The structure of a company's distribution network is of vital importance for competitiveness but also involves

considerable costs. In recent years, competitive pressure as well as regulatory measures, especially in the European

Union, have also raised awareness towards the environmental impact of supply chain activities. However, activities associated with the distribution of products are not yet subject to environmental regulations but this might change in the near future. Therefore, companies will have to consider not only economic but also environmental aspects in the design of their supply chains. Based on a case study from the petrochemical industry we present a way to evaluate (strategic) distribution network design decisions, taking into account economic as well as environmental criteria. The results of the analysis show a clear trade-off between (distribution) costs and transport carbon emissions. (author's abstract

Jammernegg, Werner

Treitl, Stefan

English

Elektronische Publikationen der Wirtschaftsuniversität Wien

THE ECONOMIC AND ENVIRONMENTAL ANALYSIS OF APETROCHEMICAL DISTRIBUTION NETWORKStefan Treitl, Werner JammerneggDepartment of Information Systems and Operations,WU Vienna University of Economics and Business, Nordbergstrasse 15, 1090 Vienna, Austriastefan.treitl@wu.ac.at, werner.jammernegg@wu.ac.atKeywords: Distribution network design, transport carbon emissions, case study.Abstract: The structure of a company’s distribution network is of vital importance for competitiveness but also involvesconsiderable costs. In recent years, competitive pressure as well as regulatory measures, especially in the Euro-pean Union, have also raised awareness towards the environmental impact of supply chain activities. However,activities associated with the distribution of products are not yet subject to environmental regulations but thismight change in the near future. Therefore, companies will have to consider not only economic but also envi-ronmental aspects in the design of their supply chains. Based on a case study from the petrochemical industrywe present a way to evaluate (strategic) distribution network design decisions, taking into account economicas well as environmental criteria. The results of the analysis show a clear trade-off between (distribution) costsand transport carbon emissions.1 INTRODUCTIONDistribution comprises all processes that are neces-sary to move and store products on their way fromthe production stage to the customer stage, thereforeconsisting of transportation and warehousing activi-ties (Chopra and Meindl, 2010). The structure of acompany’s distribution network is of vital importanceas it provides the basis for a high customer servicebut also involves considerable costs. In particular, de-cisions concerning the number, location, and capac-ity of warehouses as well as the allocation of demandare of high relevance in this regard. These decisionsare usually driven by companies’ urge to gain com-petitive advantage through reduced costs or improvedlead times (Aronsson and Huge-Brodin, 2006). How-ever, the growing concerns of both governments andcustomers about environmental protection have alsoraised awareness towards the environmental impactof supply chain activities. There are already severalregulations in force that aim at the reduction of emis-sions from manufacturing activities, for example theEU Emission Trading Scheme. Yet, activities associ-ated with the distribution of products are not subjectto strict regulations with respect to carbon emissions.One of the industries that is included in the EUEmission Trading Scheme is the petrochemical indus-try. The process of refining crude oil to final products,for instance gasoline, diesel, or other derivatives likeheating oil or lubricant, contributes to climate changeto a large extent. In order to tackle the tight regula-tions, companies have focused on implementing tech-nological innovations to reduce the environmental im-pact of refinery processes without negatively affect-ing production costs. As a matter of fact, the topicsof distribution network optimization as well as othersupply chain related issues have been neglected for along time by the petrochemical industry (McKinnon,2004). Only recently companies realized this signifi-cant potential for reducing costs (McKinnon, 2005),which has led to several changes in the distribu-tion network of some well known companies. Thesechanges often affect the number of warehousing loca-tions. As a consequence of the restructuring, also therespective primary transport flows from the refineriesto the warehouses as well as secondary transport flowsfrom warehouses to the final customers are affected.These distribution network design decisions are usu-ally based on economic criteria (mostly minimizationof costs) but environmental aspects, in particular car-bon emissions from transportation, should not be ne-glected either.Based on a case study with one of the leadingpetrochemical companies in Southeastern Europe, wepresent a way to combine economic as well as en-vironmental aspects when evaluating (strategic) dis-tribution network design decisions. The company inscope aims at restructuring its distribution networkwith special focus on reducing the number of localstorage locations. For that purpose, we develop aneasy-to-use supply chain model that shows the im-pact of different network scenarios on operational andtransportation costs as well as on transport carbonemissions.2 SUPPLY CHAIN STRUCTUREAND MODELING APPROACHThe company under consideration receives crude oilfrom three different supply sources. In all cases,the crude oil is transported to one operating refineryvia pipeline. Only a negligible share is transportedby train. In the refinery, the crude oil is processedinto several final products. We focus our analysis onfuel, making no differentiation between gasoline anddiesel. After processing, the fuel is delivered to 21storage locations spread across the country in accor-dance to the demand in the surrounding area. This socalled primary transport is mainly carried out by rail;only one storage location possesses a direct connec-tion to the refinery via pipeline. From the storage lo-cations the fuel is further moved to filling stations, lo-cated in 276 cities across the country. The transporta-tion from storage locations to filling stations (sec-ondary transport) is carried out by truck only. It isof importance for the company to supply each fillingstation from the closest storage location. Concern-ing transportation to the customers the company aimsat minimizing lead time from the storage locations,which can be achieved by the spread of storage lo-cations across the whole country. By pursuing thisstrategy the company can achieve short (secondary)transport distances and a high service level at the sametime. However, this comes at the expense of highcosts for primary transport as well as for operatingthe storage locations.By reducing the number of storage locations thecompany tries to reduce total annual distribution costswithout any negative impacts on lead time. Totaldistribution costs consist of primary transport costs,fixed and variable costs of operating the storage loca-tions and secondary transport costs. At the same timethe environmental effects of these structural changesin terms of transport carbon emissions should be as-sessed. In order to support decision-making, we de-veloped a tool for calculating these costs and the cor-responding transport emissions. We wanted to makethe application of the tool self-explanatory so that pa-rameters can be changed conveniently and quickly.For that purpose, we decided to use the spreadsheetapplication Microsoft Excel together with its VisualBasic for Applications (VBA) environment. All nec-essary data was provided by the company for a repre-sentative year.The model is static and deterministic; the coveredtime horizon is one year. In a first step, the user ofthe tool selects the storage locations that should bein operation. We assume that there occur no costsfor the closure of storage locations. By letting theuser select the locations, actual expert knowledge canbe linked with the optimization procedure of the tool.Based on the decision which storage locations are inoperation, the total annual distribution costs of thenetwork and carbon emissions from transport activ-ities are calculated automatically. If some of the stor-age locations in operation are planned to undergo cer-tain steps of revamping in the future, also nonrecur-ring investment costs are depicted. The calculationminimizes secondary transport costs by assigning thewhole demand for each city to the closest storagelocation in operation. By doing so, the amount offuel that must be held in stock at each storage loca-tion and the corresponding primary transport costs aswell as operational costs are determined for each stor-age location. Given the amount and distance the fueltravels, transport activities are calculated in terms oftonne-km for road and rail transportation. The to-tal transport emissions in terms of CO2 equivalent(CO2e) emissions are determined for each mode oftransport using conversion factors (based on assump-tions about load factors, empty runs, truck types, etc.).This activity-based approach for assessing transportcarbon emissions yields good estimations in the ab-sence of detailed process data and is well suited forthe (petro-)chemical industry (McKinnon and Piecyk,2010). The relevant conversion factors were pro-vided by the Finnish Environmental Institute (SYKE,http://www.ymparisto.fi/).3 ANALYSIS AND RESULTSIn the analysis several scenarios were consideredwhich differ only concerning the number of storagelocations in operation. Together with the companywe decided upon which storage locations should bein operation for each scenario. The Baseline scenariogives an overview of the current situation and servesas a reference point for comparison. In this scenario,all 21 storage locations are in operation, whereas inthe other scenarios the number of operating storagelocations is decreased to 17 (Scenario 2), eight (Sce-nario 3), six (Scenario 4) and three (Scenario 5). Froma modeling point of view even a network with onlyone storage location would be feasible, since no ca-pacity restrictions are included in the model. How-ever, according to the company this is not possible inreality for several reasons like, for instance, too highinvestment costs or the lack of possible alternatives incase of accidents or breakdowns. The economic andenvironmental results of the analysis, relative to theBaseline scenario, are summarized in Table 1.It turns out that a scenario with six storage lo-cations in operation leads to minimal total distribu-tion costs. Obviously, reducing the number of stor-age locations results in a decrease in operational costs,mainly due to the elimination of fixed costs. Primarytransport cost decrease only moderately, also whenswitching from 21 to six storage locations. This canbe explained by the geographical location of the op-erating storage locations which are strategically lo-cated, equally dispersed over the whole country. Bydoing so, the demanded lead time of two days max-imum can still be fulfilled. Only in Scenario 5 withthree storage locations primary transport costs dropsignificantly. The main reason for this is the selectedstorages being located quite close to each other. Fur-thermore, the major part of demand in this scenariois fulfilled from the storage location with direct andcheap pipeline supply from the refinery. Thus, pri-mary transport costs are at a minimum. Secondarytransport costs increase when the number of storagelocations decreases, since the average distance on theroad from one storage location to the supplied citiesincreases with decreasing number of operating stor-age locations. In the different scenarios the averagedistance on the road ranges from 72 km (Baseline) to239 km (Scenario 5). In Scenario 5 the reduction inoperational and primary transport costs is outweighedby the drastic increase in secondary transport costs.Therefore, it is generally not possible to say that a re-duction of operating storage locations always leads toa reduction in total distribution costs.Overall transport emissions in the distribution net-work highly depend on the amount of truck trans-portation. This is due to truck transportation beingby far more carbon intensive than rail transportation.Although the Baseline scenario with 21 storage loca-tions leads to the highest costs, this network structureresults in minimal emissions. This can be explainedby the importance of rail transport and the compara-tively low amount of road transport that goes hand inhand with this network structure. As primary trans-port decreases and secondary transport increases, to-tal emissions of the network also increase and reachtheir maximum when there are only three storage lo-cations in operation. This highly centralized networkconsequently necessitates lots of truck transportation.Compared to the Baseline situation, total distributioncosts can be reduced by approximately 7.7% whiletransport emissions are increased by more than 29%in the cost optimal scenario with six storage locationsin operation.Especially in the European Union regulations forcarbon emissions from transportation are more andmore under discussion. Several options are consid-ered, like imposing a tax on carbon emissions or in-tegrating transport emissions into the already existingEU Emission Trading Scheme. When assuming costsfor carbon emissions, a certain amount of money mustbe paid for each ton emitted by transport activities. Ina short analysis we compared the Baseline scenariowith maximal costs but minimal emissions to Sce-nario 4 with six storage locations. It turns out that(ceteris paribus) the costs for one ton of CO2e emis-sions would have to be approximately 480 Euro in or-der for these two scenarios to incur the same total dis-tribution costs. Given the fact that currently the costsfor one ton of carbon emissions are approximately 16Euro (on March 28, 2011), the impact of carbon costsin this particular case study can be questioned. How-ever, since this case study cannot be seen as an appro-priate generalization over different industries, it is ofgreat importance for future works to observe the on-going discussions and analyze the impacts of possiblepolicy regulations on a broader scale.4 CONCLUSIONSBased on an actual case study we showed the eco-nomic and environmental impacts of altering thenumber of storage locations in a petrochemical dis-tribution network. It turns out that total distributioncosts, first, decrease with the number of storage lo-cations because of a reduction in fixed costs and pri-mary transport costs. However, in a highly centralizednetwork increased secondary transport costs compen-sate the reduction in operational and primary transportcosts, resulting in again increasing distribution costs.For the company in consideration a network designwith six storage locations in operation (Scenario 4) ismost likely to be realized in the near future. Throughmajor revamping actions at the storage locations andthrough equipping them with up-to-date technology,Table 1: Economic and environmental results of scenario analysis.Baseline Sc. 2 Sc. 3 Sc. 4 Sc. 5Number of storage locations 21 17 8 6 3Operational costs 100 86.6 71.7 59.0 39.8Primary transport costs 100 100.4 98.2 94.2 48.9Secondary transport costs 100 102.3 126.6 141.1 233.2Total distribution costs 100 95.0 95.0 92.3 93.5Primary emissions 100 98.6 93.1 90.6 35.7Secondary emissions 100 106.5 161.5 192.9 385.2Total emissions 100 101.6 119.1 129.4 168.2the coordination between the locations shall be im-proved and warehousing costs shall be reduced, tak-ing advantage of scale economies. Although the neg-ative impact of this scenario on carbon emission isapparent, environmental criteria do not play any rolein the decision-making in this particular case. Severalreasons for this can be considered, however the mostimportant one is the fact that transport emissions donot have negative financial consequences for the com-pany at the moment. As long as there are no furthercosts for transport emissions, the increase in emis-sions will be compensated by the reduction of oper-ational and primary transport costs.The analysis shows a clear trade-off between costsand carbon emissions. Minimal costs are achievedwith only a small number of operating storage loca-tions, but this drastically increases carbon emissions.In contrast to this, minimal emissions are achieved ina highly decentralized distribution network, but onlyat very high costs. It is interesting to see, however,that in this case study a deviation from the cost opti-mal situation does not lead to drastic changes in totaldistribution costs. For example, when switching froma network with six storage locations (Scenario 4) toa network with 8 storage locations in operation (Sce-nario 3), total distribution costs increase only slightlycompared to the cost optimal design, but notable im-provements in total carbon emissions can be achieved.Therefore, further analyses concerning the sensitivityof distribution network optimization results with re-spect to costs and carbon emissions are mandatory.REFERENCESAronsson, H. and Huge-Brodin, M. (2006). The en-vironmental impact of changing logistics structures.The International Journal of Logistics Management,17(3):394–415.Chopra, S. and Meindl, P. (2010). Supply Chain Manage-ment - Strategy, Planning, and Operation. Pearson,Upper Saddle River, 4th edition.McKinnon, A. (2004). Supply chain excellence in the Eu-ropean chemical industry. Heriot-Watt University, Ed-inburgh.McKinnon, A. (2005). European chemical supply-chain:Threats and opportunities. Logistics and TransportFocus, 7(3):30–35.McKinnon, A. and Piecyk, M. (2010). Measuring and man-aging CO2 emissions in European chemical transport.Heriot-Watt University, Edinburgh.

The economic and environmental analysis of a petrochemical distribution network

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