april 13th 2006, bmw frequency project isye 6203, prof. j. vande vate n. garg, a. hentati, m....

1 April 13th 2006, BMW Frequency Project ISyE 6203, Prof. J. Vande VateN. Garg, A. Hentati, M. DiPace, Y. A. Chen, C. Valderrama & K. Wittek

Frequency ProjectBMW – Georgia Tech

April 13th 2006


Outline

1. Project background

2. Problem description and objective

3. Approach

4. Results

5. Recommendation for BMW

6. Future work

Cover European Part SuppliesOutline


European Parts Supplies

• 40% of parts needed by (Plant 10) Spartanburg are sourced in Europe and shipped across the Atlantic via:

– regular ocean shipments– Airfreight expediting in case of stock-out at

Spartanburg plant

Outline GeographicsEuropean Part Supplies


~1-2 days

The Geographics

Spartanburg

Wackersdorf / Steyr

European Part Supplies Current CaseGeographics

~10 days

~1-2 days

~1-2 days


Current Case

Frequency:– Three times per week

Arrival Days:– Thursday– Friday– Saturday

US Entry Port:-

Charleston

European Departure Ports:- Bremerhaven

Geographics Project DescriptionCurrent Case

Sailing Time:-10 days-12 days

-11 days


Project descriptionPrevious study suggests increasing the shipment frequency would decrease inventory costs

Objective for this Project:Suggest an optimal shipping schedule whichreduces costs related to European parts shipments

- Using real data and constraints- Considering safety stock- Considering Split- Transportation Costs

Current Case Variable ElementsProject Description


Major variable elements

– Changing the European (American) ports affects land lead times

– Frequency of shipments affects needs for inventory at plant

– Parts proportions (Split) to be shipped in each scheduled shipment may reduce stock-out?

– Shipping lines have different rates per container, shipping lead times and reliability

Project Description Approach UsedVariable Elements


Approach used

1. Collection of sailings data

2. Creation of a tool to search among the large number of Sailings

3. Selection of multiple optimal ports and lines combination for various frequencies (based on ocean and land lead time)

4. Simulation to find costs incurred with the different scenarios

5. Processing Simulation Outputs in Excel

Variable Elements Data CollectionApproach Used


Collection of Sailings

Ports were selected based on:

• Ranking in terms of TEU of European ports • Ports preferred by BMW• Duration of Sailings offered• Geography• Data obtained from www.joc.com1 (current

Tender) and material “Trans Atlantic Workshop” provided by BMW (new Tender)

1sometimes data not accurate

Approach Used Constraints on SailingsData Collection


Constraints on Sailings

• Cutoff for sailing time: 18 days• Entry Ports considered :

– Savannah, Norfolk, Charleston, New York, Montreal, Newark, Baltimore, Philadelphia, Miami, Houston, Halifax

• European Ports considered:– Hamburg, Antwerp, Bremenhaven, Le Havre,

Rotterdam, Copenhagen, Fos, Genao, Gioia Tauro, La Spezia, Le Verdon, Livorno, Montoir, Valencia, Algeciras, Barcelona

Ports preferred by BMW

Data Collection General AssumptionsConstraints on Sailings


General Assumptions

• If it arrives in port on Saturday or Sunday it cannot be shipped until Monday

(high extra charge if pulling out of port on weekends)

• We are not considering multiple arrivals at Spartanburg on the same day

• High and Low runners can not be mixed on a container

• Capital Charge: 12 %

• Non-Capital Holding Charge applied in Spartanburg: – 5% (High Runners)

– 10% (Low Runners)

Constraints on Sailings Port Selection ToolGeneral Assumptions


Port Selection Tool (Excel Model)

Assignment Model to minimize lead times under each scenario. Each scenario is a combination of:– Various ports in Europe – Various ports in the US– Various shipping lines used– Different weekdays of arrival

General Assumptions Assignment ModelPort Selection Tool


Port Selection Tool(Assignment Model in Excel)

Shipping Lines

ANL

APL

Atlantic Container L ine

China Shipping

CMA CGM Group

Minimized Lead T ime: 83 COSCO Container L ines Americas, Inc

CP Ships

Evergreen America Corp.

Hamburg Sued

Hanjin Shipping

Hapag-L loyd

Hatsu Marine

Hyundai

Lead T ime Weightage Italia Marittima

At Sea 1 K -line

In Europe 1 L loyd T riestino

In USA 1 Maersk L ine

Marfret

Mediterranean Shpg. Co.

MOL

Norasia

NYK L ines

Safmarine L ine Ltd

United Arab Shipping

Yang Ming L ine

Zim Integrated Shipping Services

Exit Port Entry Port Days Decision Total Lead Time

Algeciras Baltimore Monday 0 0Tuesday 0 0Wednesday 0 0Thursday 0 0Friday 0 0Saturday 0 0Sunday 0 0

Algeciras Charleston Monday 0 0Tuesday 0 0

Minimizing Lead Time Given a Desired Shipping Frequency

Enter # of Days per

Week to Receive

Shipments 5

In case there are multiple transportation plans with the same optimized lead time, enter :1.000 for each mode if no preference1.001 if you prefer this mode1.002 if it is your second preference1.003 if it is your last preference

Port Selection Tool Simulation InputAssignment Model


Simulation Input

• Parameters used– Holding/carrying cost:

– Values of Engines

– Detailed Expediting costs

• Major points included in the simulation– Demand uncertainty (difference between forecast and

actual usage)

– Ocean lead time variability

– Lead time variability between US-port and Spartanburg

Assignment Model Simulation in Arena (1)Simulation Input

Simulated were 5 different engines, three high and two low runners

Other parts like transmissions to be simulated at a later time


Simulation in Arena (1)

Ship Arrival

Assign Departure Port and Departure Date

Travel to Exit Port

Exit Port Travel to North America via Ship

European Transit

North America Transit

Entry PortTravel to

Spartanburg

Simulation Input Simulation in Arena (2)Simulation in Arena (1)


Simulation in Arena (2)North America Total Costs

Spartanburg Determine Shipment Size

Update Pipeline Inventory Level

Update Pipeline Costs

Plant Demand

Define Daily DemandUpdate on-hand inventory level

Update on-hand inventory costs

Are inventory levels

positive?

Update Expedited Costs

Update on-hand inventory level and costs Calculate Total Inventory +

Expediting Costs

Simulation in Arena (1) Simulation in Arena (3)Simulation in Arena (2)


Simulation in Arena (3)

Simulation in Arena (2) Output ProcessingSimulation in Arena (3)


• A Simulation with 500 runs 1000 days each was performed for each engine for each scenario

• The average values for key variables were obtained by aggregating data in Excel

• Transportation costs were calculated in Excel using the simulation output

• Costs were summed to total figure

Processing of Outputs

Simulation in Arena (3) GraphsOutput Processing


Output Processing Graphs Split

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Current Scenario Frequency 3 Frequency 4 Frequency 5

Relative Total Cost for Three Cases by Frequency

Total Inventory Costs Pipeline Inventory Costs Expediting Costs

*Note: Total cost does not include the transportation cost


Split

Split policies were simulated for:

• One high and one low runner engine

• For a frequency of three

Graph Split GraphsSplit


Split Split Graphs(2)Split Graphs(1)

Progress of Split Optimization

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91

Iteration

To

tal C

ost

Split Graphs

Optimizing the split implies important savings in the Total Cost


Split Graphs(1) Split Graphs(3)Split Graphs(2)

Split Graphs

• Models were run using OptQuest Analyzer for Arena.

• We defined variables (split variables) to identify the amount of engines (on a day basis) for each shipment route.

• The variables implemented in the simulation then were evaluated minimizing total cost under changes in the split variables.

• 500 replications of 1000 day runs were done for 100 scenarios to find the behavior of near optimal solutions.

• Convergence can be seen from the results.

• Best policy is inclined toward sending more through the fastest vessel, but trying to keep a homogeneous distribution among routes.


Split Graphs(2) RecommendationsSplit Graphs(3)

Split Graphs

• Vessel 2 refers to the fastest vessel in each case.

• Different cases for two engines, related by equal and unequal interarrival times.

• Split validates that the most should be allocated to the fastest vessel and the split is also trying to be more equally divided among shipments.

• First case has variable delay upon arrival at Charleston for the second vessel, volume movement is not enough to make this vessel significantly more prone to more shipping.

Minimize Total Costs Split Variable1 Split Variable2 Split Variable3242,315.00$ 33.93% 29.81% 36.26%




Engine 1552166 (low runner) - Restricted/Equal

Engine 1552166 (low runner) - Restricted/Unequal

Engine 1552145 (high runner) - Restricted/Equal

Engine 1552145 (high runner) - Restricted/Unequal


Recommendations

Split Graphs(3) TransportationRecommendations

• Use frequency of shipments of four times a

week.

• Move towards evenly spaced shipments.

• Move towards more shipping routes

•Unrestricted case, approximately 7% savings per engine based on Inventory cost+pipeline cost+expedited cost.

•Transportation Costs


Total Cost incl. Transportation

Recommendations Transportation Future Work

0

0.2

0.4

0.6

0.8

1

1.2

Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7 Case 8 Case 9 Case 10 Case 11 Case 12

Relative Total Costs For All Cases (Routes)

Inventory Pipeline Transportation Expediting

*Note: Case 1 identifies the current case


Future Work

• Improve model to consider only integer number of container loads

• Come up with a policy concerning mix of parts on a container

• Simulate other plant 10 parts• Improve modeling of safety stock• More sensitivity analysis• Risk reduction by having multiple arrivals on one

day

Transportation Future Work

april 13th 2006, bmw frequency project isye 6203, prof. j. vande vate n. garg, a. hentati, m....

Documents

bmw frequency project

project background

vande vate

days slide

shipment frequency

current case frequency

supplies slide

supplies outline slide