Measuring the Capacity of a Port Systemctl.mit.edu/sites/ctl.mit.edu/files/library/public/03...Liquid Bulk Terminal Yard (Volume) – Static only 5. Dry Bulk Terminal Yard (Mass) 6

Measuring the Capacity of a Port System A Case Study on a Southeast Asian Port

Author: Jason Salminen

Advisors: Mr. James Rice & Dr. Ioannis Lagoudis

Sponsor: MIT Center for Transportation & Logistics

MIT SCM ResearchFest May 22-23, 2013

Agenda

• The Southeast Asia Maritime Landscape

• Objective & Motivation

• The Proposed Framework

• Key Findings

May 22-23, 2013 MIT SCM ResearchFest 2

The Southeast Asia Maritime Landscape


Average port utilization to increase from 71% in 2011 to 87% by 20171

1 Drewry Maritime Research, 2012

Objective & Motivation

To enhance the investment decision-making process for port infrastructure through the:

• Application and modification of two existing methodologies

• Development of both an investment tool and a framework for selecting optimal investment strategies to address bottlenecks


Objective

• Extend the application of the existing methodologies • Evaluate potential investment strategies under uncertainty • Improve profitability and increase capacity

Motivation

The Proposed Framework


The framework is an 8-step process using 2 modified methodologies

Step Action Methodology

1 Identify Port Components Measure Port Capacity to Identify Bottlenecks 2 Measure Capacity at Each Port Component

3 Identify Scenarios of Uncertainty

Evaluate Potential Investment Strategies Under Uncertainty

4 Run Simulation to Generate Profitability Results

5 Select Components for Further Evaluation

6 Determine Potential Investment Strategies

7 Run Simulation Again to Generate Profitability Results

8 Select the Optimal Strategy After Comparison

Identify the Components in the Port System Step One

May 22-23, 2013 MIT SCM ResearchFest 6 A Port System (Lagoudis & Rice, 2011)

• The case study port has 22 components and handles 4 cargo types • The case study port does not have available land to expand

• Objective is to identify current and potential bottlenecks

• Theoretical & actual capacity are measured along 2 dimensions:

• Static: Point in time

• Dynamic: Period in time

• Based on Lagoudis and Rice methodology (2011)


Measure Capacity at Each Port Component Step Two - First Methodology


Measure Capacity at Each Port Component Step Two: An Example

7 current or potential bottlenecks identified at the 22 port components:

1. Container Berths

2. Container Terminal Yard

3. Liquid Bulk Terminal Yard (Mass) – Static only

4. Liquid Bulk Terminal Yard (Volume) – Static only

5. Dry Bulk Terminal Yard (Mass)

6. Dry Bulk Warehouse (Mass)

7. Break Bulk Warehouse (Mass)


Measure Capacity at Each Port Component Step Two

• Objective is to evaluate potential investments under multiple scenarios of uncertainty

• Achieved through a modified simulation screening model

• Based on an existing methodology developed by Dr. Richard de Neufville & Dr. Stefan Scholtes


Evaluate Investment Strategies under Uncertainty Step Three to Eight - Second Methodology

1. Macroeconomic developments

2. Regional hub for products and services

3. Recurring national political events


Identify Scenarios of Uncertainties Step Three

Three Scenarios of Uncertainty


Run Simulation to Generate Profitability Results Step Four

Bottlenecks occur! • 95% chance at Warehouse • 40% chance at Liquid Bulk Terminal


Select Components for Further Evaluation Step Five

• Confirms results from the Measuring Port Capacity methodology

• Liquid Bulk Terminal and Warehouse are the two components where potential investment strategies should be explored

• The Warehouse is selected due to high bottleneck probability and highest profitability


3 potential investment strategies are explored:

Determine Potential Investment Strategies Step Six


For similar scale strategies, the one with a flexible option is optimal

Run Simulation to Generate Profitability Results Step Seven

The flexible option is valued at USD 205 mill. with a cost of just USD 24 mill., equal to 5% of the initial capital expenditure.


Select the Optimal Strategy After Comparison Step Eight

• The 4 level flexible warehouse is compared vs. its best alternatives • The results indicate that the 5 level non-flexible warehouse is the

optimal strategy

Key Findings

• The proposed framework can be successfully applied at a multi-purpose port to identify optimal investment strategies.

• 7 of the 22 port components are current or potential bottlenecks.

• The simulation screening model narrows the focus on bottlenecks to the warehouse and the liquid bulk terminal.

• When comparing investment strategies of the same scale, the investment strategy with the flexible option is often preferable to the investment strategy without flexibility.

• The optimal investment strategy is the 5 level non-flexible warehouse, outperforming a comparable 4 level flexible warehouse.


Thank You


Q & A

Appendix

Appendix Trend R-Squared Adj. R-Squared GDP t-stat p-value Intercept t-stat p-value

Container 5-Yr (2007-2011) 0.71 0.62 -25,165 -2.73 0.07 958,274 31.38 0.00

Container 9-Yr (2003-2011) 0.43 0.35 16,274 2.43 0.04 748,215 18.03 0.00

Liquid Bulk 5-Yr (2008-2012) 0.56 0.41 748,609 1.95 0.15 8,861,478 6.97 0.01

Liquid Bulk 10-Yr (2003-2012) 0.01 0.10 -31,937 -0.23 0.82 11,317,470 12.24 0.00

Break Bulk 3-Yr (2010-2012) 0.75 0.50 56,522 1.73 0.33 1,011,594 14.35 0.04

Break Bulk 5-Yr (2008-2012) 0.63 0.51 -183,913 -2.28 0.11 1,900,435 7.12 0.01

Break Bulk 10-Yr (2003-2012) 0.09 0.01 -38,340 -0.93 0.38 78,519,763 0.95 0.37

Dry Bulk 5-Yr (2008-2012) 0.07 0.25 28,261 0.46 0.68 3,802,174 18.57 0.00

Dry Bulk 10-Yr (2003-2012) 0.04 0.07 -14,822 -0.60 0.57 4,108,696 24.32 0.00

Source: Author

Terminal Type Average Standard Deviation

Container Terminal 2.3% 5.7%

Liquid Bulk Terminal 2.0% 12.5%

Break Bulk Terminal 2.9% 19.4%

Dry Bulk Terminal 1.0% 6.4%

Historical data time period (2003-2012),

except for the container terminal data (2003-2011)

Source: Author

Appendix

Container Liquid Bulk Break Bulk Dry Bulk Warehouse

a 0.10 0.50 0.10 0.10 0.10

b 0.15 0.10 0.05 0.10 0.10

MD 304,640 683,400 713,129 304,640 13,605

MAD 311,192 4,123,116 1,581,615 311,192 35,825

RMSE 125,981 1,539,902 574,478 125,981 12,744

MPE 12% 1% 6% 12% 5%

MAPE 12% 13% 32% 12% 22%

MD/MAD 98% 17% 45% 98% 38%

CoV 41% 225% 81% 41% 94%

Note that RMSE stands for Root Mean Squared Error and MPE stands for Mean Percentage Error

Source: Author

Appendix

Cost of Option New Warehouse Current

(% of Initial Capex) 0% 5% 10% 20% 30% 40% 50% without Flexibility Warehouse

ENPV 9,008 8,984 8,959 8,911 8,862 8,814 8,765 8,794 5,287

Min result 6,051 6,027 6,003 5,954 5,906 5,857 5,809 6,019 5,151

Max result 11,340 11,316 11,292 11,243 11,195 11,146 11,098 9,666 5,287

All figures in USD mill.

Adapted from Lin (2008)

New Warehouse with Flexibility

Documents

Measuring the Capacity of a Port Systemctl.mit.edu/sites/ctl.mit.edu/files/library/public/03...Liquid Bulk Terminal Yard (Volume) – Static only 5. Dry Bulk Terminal Yard (Mass) 6