Measuring the Capacity of a Port System A Case Study on a Southeast - - PowerPoint PPT Presentation

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Measuring the Capacity of a Port System A Case Study on a Southeast - - PowerPoint PPT Presentation

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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,

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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

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Agenda

  • The Southeast Asia Maritime Landscape
  • Objective & Motivation
  • The Proposed Framework
  • Key Findings

May 22-23, 2013 MIT SCM ResearchFest 2

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SLIDE 3

The Southeast Asia Maritime Landscape

May 22-23, 2013 MIT SCM ResearchFest 3

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

1 Drewry Maritime Research, 2012

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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

May 22-23, 2013 MIT SCM ResearchFest 4

Objective

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

Motivation

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The Proposed Framework

May 22-23, 2013 MIT SCM ResearchFest 5

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

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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
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SLIDE 7
  • 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)

May 22-23, 2013 MIT SCM ResearchFest 7

Measure Capacity at Each Port Component

Step Two - First Methodology

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SLIDE 8

May 22-23, 2013 MIT SCM ResearchFest 8

Measure Capacity at Each Port Component

Step Two: An Example

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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)

May 22-23, 2013 MIT SCM ResearchFest 9

Measure Capacity at Each Port Component

Step Two

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SLIDE 10
  • 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

May 22-23, 2013 MIT SCM ResearchFest 10

Evaluate Investment Strategies under Uncertainty

Step Three to Eight - Second Methodology

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SLIDE 11
  • 1. Macroeconomic developments
  • 2. Regional hub for products and services
  • 3. Recurring national political events

May 22-23, 2013 MIT SCM ResearchFest 11

Identify Scenarios of Uncertainties

Step Three

Three Scenarios of Uncertainty

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SLIDE 12

May 22-23, 2013 MIT SCM ResearchFest 12

Run Simulation to Generate Profitability Results

Step Four

Bottlenecks occur!

  • 95% chance at Warehouse
  • 40% chance at Liquid Bulk Terminal
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SLIDE 13

May 22-23, 2013 MIT SCM ResearchFest 13

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

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SLIDE 14

May 22-23, 2013 MIT SCM ResearchFest 14

3 potential investment strategies are explored:

Determine Potential Investment Strategies

Step Six

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SLIDE 15

May 22-23, 2013 MIT SCM ResearchFest 15

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.

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SLIDE 16

May 22-23, 2013 MIT SCM ResearchFest 16

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
  • ptimal strategy
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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.

May 22-23, 2013 MIT SCM ResearchFest 17

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Thank You

May 22-23, 2013 MIT SCM ResearchFest 18

Q & A

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Appendix

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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

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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

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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