1. Intermodal Supply Chain Optimization at a Large Retailer Part 2: Experimentation and Results
Scott J. Mason, Ph.D.
Fluor Endowed Chair in Supply Chain Optimization and Logistics
Professor of Industrial Engineering

2. k distribution centers
The Logistics Network
Mode: LTL
Modes: LTL or TL
Modes:
Intermodal and TL
i suppliers
j facilities
k distribution centers
Suppliers Consolidation Points Distribution Centers
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3. Client-Desired Experiments
CP closings as a function of DC demand fill rate
Closed due to low volume considerations
CP expansions as a function of DC demand fill rate
Expanded due to high volume issues
CP openings as a function of DC demand fill rate
Opened due to high volume issues
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4. Phase 1: Product Mix and Data File Generation
Example data for actual DC demand for each supplier was provided as a suitable product mix
During first 16 weeks of the year, an average of 81M pounds of total demand was experienced across all 19 DCs
April product mix used to scale up demand requirements to appropriate 6-month demand for first half of year of 2.1B pounds
Cube restrictions were not found to be binding
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5. CP Closings as a Function of DC Demand Fill Rate
Using base case 6-month data file, CPs assessed for closing in terms of reverse Pound Rank
CP 6910 (Pound Rank of #19)
CP 6911 (#18)
CP 6992 (#17)
CP 6917 (#16)
CP 6940 (#15)
Baseline model with 19 CPs
18 CPs (close CP 6910)
17 CPs (close CP 6910 and CP 6911)
And so on
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6. CP Closings as a Function of DC Demand Fill Rate
In conjunction with CP closings, required DC demand fill rate is varied
Current level of 100% down to 95% in 1% increments
The “as is” condition is 19 CPs and 100% demand fill rate for every DC
Six different CP configurations evaluated at six different DC demand fill rates
6 x 6 = 36 model runs with baseline 6-month data file
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7. CP Closings as a Function of DC Demand Fill Rate
Performance metric is ratio of the cost of CP and fill rate combination to the “as is” configuration
1.00 is the baseline
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8. CP expansion options pre-determined by client
CP Expansions
CP expansion options pre-determined by client
Examine CPs with 100% utilization that can be expanded
For example, CP 6910 cannot be expanded
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9. CP Expansions Expand each CP to its maximum capacity
Also consider expanding each CP by one-half of its available dock door capacity
CP 6935 currently has 50 dock doors can be expanded to 100
Analyze this CP being expanded to both 75 doors and 100 doors
Both CPs expanded individually, as well as simultaneously
CP expansions also examined in concert with potential CP closings
Determine trade-offs for client to drive towards reduced CP network transportation costs
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10. Expanding Each CP Individually
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11. Expanding CP 6915 and CP 6935 Simultaneously
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12. CP Openings
Examination experimental results in concert with assessment of current CP locations reveals
California-based third-party consolidators CAC and CAI are
Located in isolation
Handle the majority of west coast demand
Fully utilized at 100%
CP 6927 and CP 6938 are primary CPs responsible for serving northeastern U.S. (with some demand supported by third party consolidator JNJ)
Both CPs are utilized 100% in base case optimization results
Both CPs not expandable past current number of 20 dock doors
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13. CP Openings: California Study
CAI operating at maximum capacity of 124 dock doors
CAC operates 50 doors, with expansion to 80 doors a possibility
Expand CAC to 80 dock doors with varying DC demand rates
Evaluate attractiveness of constructing new CP with 100 dock doors at same basic CAC site
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14. CP Openings: California Study
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15. CP Openings: Northeast US Study
Investigate the effect of consolidating CP 6927 and CP 6938
Construct new CP facility at each current location independently
Client stated that all new CPs will have a minimum of 50 doors, with a maximum of 100
Investigate new CP opening with 50, 75, or 100 dock doors
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16. CP Openings: Northeast US Study
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17. CP Openings: Northeast US Study Results
More cost effective to locate new CP in New York than in Pennsylvania
Should be built with 50 doors
No additional transportation cost decrease results for >50 doors due to insufficient demand volume at the current time
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18. 5 Year Strategic Analysis
Results suggest client’s CP network is 82.5% utilized based on first half of year demand data
Retailer stated that it expects an average annual demand growth rate of 10%-12%
Investigate CP network changes that must occur (and their respective timing) in order to accommodate demand growth over five years using sequential optimization
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19. 5 Year Strategic Analysis Results
Resulting transportation costs expressed relative to current CP network base case conditions
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20. 5 Year Strategic Analysis Summary
Eight CP locations need to be expanded to maximum dock door capacity over next four years to accommodate projected DC demand growth
Expansions suggested for second half of year 5 leaves CP network 98.8% utilized
Additional expansion warranted for subsequent periods
Second half of year 5 optimization results suggest only two CPs not 100% utilized
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21. Network Equilibrium—What is the Desired Operating State?
Assume each CP has infinite capacity
Modeled by assuming very large number of doors
One can think of the minimum achievable transportation cost solution as the CP network’s equilibrium or desired operating state
A number of model inputs are responsible for some portions of this equilibrium solution
Transportation lane restrictions
Fixed and variable transportation costs
Investigated equilibrium solutions for Period 1 and Period 10 of 5-year strategic analysis
Highlighted expansion and contraction opportunities
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