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
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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)
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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
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•
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•
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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
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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
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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
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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?
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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
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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
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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, …
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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
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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
iI jJ
s.t.
x
iI
ij
1
xij  yi
y
iI
i
jJ
i  I, j  J
k
xij , yi  {0, 1} i  I , j  J
Source: Camm et al. 1997