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