Green supply chain network optimization and the trade-off between environmental and economic objectives

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Green supply chain network optimization and the trade-off between environmental and economic Objectives Alice Tognetti, Pan Stephan M. Wagner

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Received date: 7 May 2014 Accepted date: 5 May 2015 Cite this article as: Alice Tognetti, Pan Theo Grosse-Ruyken, Stephan M. Wagner, Green supply chain network optimization and the trade-off between environmental and economic Objectives, Int. J. Production Economics, http://dx. doi.org/10.1016/j.ijpe.2015.05.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Green Supply Chain Network Optimization and the Trade-off between Environmental and Economic Objectives Alice Tognetti, Pan Theo Grosse-Ruyken, Stephan M. Wagner Chair of Logistics Management Department of Management, Technology, and Economics Swiss Federal Institute of Technology Zurich Weinbergstrasse 56/58, 8092 Zurich, Switzerland [email protected]; [email protected];[email protected]

Green Supply Chain Network Optimization and the Trade-off between Environmental and Economic Objectives

Alice Tognetti Regensbergstrasse 70, 8050 Zurich, Switzerland Pan Theo Grosse-Ruyken * Chair of Logistics Management Department of Management, Technology, and Economics Swiss Federal Institute of Technology Zurich Weinbergstrasse 56/58, 8092 Zurich, Switzerland Stephan M. Wagner Chair of Logistics Management Department of Management, Technology, and Economics Swiss Federal Institute of Technology Zurich Weinbergstrasse 56/58, 8092 Zurich, Switzerland

Final accepted manuscript: International Journal of Production Economics IJPE-D-14-00782R1 Accepted: May 5, 2015

Elsevier Reference: PROECO_6077

* Corresponding author. Tel. +41 44 632 0987, fax: +41 44 632 1526. Email: [email protected].

Green Supply Chain Network Optimization and the Trade-off between Environmental and Economic Objectives Abstract Growing public pressure, stricter environmental standards but also the awareness that greener supply chains can be more attractive for investors have made it more important than ever for firms to go “green.” While scholars have investigated technological production options, recycling and logistics flows, there is no study that jointly optimizes the emissions and the costs of the supply chain by looking at the production volume allocation and at the energy mix. The results, based on a case study in the German automotive industry, show that by optimizing the energy mix, the CO2 emissions of the supply chain can be reduced by 30% at almost zero variable cost increase.

Keywords: Supply chain network optimization; Green supply chain management; Life cycle assessment; Energy sources; Multi-objective optimization; Automotive industry

1.

Introduction

Firms become increasingly aware of the importance of environmental measures such as emissions reduction or efficiency improvement within the corporate organizational boundaries. The reasons for becoming “green” are manifold, ranging from adherence to stricter environmental standards (Walton et al. 1998) to expected reputational effects and profit implication (Hofmann et al. 2014). A benchmark among 300 global firms shows that 83% of firms plan to establish a “green design” of their supply chain networks (SCNs) (Shecterle and Senxian 2008). Through the implementation of environmental practices, firms can increase their attractiveness to investors. Jacobs et al. (2010) show that ISO 14001 certifications are associated with significant positive market upturns. A “green reputation” increases unit sales and prices, as the willingness to pay for “green” products is higher (Moon et al. 2002). Purchasing decisions are also more and more influenced by environmental considerations and reputational risk concerns (Schoenherr et al. 2012). As a result, leading firms proactively implement “green” initiatives (Wang et al. 2011). Examples are Mondi’s “as green as it gets” (Mondi 2012), MGM Grand’s “green, going greener” (MGM Grand 2012), or PepsiCo’s “performance with purpose” (PepsiCo 2013) claims. Contrarily, poor environmental practices can erode firms’ benefits by driving away sales, increasing costs, or ruining a firm’s rating or share value. For example Cailler, a chocolate brand owned by

Nestlé, had to rescind its packaging strategy as the rebranding exercise had generated strong opposition from environmental groups, retailers and customers. The cost of repackaging was estimated to exceed USD 39-47 million (Swissinfo 2006). Costs can also sharply increase due to high amendments triggered by poor environmental practices. Pfizer, for example, approved to pay a penalty of USD 975,000 to resolve purported violations of the Clean Air Act at its former manufacturing plant in Groton, Connecticut (EPA 2008). These examples show that developing green supply chains which fulfil environmental standards become more common practice (Gavronski et al. 2011). In particular in green supply chain management (GSCM), the SCN footprint plays a principal role for firms’ environmental and economic performance (Pishvaee and Razimi 2012). Adopting stricter environmental standards comes at a cost, and can be expensive. The Environmental Protection Agency in the U.S. has estimated that the environmental expenses for clean-up, regulatory compliance, and management impose annual compliance costs of more than USD 30 billion. Firms lack the practical tools to handle the joint operational, economic and environmental decision-making involved in green SCN design (Gupta and Palsule-Desai 2011) and managers often times fear extremely high implementation costs. In the literature about various aspects and facets of GSCM (Srivastava 2007), model-based research that extends quantitative models, is rare. Existing literature focuses either on minimizing cost or maximizing profit, not considering enough the carbon footprint (Benjaafar et al. 2013). And practical applications investigating environmental management strategies need to be developed (Hugo and Pistikopoulos 2005). In particular, energy efficiency along the supply chain has been neglected or research is this area just began to emerge (Halldorsson and Kovacs 2010). Therefore, this study aims to provide an optimization model considering environmental and financial aspects which quantifies and optimizes the environmental impact of firms’ SCN.

2.

Literature review

2.1. Green supply chain management Green supply chain management (GSCM) received with more than 300 published papers between 1997 and 2012 much attention from academia and business alike (Seuring 2012). GSCM can be defined as “integrating environmental thinking into supply-chain management, including product design, material sourcing and selection, manufacturing processes, delivery of the final product to the consumers as well as end-of-life management of the product after

its useful life” (Srivastava 2007, pp. 54-55). GSCM encompasses a set of environmental practices along the product value chain (Zhu et al. 2006; Zhu et al. 2008). Many studies have investigated approaches and methodologies to integrate environmentally friendly practices in supply chain management. We distinguish two methods in the literature: green design and green operations (Srivastava 2007). Green design considers the environment as a design objective instead as a constraint on operations (Hugo and Pistikopoulos 2005). This approach is widely applied on the use phase and design for disassembly (Alting and Legarth 1995), and for supply chain re-engineering (Aikten et al. 2003). An implementation in strategic production planning was proposed (Hugo and Pistikopoulos 2003; 2005). Green operations involve reverse logistics and network, green manufacturing and remanufacturing and waste management. Empirical studies identified the drivers of GSCM adoption (Khiewnavawongsa and Schmidt 2008) and have underlined the importance of proactive strategies (Walton et al. 1998) as well as the integration of additional environmental criteria throughout the supply chain (Koplin 2005). In the European automotive industry for example, empirical research investigated the influence of certified environmental management systems, i.e. ISO 14001 or Environmental Management System (EMS), on the incorporation of green practices, such as the reduction of raw materials or recycling (Gonzalez et al. 2008; Thun and Müller 2010). Due to multiple-stage execution of supply chain practices and the multi-faceted performance implications, measuring GSCM practices and green supply chain performance is complex (Sundarakani et al. 2010). Researchers have analyzed the influence of an environmentally based supplier rating on firm performance (Green et al. 1998) and have estimated the impact of GSCM on the competitive position and economic performance (Rao and Holt 2005). A performance assessment index has been introduced (Wang et al. 2005) and an analysis approach to prove the validity of a performance measurement construct has been developed (Olugu et al. 2011). For example, Azevedo et al. (2011) show that the type of adopted GSCM influences supply chain performance, and Simpson et al. (2007) highlight that the integration of standards, i.e. the ISO norms, make suppliers more receptive to their customers’ environmental performance requirements.

2.2. Green supply chain network optimization From the vast opportunities that GSCM practices offer to reduce the environmental impact, we focus our attention on measures related to the optimization of the firm’s SCN. “Supply chain network optimization refers to models supporting strategic and tactical planning across the geographically dispersed network of facilities operated by the company and those

facilities operated by the company’s vendors and customers” (Shapiro 2004, p. 4). Given the motivation of our research to shed light on the trade-off between environmental and economic objectives in optimizing firms’ SCNs, this study focuses on green supply chain network (GSCN) optimization, specifically by optimizing the energy mix. As GSCN optimization investigates the selection of the best activities for the coordination and design of a green supply chain over time, it provides crucial support to decision-making (Fleischmann et al. 2010). Classical SCN design problems are one of the most comprehensive strategic problems that must be solved for long-term efficiency of the supply chain (Wang et al. 2011). It determines several configuration parameters, such as the allocation of the production volumes, the number, location, capacities, or types of facilities in the network. The problem covers deterministic models where the parameters are fixed and stochastic ones where some parameters are random (Rader 2010). Numerous studies have suggested methods for SCN design (e.g., Beamon 1998; Goetschalckx and Fleischmann 2010; Min and Zhou 2002). Single objective problems have been widely implemented (Melkote and Daskin 2001; Santoso et al. 2005) and classical models have been extended to include risk management (Goh et al. 2007), taxes (Kuo et al. 2001), or transportation modes (Cordeau et al. 2006). However most “design” problems involve research trade-offs among incompatible objectives. Only a few applications have developed integrative, holistic models that investigate the trade-offs between environmental and operational performance to support managers’ decisions (Gupta and Palsule-Desai 2011). Wang et al.’s (2011) multi-objective optimization models consider CO2 emissions and costs. For a number of carbon- and fuel-pricing scenarios, Fahimnia et al. (2014) examine economic and environmental trade-offs. Sheu et al.’s (2005) model optimizes the operations of integrated logistics networks. They have suggested that supply chain profits can be improved by more than 20%. Also, Hugo and Pistikopoulos (2003, 2005) using a mixed integer linear programming model integrating life cycle assessment (LCA) for investment planning, within a supply chain context, highlighted that there are margins for significant performance improvement. Nevertheless, practical applications investigating other dimensions and environmental management strategies are still needed (Hugo and Pistikopoulos 2005). For example, Pishvaee and Razimi (2012) show that addressing operational planning problems can offer attractive research opportunities. A broader look at emissions categories other than CO2 is proposed by Hugo and Pistikopoulos (2003; 2005) and Jamshidi et al. (2012), who study the integration of LCA into strategic production planning. Bloemhof-Ruwaard et al.

(1996) have presented a life cycle optimization model for the pulp and paper industry. Finally, energy efficiency has been largely neglected in SCM (Halldorsson and Kovacs 2010). Hence, we propose a model supporting the integration of those aspects in SCN design. The approach is consistent with the environmental standards (ISO 2006a, b) and strives at the increase of profitability, appeal to investors and decrease of risks. The approach is based on the source of energy used (i.e. the energy mix) and the allocation of the production volume across the plants. To handle the environmental and the economic objectives, we implement a linear multi-objective optimization. We measure the economic performance with the net present value (NPV) of variable costs and the environmental impact through the global warming potential (GWP) that is a measure of the CO2 equivalent emissions based on the LCA methodology.

3.

Conceptual framework and problem formulation

GSCM planning is a broad field, because environmentally friendly or “green” practices can be implemented over different planning horizons and at different levels of the supply chain (Chopra and Meindl 2013). In order to understand the trade-off between environmental impact reduction and costs minimization, we present the GSCM planning problem in Figure 1.

Analyzed aspects:

Network configurations: Different network size

Network size influence

Variable production restrictions

Production flexibility impact

Decision variables:

Production volume allocation influence on environmental impact

Production volume allocation Energy mix Performance measures: Economic profit Environmental impact

Optimization

Energy mix influence on environmental impact Environmental optima Economic optima Trade-off solutions between economic and environmental optimization

Figure 1. Conceptual framework for a jointly economic and ecological GSCM planning. The network configurations determine the network structure and therefore affect the optimization output by limiting the feasible solutions area. The two drivers of environmental impact and costs are network size and production restrictions. The size of the network

determines the location and number of factories; production restrictions define the flexibility of the production network. The constraints on production capacity and product allocation set the maximal number of products and product variants that can be manufactured in each factory, respectively. The network configurations are analyzed by looking at the production volume allocation, which encompasses the whole supply chain, and the “energy mix”, which focuses on production. We chose the production volume allocation because it is the rationale for all strategic production planning problems and includes all supply chain stages. It includes the selection of the optimal production volume allocation among the set of production plant candidates and the definition of the optimal transportation network between the suppliers and the selected sites, in addition to the one between the selected sites and the markets. The second decision variable is at the production level and consists of energy sources or “energy mix” selection. Together with production efficiency, most automotive companies consider energy as a means of decreasing their environmental impact (e.g., Daimler 2011; Fiat 2010; Toyota 2011; Volkswagen 2011). In fact it is one of the larger sources of emissions in the industrial sector (European Commission 2010), and a good management of the energy sources can involve consistent costs reduction. In the future, energy savings can be even larger as resources are depleted and the energy prices rise (EIA 2012). The best way to reduce the carbon footprint of the supply chain and the production process from an economic perspective is through the analysis of the production volume allocation and the energy mix selection. The values of the decision variables are determined by optimizing economic and ecological performance measures. The first consists in the minimization of the NPV of variable costs; the second in the minimization of the environmental impact. Given a SCN structure constituted by a set of suppliers, a set of factories and a set of markets, the goal is to design the SCN of the integrated facilities that would satisfy demand over the planning horizon such that both NPV of the variable costs and the GWP of the entire network are minimized. All decisions are made on a yearly basis and over a 10-15 year planning horizon (where prices, the demand, and availabilities of the products and the operating costs of the plants can vary). The proposed structure is illustrated in Figure 2. Global and local suppliers supply the factories. The final products are then distributed in the main markets, which have specific demands for the product variants. The unit of analysis defines the boundaries of the system on which the variable costs and the emissions are traced and quantified (gray area in Figure 2). It includes three life cycle stages: inbound logistics (IBL), outbound logistics (OBL), and production. The first two refer to the supply of sub-products to the factory and the

distribution of end productions to the markets respectively; the latter refers to the assembly of those subcomponents into the end-product.

Suppliers

Inbound logistics (IBL)

Production

Outbound logistics (OBL)

Markets

Unit of analysis

Figure 2. Overview of the network structure.

4.

Methodology

4.1. Model design To integrate ecological concerns in supply chain and production design it is necessary to define the environmental impact assessment approach for the measurement of the ecological performance, to quantify the suitable economic performance measurement and to select a mathematical optimization approach that can balance environmental criteria against traditional financial incentives. First, to measure ecological performance, we choose the GWP. The GWP is measured in tons of CO2 equivalent and corresponds to the possible contribution to the anthropogenic greenhouse effect on a time horizon of 100 years. It is the impact category with the larger average impact per inhabitant (Volkswagen 2008), has the strictest regulations, and its quantification is based on the LCA methodology, which is recognized by the International Standards Organization (ISO 2006a) and which is widely implemented (Rebitzer et al. 2004). It also allows working with consistent and homogeneous international emissions databases. According to ISO norms, the analysis comprises four phases: scoping, inventory, impact assessment, and interpretation (ISO 2006a). Scoping is the selection of the functional unit and the determination of the system boundaries (Rebitzer et al. 2004). The functional unit states the system’s function: the delivery of the final products to the market over the planning horizon. The boundaries define the involved processes as shown in Figure 2 and include the life cycle stages of IBL, production, and OBL. For IBL and OBL the emissions are based on the transport mean, the covered distances, and carried weight. In the production process, the emissions depend on the energy consumption according to the energy mix composition. We have obtained the inventory data for each life cycle stage from the EcoInvent database (Frischknecht and Jungbluth 2007). Several impact assessment methods are available for the

aggregation of the inventory data into impact categories (Bare and Gloria 2006; Pennington et al. 2004). We have selected the CML methodology developed by the University of Leiden (Guinée 2002). Finally, we have interpreted the results based on the environmental performance of the various production sites and the production allocation alternatives as well as the energy mix composition. Second, we quantify economic performance by looking at the NPV of the variable costs. The NPV is a time measure of money providing a comprehensive basis for profitability analysis (Hugo and Pistipokulos 2005). It is used in the economic environment and is an important investment indicator. The optimal network structure is the one with the lowest costs over the planning horizon. In the logistics costs we have included the taxes for the importation of the sub-products, the taxes for the exportation of the finished products, and the transport costs. In the production costs we have considered the energy and the labor costs. Two assumptions on the latter’s quantification need to be highlighted. First, the cash flow is constituted by variable costs: no fixed costs, investment or profits have been taken into account. Second, the labor costs have been adapted to inflation and to the expected salary evolution. Finally, we apply a multi-objective optimization to handle the large number of alternatives of the planning process balancing environmental criteria against the traditional financial incentives. This allows for better decisions, coordination and control (Natarajan et al. 2006). In this study, the linear e-constraint optimization consists of optimizing a preferred linear objective treating the others as constraints (Sunar and Kahraman 2001). These constraints are also linear functions of the decision variables (Shah et al. 2007). Hence, the main model components are a set of parameters, decision variables, constraints, and an optimization function (DeWeck 2004).

4.2. Model formulation In most industries the strategic production network planning is made using linear economic optimization models. This is also the case in the present study. In the mathematical model we used, the configurations C represent different network designs, which, in diverse scenarios S and periods T, have to satisfy different demands of the markets M. The supplier factories F2 produce the pre-products P2 manufactured in the line L2 and in the node N2. The preproducts P2 yield in the end product P, which are produced in the factory F, node N and line L. Investments I can be made on the factories F, on the nodes N or on the lines L. The new

dimensions integrated to consider the environmental aspects are the monitored and measured emissions categories E, the mean of transport used U, the different electricity mix compositions Ele and the fuel and heating sources Ene. The input parameters to cover the environmental aspects include the following: _D OBL _ D E pIBL ,u , f , f 2,t , c E p , u , f , m , t , c

IBL/OBL distances covered [tkm]

EeLOG ,u , t , c

Emissions produced with the transport mean [kg emission/tkm]

C Eleele , f ,t , s , c

Electricity costs [EUR/MWh]

mix _ max mix _ min Eleele , f , t , s , c Eleele , f , t , s , c

Maximum/minimum share of electricity available [%]

MixMax Eleele , f ,t , s,c

Maximum amount of electricity available [MWh]

Emi Eleele , e,t ,c

Emissions per MJ electricity mix [kg emission/MWh]

Var Ele Fix f , t , c Ele p , l , n , f , t , c

Fix/variable electricity consumption [MWh]/[MWh/product unit]

C Eneene , f ,t , s, c

Costs of the heating/fuels [EUR/MWh]

Emi Eneene ,e,t ,c

Emissions e generated by the use of fuel/heating [kg emission/MWh]

Fix Var Eneene , f , t , c Eneene, p ,l , n, f ,t , c Fix/variable fuel/heating consumption [MWh]/[MWh/ product unit] Var EOeFix , f ,t ,c EOe , p , l , n , f , t , c

Other fixes/variables emissions generated [kg emissions]/[kg/product unit]

_T ECOered ,t , s , c

Reduction target for the emission e [%]

EeT,t_,sIn,c

Total emissions e when a purely economic optimization is made without considering any environmental aspect. [kg emissions]

EeT,s_,tV,c_ In

Total emissions e per end-product manufactured, when a purely economic optimization is made without considering any environmental aspect. [kg emissions/end-product unit]

Five sets of independent continuous decision variables include: _ fm logTV p ,l , n , f , m ,t , s , c

Amount of transported end-products [product unit]

_ ff log TV p , p 2 ,l ,l 2 ,n ,n 2 , f , f 2 ,t , s ,c

Amount of transported pre-products [product unit]

prod pPV,l ,n, f ,t ,s ,c

Amount of products manufactured [product unit]

prod lVarLS , n , f ,t , s ,c

Shortage of labour due to an exceeding of the production constraint for fixed labor [capacity units]

Buy Eleele , f ,t ,s ,c

Amount of electricity of type ele acquired [MJ]

Integrating the above environmental parameters to the economic ones and using the decision variables, two model objective functions are formulated in Equation (1) and (2). The first corresponds to the NVP maximization and the second to GWP minimization. v TTC TFixC DCVAtRe − DCVAtTOHC − DCVAtTVarC , s ,c − DCVAt ,s ,c − DCVAt ,c ,c , s ,c

∀c , t , s

(1)

In Equation (1), Term 1 represents the total revenues achieved for all products sales occurring in one period. These are calculated by multiplying the unit price per sale with the transported volume from the factories to the markets. Term 2 expresses the total transportation costs occurring in one period that correspond to the sum of the inbound (factory to factory) and outbound (factory to market) logistics costs. Term 3 corresponds to the total fix costs resulting from a configuration including the sum of fixed labour costs, material and production overhead, material costs and the fixed costs for starting a production on a line or modifying the line setup. Term 4 represents the total overhead costs occurring in one period. Those results from the addition of the overhead costs per period for common practices (e.g. heating,…), administration (e.g. controlling, facility management, catering,…), warehouses and consolidation centers, for infrastructure and the yearly costs for operating a node to the total overhead costs per period in the whole production network. Finally, Term 5 expresses the total variable costs which results from the addition of the corrected total variable labor costs with the sum of the costs for capital bound at factories, the variable costs of material, production overheads (e.g. lubricants), start-up and modifications of line setup and other costs multiplied by the produced volumes.

∑ (E

Pr od _ T _ Out e ,t ,s ,c

_ T _ Out _ T _ Out ) + EeIBL + EeOBL ,t ,s ,c ,t ,s ,c

∀c, t, s

e

In Equation (2), Term 1 represents the total production emissions resulting from the addition of the total emissions by electricity consumption to the total emissions by energy consumption and other emissions. In fact, each production plant has a different composition of the energy mix, fix and variable energy consumptions. Term 2 expresses the total inbound logistics emissions that results from the multiplication of the distance realized with each transport mean with the emissions per transport mean and ton kilometer of the transported goods from factory to factory. Term 3 represents the total outbound logistics emissions, which results from the multiplication of the distance realized with each transport mean with the emissions per transport mean, and ton kilometer of the transported goods to the markets. Equations (1) and (2) are subject to the following constraints: first, as many pre-products can go in a product as many are necessary for its production; second, the amount of goods

(2)

produced must satisfy the amount of goods transported in the transport relations; third, the capacity consumption by the production of products cannot exceed the maximum capacity of the lines; fourth, the given demand must be fulfilled; fifth, the total production volume capacity for a given product in a line cannot be exceeded; sixth, the compliance of the minimum production quantity of products in a line must be ensured; seventh, the shortage of labor due to an exceeding of the production constraint for fixed labor has to be larger or equal to the variable labor costs corrected inner term minus the labor capacity. Five additional constraints were integrate for the environmental aspects: Fulfilment of the electricity need for the production

∑ Ele

Buy ele , f ,t , s ,c

_f ≥ Ele Cons f ,c , s ,t

∀f , t , s, c

(3)

∀f , t , ele, s, c

(4)

ele

Electricity maximum capacity Buy MixMax Eleele , f ,t , s ,c ≤ Eleele , f ,t , s ,c

Electricity maximum and minimum share of each electricity mix type Buy Max Eleele , f ,t ,s ,c ≤ Ele ele , f ,t ,s ,c

Buy Min ∀f , t , ele, s, c , Eleele , f ,t ,s ,c ≥ Ele ele , f ,t ,s ,c

∀f , t , ele, s, c

(5,6)

E-Constraint – Emissions EeT,t_,sOut ,c ≤

(

_T EsT,e_,cIn,t ⋅ 100− ECOsred ,e,c,t

100

)

∀e, t, s, c

Equation (7) is a constraint specific to the economic optimization with reduction targets. It states that the total emissions per period have to be smaller or equal to the emissions on the reference year reduced by the percentage reduction target.

4.3. Model implementation We propose a numerical application of the green supply chain optimization model based on an example in the automotive industry. The network of the case study firm, a German automotive manufacturer, is based on the following assumptions: four production plants already exist and are located in Central Europe, Eastern Europe, Central America and Asia. The total production of eight product variants (p1 to p8) in those plants is already scheduled between 2016 and 2019. The production of 11 new product variants (p9 to p19) has to be added to this schedule. For that, the use of three additional production plants located in North America, South America and Africa is being considered. The production has to meet demand

(7)

in Europe, North America, South America, China and the Rest of the World. All seven production plants have global and local suppliers: Those located in Europe supply the engine, the front and rear axle, and the gear box; the others are located near each production plant and supply all other remaining parts. The product variants differ by series, model, engine, wheel type, and the body shape. To investigate how resources should best be allocated in order to reduce the environmental impact and at the same time maximize the economic performance, we implemented and compared three different modelling approaches based on the parameters, decisions variables, objective functions and constraints presented in Section 4.2 above: first, we applied a linear economic optimization for the maximization of the NPV of the profit – Equation (1). Second, to understand the maximum environmental optimization potential, we used a linear environmental optimization for the minimization of the emissions – Equation (2). Finally, to deeper understand the trade-offs between the environmental and the economic objectives, e-constraint, a multi-objective optimization, was applied to the economic optimization with environmental impact reduction targets – Equation (1) with additionally the constraint (7). Following Dale et al. (2004), we assume an increase of 5% of the variable costs when wind power is used instead of the other energy sources. To avoid the bias caused by the bell-shaped demand, the emissions as well as the market demand and the production costs are fixed to the 2020 levels. In the optimization, we use the unconstrained configuration and we test two networks with four and seven production plants.

4.4. Model verification and validation An important concern for the developers and the users of mathematical models is the quantification of the simulation’s confidence through verification and validation. Verification ensures that the conceptual description and the solution of the model are implemented correctly and reflect the real world. In the model validation, the computational simulation is compared with empirical data, and the accuracy of a the simulation is evaluated (Oberkampf et al. 2004). We conducted verification by applying the optimization to a simplified production flow. Here we checked that the model predictions are consistent with the data. Each equation was checked and the discounted value added (DCVA) calculations were verified by the controlling department of the automotive manufacturer. Moreover, sensitivity analyses were implemented on model parameters and inputs. The validity of the model was verified through experimental analysis (Shah et al. 2007). The model numbers are reasonably close to actual numbers and historical data. For that, inputs from all members of the supply chain design team were necessary (Bassett and Gardner 2010). The emissions output were

compared with the life cycle analysis results of the environmental statements and with the internal data of the energy reports. The results were checked by the experts.

5.

Results, discussion and implications

As described above, our results are based on a case study in the German automotive industry and focuses on the optimizing of the energy mix which is based on the emissions of the supply chain, i.e., we show the results of the production volume allocation and energy mix (not additionally the results of the production volume allocation alone). Figure 3 depicts the trade-off between the variable costs and the reduction of the GWP in relation to the corresponding change in the volume allocation. Hereby, results of the volume allocation and energy mix optimization show that by optimizing the energy strategy, the emissions of the supply chain can be reduced by 30% at almost zero variable cost increase.

60%

GWP reduction [%]

50%

40%

30%

20%

10%

0% 0%

5%

10%

15%

20%

25%

30%

35%

Variable costs increase [%]

Figure 3. Trade-off solutions of economic and ecological optimization with seven production plants. A summary on the maximal relative reduction of the production and transport emissions as well as their GWP reduction, the costs involved and the volume allocation change based on four production plants is provided in Table 1.

Production

Absolute GWP reduction 28%

Absolute NPV variable costs increase 0.05%

Corresponding volume allocation change 0%

No changes

IBL

4%

2%

13%

OBL

23%

34%

67%

Total

55%

36%

80%

Central America
Eastern Europe Central America
Central Europe Eastern Europe
Central Europe

Table 1. Absolute GWP reduction and corresponding costs increase and volumes allocation changes (%). Looking at the NPV of variable costs of our case study firm, the economic and the environmental objectives appear to be in conflict. The first part of the impact reduction is achieved by greening the energy mix. This increases costs only slightly as the energy accounts for only 1% to 2% of the total costs. GWP reduction is further achieved through a decrease of the IBL/OBL emissions. The allocation of the production volumes is kept constant and the acquisition of wind electricity decreases production emissions. The IBL emissions are reduced by moving production from Central America to Eastern Europe. Finally production in Eastern Europe is moved to Central Europe where there are lower OBL emissions. Looking at the effect of the energy mix, it appears that the environmental impact can be decreased by 30% by acquiring wind-produced electricity. Of this, the first 20% is achieved by greening the energy mix in Asia, which is the plant with the highest emissions per MWh electricity consumed (a large share of electricity is produced from coal). In the other production plants, wind-produced electricity is acquired. The nuclear electricity is not replaced because it does not involve high CO2 equivalent emissions. Results indicate that the environmental impact decreases always increase the variable costs, however, depending on how the emissions reduction is done, the costs are of significant different magnitudes. Considering the energy mix composition additionally and the volume allocation, the “environmental optimal solution” consists in producing locally, as close as possible to the target markets. The analyses highlight that the differences in production emissions among the production plants do influence the optimization results. These differences are determined by the energy mix composition and the variable energy consumption. The production emissions can therefore be decreased by improved energy efficiency and changes in the energy mix composition. Based on the results and our findings, we derive several of managerial implications. Focus on the production emissions. Although the main impact contribution is caused by logistics, the first priority for future investments should be the reduction of production emissions as the costs involved are much lower. The production emissions can be largely reduced by improving the energy efficiency in all production plants and by going green with the energy mix. All plants should be held to Europe’s consumption levels and standards. From

an environmental perspective this entails the highest reduction in emissions. This could also bring a significant boost to productivity within the industry (Worrell et al. 2003) and improve the firm’s attractiveness to investors. Moreover, the field of energy is being increasingly regulated (Denne and Bond-Smith 2010) since governments consider energy efficiency a critical feature of sustainability (Hanley et al. 2009). Results show that when the acquisition of renewable electricity is left flexible, it is possible to achieve a higher decrease in environmental impact with a low increase in cost. The energy mix seems therefore to be a powerful means of emissions reduction. For example, the BMW manufacturing plant in Greer, South Carolina, is already getting more than 60% of its electricity from gas siphoned from a nearby landfill. This project is already saving the firm $2 million annually (BMW 2012). Focus on the reduction of the CO2 emissions, the other factors will follow. Our results show that the GWP is highly correlated with the other measured impact categories (e.g. acidification potential, abiotic depletion potential, eutrophication potential and photochemical oxidation). Therefore it constitutes a good measure for the production and logistics emissions. This is an important finding for the strategic network planners because it allows to decrease the number of variables and simplify the optimization model. Instead of including all the emissions categories in the optimization model, the GWP can be taken as reference as the other emissions categories will follow. Go local with sourcing and production. After decreasing the production emissions by improving the energy efficiency and going green with the energy mix, local sourcing should be enhanced. We found that IBL emissions have strong impact. When looking only at the volume allocation, the production was centralized in Central Europe to cut the IBL emissions in half. When considering the energy mix, the inbound logistics appeared to be the second best approach. The final step is going local with the production to decrease OBL emissions. For that the production plants must be close to the markets with the highest demand and have a high manufacturing flexibility, which means the ability to assemble different product variants. Many industries make impact driver consistent efforts to go in this direction because cost reduction and value increases can lead to improved profitability (Brylawski 1999; Chandra et al. 2005). The risk can be reduced, especially for firms facing inelastic demand (De Meza and Van der Ploeg 1987). To benefit from flexible manufacturing practices, high performance has to be ensured (Zhang et al. 2003), for instance, with modular product platforms which allow building many variations of final products while controlling the internal complexity (Ericsson and Erixon 1999). Many successful applications are found in

the industry. For example, on the same platform, Volkswagen produces the VW New Beetle, Jetta, and Golf, Audi A3 and TT, and two European models (Brylawski 1999).

6.

Conclusion

The results of this study highlight that by optimizing the energy mix, the emissions of the supply chain can be reduced by 30% at almost zero cost increase. Second, with the inclusion of regional specifications on energy efficiency and energy mix composition, we investigate a new dimension and environmental management strategy compared to previous research (Hugo and Pistikopoulos 2005; Jamshidi et al. 2012). Furthermore, the implementation of a practical case allows us to show that energy efficiency and sourcing strategies are the best solution, especially for the short-term investment decisions and for the production networks that are already in place. Finally, findings support the argument about the power of LCAbased approaches. They have been defined in the ISO norms and have already been implemented, especially for green design (Bare and Gloria 2006; Pennington et al. 2004; Rebitzer et al. 2004). Dealing with environmental impact allows for comprehending and modelling product-related impacts (Seuring 2012). In our effort to provide insights into GSCM, we faced several limitations. First, from the mathematical model perspective one important limitation is the use of classical methods for solving the multi-objective optimizations. The use of evolutionary algorithms for the multiobjective optimization is suitable. Moreover stochastic modelling should be implemented for the analysis of the influence of the volatility of energy and electricity price on the network planning process. Second, from an environmental perspective, the most relevant limitation is the definition of the boundaries, as only in- and outbound logistics and the manufacturing of the end product were included. Also, assumptions for the input data were necessary: Data concerning the energy mix composition were available only at the national level and for emissions only at a continental level. To mitigate those problems, the unit of analysis (Figure 2) should be enlarged to include the emissions for the production of the sub-products and for the extraction of the raw materials. Moreover, the emissions generated when enlarging the capacity and constructing new plants or when investing in renewables should be integrated. Third, from the economic perspective an important omission are fixed costs. Finally, nonquantifiable factors such as the benefits on the firm’s image because of going green were not included (Natarajan et al., 2006). For that, it would be worthwhile to pay closer attention to the influence of environmental policies and emissions taxes. Notwithstanding these

limitations, our research should stimulate future research into new factors influencing GSCN design, optimization and operation.

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