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Panama Canal Authority

Panama Canal Authority extracts valuable insights from big data to grow market share

With responsibility for managing one of the world’s foremost shipping channels, this agency set out to estimate how a planned $5 billion expansion would impact its revenues. Its managers successfully predicted how the canal’s expanded capacity might transform its market share – including a 140% increase in petroleum products transit by 2017.

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  • Quantify the revenue-generating impact of the canal’s expansion
  • Revise existing model to reflect the canal’s ability to accommodate larger ships
  • Protect and advance global shipping market share


IHS Advanced Analytics Solutions


  • Captured growth opportunities that would have otherwise been neglected–including a 140% forecasted increase in petroleum products to transit through the canal by 2017
  • Replaced traditional model with a more robust accurate model for conducting market planning and preparing budgets
  • Benchmarked competitor routes in terms of cost and risk
  • Set and adjusted transit fees as needed to compete with alternate routes

Panama Canal Authority Discovers Growth Opportunity

Data is a primary intelligence tool: it enables companies to analyze the market and position themselves competitively within it. Similarly, government agencies can use data to create more effective, fiscally responsible policies. Managers, however, are often overwhelmed by the sheer volume of data arising from greater global connectivity and the Internet of Things. While no universally agreed upon definition exists for the term “big data”, it generally refers to the four “Vs”: volume (quantity), velocity (quickly collected or produced), variety (multiple types), or value (advantageous). Identifying and gathering the right data, effectively analyzing it with the strongest possible model, and unearthing helpful insights is a persistent and sizeable problem most organizations confront today.

Determining the Need for a New Model

The Panama Canal Authority (ACP) faced this very challenge when it set out to estimate how a planned $5 billion expansion of its canal would impact its revenues. The ACP already collected data on the nearly 11,000 ships that passed through its corridors each year. It was able to project, based on historical data, how changes in the number of ships would likely affect profits. However, the canal’s proposed expansion called into question the use of this traditional model since the canal would be able to accommodate much larger ships. Historically, ships 106 feet wide by 956 feet long could cross the canal; after the expansion, Supermax ships 160 feet wide by 1,200 feet long would meet clearance requirements. The expansion would also allow each ship to triple its number of transportable containers, from 4,800 to 12,500.

With shipping companies having multiple alternatives to the Panama Canal—such as the Suez Canal, Cape Horn, Cape of Good Hope, and water-overland combinations—the ACP needed to accurately and quickly predict how the canal’s expanded capacity might transform its market share. A robust model would enable the ACP to position itself more attractively with regard to key factors that shippers use to select their routes: items such as bunker fuel costs, canal transit fees, and port duties.

Imaging Possibilities over Following Instinct

The ACP could have resorted to traditional forecasting based on the use of historical data to predict future revenues. Indeed, the ACP’s first model took this approach with a view to answering one question: How will the canal’s expansion impact my available market? However, after expert analysis and discussion, the canal authorities realized the picture was more complex. They needed to incorporate a much broader set of data into the analysis if they were to connect the dots among the multiplicity of changes occurring in globally traded goods and commodities. Quite simply, they needed to architect a new model based on a new set of interdependent questions.

To craft a solution, a team of ACP representatives and IHS experts in maritime, trade, energy, chemical, automotive, and economics met at a workshop. The team’s goals were specific:

  • Identify the data needed for accurate forecasting
  • Define the analytics and models required to extract insights from the data
  • Develop the tools and visualizations needed to communicate these insights

A workshop leader moderated the discussion, asking stakeholders for information and insight and drafting outlines and visualizations to critique. The discussion allowed stakeholders to hear alternative perspectives and build consensus. As a result, the participants agreed the right solution would require a larger dataset along with changes to the model’s design, calculations, and proposed visualization.

By tracking the metric tons of refined petroleum products that travel from the United States to Asia and South America via the canal, the model revealed a significant, potential gain in the canal’s future market share… 140% relative to 2009.

Enhancing Predictive Power

The interactive, meta-model that the team designed detailed trade flows by good, vessel, origin, and destination to reveal new shipping routes and requirements—findings the historical trade model could not have anticipated. Whereas the old model focused on one question, the new model aspired to answer three:

  • How is the world’s fleet evolving?
  • How will demand-supply shifts for commodities impact shipping?
  • How will carriers optimize shipping methods and routes to minimize costs?

Answers to these questions equipped the modelers with greater predictive power and cause for optimism. They were able, for example, to forecast the likely effects of the US unconventional oil and gas revolution on the canal’s revenues. By tracking metric tons of US refined petroleum products that travel to Asia and South America via the canal, the model revealed a significant, potential gain in the canal’s future market share. It predicted that the annual tonnage of refined petroleum products that would pass through the canal by 2017 would increase by 140% relative to 2009. To arrive at this rate, the model integrated data for well counts, production volumes, refinery capacity, and buildout plans—predicting how production and, therefore, exports could increase tonnage crossing the canal. If the ACP had relied on its old model based on historical trends and GDP-to-tonnage shipped relationships, the opportunity cost would have been huge. By leveraging a more holistic model, the ACP unlocked a substantial growth opportunity.

Leveraging Deep Expertise to Extract Value

The ACP’s use of big data illustrates how applying best practices leads to better results. Because the ACP was willing to combine its data with data from other industries in a way it had never done before, it gained a strategic planning tool that enabled it to:

  • Capture market growth opportunities that would have otherwise been neglected
  • Conduct market planning and prepare budgets
  • Benchmark competitive routes in terms of cost and risk
  • Set and adjust transit fees to make them competitive relative to alternate routes

To successfully harvest valuable insights from big data, however, organizations need experts who have a deep understanding of one’s markets, industries, and related industries. These experts can help narrow down which data to use, ask the right questions, and engineer an iterative model that integrates real-time data while enabling fine-tuning when needed. By tapping into the knowledge of IHS advanced analytics experts, the ACP avoided strategic errors, getting waylaid by external forces, and hitting unforeseen roadblocks based on outdated information or faulty analysis. Most importantly, the agency uncovered a huge revenue opportunity and gained a robust instrument for effectively navigating the global shipping market.

By tracking the metric tons of refined petroleum products that travel from the United States to Asia and South America via the canal, the model revealed a significant, potential gain in the canal’s future market share… 140% relative to 2009.

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