Download - Logistics Network Configuration
Logistics Network Configuration
Designing & Managing the Supply Chain
Chapter 2
Byung-Hyun Ha
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 facilities
Supply
Sources:plantsvendorsports
RegionalWarehouses:stocking points
Field Warehouses:stockingpoints
Customers,demandcenterssinks
Production/purchase costs
Inventory &warehousing 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
$-
$10
$20
$30
$40
$50
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$70
$80
$90
0 2 4 6 8 10
Number of Warehouses
Cos
t (m
illio
ns $
)
Total CostTransportation CostFixed CostInventory Cost
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
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070 0.080 0.090 0.100
Volume (pallets per case)
Wei
ght (
lbs
per c
ase)
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:$104,564,000Total Products: 46
Total Cost:$104,599,000Total Products: 4
Impact of Aggregation on Variability Measure of variability?
Average and standard deviation• Enough?
Which one has bigger SD than the other?
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SDnX
X ii
22 )(
0
15
30
0
200
400
Impact of Aggregation on Variability Measure of variability
Coefficient of variation
CVA CVB
XSDCV
0
15
30
0
200
400
A B
Impact of Aggregation on Variability Historical data for the two customers
Summary of historical data
Year 1992 1993 1994 1995 1996 1997 1998Customer 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
Average Standard deviation CoefficientStatistics annual demand annual demand 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, …
$-
$10
$20
$30
$40
$50
$60
$70
$80
$90
0 2 4 6 8 10
Number of Warehouses
Cos
t (m
illio
ns $
)
Total CostTransportation CostFixed CostInventory Cost
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
Industry Benchmarks:# of Distribution Centers
Sources: CLM 1999, Herbert W. Davis & Co; LogicTools
Avg.# ofWH 3 14 25
Pharmaceuticals Food Companies Chemicals
- High margin product- Service not important (or easy to ship express)- Inventory expensiverelative to transportation
- Low margin product- Service very important- Outbound transportationexpensive relative to inbound
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
Facilitywarehouse p1 p2 c1 c2 c3
w1 0 4 3 4 5
w2 5 2 2 1 2
Heuristics and Exact Algorithms A distribution system
D = 50,000
D = 100,000
D = 50,000Cap = 60,000
$4
$5
$2
$3
$4
$5
$2
$1
$2
Production costs are the same, warehousing costs are the same
$0
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
D = 100,000
D = 50,000Cap = 60,000
$5 x 140,000
$2 x 60,000
$2 x 50,000
$1 x 100,000
$2 x 50,000
Total Costs = $1,120,000
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)
D = 50,000
D = 100,000
D = 50,000Cap = 60,000
$4
$5
$2
$3
$4
$5
$2
$1
$2
$0
P1 to WH1 $3P1 to WH2 $7P2 to WH1 $7P2 to WH 2 $4
P1 to WH1 $4P1 to WH2 $6P2 to WH1 $8P2 to WH 2 $3
P1 to WH1 $5P1 to WH2 $7P2 to WH1 $9P2 to WH 2 $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)
D = 50,000
D = 100,000
D = 50,000Cap = 60,000
$5 x 90,000
$2 x 60,000
$3 x 50,000
$1 x 100,000
$2 x 50,000
$0 x 50,000
P1 to WH1 $3P1 to WH2 $7P2 to WH1 $7P2 to WH 2 $4
P1 to WH1 $4P1 to WH2 $6P2 to WH1 $8P2 to WH 2 $3
P1 to WH1 $5P1 to WH2 $7P2 to WH1 $9P2 to WH 2 $4
Total Cost = $920,000
Heuristics and Exact Algorithms xij: the flow from i to j
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Total Cost = $740,000
Heuristics and Exact Algorithms Network configuration problem is generally
formulated as integer programming Hard to obtain the optimal solution
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Source: Camm et al. 1997