact on a closed-loop supply chain network, try to limit CO 2 emissions by encouraging customers to use recycled products. They differentiate transportation choices according to CO 2 emissions. A series of simple models based on classical lot-sizing models presented in [2] show that firms could effectively reduce their carbon emissions without significantly increasing costs by employing only operational adjustments. Fahimnia et al. [12] introduce a multidimensional optimization model for tactical supply chain management, and apply this model to real world situations. Their complex nonlinear problem, as solved by a novel solution approach, provides practical insights into the decision facing managers related to the green-or-lean debate. Abdallah et al. [13] develop a mixed integer program for a carbon-sensitive supply chain, integrating transportation costs and carbon emission costs via carbon market trading. Ozsen et al. [5] form a two-echelon single product logistics system to analyze the potential savings that can be achieved by allowing a retailer to be sourced by more than one distribution center. In this study, we assume that the supply chain network is given, that is, the long-term strategic level decisions about the locations of the suppliers, distribution centers, and retailers have already been made and will not change. On the other hand, we analyze the economic and environmental effects of different sourcing strategies at the mid-term tactical level. In particular, the sourcing strategy of a given supply chain network refers to the decisions governing how the agents at one echelon of the network supply the product of interest to the agents at the lower echelons. For instance, a distribution center can receive products from multiple suppliers, whereas a retailer can be restricted to receive shipments from one distribution center only. In this study, our focus is on the sourcing strategy at the retailer level as distribution centers are generally larger facilities that can effectively handle deliveries from multiple sources. Figure 1 below demonstrates the three-echelon supply chain network we consider in this study. Figure 1: Illustration of the three-echelon supply chain network Most of the studies that integrate environmental considerations within supply chain network design models adopt multi-objective optimization approaches [10-12]. Particularly, these studies include an environmental objective such as emission minimization along with the classical economic objectives such as cost minimization or profit maximization. Different than these studies, we do not consider environmental objectives in addition to the economic objectives. Our motivation in this study is to demonstrate how the distribution policy optimizing the economic objective affects the environmental objective and how the distribution policy optimizing the environmental objective affects the economic objective under different sourcing strategies at the retailer echelon of a supply chain network. The economic objective considered in this study is cost minimization and we consider the transportation costs between suppliers and distribution centers, and distribution centers and retailers. We use CO 2 emission generated throughout the distribution network as the environmental performance because carbon footprint is a widely used environmental index and it can be measured throughout supply chains [10]. The environmental objective is therefore defined as

3 emission minimization and we model the emissions generated due to the shipments between suppliers and distribution centers, and distribution centers and retailers. To the best knowledge of the authors, this study is the first to look at the economic as well as environmental effects of sourcing decisions. Our contribution is to highlight how economic and environmental performance of cost and emission minimizing policies change with the sourcing decisions. 2. Problem Formulation Consider a three-echelon supply chain network with n suppliers, m distribution centers, and p retailers. Let the suppliers be indexed by i such that i ={1,2,,n}. The suppliers are the origins of the product and they ship the product to the distribution centers. We assume that each supplier has limited supply and let S i denote the supply capacity of supplier i. Let the distribution centers be indexed by jj such that j ={1,2,,m}. The distribution centers ship the products they received from the suppliers to the retailers and we assume that the distribution centers can receive and ship as many products as possible. That is, there are no inbound and outbound restrictions at the distribution centers. Let the retailers be indexed by k such that k ={1,2,,p}. Each retailer has a demand for the product and let D k denote nn pp retailer k s demand. We assume that ii=1 SS ii DD kk to assure feasibility. kk=1 The problem is to determine the best distribution plan from the suppliers to the retailers through distribution centers. Let us define Q ij as the quantity shipped from supplier i to distribution center j. Furthermore, let us define Q jk as the quantity shipped from distribution center j to retailer k. As a result of the distribution decisions, the supply chain incurs transportation costs and generates carbon emissions. We let c ij denote the unit transportation cost from supplier i to distribution center j and c jk denote the unit transportation cost from distribution center j to retailer k. Then, one can nn pp note that the total distribution costs amount to TTTT = ii=1 jj=1 cc iiii QQ iiii + jj=1 kk=1 cc jjjj QQ jjjj. Now, let us define e ij as the carbon emissions generated due to shipping one product from supplier i to distribution center j and e jk as the carbon emissions generated due to shipping one product from distribution center j to retailer k. Then, the total distribution nn pp emissions amount to EE = ii=1 jj=1 ee iiii QQ iiii + jj=1 ee jjjj QQ jjjj. kk=1 We model the sourcing strategy at the retailer echelon as follows. We assume that any retailer can receive delivery from at most Z distribution centers. Note that ZZ m. In the case ZZ = 1, the retailers are restricted to single sourcing and in the case ZZ 2, multisourcing is allowed at the retailer level. Let us define XX jjjj = 1 if distribution center j ships to retailer k and XX jjjj = 0 otherwise. Note that if XX jjjj = 0, then QQ jjjj = 0 as well. Then, sourcing strategy with Z indicates that jj=1 XX jjjj ZZ for any retailer kk. Below, we give the mathematical formulation of the distribution problem. nn P: MMMMMM TTTT = cc iiii QQ iiii + cc jjjj QQ jjjj ii=1 jj=1 jj=1 kk=1 pp (1) OR MMMMMM ss. tt. nn pp TTTT = ee iiii QQ iiii + ee jjjj QQ jjjj (2) ii=1 jj=1 jj=1 kk=1 QQ jjjj = DD kk kk (3) jj=1 QQ iiii SS ii ii (4) jj=1 QQ jjjj DD kk XX jjjj kk, jj (5) XX jjjj ZZ kk (6) jj=1

4 nn pp Konur, Telles, and Farhangi QQ iiii QQ jjjj = 0 jj (7) ii=1 kk=1 QQ iiii 0, QQ jjjj 0, XX jjjj {0,1} ii, jj, kk (8) In P, the objective is to minimize either costs in (1) or emissions in (2), while following the restrictions imposed by constraints (3)-(8). In particular, constraints (3) assure that the demand at each retailer is shipped while constraints (4) enforce the supplier capacities. Constraints (5) guarantee that a distribution center will not ship to a retailer if it is not a source of supply for the retailer. Constraints (6) are the sourcing restrictions for the retailers and constraints (7) ensure that the distribution centers are the transshipment facilities, i.e., they ship what they receive. Finally, constraints (8) are the non-negativity and binary definitions of the decision variables. In our numerical analyses, while minimizing the total cost of transportation, we increase the number of sources allowed at the retailer level and observe how both the cost and emissions behave. Likewise, the total emissions created are minimized, while increasing the souring options, to see how it impacts the emissions and cost. In the next section, we present the details of our numerical study. 3. Numerical Study Our model was solved for eight different scenarios: the number of retailers (n), distribution centers (m), and retailers (p) would be either 5 or 10. For each of these combinations, the number of sourcing options was then varied from 1 to m. We first needed to assign values for each supplier s inventory amount and each retailer s demand. As the number of retailers was not fixed relative to the number of suppliers, we needed to assure that even if the number of retailers were larger than the number of suppliers (i.e., if n = 10 while p = 5), the supply would be sufficient to make the problem feasible. For this reason, we fixed both the total supply and total demand to 1000 units, and distributed the supply and demand randomly between the nodes. In this construction, both the supply and the demand values had two different sets of values, depending on whether the number of nodes was 5 or 10. We then assigned both cost and emission values for each link between suppliers and distribution centers, and distribution centers and retailers. Cost values were randomly generated between 10 and 20, and emissions values were randomly generated between 0 and 5. The problem P is a mixed-integer-linear prograing model and we use the MATLAB function intlinprog to solve any problem instance with first the cost minimization objective, and then separately the emissions minimization objective. Overall, there was no specific relation that can be generalized about how the emissions of the cost minimizing and the costs of the emissions minimizing solutions change. In particular, Figures 2 and 3 show the results of solving these problems for two representative scenarios. Figure 2: Costs and Emissions vs. Z under cost and emission minimizing distribution plans (nn = 10, = 5, pp = 10)

5 Figure 3: Costs and Emissions vs. Z under cost and emission minimizing distribution plans (nn = 10, = 10, pp = 5) As can be seen in Figures 2 and 3, as the number of distribution centers that can supply a retailer (ZZ) increases, the cost of the cost minimizing distribution policy and the emissions of the emission minimizing policy decrease. This result is expected as larger ZZ implies more freedom in the distribution management. Therefore, we discuss the effects of ZZ on the emissions of the cost minimizing distribution policy and the costs of the emissions minimizing distribution policy. We have the following insights from the sample scenarios solved: As ZZ increases, the emissions of the cost minimizing policy might increase or decrease, As ZZ increases, the costs of the emission minimizing policy might increase or decrease. These suggest that the sourcing strategy adopted for a supply chain has substantial effects on the costs and emissions. For instance, by restricting the number of sources, if the objective is cost minimization, the costs will increase but it might reduce emissions. On the other hand, such a restriction might be increasing emissions as well. Similarly, by restricting the number of sources, if the objective is emission minimization, the emissions will increase however the costs might decrease or increase. Therefore, we conclude that a supply chain agent should carefully plan sourcing strategies considering not only costs but also emissions as a sourcing strategy can have an economic as well as environmental advantages or economic (environmental) advantages with environmental (economic) disadvantages. 4. Conclusions and Future Research Intuition may indicate that, while the number of available sourcing distribution centers increases, both the cost and emissions output will be reduced. However, even our very simplified model shows that this is not always the case: as the number of sourcing distribution centers in a given supply chain network increases, and one objective is optimized, the other objective shows no relationship in how its value changes. In other words, under a cost minimizing distribution plan, emissions might increase or decrease as the number of sourcing distribution centers increases. Even in these simple settings, our model gives us important insights on how distribution planners can balance the often incompatible needs of maximizing profit while minimizing environmental impact with changing their allocation strategies at the tactical level. Environmental costs are not negligible; they may have a profound impact on the optimal configuration of a logistical network [3]. The investigation presented here, while a first-step model, could have significant impact on future industry decisions. Operations decisions like the system explored in this paper are gaining increased interest as a way to significantly reduce emissions without significantly increasing cost [2]. With an increasing interest in greening operations (while holding onto profits), a well-designed supply chain network design has become an important strategic competitive weapon [14]. Acknowledgements This study has been supported by the Department of Engineering Management and Systems Engineering at Missouri University of Science and Technology.

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