1. Logistics Network Configuration
Designing & Managing the Supply Chain
Chapter 2
Byung-Hyun Ha

2. Outline Case: Bis Corporation What is logistics network configuration?
Methodology
Modeling
Data Aggregation
Validation
Solution Techniques

3. 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

4. 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

5. 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, …

6. 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?

7. 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

8. 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.

9. 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

10. Network Design
Tradeoffs

11. 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

12. Visualize Your Supply Chain

13. Compare Scenarios

14. Data Collection Data for Network Design Customers and Geocoding
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)

15. 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/…

16. 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%

17. 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 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

18. 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

19. Data Aggregation: Product
Aggregation by distribution pattern

20. 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

21. 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

22. Impact of Aggregation on Variability
Measure of variability?
Average and standard deviation
Enough?
Which one has bigger SD than the other?

23. Impact of Aggregation on Variability
Measure of variability
Coefficient of variation
CVA  CVB A B

24. Impact of Aggregation on Variability
Historical data for the two customers
Summary of historical data
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
Average
Standard deviation
Coefficient
Statistics
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

25. 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, …

26. 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

27. Industry Benchmarks: # of Distribution Centers
Pharmaceuticals
Food Companies
Chemicals
Avg.
# of WH 3 14 25
- High margin product
- Service not important (or
easy to ship express)
- Inventory expensive
relative to transportation
- Low margin product
- Service very important
- Outbound transportation
expensive relative to inbound
Sources: CLM 1999, Herbert W. Davis & Co; LogicTools

28. Other Issues Potential Warehouse Locations Service level requirements
Geographical and infrastructure conditions
Natural resources and labor availability
Local industry and tax regulations
Public interest
Service level requirements
Future demand

29. Model and 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?

30. 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

31. 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 4 3 5 w2 2 1

32. Heuristics and Exact Algorithms
A distribution system $0
D = 50,000 $3 $4 $5 $5
D = 100,000 $2 $4 $1 $2
Cap = 60,000 $2
D = 50,000
Production costs are the same, warehousing costs are the same

33. Heuristics and Exact Algorithms
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
$1 x 100,000
$2 x 60,000
Cap = 60,000
$2 x 50,000
D = 50,000
Total Costs = $1,120,000

34. Heuristics and Exact Algorithms
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 $3
P1 to WH2 $7
P2 to WH1 $7
P2 to WH 2 $4 $4 $5 $5
D = 100,000 $2
P1 to WH1 $4
P1 to WH2 $6
P2 to WH1 $8
P2 to WH 2 $3 $4 $1 $2
Cap = 60,000 $2
D = 50,000
P1 to WH1 $5
P1 to WH2 $7
P2 to WH1 $9
P2 to WH 2 $4

35. Heuristics and Exact Algorithms
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 $3
P1 to WH2 $7
P2 to WH1 $7
P2 to WH 2 $4
$5 x 90,000
D = 100,000
P1 to WH1 $4
P1 to WH2 $6
P2 to WH1 $8
P2 to WH 2 $3
$1 x 100,000
$2 x 60,000
Cap = 60,000
$2 x 50,000
D = 50,000
P1 to WH1 $5
P1 to WH2 $7
P2 to WH1 $9
P2 to WH 2 $4
Total Cost = $920,000

36. Heuristics and Exact Algorithms
xij: the flow from i to j
Total Cost = $740,000

37. Heuristics and Exact Algorithms
Network configuration problem is generally formulated as integer programming
Hard to obtain the optimal solution
Source: Camm et al. 1997