Location Decision in Distribution Centers

Transcription

1 การประช มว ชาการ : การว จ ยด าเน นงาน ประจ าป Location Decision in Distribution Centers Ninlawan Choomrit Department of Industrial Engineering, Srinakharinwirot University ninlawan@swu.ac.th Abstract Location decision in distribution Centers is often one of the most critical fundamentals in an organization s success. This paper gathers some recent applications related to DC location decision for general approaches in logistic environment and in supply chain environment. The sight in location variables and methodology also illustrates and guides to future prospects. Keywords: Distribution Center, Location Decision, Supply Chain Management 1. Introduction In designing a production distribution system, locating distribution centers (DCs) is one of important concern. The distribution center system consists of one or more production plants, a group of distribution centers, and retail outlets and customers (see figure 1). Two questions related to DC location decision are How many DCs should there be? and Where should they be located? which refer to the numbers and locations of distribution centers under different circumstances. Figure 1 A basic distribution center system In general, the main goal in site selection is to optimize customer service while minimizing costs associated with transportation, labor, real estate and taxes. For the most important selection criteria, Atkinson (2002) concluded that Transportation consideration are the most important factor with different aspects: the proximity to customers (being able to make quick and dependable JIT deliveries), the expense, with transportation costs being a large part of the supply chain budget, the shortage of drivers who are interested in long-haul driving, and the proximity to transportation routes (interstates, rail service, waterways, ocean ports). Other criteria are in concern: Labor costs and availability, Real estate costs, Taxes, Incentives (from governments and economic development group), and Utilities. Applications of location problems are

2 238 การประช มว ชาการ : การว จ ยด าเน นงาน ประจ าป 2548 abundant and are found in both manufacturing and service. Some applications provide graphical solutions showing numbers and locations of distribution centers as see in Figure 2. Figure 2 Graphical solution of distribution centers (From Nozick and Turnquist 2001, p.439) 2. Some recent approaches of DC location decision 2.1 General approaches in logistic environment In automotive manufacturers, locating distribution centers is one of important focus in their logistic system. Nozick and Turnquist (2001) proposed a trade off between costs and customer service coverage. Only inventory costs and transportation costs are considered in cost reduction meanwhile demand coverage also is another concern in customer fulfillment. To minimize costs and to maximize customer coverage are multiple objectives in making decision of DC locations. Integration of both objectives is appeared by using the fixed charge facility location model with given a weight related to the objective of customer demand. Applying this methodology in an automotive manufacturer, number of distribution centers as a trade off solution with minimum costs and maximum a percentage of demand coverage is derived. Nozick and Turnquist (2001) presented another approach of the location optimization in different matter. Inventory stocking policy in each product turns on to be joined with DC locations for making decision. What products should have safety stock at the DCs and the plant, and those should be stocked only at the plant are chosen for the stocking policy. A fix charge facility location model is used to decide in DC locations; also an inventory allocation model fulfills answers for stocking policy. Integration in both models leads to obtain the number and location of DCs, and what products to stock at each level. Making decision in DC locations under fuzzy environment is in concern when the values for the qualitative criteria in situation are often imprecisely defined, also the desired value and importance weight of criteria are usually described in linguistic terms, for example, low, medium, high. Fuzzy set theory was developed on the principle that the key elements in human thinking are not numbers, but linguistic terms. Chen (2001) interested in a fuzzy decision making method under multiple criteria consideration for location selection by integrating linguistic assessments and weights. Five criteria to select the most suitable location are investment cost, expansion possibility,

3 การประช มว ชาการ : การว จ ยด าเน นงาน ประจ าป availability of acquirement material, human resource, and closeness to demand market. Results reveal in both the ranking order of all alternatives and the degree of preference of each pair of alternatives. Chan et al. (2001) proposed another method for a multiple-depot, multiple vehicle, location routing problem with stochastically processed demands. The requirement for raw materials as stochastically processed demands refers to agile logistics which raw materials are stored at supply depots and surplus inventory is strictly disallowed at the plants. Formulation in a stochastic location routing problem (SLRP) with stochastic demands is generated by a queuing network. At first, the lower bound for heuristic validation purposes is served with formulating multiple depot location and multiple vehicle routing problem. Then a stochastic formulation, called the stochastic multiple vehicle routing multiple facility location problem is created for stochastic demands. A medical evacuation of U.S. Air Force is used as a time sensitive case study to allocate and route aircrafts to cover most patients. In addition, this study is to test the adequacy of both the delivery system and hospitals in the emergency responsiveness. Solving the multi-depots location routing problem (MDLRP) in logistic environments is proposed by Wu et al. (2002) in order to find the optimal solution for DC locations, allocation of customers to each service area, and transportation plans connecting customers. The multiple objective is to minimize total costs of fixed depot establishing cost, delivery cost, and dispatching cost for the vehicles assigned. Location allocation problem (LAP) to select DC sites and a plan for allocating customers to each chosen DC site is used to drive input to the Vehicle Routing problem (VRP) for generating a starting feasible set of routes. Simulated annealing (SA) is used as the basis for developing search methods for both the LAP and VRP. Comparison results derived from the proposed method and another two methods is done for a better solution with shorter CPU time. Hwang (2002) concerned in both required service levels (in the number of warehouse or distribution center, W/D) and vehicle routing schedule in order to reach high logistic performance. Stochastic set covering problem is used to determine the minimum number of W/D centers among a discrete set of possible location sites so that the probability of each customer to be covered it not less than a critical value (required service level) and 0 1 programming method is a solver of this problem. Meanwhile, an integrated vehicle routing problem (VRP) solver based on improved genetic algorithm (GA) is developed to minimize the total logistic cost for the service of the set of customers without being tardy or exceeding the capacity and available travel time of the vehicles and GUI type (Graphical User Interface) programming is developed for computational purpose. Ridlehoover (2004) presented the facility location problems with the utilization of Monte Carlo simulation and risk analysis to determine the best economic risk location through the expected annual worth. Starting with a P-median model, the weighted distance from the depot to all customer sites is minimized which candidate locations are selected. For financial analysis, Monte Carlo Simulation is used to determine the costs and benefits of each site which consisting of initial investment, annual operating costs, annual benefits (transportation savings), and interest rates (capital costs). For risk analysis, labor, transportation, and real estate appreciation are key geographic factors in assessing the risk in each site. The use of the Certainty Equivalence Method is to compare in company s risk profile. Avittathur et al. (2005) developed the model to locate distribution centers with minimizing central sales tax (CST) in India. The CST is a tariff that is applicable to inter state trade and is paid by a manufacturer when trading goods to his retailer in different

4 240 การประช มว ชาการ : การว จ ยด าเน นงาน ประจ าป 2548 state. To analyze DC locations, fixed cost, transportation cost, product variety, service level and demand distribution across the demand point are involved with the CST rate. Using the approximate mixed integer programming (AMIP) instead of the complex non linear mixed integer programming (NLMIP) provides a near-optimal solution for a manufacturer s distribution networks. 2.2 Approaches in supply chain environment With consideration in the Chinese PC domestic market, an integrated supply chain based spatial intersection model is developed for the oversea facility network design problem. This model also leads to the supply chain management (both manufacturing and distribution centers), and the inbound and outbound logistics related factors; for example, material source accessibility, transportation and inventory cost, potential benefits, inter province distribution restrictions, and long term regional market conditions to alleviate the decision bias for locating the facilities. Sheu (2003) proposed this model to determine facility location with two aspects: higher rate of return on investment (reflected by the financial measures in model), and the demand over supply operational status (urged to expand local facilities for satisfying the deficiency of PC supply volumes). In the PLOT (Production, Logistics, Outbound, Transportation) design system, a class of distribution network design problems is illustrated by multiple product families, a central manufacturing plant site, multiple distribution center and cross docking sites, and retail outlets (customer zones). Jayaraman and Ross (2003) developed the PLOT system into two models: P1 and P2. Model P1 is a strategic model which offers the strategic choice of deciding which warehouses and cross docks need to be opened and functional in any given time period. The objectives of this model are to minimize fixed costs to open warehouses and cross docks, costs to transport products from warehouses to cross docks, and costs to supply products from cross docks to satisfy customer demand. Model P2 is an operational model which deals with the optimal flow of product families from warehouse through cross-docks to satisfy the customer demand. Minimizing transportation costs incurred by shipping product families to warehouses, transshipment costs to ship product families from warehouses to cross docks, and distribution costs to ship product families from cross docks to customer zones are model p2 s objectives. Simulated Annealing (SA) procedure is adopted to combine the strategic model and operational planning scheme into a single large problem. The results determine the best set of warehouses and cross docks to operate while minimizing fixed costs, transportation costs from warehouses to cross docks, and costs to supply products based on customer demands. Oum and Park (2004) identified the important factors for regional distribution centers location decision in the Northeast Asian (NEA) of multinational companies (MNCs). A structured questionnaire based survey of MNCs distribution managers, and personal interviews with MNCs senior executives and governments officials are used for data collection and analysis. The important factors for MNCs distribution centers relate to the market and service in issues of market size and growth potential, geographic location and market accessibility, transport facilities, political stability, skilled labor and labor peace, flexible government, and logistics service providers. Market size is a major driving force for MNCs location selection. Findings from doing interviews with senior executives/officials of 29 institutions (MNCs and government agencies), the way to be a major logistics hub in the NEA region starts by making target in strategically

5 การประช มว ชาการ : การว จ ยด าเน นงาน ประจ าป important industries, for which the size of the domestic market is large enough to provide economic incentives for foreign MNCs to locate a distribution center. For outbound supply chain network of an automobile company, location and size are concerned for vehicle distribution centers (VDCs) in transporting vehicles from plants to dealers. Eskigun et al. (2005) interested in the lead time reduction which widely mentions in supply chain management. Transportation costs, fixed costs for VDCs and lead time costs are minimized as a multiple objective in a proposed capacitated network design model (NDMC). For short computational time in a large-scale integer linear programming of NDMC model, Lagrangian heuristic is used to obtain near optimal solutions. The results show the average delivery lead time gains and the percentage of truck usage which refers to transportation costs and the number of open VDCs. The US based companies are encouraged to do their business in Mexico under the North American Free Trade Agreement (NAFTA). Decision support for the strategic design of an assembly system is needed before making investment. Wilhelm et al. (2005) contributed the mixed integer programming model to deal with the complexities of the international design problem and to integrate relevant enterprise-wide decisions in the US Mexico business environment under NAFTA. The objective is to maximize total after tax profit under constraints of eight categories: revenues and cost, income taxes, international considerations (transfer prices, the safe harbor rule, local content rules, and transportation charge allocation), logical constraints for system design, logical constraints for material flows, flow scaling, design of the assembly system and its supply chain, and prescribing material flows through the supply chain. The model application in an enterprise of laptop computer shows the number of trade offs, for example, centralization versus decentralization, make versus buy, outsourcing versus in house assembly, flexible versus dedicated technologies, and economies of scale versus economies of process. In addition, this model can evaluate a variety of factors such as limitations on transfer prices, facility locations, tariff impacts, exchange rate impacts, tax impacts, dollar valuation, local content rules, and the costs of transportation and distribution. 3. Conclusion and future prospect Most applications use the multiple objective functions, basically to minimize costs, in DC location decision with different solution methods up to situations. Look in supply chain management (SCM) is widely seen in recent applications. Atkinson (2002) concluded that the growth in popularity of strategically located DCs is by reason of lack of inventory control (due to better inventory management via SCM, visible pipeline activities, e commerce and internet), customer delivery demands (within a specific time), methods of distribution (require different types of handling and technology), and volume of import/export business (due to evolution of off-shore manufacturing/sourcing). In addition, some aspects are proposed for location decisions such as Top 10 most powerful factors in location decisions : Reasonable cost for property, Roadway access for trucks, Nearness to customers, Cost of labor, Low taxes, Tax exemptions, Tax credits, Low union profile, Ample room for expansion, Favorable attitude of community/residents to industry (from Transportation & Distribution magazine). Population site location : in rural areas (somewhat near larger urban areas for quick access to transportation routes and sufficient labor at reasonable rates), near airports and hubs (as a result of the increased demand for JIT delivery, on line retailing, and rising customer expectations), at ports (focusing on lower-cost, less skilled manufacturing around the world, port-related facilities), in urban areas (having a trend toward DCs

6 242 การประช มว ชาการ : การว จ ยด าเน นงาน ประจ าป 2548 away from urban areas to more rural areas), near manufacturing sites (becoming a popular alternative due to getting more direct shipments). Geographic information systems (GIS), especially when coupled with location models and efficient algorithms, have the ability to vividly display the results of the user input and, most importantly, the changes that result from input modifications. This display tool, coupled with techniques designed to deal with fuzzy input from multiple decision makers may, after all, result in many more location models being applied by practitioners (Revelle and Eiselt, 2005). Also, qualitative factors or non tangible features are directed to location models for more effective results, for example, reliability, quality, security, labor skill, attitude, reputation. References Atkinson, W. (2002) DC Siting What Makes the Most Sense?, Logistics Management & Distribution Report, 41(5), pp 63, Avittathur, B., Shah, J. and Gupta, O.K. (2005) Distribution Centre Location Modeling for Differential Sales Tax Structure, European Journal of Operational Research, 162, pp Chan, Y., Carter, W.B. and Burnes, M.D. (2001) A Multiple Depot, Multiple Vehicle, Location-Routing Problem with Stochastically Processed Demands, Computer & Operations Research, 28, pp Chen, C.T. (2001) A Fuzzy Approach to Select the Location of the Distribution Center, Fuzzy Sets and Systems, 118, pp Eskigun, E. et al. (2005) Outbound Supply Chain Network Design with Mode Selection, Lead Times and Capacitated Vehicle Distribution Centers, European Journal of Operational Research, 165, pp Hwang, H.S. (2002) Design of Supply Chain Logistics System Considering Service Level, Computer & Industrial Engineering, 43, pp Jayaraman, V. and Ross, A. (2003) A Simulated Annealing Methodology to Distribution Network Design and Management, European Journal of Operational Research, 144, pp Nozick, L.K. and Turnquist, M.A. (2001) A Two Echelon Inventory Allocation and Distribution Center Location Analysis, Transportation Research (Part E), 37, pp Nozick, L.K. and Turnquist, M.A. (2001) Inventory, Transportation, Service Quality and the Location of Distribution Centers, European Journal of Operational Research, 129, pp Oum, T.H. and Park, J.H. (2004) Multinational Firms Location Preference for Regional Distribution Centers: Focus on the Northeast Asian Region, Transportation Research (Part E), 40, pp Revelle, C.S. and Eiselt, H.A. (2005) Location Analysis: a Synthesis and Survey, European Journal of Operational Research, 165, pp Ridlehoover, J. (2004) Applying Monte Carlo Simulation and Risk Analysis to the Facility Location Problem, The Engineering Economist, 49, pp Schwartz, B.M. (1999) Map Out a Site Route, Transportation & Distribution, 40(11), pp Sheu, J.B. (2003) Locating Manufacturing and Distribution Centers: an Integrated Supply Chain Based Spatial Interaction Approach, Transportation Research (Part E), 39, pp

7 การประช มว ชาการ : การว จ ยด าเน นงาน ประจ าป Wilhelm, W. et al. (2005) Design of International Assembly Systems and Their Supply Chains under NAFTA, Transportation Research (Part E), Article in Press (Appear in doi: /j.tre Wu, T.H., Low, C. and Bai, J.W. (2002) Heuristic Solutions to Multi Depot Location Routing Problems, Computer & Operations Research, 29, pp