Evaluating the Consequences of an Inland Waterway Port Closure with a Dynamic Multiregional Interdependency Model Cameron MacKenzie and Kash Barker School of Industrial Engineering University of Oklahoma Society for Risk Analysis Annual Meeting December 6, 21
Motivation 2.5 billion tons of commerce via water annually 2
What s new Focusing on inland waterway ports Combining simulation with multiregional input-output model Incorporating companies decision-making process into simulation Integrating publicly available databases for a case study examining effects of closing an Oklahoma port 3
Outline 1. Simulation 2. Multiregional Dynamic Inoperability Input- Output Model (DIIM) 3. Port of Catoosa case study 4
Simulation + model Port suddenly closes Port officials announce or revise expected opening Each company updates probability of expected opening of port Ship via alternate route? No Commodities not yet shipped flow through port Yes Port opens? No adverse economic effects Multiregional DIIM No Yes 5
Ship now or wait for port to open? Premium company willing to pay to ensure on-time delivery Cost of shipping via alternate route Expected penalty cost from waiting Cost of shipping via port 6
Ship now or wait for port to open? Cost of shipping via alternate route Premium company willing to pay to ensure on-time delivery Expected penalty cost from waiting Cost of shipping via port 7
If company chooses to wait Commodities not exported Effect is equivalent to reducing demand for those commodities Commodities not imported Companies needing those commodities suffer supply shortages Multiregional DIIM Loss of production 8
Multiregional Dynamic Inoperability Input-Output Model (DIIM) Ref: Lian and Haimes 6 Crowther and Haimes 21 np x np diagonal matrix describing how quickly perturbations reverberate through economy n industries per region, p regions np x np matrix describing interdependencies among industries q( t 1) I K q( t) K T * A * q( t) T * c * ( t) np x 1 vector describing production loss of each industry np x np matrix describing interdependencies among regions np x 1 vector describing reduction in customer demand for each industry at time t 9
Port of Catoosa case study McClellan-Kerr Arkansas River 2 million tons of cargo 1
Transportation hub 3 different rail lines 5 1 trucks per day 11
Catoosa daily schedule Create daily schedule of shipments through Catoosa Combine publicly available databases 12
Value of products through Catoosa (in millions of dollars) From Okla. To Okla. Total Food and Beverage and Tobacco Products Petroleum and Coal Products Chemical Products Nonmetallic Mineral Products INDUSTRY Primary Metals Fabricated Metal Products Machinery Misc. Manufacturing Ala. 9 9 Ill. 3 3 Kent. 18 18 Louis. 131 49 3 21 Miss. 71 71 Tex. 8 78 6 92 Ala. 165 38 23 Ark. 1 1 Ill. 1 2 4 Iowa 2 2 Louis. 3 9 131 93 21 257 Miss. 2 2 Ohio 55 12 67 146 66 223 4 313 71 18 6 937 Total 13
Key assumptions Each shipment < 9 tons (six barges) Railroad is alternate route A little less than 3 times more expensive than barge No capacity constraints 14
Results: no penalty Value of product not transported while port is closed Industry Mean Standard deviation Food and beverage and tobacco products 17 7 Petroleum and coal products 8 4 Chemical products 26 1 Nonmetallic mineral products.5.4 Primary metals 37 14 Fabricated metal products 8 4 Machinery 13 14 Misc. manufacturing 1 2 Total 11 36 In millions of dollars Production loss per state due to interdependencies State Mean Standard Deviation Alabama 68 31 Arkansas 61 28 Illinois 116 7 Iowa 32 14 Kentucky 6 32 Louisiana 798 391 Mississippi 277 135 Ohio 132 6 Oklahoma 2,993 1,449 Texas 525 277 Total 5,61 2,26 15
Frequency Frequency Distribution of production losses Oklahoma s lost production Region s lost production 15 15 1 1 5 5 5 1 15 Billions of dollars 5 1 15 Billions of dollars 16
Results:.2% penalty Value of product not transported while port is closed Industry Mean Standard deviation Food and beverage and tobacco products 3.9 2.8 Petroleum and coal products 1 1 Chemical products 4 3 Nonmetallic mineral products.2.3 Primary metals 2 3 Fabricated metal products.2.9 Machinery Misc. manufacturing Total 12 6 In millions of dollars Production loss per state due to interdependencies State Mean Standard deviation Alabama 7 6 Arkansas 5 4 Illinois 27 36 Iowa 3 2 Kentucky 6 6 Louisiana 137 131 Mississippi 23 34 Ohio 1 8 Oklahoma 218 234 Texas 29 25 Total 465 361 17
Frequency Frequency Distribution of production losses Oklahoma s lost production Region s lost production 8 8 7 7 6 6 5 5 4 4 3 3 1 1 1 2 3 1 2 3 Billions of dollars Billions of dollars 18
Millions of dollars Impact of penalty 1 4 1 3 Oklahoma's lost production Region's lost production Extra transportation cost paid by companies 1 2 1 1 1 1-1 %.1%.2%.3%.4%.5%.6%.7%.8%.9% 1.% Penalty 19
Frequency Temporal impact (with.2% penalty) Port closed Jan 1 Port closed Feb 1 Port closed Mar 1 1 2 1 2 1 2 Port closed Apr 1 Port closed May 1 Port closed Jun 1 1 2 1 2 1 2 Port closed Jul 1 Port closed Aug 1 Port closed Sep 1 1 2 1 2 1 2 Port closed Oct 1 Port closed Nov 1 Port closed Dec 1 1 2 1 2 Region s production loss (billions of dollars) 1 2 2
Conclusions 21 Model Integrating simulation with Multiregional DIIM provides powerful analytical tool Incorporating companies reactive strategies to port closures delivers a more complete picture of consequences Catoosa case study If commodities sit at port, losses around $5 billion If 9% of commodities move before port reopens, losses around $46 million Policymakers may want to incentivize companies to move commodities before port reopens 21
This work was supported by The U.S. Federal Highway Administration under awards SAFTEA-LU 1934 and SAFTEA-LU 172 The National Science Foundation, Division of Civil, Mechanical, and Manufacturing Innovation, under award 927299 Email: cmackenzie@ou.edu 22
Backup
Inoperability Daily inoperability for Oklahoma and Louisiana with no penalty.3.2.1 -.1 Oklahoma sectors.3.2.1 -.1 1 2 3 4 5 6 Days Catoosa is closed Louisiana sectors 24
Inoperability Daily inoperability for Oklahoma and Louisiana with.2% penalty.2 Oklahoma sectors -.2.2 Louisiana sectors.2 1 2 3 4 5 6 Days Catoosa is closed 25
References C. Lian and Y.Y. Haimes. 6. Managing the Risk of Terrorism to Interdependent Infrastructure Systems through the Dynamic Inoperability Input-Output Model. Systems Engineering 9 (3): 241-258. K.G. Crowther and Y.Y. Haimes. 21. Development of the Multiregional Inoperability Input-Output Model (MRIIM) for Spatial Explicitness in Preparedness of Interdependent Regions. Systems Engineering 13 (1): 28-46. 26