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A customer oriented approach to warehouse network evaluation and design

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A customer oriented approach to warehouse network evaluation and design
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  * Corresponding author. # 358 53 6212 652; fax: # 358 536212 699; e-mail: antti.lehmusvaara @ lut.fiInt. J. Production Economics 59 (1999) 135 —  146 A customer oriented approach to warehouse network evaluationand design Jukka Korpela  , Antti Lehmusvaara   *  UPM-Kymmene Oy FIN-53200 Lappeenranta, Finland    Lappeenranta Uni v ersity of Technology, Department of Industrial Engineering and Management, P.O. Box 20, FIN-53851 Lappeenranta, Finland  Abstract Cost or profit based optimisation with capacity restrictions is the most widely used method for distribution networkdesign. This approach is based on production or company oriented logistics thinking. However, in the presentcompetitive business environment, a more customer driven and holistic approach to supply chain management isrequired. In this paper, the focus is on warehouse network evaluation and design. The aim is to present a customeroriented approach to the evaluation and selection of alternative warehouse operators. The Analytic Hierarchy Process(AHP) is used for analysing the customer-specific requirements for logistics service and for evaluating the alternativewarehouse operators. The AHP-based analysis results in a customer-specific priority for each alternative warehouseoperator. This priority describes how well a certain warehouse operator is expected to satisfy a certain customer’sperformance requirements. The priorities are then entered to a Mixed Integer Linear Programming (MILP) -modelwhich is used for maximising the overall service performance of the warehouse network under relevant restrictions. Thus,the warehouse network can be designed based on multiple quantitative and qualitative criteria instead of just costs orprofits alone.    1999 Elsevier Science B.V. All rights reserved.  Keywords:  Analytic hierarchy process; Warehouse network evaluation 1. Introduction The distribution center or warehouse locationproblem is a strategic level network design problem[1]. This means that the nature of the decision islong-term and the influence of the warehouse loca-tion decision on the profitability of the companywill last for years. This partly explains the greatinterest of practitioners and scientists on the issue.Partly the same techniques and frameworks havebeen used for warehouse and plant location prob-lems, although qualitative elements like businessculture, financial subsidies and professional staff have a greater influence in the plant locationproblem. Because the same techniques are utilized, 0925-5273/99/$-see front matter    1999 Elsevier Science B.V. All rights reserved.PII: S0 9 2 5 -5 27 3 (9 8 ) 00 0 9 6- 6  these problems are often included in the facilitylocation problem. For the basic theory concern-ing facility location problems, see e.g. Francis et al.[2].Today, it is rather common practice for com-panies to outsource distribution logistics functionsat least to some extent. Thus, ensuring that thedistribution network achieves the requested perfor-mance level calls for analytical approaches forevaluating the alternative third party logisticsservice providers. Costs are often used as themajor factor in selecting the service providers,whereas enough attention is not paid to the variousquantitative and qualitative customer serviceelements.In Section 2 of this paper, we discuss the benefitsand problems related to logistics modelling. In Sec-tion 3, some approaches to supply chain optimisa-tion are presented. In Section 4, we present anapproach to warehouse network evaluation anddesign which is based on the integrated utilisationof the Analytic Hierarchy Process and Mixed Inte-ger Linear Programming. 2. On the benefits and difficulties in logisticsmodelling 2.1. The utilization of mathematical models inlogistics network design In logistics literature, many practically orientedpapers on the optimisation of logistics have beenpublished since 1990. This is due to the improvedpower of the mathematical tools, improved avail-ability of the basic data resources in companies,and managers’ increased interest on utilizing theresults of mathematical models. For exampleBaunack et al. [3] have predicted that in the case of Germany’s construction industry the opening of two main depots and the closure of five sub-depotswill increase the turnover of their case company by5% and the profit by 20%. In a paper by van denBruggen et al. [4] which deals with depot locationand the client assignment to depots for a large oilcompany,the simulation study pointed out that thetotal logistics cost savings were in the range of 5 —  6% per year. These two examples give an idea of the potential savings through optimisation andsimulation tools on logistics. 2.2. On problems of implementing logistics network design models From some published papers we may concludethat there have been problems in implementing theresults of logistics models in reality. For example,according to Mourits et al., many logistics supportsystems have the following shortcomings [5]:   they focus on a subset of activities contained inthe supply chain.   they focus only on a subset of problems relatedto the development of distribution networks or   they are difficult to apply to real cases due to thelarge data requirements.Mourits et al. have even stated that the availablesupport systems do not facilitate a coherent ap-proach to all issues involved in the development of large-scale distribution networks.LeMay et al., added to the problems of imple-menting logistics systems the poor understandingof logistics functions by system designers,accountants and top managers [6]. Solving thisproblem of poor understanding requires above allseveral negotiations and coordination betweenlogistics specialists. 3. Different approachesto supply chain optimisation 3.1. The traditional approach In the traditional supply chain network designproblem the focus has been on achieving the min-imum cost or maximum profit within given restric-tions. This is mainly a consequence of the highstrategic importance of the network structure forthe delivering company which wants to achieve thebest possible profit. Normally, companies deter-mine the logistics structure, e.g. warehouse loca-tion, based on costs or profits by implementingquantitative elements as restrictions like minimumtransportation time or distance, minimum safety 136  J. Korpela, A. Lehmus v aara /   Int. J. Production Economics 59 (1999) 135 —  146   stock requirement or minimum delivery frequency.The values of these restrictions can be rated frominternal knowledge of the customers’ behaviour orfrom performed customer surveys. However, due tothe qualitative nature of these restrictions, they arevery seldom taken into account in the objectivevalue itself. According to Tampoe et al., untilrecently, most models used quantitative data andmathematical relationships, which limits the abilityof the model to mimic reality [7].In order to fully support the analysis of theperformance of an integrated logistics chain, thetraditional approach, in which the model is thestarting point of the analysis and in which the datastructure is derived from the model, is insufficient.In integrated logistics chain modeling the choice of suitable models should be determined by the in-formation structure required and by the informa-tion technology available [8]. This means that themodel designer must have knowledge on both theoperational rules and tactics and on the infor-mation systems (data resources) of the targetcompany. 3.2. Ser  v ice- sensitive location problems Ho and Perl have defined the Service-sensitiveWarehouse Location Problem (SSWLP) as theproblem of determining the number and locationsof warehouses, and the allocation of markets to theestablished warehouses, so as to maximize totalprofit [9]. In the SSWLP, according to Ho andPerl, the demand generated by each market is de-pendent on two customer service elements: productavailability and order cycle time. To product avail-ability they have given a pre-specified minimumlevel, which cannot be exceeded at any warehouse,and to each market they have a maximum allow-able order cycle time. They handle these two ele-ments, product availability and order cycle time, asquantitative elements and exclude other qualitativeaspects.From a customers’point of viewthis canbeconsidered as a shortcoming. Nevertheless, theformulation of the SSWLP is a step towards amore customer oriented, holistic and integratedlogistics approach compared to the traditionalapproach. 4. An integrated approach to warehouse networkevaluation and design 4.1. Proposed approach In this paper, we propose an approach to evalu-ate and design a warehouse network for a companybased on integrating the Analytic Hierarchy Pro-cess (AHP) and Mixed Integer Linear Program-ming (MILP). The basic premises for the proposedapproach are the following: (1) warehousing activ-ities have been outsourced, i.e. only  “ third party ” warehouse operators are used, and (2) this ap-proach is preceded by an analysis in order to definethe best potential locations for the warehouses, todetermine the feasible alternative warehouse oper-ators and to gather extensive information on them.Thus, the objective for the proposed approach is toassist in deciding which warehouse operators of thefeasible alternatives will be included in the distribu-tion network of a company. By incorporating theAHP in the process, the individual customer’s re-quirements and preferences for logistics service canbe analysed and prioritised, and the alternativewarehouse operators can be analysed in a cus-tomer-focused manner. By using the results of theAHP-analysis, i.e. the priorities for each alternativeoperator from each customers’ point of view, as thebasis for the MILP-optimisation, the warehousenetwork can be designed based on all relevantcustomer service elements instead of the costsonly.The warehouse network design process is a com-plex decision problem involving numerous quantit-ative and qualitative elements, and full informationconcerning these elements is not always available.In order to reach the decision under these circum-stances, the available objective information has tobe combined with subjective judgments. Therefore,the AHP is a suitable tool for assisting this decisionprocess by allowing the utilisation of both subjec-tive judgments and objective information.The proposed approach is presented in Fig. 1,and it consists of the following steps: 4.1.1. Preliminary analysis The first phase of the approach involves e.g.stating the objectives for the warehouse network  J. Korpela, A. Lehmus v aara /   Int. J. Production Economics 59 (1999) 135 —  146   137  Fig. 1. The proposed approach for warehouse network evalu-ation and design. design problem, determining the best potentiallocationsforthe warehouses,and definingthe alter-native warehouse operators on the locations andgathering and analysing information concerning allaspects of their operations. The information can begathered e.g. through direct visits to and meetingswith the alternative warehouse operators. 4.1.2. De  fi ning the  fi nal e v aluation problem This phase involves defining the alternativewarehouse operators among which the final selec-tion will be made. The most feasible alternativewarehouse operators are defined based on the in-formation gathered in the previous phase, i.e. thewarehouse operators that do not satisfy the basicrequirements set on e.g. customer service and costlevel are eliminated from the final evaluation. 4.1.3. The AHP- based analysis The following steps are included in the thirdphase: (1) representatives of each customer affectedby the decision define the criteria they use foranalysing the alternative warehouse operators, anddetermine their requirements concerning eachcriterion, (2) the criteria are structured into acustomer-specific AHP-hierarchy, and (3) the rep-resentatives of each customer derive priorities forthe criteria and the corresponding requirements.The structured AHP-models are then used foranalysing the alternative warehouse operators, re-sulting in a preference priority for each alternativewarehouse operator from each customer’s point of view. In the proposed approach, this preferencepriority is labelled  “ customer satisfaction priority ” as it measures the overall estimated satisfactionprovided by a certain warehouse operator. As therepresentatives of the customers normally do nothave direct information or knowledge concerningthe alternative warehouse operators, the represen-tatives of the company conductingthe analysis helpthem to evaluate the warehouse operators.Notably, the analysis of the warehouse operatorsis based on both factual information and subjective judgements, which is allowed for in the AHP-method. 4.1.4. MILP- based optimisation MILP-based optimisation is based on maxi-mising customer satisfaction while taking intoaccount the relevant constraints. Instead of costs,customersatisfactionpriorities are used as the basisfor the optimisation, i.e. the objective function is tomaximise the overall customer satisfaction pro-vided by the warehouse network under the definedconstraints (such as capacity limits). 4.1.5. Implementation and follow- up After implementing the decided warehouse net-work in practice, the AHP-models can be used tosupport periodical reviews of the actual perfor-mance of the warehousenetwork. The MILP-basedoptimisation model can then be used to reviewthe overall warehouse network. New alternativewarehouses can also be included very easily in theanalysis. 138  J. Korpela, A. Lehmus v aara /   Int. J. Production Economics 59 (1999) 135 —  146   Fig. 2. The logistics network of corporation A. In this paper, we extend the work by Korpelaand Tuominen [10] in which they used the AHPand benefit/cost-analysis for evaluating and select-ing the warehouse operators. For earlier work onthe utilisation of the AHP for the site selectionproblem, see also e.g. Hegde and Tadikamalla [11].The AHP and linear programming have earlierbeen used together by e.g. Liberatore [12] for se-lecting R and D projects, by Korhonen andWallenius [13]for formulatinga marketing strategy,by Gass [14] for large-scale personnel planningmodels and by Olson et al. [15] for an exportplanning model for a developing country.We demonstrate the utilisation of the proposedapproach by a numerical, illustrative example. 4.2. Preliminary analysis and de  fi ning the  fi nal e v aluation problem The logistics network of process industry cor-poration A is shown in Fig. 2. The corporationoperates worldwide, but most of the corporation’sproduction and customers are located in Europe.The main flow of the corporation’s products isexported via loading and destination ports to mar-ket area warehouses, from where the products aredelivered to the customers. The corporation is nowrestructuring the warehouse network. The poten-tial locations for the warehouses have been definedand the potential warehouse operators have beenmapped and analysed.Based on the gathered information, the objectivenow is to determine which third party warehouseoperators will be selected. In this illustrativeexample, there are five alternative warehouse oper-ators and five customer companies of corporationA to be served. The service capabilities of the alter-native warehouse operators will be analysed basedon the requirements of each individualcustomer. Inorder to establish a warehouse network whichmatches the customers’ requirements as well aspossible, the final selection of the warehouse oper-ators to be used will be based on the customers’requirements and preferences under relevant re-strictions. 4.3. AHP- based analysis The third step in our approach involves analys-ing the capabilities and characteristics of thealternative warehouse operators by using AHP-supported analyses. In order to achieve a strong  J. Korpela, A. Lehmus v aara /   Int. J. Production Economics 59 (1999) 135 —  146   139
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