Next along its path of transformation and enlightenment, Customs data is enhanced with a number of ancillary but related (and connectable) data.  Once the company location and name has been successfully resolved, its location can be assigned to a respective county, city, MSA, CSA, CBSA, congressional district, area code, time zone or even latitude-longitude.  Thereafter it can be integrated into a dynamic mapping application or be used in detailed geo related analysis and reporting.  Any number of “groupings” or consolidating factors can thus be applied.

GEO MAPPING of customs data after resolution of company name iterations, disparate locations and other aberrations.

Expanded information about the vessels (ships), containers (types, sizes), container owners, ports, carriers (SCAC reference) and any number of “connectable” relevant databases can also be linked during this step of the process. The possibilities are as infinite as imagination and business requirements dictate.

Referential database utilized to enhance normalized AMS Customs data. Click to enlarge and open in a new window.

At this point, the U.S. Customs data has been imported, organized, cleaned, groomed and dressed.  It is now ready for “prime time”.  It’s show time in the data world.  The data is now ready to move into an “exportable” mode.  Therefore, it is organized (and refreshed) within a distinct database of its own.

All the processes, detailed over the last several days, tens of thousands of individual BOLs, have been completed in a number of hours.  These routines run habitually every day.  All databases and processes – through the creation /update of the “AMS Trade Data Export” DB – occur internally and securely (behind the veil).

Now four processes, involving six databases have been completed.  Thereafter, two additional processes are initiated on a weekly basis. One integrates various client’s proprietary data with our completed Customs Data and updates the databases upon which their respective web applications sit.  The second process refreshes the reporting repository from which the host of commercially available web applications draw.

The Journey of Customs Data Transformation from Raw data to Trade Intelligence. Click to enlarge.

Transformed Customs data, integrated with other statistical, company trade and economic data sources can be a powerful tool to navigate and succeed within the multi-trillion dollar international trade marketplace.  A plethora of applications and services can be enhanced by the skilled utilization of Trade Intelligence.

Customs data tracks over $1 Trillion of U.S. Imports annually.

The collection of programs, procedures and referential databases with which we transform raw data into usable business intelligence we refer to as our “A.I.” (Artificial Intelligence Engine). It, along with our huge data repositories of statistical, company and transactional data collected over the years, together represent the primary assets (along with the human intelligence and experience by which to integrate, develop and deploy them) that we are offering for license, sale or joint venture consideration.

A.I. Artificial Intelligence Engine transforms data into intelligence to support international trade services and applications.

Please refer to our Commercial Services Menu on the top navigational bar of this site for information on Application Licensing, Research & Writing Services, Database Repositories, Artificial Intelligence Engine as well as other Consulting, Project Management and Application Development Services.

Two of the most important normalization processes are accounting for the many iterations of company names and establishing an accurate company location.  See the previously published article, “The ABCs of U.S. Customs Data- Issues & Shortcomings“.  There can be many dozen iterations of the same company name.  This wreaks havoc with the veracity of the data under analysis.  The problem is evident is a cursory review of trade intelligence applications offered by most data vendors.

Text Strings converted into Data tokens for analysis and processing

In order to resolve these issues, the name and address fields contained on the bills of lading (for both shipper and receiver) are broken down into “tokens” and compared with a dynamically evolving referential database of “resolved” names and addresses.  Actually, accurately “geo-locating” the entity is the simplest of the two tasks. Zip codes, for the U.S. at least, follow a predictable pattern and typically occur at the end of the text string in the  “address” block of the flat file.

The two diagrams below are tables utilized within the fourth database involved in the third step of the transformation.  The first diagram shows elements that are utilized to resolve company location.  The second shows those necessary to resolve company name.

A separate, complimentary and very important utility – called the company-location resolver – is THE essential cornerstone of the A.I. (Artificial Intelligence) Engine and is required to dynamically evolve and “educate” the system.  More on that later.

Database used to "normalize" a location for an entity. This is the basis of the innovative geo-location feature utilized in the Prospects Trade Intelligence platform.

Normalization of the shipper or importer is essential to veracity in analysis and reporting. It's a challenging task to say the least. Most Purveyors of Trade Intelligence products using Customs Data don't even bother.

The location – company match utility is a very nifty accessory and vital component of the A.I. Engine.  Although the system is set up to quickly, accurately and automatically normalize U.S. Customs data, it also has the capacity to “learn” and improve its performance over time. Some of this learning takes place automatically over time as it gains more and more experience performing its daily processing rituals.  Adjunct education is interjected manually.

For instance, perhaps during the last several days/weeks/months processing routines, our A.I. Engine encountered some company name iterations that it hadn’t handled before and wasn’t in its library of established “tokens”.  Conveniently, it would display these unresolved iterations, ranked by the number of occurrences along with likely matches.  With one stroke an operator could resolve and match all particular aberrations or variations on a particular supplier or importer name or location… sometimes representing several hundred or thousand individual BOLs.

Thus the A.I. Engine learned something new.  And unlike its human counterparts, it will never have to ask the same question again.

Despite the sophisticated array of advanced technology deployed to automate the process of transformation, sometimes semi-automated manual intervention is called for.

The location – company match utility also can be used to link unlinked branch locations to their respective parent company or regional/ divisional headquarters.  Furthermore, it can process and link a proprietary client’s database of customers as well.  In this fashion, one can monitor customer’s trading activity and supply chain operations on a daily basis! This information can be incorporated into a web application which is distributed within the secure company intranet or protected proprietary web site.  An example is Panalpina, one of our  previous (CenTradeX) clients wherein we integrated their proprietary information into a customized web application for distribution to their regional sales offices.

It took us several years (at CenTradeX) to develop an intelligent system by which to quickly and seamlessly assimilate the daily Customs feeds.

Over time we developed and incorporated automated procedures and administrated them under an umbrella control panel. Statistical data update processes from U.S. Census and U.N. Comtrade were initiated from this centralized control panel.  U.S. Customs data, initial processing and normalization as well as company, parent and location matching, were also conducted from the same control panel.

A detailed diagram of the individual components that make up the control panel (as a constituent part of the A.I.Engine) can be downloaded from Google Docs by clicking this link.

Component of the A.I. Engine. Control panel by which to initiate data import

Company data collections (from sundry vendors because each contained its own unique non-standardized characteristics) were initially processed utilizing different arrays of queries and procedures.  They were then integrated into the combined company repository which, in turn, were correlated with the U.S. Customs and statistical data.  See U.S. Customs Data Primer Part 4: Enlightenment Through Graphics & Diagrams for illustrative diagrams.

Data sources. Processing schedule

U.S. Customs Data that we referred to as AMS – automated manifest system – went through six distinct processes which are depicted below.  An illustrative diagram of all six processes and eight sequential databases (or collections) can be viewed by clicking this link.

Customs data is received and processed on a daily basis, but the final, resultant databases utilized to serve up web reports were refreshed weekly to allow for enhancements (beauty treatments) and interconnectivity with other data collections.

AMS (Customs) Data requires many steps of parsing, normalizing, refining, integrating and optimizing before it is ready for “prime time”.

Let’s look at the steps from the beginning.  Roughly speaking, the first task is to import all the data properly – correctly parsing all the elements contained in the original “flat file” and organizing them within a relational database.  Every data element and every permutation and aberration must be accounted for.  The diagram below depicts the second of seven databases (the first “DB” is really just a collection of the all the raw AMS or Customs data itself). This database is resultant and refreshed daily from the first processing step.

Parsed Customs data sorted and organized within a relational database structure. Click to open image in a new window.

A high(er) resolution depiction of the above diagram can be obtained from our Google Docs site, by clicking this link.

Next comes the “normalization” process, wherein each element of parsed data is refined and standardized. For instance, a simple Port code, whether foreign or domestic,  has its corresponding state, province /region, country and normalized name.  Each container code is translated into presentable information about its type such as refrigerated or non, height, length and particular identifying number. Within this normalization process company name, address, and contact iterations are resolved as well.

Below is a diagram depicting the third of eight databases after the second step along the Customs data transformation journey.  A high(er) resolution image is available for download from our Google Docs site.

The second step in the Customs data transformation process. Click to open in a new window.

The original CenTradeX Trade Intelligence platform was developed over a three to four-year time frame from Spring 2000 to Spring 2004.  Initially, we focused on bringing a host of value added features to statistical U.S. import and export data from U.S. Census.  Thereafter, we layered and connected this U.S. centric data with global import and export data (from U.N. Comtrade) on approximately 190 countries.  In time, we also incorporated U.S. state export data into the mix.  Surprisingly, no one had ever layered these data sets together before.

HTS (Harmonized Tariff System) Classification schema. The essential “language” of international trade statistics.

Atop these statistical data collections, we crafted a graphic, interactive interface wherein users after selecting an “X” product vector (one of the 6,000 Harmonized System product classifications) and a “Y” location vector (one of 200 countries listed) were (almost) instantaneously presented with dozens of dynamically created reports representing many perspectives pertaining to the intersection of their choice.  Over a billion unique reports could be potentially generated by the system. These included historic analyses spanning 20 years, trends 1, 3, 5 years into the future, contextual reports for the respective region and industry, competitive analyses, product/industry segmentation and trending, etc.  One economics /international trade professor remarked that the system made “data dance”.

Over a billion dynamically generated reports could be produced by the CenTradeX system

Market testing and resulting feedback, compelled us into the task of finding and incorporating company data – both foreign and domestic – toward the objective of uncovering the actual traders behind the statistics.  Economics and trending with numbers is one thing… pinpointing buyers and sellers is quite another.  Consequently, we apprehended and assimilated the best known company sources (at the time) including; Kompass, Harris Info, Hoovers, D&B, PIERS and others.

One of the most challenging aspects was that statistical data is organized under one (HTS) classification schema while company information is organized under other (unrelated) systems (SIC, NAICS, or a vender’s particular proprietary taxonomy). However, after successfully tackling that enigmatic brainteaser, we were able to incorporate other data sets, tariffs (for all countries and products), estimated shipping costs (from four U.S. port regions to any /all countries), foreign exchange as well as our clients’ proprietary data collections and others… with relative ease.

Connecting disparate data collections is challenging

By far the most arduous of our data transformation and enhancement endeavors was to understand, normalize and intelligently incorporate U.S. Customs data into this dynamic mix.  ALL OTHER purveyors of Customs data started the other way around.  Some (the best) have consequently connected some other data elements.  Most notable among the few is PIERS, who several years back contracted with D&B to “tag” their data collection of U.S. Importers & Exporters.  Datamyne is presumably undertaking a similar process now.  Panjiva has connected with reasonable success many other vendor’s data pertaining to foreign sourcing. To my knowledge, at the present, no others have made those necessary connections.  Zepol has begun offering statistical data, but not connected to Customs data. They remain in separate unrelated silos.  Suffice it to say, there are still significant vistas to explore and develop.

The 45 second video (slide show) below is an irreverent depiction of  what many users have reported experiencing when trying to find solid “trade intelligence” amidst the seemingly endless sea of  Customs data obscurity.

In the past we published a series of 5 articles; “U.S. Customs Data Primer”, Parts 1-5, about the particulars of understanding, processing and enhancing the daily transactional inbound shipping records published by DHS/Customs.  This article will expand upon the fourth article in that series, “U.S. Customs Data Primer Part 4: Enlightenment Through Graphics & Diagrams” which provides a visual guide for the processes we at CenTradeX employed in transforming raw data into trade intelligence.

The 90 second video (slide show) below portrays the original Trade Intelligence vision and mission that fueled the innovative growth and development of CenTradeX.

Developing innovative and powerful trade intelligence applications involves attending to three major areas: Access, Integration and Delivery.

• Access: Helping the target audience(s) understand, find and use the data and application.
• Integration: Normalizing of base data and connecting it with other relevant data sources.
• Delivery: Enhancing the speed, efficacy and beauty with which the combined data is organized and presented.

For the purposes of this series, we will only focus on the second aspect, Integration.  Furthermore, having provided a foundation of understanding through the above referenced (linked) article, we will proceed to explore the more technical (under the hood) facets involved.

I refer to U.S. Customs waterborne import manifest data as the “base” data because it is considered (by myself and many others) the most intrinsically valuable, if challenging, international trade data set available.  It’s daily. It’s transactional. The U.S. is considered the easiest market to access. It contains a wealth of detailed information about the global supply chain. It represents $1 trillion dollars of trade a year.

U.S. Customs data is #1.  It’s THE KING of the international trade jungle.  However, a powerful Kingdom is more than just one regal personage.  It must include a capable entourage as well. Thus the need for complimentary data sets.

The first, primary step in building a powerful trade intelligence “kingdom” is attending to the King. PIERS has an easy to understand graphic portraying  the processes of normalizing “base” Customs data layer.

PIERS graphic portraying the processes involved in Normalizing Customs data

In performing the seven steps highlighted above, we at CenTradeX developed and refined many sophisticated procedures.  Over time, and through much scrutiny and evolution, we constructed a reliable, interconnected system of transforming data into intelligence.

• It involved an array of automated queries and stored procedures for importing new data on a regular basis.
• It involved created programs and “scripts” that would parse, tokenize and reference selected data elements, compare and contrast them with its expanding library and referential databases as well as “learn” better ways of matching and connecting.
• It involved scouring the planet for the best, most reliable, accurate and timely ancillary databases to enhance and expand the KING and the Kingdom.

This week we went under the hood to look at nature and application of Customs data that tracks U.S. Waterborne Import Shipments from Overseas Suppliers and Sellers.

• U.S. Customs Data Primer Part 1: You Can’t Always Get What You Want… BUT
• U.S. Customs Data Primer Part 2: “Holes” in the Data & Other Frustrating Anomalies
• U.S. Customs Data Primer Part 3: The Devil (or a Worthwhile Treasure) is in the Details
• U.S. Customs Data Primer Part 4: Enlightenment Through Graphics & Diagrams

There are a number of previous articles wherein I have referred to other shortcomings and challenges inherent with the understanding and applying U.S. Customs data.  Please note the following:

• TI Transformation: Data into Information into Knowledge into Intelligence into Application. Current trends and commoditization of U.S. Customs data.
• Trade Intelligence or TI: IT all depends upon how you define “IT” and “TI”. The plethora of current “Trade Intelligence” suppliers of U.S. Customs data.
• Understanding Data: Normalization Procedures with U.S. Customs Data.  Normalization procedures for U.S. Customs data with accompanying charts
• U.S. Customs Waterborne Import Data: Perspective is Everything.  Need for a broader understanding and application of trade data.
• U.S. Customs (AMS) Waterborne Shipping Manifest (BOL) Import Data.  BOL fields listed with an excellent chart depicting relevant trade flows.  A must read.
• The Use and Application of Trade Intelligence Can Be a Matter of Life and Death. Case Study for application of U.S. Customs data in “tainted toy” (lead paint) fiasco.
• Three part series: The ABCs of U.S. Customs Data – Issues & Shortcomings.
  • Read Part 1. Problem with Supplier /Importer name iterations.
  • Read Part 2. Problem with Product identification and valuation.
  • Read Part 3. Problem with suppressed names and Master Bill of Lading versus House Bill of Lading.

We also published several dozen articles focusing on the current Trade Intelligence purveyors of Customs data.  The links provided below will pull up a handful of articles each – for a particular company, group of companies (in cases where they are “minor, second tier” providers) and summary evaluations. You can also find these articles, and others grouped by various categories, on the top navigation menu of this site.

• Overview of T.I. Providers 
• UBM Global Trade /PIERS
• ZEPOL
• DataMyne
• Import Genius
• Panjiva
• Other Providers

Please refer to our Commercial Services Menu on the top navigational bar of this site for information on Application Licensing, Research & Writing Services, Database Repositories, Artificial Intelligence Engine as well as other Consulting, Project Management and Application Development Services.

So now that we have addressed a few of the issues relating to understanding the inherent limitations contained within the U.S. Customs data, let’s look at the processes we (at CenTradeX) employed in parsing, normalizing and enhancing this data.  Every Trade Intelligence provider has their own approach to processing the data, along with their own particular brand of “spice” they add as well as the tools utilized to search through and display the data. Notwithstanding, the best few have many things in common.

Therefore I believe the following explanation may be both enlightening and educational, whether or not it is precisely mimicked.  First of all, let’s take a look at the “big picture”.  As reflected in the illustration below, Customs data contains detailed records – bills of lading – of the particulars of each and every transaction between foreign shipper and U.S. receiver for waterborne freight.

The Big Picture of Customs Data and the trade related information packed therein. 

As I’ve mentioned, Customs data is distributed (as a “flat file”) on a daily basis as the BOLs for various arriving “vessels” are cleared at the  respective U.S. ports.  The first, rather arduous process, is to “normalize” data into usable, organized elements contained in a relational database.

Normalization process for daily AMS Customs Flat files

We found that the best, most efficient method to add accuracy and value to Customs data, after the initial normalization process had been completed, was to connect it with our other comprehensive company and referential databases.  After going through many elaborate transitions, this enhanced customs data was ready for “show time”.

Diagram illustrating the processes involved in normalizing and transforming Customs data

We found that the more relevant ancillary databases we were able to connect to the Customs data, the more dimensionalized and powerful the individual portraits of trade and the underlying traders became and the broader the business applications and potential.

Connecting the Dots. Ancillary data sources to Customs data.

Trade Intelligence begins with data. It is the fundamental building block from which dynamic business applications are crafted.  To make delicious, even digestible Trade Intelligence you must adhere to some basic steps.

1. Get good, accurate, timely data.
2. Scrub it up, remove the “dirt”.
3. Mix it with other good data.
4. Cook it well.
5. Serve it with style.

Deriving Digestible Trade Intelligence from Customs Data takes finesse and diligence

Prospects, Trade Finance & Stats Plus; T.I. Applications developed by CenTradeX, acquired now marketed by PIERS

Several other Trade Information providers have developed some very powerful and cool applications incorporating U.S. Customs data.  Check out the top navigational menu of this site under T.I. Providers Links> Transactional, Article Categories> Suppliers as well as Trade Blogs> T.I. Providers and Video Library> T.I. Providers for more information on these companies and the products and services they offer.

Also, please refer to our Commercial Services Menu on the top navigational bar of this site for information on Application Licensing, Research & Writing Services, Database Repositories, Artificial Intelligence Engine as well as other Consulting, Project Management and Application Development Services.

Let’s go back to the intrinsic nature of the U.S. Customs Data itself.

U.S. Customs data is gathered electronically through the AMS (Automatic Manifest System), for sea, air and rail. However, only waterborne manifests are available publicly.  Each daily tally contains detailed records of the tens of thousands of shipments that arrive at U.S. ports, many millions of shipments each year.  Since we are a country of consumers and most imports arrive via ship, U.S. Customs Waterborne Import data represents MOST of our trade activity… to the tune of $1 trillion annually.

In spite of its inherent shortcomings, pause to appreciate the fact that detailed records of virtually every waterborne shipment, every foreign seller, every corresponding U.S. buyer, every product and component, every carrier, every port, for every day is made available publicly.  The potential value contained therein is staggering.  Most countries (perhaps wisely) don’t publish such information.  In some countries releasing such information would be /is a capital offense.

First of all, U.S. Customs data comes as a “flat file”.  It is not conveniently delimited for easy assimilation.  For each and every Bill of Lading (BOL) the respective data fields have a reserved number of characters rigidly assigned; some fields are filled with interesting data, others remain completely empty.  Analogous to a train hauling rail cars of varying lengths, each BOL must have its fields carefully unloaded and organized.  One mishandled BOL field can wreak havoc with accurate assimilation and analysis of the data.

For instance, there is a very important single character field contained in the data string that signifies whether the proceeding data for the BOL is original and new or whether it represents a revision of an already processed BOL (from a previous day!).  I have seen cases where there are several dozen revisions published to a single record occurring over a span of several months!

If not accounted for, you have multiple (and inaccurate) shipments counts.  The difficulty is that the only way to adequately correct the problem is to go back into already processed and published data to completely erase and replace the previous record or retain the inaccuracy.

Another significant problem is that there are many times multiple containers for one BOL AND/OR multiple BOLs for one container (LCL –less than container loads).  If not prudently accounted for there will be huge discrepancies when calculating TEUs (the standard measure for shipment volumes).

Implications?  If you are evaluating whether or not to construct a new distribution center, expand port capacity, open up a freight forwarding office, evaluate economic development, perform competitive analyses on a particular U.S. buyer or foreign seller, or deconstruct and improve supply chain logistics, what you don’t know or what you think you know (but is really fictitious) can kill you.

Once you know where the holes are, many times you can fill some of them. U.S. Census (statistical) data – which is published on a monthly basis – can give you an accurate measure of the value, number of units (and thus by computation the average cost per unit), country of origin and U.S. port for a particular product grouping (arranged within the Harmonized Tariff System) and method of transport (air, water, and again by computation “other” which would typically be rail /truck from Canada or Mexico).

Unfortunately, U.S. Customs data and U.S. Census data are asynchronous in many important ways.  For reasons beyond the scope of this article, it is impossible to take a record of all waterborne shipments for the month of January from U.S. Customs and seamlessly overlay it with the aggregate statistical record of imports provided by U.S. Census.  Further, the HS product categorization system is many times either too specific or too broad to apply.

Another problem is that several thousand U.S. importers and their corresponding foreign suppliers have been “suppressed” from appearing in the U.S. Customs data publications through the “trade secrets” exclusion to the Freedom of Information Act.  This “suppression” results in about 1/7 of all shipment records having blank fields where the “foreign shipper” and “U.S. importer” identification would normally be.

Again, once you know where the holes are, there are ways to work around them.  Wal-Mart is an obvious entity that attempts to mask its supply chain activities and valuable suppliers.  Notwithstanding, in a landmark report done on the “tainted toy” fiasco several years ago, we were able to extract 40,000 imported shipments of toys by Wal-Mart (of the 400,000 we retrieved) over an 18 month period of time.

How? Several methods. Although presumably “suppressed”, tens of thousands of transactions slip through the filtering methods applied by U.S. Customs technologies. Port pairs (matching foreign port with U.S. port) for a particular product also yield significant dividends.  The “product description” and “marks and numbers” fields contained on the shipping manifest sometimes contain references to either Wal-Mart or one of its known suppliers.  Product identification information – SKUs, trademarks, etc.- are also sometimes found.

It’s all a matter of sleuthing: trying to put together a complex puzzle from the resources at hand.  In the end, it’s an imperfect world with incomplete data.  However, with some effort, technological tools, multiple data sources along with intelligence and knowledge, you can discover an amazing amount of very valuable trade /business intelligence.  You just need to increase your awareness in order to align your expectations to what’s real and possible.

Most people seem to want what they don’t have.  I guess it’s human nature.  It’s that way with trade intelligence. Folks want to extract more information from it than what is intrinsically possible.  You just can’t get soda pop from milking a cow.

You aren’t going to get a complete picture of importers, supply chain, current inventory, shipment valuations and intrastate transport patterns from U.S. Customs data.  However, just because you can’t have everything, doesn’t mean you can’t get a lot.  As Mick Jagger sang, “You can’t always get what you want, but if you try sometime, you may find, you get what you need.”

To understand what you can and can’t get from U.S. Customs data… we need to dig into what it is and why it is… what it has and what it doesn’t have… what current T.I. providers are doing to enhance the base data…  where the holes are and how best to fill them.

U.S. Customs, now under the auspices of Homeland Security, requires detailed documentation of all waterborne shipments entering into the United States.  This information must be filed 24 hours before the shipment disembarks from its originating foreign port.

Once the carriers dock at their respective domestic port, each day’s documentation of shipments (midnight cut off point) is published and distributed via FTP (used to be sent via overnight on a DVD) to awaiting subscribers (of which there are only a couple handfuls). This is made available through the Freedom of Information Act.

First of all, with the exception of UBM Global Trade /PIERS, who has special reciprocal information exchange deals with many ports and carriers as well as a cadre of data gathers assigned to many U.S. ports, only daily transactional data on U.S. IMPORTS is available, NOT EXPORTS.  Thus, as a U.S. manufacturer, you aren’t going to find a list of foreign buyers for your particular product within the confines of U.S. Customs data.

Secondly, only commodities and products that enter the U.S. via SHIP (waterborne freight both containerized and non-containerized) are accounted for.  Shipments that come via TRUCK or RAIL, let’s say from our North American neighbors – Canada or Mexico – are invisible.  Also absent are shipments that come via AIR.

Therefore, if you’re looking for U.S. Customs data to provide information on shipments of high-tech components, you’re going to be very disappointed (because they are mostly shipped by air).  If you want competitive intelligence on a company who largely imports from suppliers in Mexico, again, you’re going to become very frustrated.  If you’re after an accurate analysis of all foreign suppliers and U.S. importers for a particular component that may have originated from several countries (including our NAFTA neighbors) and shipped by multiple means (air, rail, truck, ship), it just isn’t going to happen.  There are going to be huge, gaping holes in your report.

Available U.S. Customs data ONLY details inbound waterborne shipments, NOT U.S. exports and NOT trade activity by rail, truck or air.

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