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Logistics Network Configuration Designing & Managing the Supply Chain Chapter 2 Byung-Hyun Ha bhha@pusan.ac.kr Outline Case: Bis Corporation What is logistics network configuration? Methodology Modeling Data Aggregation Validation Solution Techniques Case: the Bis Corporation Background Produce & distribute soft drinks 2 manufacturing plant 120,000 account (retailers and stores), all over the US 3 existing warehouse (Chicago, Dallas, Sacramento) 20% gross margin $1,000 for each SKU (stock-keeping unit) for all products Current distribution strategy (designed 15 years ago) Produce and store at the manufacturing plant Pick, load, and ship to a warehouse/distribution center Unload and store at the warehouse Pick, load, and deliver to store Case: the Bis Corporation You, consulting company Proposal as reengineering the sales and distribution functions First phase, identifying 10,000 direct delivery account, based on • • • • • • • • Dock receiving capabilities Storage capability Receiving methodologies Merchandising requirements Order-generation capabilities Delivery time window constraints Current pricing Promotional activity patterns Case: the Bis Corporation Redesign distribution network Grouped accounts into 250 zones, products into 5 families Data collected • • • • • Demand in 1997 by SKU per product family for each zone Annual production capacity at each manufacturing plant Maximum capacity for each warehouse, new and existing Transportation costs per product family per mile for distributing Setup cost for establishing a warehouse Customer service level requirement No more than 48 hours in delivery Additionally, Estimated yearly growth, variable production cost, cost for increasing production capacity, … Case: the Bis Corporation Issues How can the Bis Corporation validate the model? Impact of aggregating customers and products Number of established distribution centers and their locations Allocation of plant’s output between warehouses When and where should production capacity be expanded? The Logistics Network The Logistics Network consists of: Facilities: Vendors, Manufacturing Centers, Warehouse/Distribution Centers, and Customers Raw materials and finished products that flow between the Customers, facilities demand Sources: plants vendors ports Regional Warehouses: stocking points Field Warehouses: stocking points centers sinks Supply Inventory & warehousing costs Production/ purchase costs Transportation costs Inventory & warehousing costs Transportation costs The Logistics Network Strategic Planning: Decisions that typically involve major capital investments and have a long term effect Determination of the number, location and size of new plants, distribution centers and warehouses Acquisition of new production equipment and the design of working centers within each plant Design of transportation facilities, communications equipment, data processing means, etc. Network Design Key Issues Pick the optimal number, location, and size of warehouses and/or plants Determine optimal sourcing strategy • Which plant/vendor should produce which product Determine best distribution channels Which warehouses should service which customers The objective is to balance service level against Production/ purchasing costs Inventory carrying costs Facility costs (handling and fixed costs) Transportation costs Network Design Tradeoffs $90 $80 Cost (millions $) $70 $60 Total Cost Transportation Cost Fixed Cost Inventory Cost $50 $40 $30 $20 $10 $- 0 2 4 6 Number of Warehouses 8 10 Network Design DSS: Major Components Mapping Mapping allows you to visualize your supply chain and solutions Mapping the solutions allows you to better understand different scenarios Color coding, sizing, and utilization indicators allow for further analysis Data Data specifies the costs of your supply chain The baseline cost data should match your accounting data The output data allows you to quantify changes to the supply chain Engine Optimization Techniques Visualize Your Supply Chain Compare Scenarios Data Collection Data for Network Design A listing of all products Location of customers, stocking points and sources Demand for each product by customer location Transportation rates Warehousing costs Shipment sizes by product Order patterns by frequency, size, season, content Order processing costs Customer service goals Customers and Geocoding sales data in a geographic DB rather than accounting DB Geographic Information System (GIS) Data Aggregation Optimization model for the problem? Typical soft drink distribution system: 10,000~20,000 accounts Wal-Mart or JC Penney: hundreds of thousands! Too much Customer Aggregation Aggregating customers located in close proximity • Using a grid network or clustering techniques All customers within a single zone • Replaced by a single customer located at the centroid of the zone Aggregation by classes • Service levels/frequency of delivery/… Data Aggregation: Customer The customer zone balances Accuracy loss due to over aggregation needless complexity Why aggregation? The cost of obtaining and processing data The form in which data is available The size of the resulting location model The accuracy of forecast demand Recommended Approach Use at least 300 aggregated points Make sure each zone has an equal amount of total demand Place the aggregated point at the center of the zone In this case, the error is typically no more than 1% Testing Customer Aggregation Experimental results: cost difference < 0.05% Considering transportation costs only Customer data • Original Data had 18,000 5-digit zip code ship-to locations • Aggregated Data had 800 3-digit ship-to locations • Total demand was the same in both cases Total Cost:$5,796,000 Total Customers: 18,000 Total Cost:$5,793,000 Total Customers: 800 Data Aggregation: Product Product aggregation Hundreds to thousands of individual items in production line • Variations in product models and style • Same products are packaged in many sizes Collecting all data and analyzing it is impractical Aggregation by distribution pattern Place all SKU’s into a source-group • A source group is a group of SKU’s all sourced from the same place Aggregate the SKU’s by similar logistics characteristics • Weight • Volume • Holding Cost Aggregation by product type Data Aggregation: Product Aggregation by distribution pattern 70.0 60.0 Weight (lbs per case) 50.0 40.0 30.0 20.0 10.0 0.0 0.000 0.010 0.020 0.030 0.040 0.050 0.060 Volume (pallets per case) 0.070 0.080 0.090 0.100 Test Case for Product Aggregation Setting 5 Plants 25 Potential Warehouse Locations Distance-based Service Constraints Inventory Holding Costs Fixed Warehouse Costs Product Aggregation • 46 Original products • 4 Aggregated products • Aggregated products were created using weighted averages Test Case for Product Aggregation Results: cost difference < 0.05% Total Cost:$104,564,000 Total Products: 46 Total Cost:$104,599,000 Total Products: 4 Impact of Aggregation on Variability Measure of variability? Average and standard deviation • Enough? X Xi n SD2 2 ( X X ) i n Which one has bigger SD than the other? 30 400 15 200 0 0 Impact of Aggregation on Variability Measure of variability Coefficient of variation CV SD X CVA CVB 30 400 A B 15 200 0 0 Impact of Aggregation on Variability Historical data for the two customers Year 1992 1993 1994 1995 1996 1997 1998 Customer 1 22,346 28,549 19,567 25,457 31,986 21,897 19,854 Customer 2 17,835 21,765 19,875 24,346 22,876 14,653 24,987 Total 40,181 50,314 39,442 49,803 54,862 36,550 44,841 Summary of historical data Statistics Average annual demand Standard deviation annual demand Coefficient of variation Customer 1 24,237 4,658 0.192 Customer 2 20,905 3,427 0.173 Total 45,142 6,757 0.150 Transport Rates Internal/external fleet Truckload (TL)/less than truckload (LTL) Cost structure is not symmetric LTL industry (3PL?) Class, exception, commodity Additionally, Mileage estimation, … Warehouse Costs Three main components Handling costs: labor costs, utility costs Fixed costs: not proportional to the amount of material the flows through the warehouse Storage costs: proportional to the inventory level • Inventory turnover ratio = annual sales / average inventory level $90 $80 Cost (millions $) $70 $60 Total Cost Transportation Cost Fixed Cost Inventory Cost $50 $40 $30 $20 $10 $- 0 2 4 6 Number of Warehouses 8 10 Industry Benchmarks: # of Distribution Centers Pharmaceuticals Avg. # of WH 3 - High margin product - Service not important (or easy to ship express) - Inventory expensive relative to transportation Food Companies 14 Chemicals 25 - Low margin product - Service very important - Outbound transportation expensive relative to inbound Sources: CLM 1999, Herbert W. Davis & Co; LogicTools Other Issues Potential Warehouse Locations Geographical and infrastructure conditions Natural resources and labor availability Local industry and tax regulations Public interest Service level requirements Future demand Model and Data Validation Model? Data validation Ensuring data and model accurately reflect the network design problem Done by reconstructing the existing network configuration using the model and collected data comparing the output of the model to existing data Can identify errors in the data, problematic assumptions, modeling flaws, … • • • • Does the model make sense? Are the data consistent? Can the model results be fully explained? Did you perform sensitivity analysis? Solution Techniques Mathematical optimization techniques Exact algorithms: find optimal solutions Heuristics: find “good” solutions, not necessarily optimal Simulation models provide a mechanism to evaluate specified design alternatives created by the designer Heuristics and Exact Algorithms A distribution system Single product Two plants p1 and p2 Plant p2 has an annual capacity of 60,000 units The two plants have the same production costs There are two warehouses w1 and w2 with identical warehouse handling costs. There are three markets areas c1, c2 and c3 with demands of 50,000, 100,000 and 50,000, respectively Distribution cost per unit Facility warehouse p1 p2 c1 c2 c3 w1 0 4 3 4 5 w2 5 2 2 1 2 Heuristics and Exact Algorithms A distribution system $0 D = 50,000 $3 $4 $5 D = 100,000 $2 $4 Cap = 60,000 $5 $2 $1 $2 D = 50,000 Production costs are the same, warehousing costs are the same Heuristics and Exact Algorithms Heuristic 1 For each market, choose the cheapest warehouse to source demand. Then, for every warehouse, choose the cheapest plant. D = 50,000 $5 x 140,000 D = 100,000 $2 x 50,000 Cap = 60,000 $2 x 60,000 $1 x 100,000 $2 x 50,000 Total Costs = $1,120,000 D = 50,000 Heuristics and Exact Algorithms Heuristic 2 For each market area, choose the warehouse such that the total delivery costs to the warehouse and from the warehouse to the market is the smallest. (i.e. consider inbound and outbound costs) $0 D = 50,000 $3 P1 to WH1 P1 to WH2 P2 to WH1 P2 to WH 2 $4 $5 D = 100,000 $2 $4 Cap = 60,000 $5 $2 $3 $7 $7 $4 $1 $2 P1 to WH1 P1 to WH2 P2 to WH1 P2 to WH 2 $4 $6 $8 $3 D = 50,000 P1 to WH1 P1 to WH2 P2 to WH1 P2 to WH 2 $5 $7 $9 $4 Heuristics and Exact Algorithms Heuristic 2 For each market area, choose the warehouse such that the total delivery costs to the warehouse and from the warehouse to the market is the smallest. (i.e. consider inbound and outbound costs) $0 x 50,000 D = 50,000 $3 x 50,000 P1 to WH1 P1 to WH2 P2 to WH1 P2 to WH 2 $5 x 90,000 Cap = 60,000 $2 x 60,000 D = 100,000 $1 x 100,000 $2 x 50,000 Total Cost = $920,000 $3 $7 $7 $4 P1 to WH1 P1 to WH2 P2 to WH1 P2 to WH 2 $4 $6 $8 $3 D = 50,000 P1 to WH1 P1 to WH2 P2 to WH1 P2 to WH 2 $5 $7 $9 $4 Heuristics and Exact Algorithms xij: the flow from i to j min. 0 x p1w1 5 x p1w2 4 x p2 w1 2 x p2 w2 3xw1c1 4 xw1c2 5 xw1c3 2 xw2c1 xw2c2 2 xw2c3 s.t. x p2 w1 x p2 w2 60,000 x p1w1 x p2 w1 xw1c1 xw1c2 xw1c3 x p1w2 x p2 w2 xw2c1 xw2c2 xw2c3 xw1c1 xw2c1 50,000 xw1c2 xw2c2 100,000 xw1c3 xw2c3 50,000 xij 0 i, j Total Cost = $740,000 Heuristics and Exact Algorithms Network configuration problem is generally formulated as integer programming Hard to obtain the optimal solution min. cij xij iI jJ s.t. x iI ij 1 xij yi y iI i jJ i I, j J k xij , yi {0, 1} i I , j J Source: Camm et al. 1997