XYZ Company Supply Chain Optimization Project Network Optimization
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1 XYZ Company Supply Chain Optimization Project Network Optimization Date: 04/25/2006 ISyE 6203: Transportation and Supply Chain Management Prepared By: Jayson Choy Christie Williams Andy Ang Thomas Ou Naragain Phumchusri Raghav Himatsingka
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ISyE 6203: Transportation and Supply Chain Management. XYZ Company Supply Chain Optimization Project Network Optimization. Prepared By: Jayson Choy Christie Williams Andy Ang Thomas Ou Naragain Phumchusri Raghav Himatsingka. Date: 04/25/2006. Agenda. Introduction Key Deliverables - PowerPoint PPT Presentation
Transcript of XYZ Company Supply Chain Optimization Project Network Optimization
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XYZ CompanySupply Chain Optimization ProjectNetwork Optimization
Date: 04/25/2006
ISyE 6203: Transportation and Supply Chain Management
Prepared By:
Jayson Choy
Christie Williams
Andy Ang
Thomas Ou
Naragain Phumchusri
Raghav Himatsingka
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A. Introduction
B. Key Deliverables
C. Data Analysis
D. Mathematical Model
E. Recommendation
F. Sensitivity Analysis
G. Conclusion
Agenda
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• Locations in Florida and California
• Each location has Multiple Operations
• Suppliers across USA
• Supplier shipments may be parcel, less-than-truckload or full truckload, some must be frozen or chilled
Introduction
Project goal: Reduce inbound transportation costs across the business while meeting customer service requirements.
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Timeline
PhaseProject kick-off & Deliverables Rationalization
Data Cleansing & Validation
Preliminary Modeling
Validation of Model
Generation of results & sensitivity Analyses
Jan Feb Mar Apr
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A. Introduction
B. Key Deliverables
C. Data Analysis
D. Mathematical Model
E. Recommendation
F. Sensitivity Analysis
G. Conclusion
Agenda
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Key Deliverables Create a graphic illustration of Current North American Supply
Chain network
Document Current Volumes and Freight spend by mode to each location and in total
Identify and recommend North American Consolidation Points for most efficient route and capacity utilization
Create a graphic illustration of the Recommended New Supply Chain Network with all consolidation facility representations and conceptual lanes to each location
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California and Florida Supplier Locations
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Problem DefinitionBreakdown of Volume by Transportation
Costs
54%
46%
Florida California
Breakdown of Volume by Weight
96%
4%
Florida California
Breakdown of Volume by Shipment Frequency
76%
24%
Florida California
Florida
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Problem DefinitionBreakdown of Florida's Volume by
Cost
FTL LTL Parcel - Ground
Parcel - Air Produce Frozen
36%
26%
13%8%
11%
6%
LTL: Greatest Opportunity for Savings
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Problem Definition
• Focus on Consolidation of LTL shipments to Florida
• Eliminated Frozen and Chilled shipments from the Optimization model
• Included most FTL shipments by breaking them down
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A. Introduction
B. Key Deliverables
C. Data Analysis
D. Mathematical Model
E. Recommendation
F. Sensitivity Analysis
G. Conclusion
Agenda
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Data Analysis
Florida Breakdown of Individual Business Units by Total Amount Paid
Merchandise58%
Food34%
Costuming0%
General Supplies8%
Others0%
Food (FOB)0%
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Data Analysis
Florida Breakdown of Individual Business Units by Shipment Count
Merchandise88%
General Supplies0%
Costuming0% Others
0%
Food2%
Food (FOB)10%
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Data Analysis
Florida Breakdown of Individual Business Units by Weight
Food49%
Others0%
General Supplies5%
Costuming0% Merchandise
21%
Food (FOB)25%
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A. Introduction
B. Key Deliverables
C. Data Analysis
D. Mathematical Model
E. Recommendation
F. Sensitivity Analysis
G. Conclusion
Agenda
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Supplier Locations (LTL Florida)(Data Aggregation by 3-Digit Zip Code)
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To Consolidate LTL Shipments into FTL
Shipper
XYZ Company
Shipper
Shipper
Shipper
CP
FTL
LTL
LTL
LTL
LTL LTL
LTLLTL
LTL
Present Situation
Proposed Solution
LTL
LTL
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Proposed 2-Step Model
Step 1: Set Covering Model (SCM)
Step 2: Network Design Model (NDM)
To Generate Potential Consolidation Point Candidates
To Determine which Consolidation Points to Open/ Close
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Step 1: Set Covering Model (SCM)
• Maximize sum(i in Suppliers) y[i]
• s.t {sum(i in Suppliers) x[i] <= 30; forall(i in Suppliers)
• y[i] <= (sum(j in Suppliers) Matrix[i,j]*x[j])
• Integer Programming Model
• Model will decide 30 Potential Consolidation Points within 300 mile Radius
from Suppliers.To Maximize the Number of Suppliers which can be Covered by the CPs To Generate at
most 30 Potential CP locations To Ensure that
CPs are within 300 mile radius from Suppliers
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SCM Results: 30 Consolidation Points
Next Step:
CP Candidates will be fed into the Network Design Model (NDM)
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Step 2: Network Design Model (NDM)Model Objectives :• To Decide which Consolidation Points to open or close
• To Determine whether Suppliers should Ship Direct to the company
• To Assign Suppliers to Consolidation Points
To Open or Close?
To Open or Close?
To Open or Close?
To Open or Close?
To Open or Close
To Open or Close?
To Open or Close?
To Open or Close?
To Open or Close?
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Objective : To Minimize Total Transportation Costs (Direct shipments + Shipments via opened CP)
XYZ Company
Shipper
Direct LTLShipment
Constraint I: If Supplier is not a Candidate CP• We either serve this 3 Digit zip via LTL shipments to the destination or via a consolidation point
CP
LTLShipment
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Step 2: Network Design Model (NDM)Constraint II: If Supplier is a Candidate CP • Case 1: If NOT OPEN We send LTL direct or via a designated CP
CP
XYZ Company
LTL
Case 1CP
FTL
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Step 2: Network Design Model (NDM)
CP
XYZ Company
Case 2
FTL
Constraint II: If Supplier is a Candidate CP • Case 2: If OPEN
We consolidate at CP and send FTL direct
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Step 2: Network Design Model (NDM)Constraint III: Load Factor of 0.8• Total Inflow into CP < = [ 0.8 * Total Truckloads ]
CP 0.8 *
Constraint IV: Frequency• Minimum Truckloads going through
an Open CP per year > = 52
LTL
LTL
LTL
ie at least 1 truckload per week
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A. Introduction
B. Key Deliverables
C. Data Analysis
D. Mathematical Model
E. Recommendation
F. Sensitivity Analysis
G. Conclusion
Agenda
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5 CP Locations
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Assignment of Suppliers
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CP Location I: Charlotte, NC
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CP Location II: Atlanta, GA
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CP Location III: Los Angeles, CA
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CP Location IV: Gulfport, MS
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CP Location V: Jackson, KY
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LTL Direct Shipments
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Cost Savings
1920
1770
1650
1700
1750
1800
1850
1900
1950
Cost Before Optimization ('000) Cost After Optimization ('000)
Less than 8% Reduction
8% Reduction
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Summary
CP LocationTruckloads
Per YearVolume
('000 lbs)
Number ofAssignedSuppliers
CharlotteNC 190 3800 102AtlantaGA 52 1040 48JacksonKY 52 1040 53GulfportMS 52 1040 65 Los Angeles CA 52 1040 28
Direct LTL - - 136
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A. Introduction
B. Key Deliverables
C. Data Analysis
D. Mathematical Model
E. Recommendation
F. Sensitivity Analysis
G. Conclusion
Agenda
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Sensitivity Analysis
Effect of Shipment Frequency on CP
0
1
2
3
4
5
6
1 2 3 4 5 6 7
Shipments Per week
No.
of C
onso
lidat
ion
Poin
ts
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Sensitivity Analysis
Shipments
Per Week Charlotte Atlanta LA Gulfport Jackson
1 ☻ ☻ ☻ ☻ ☻2 ☻ ☻ ☻ ☻ ☻3 ☻ ☻ ☻ 4 ☻ ☻ 5 ☻ 6 ☻ 7 ☻
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A. Introduction
B. Key Deliverables
C. Data Analysis
D. Mathematical Model
E. Recommendation
F. Sensitivity Analysis
G. Conclusion
Agenda
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Conclusion
• Key Learning– Counter intuitive peculiarities of LTL cost structure
(small volumes, backhauling …etc)
• Moving Forward– “Milk run” study on remaining LTL direct volumes– Optimization of other shipment modes
(e.g. parcel, frozen, chilled …etc)– Optimization of Florida bound shipments
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Q & A
