by Mikael Lind, Research Institutes of Sweden (RISE), Richard Watson, University of Georgia,
Chye Poh Chua, ShipsFocus Group, David Levy, OrbitMI, Socrates Theodossiou, Tototheo Maritime,
Omer Primor, Windward, Alexio Picco, Circletouch
There are two key measures central to transforming the maritime industry to increase its sustainability and profitability. In this article, we introduce both of them, Eʹ (E prime) and Cʹ (C prime), and we discuss their relevance to operational efficiency, predictability, and strategic competitiveness. (1)
The world consists of two major ecosystems. The natural ecosystem produces the food we eat. Humans have created a capital creation system that produces the goods and services we consume. Success in the natural system depends upon capturing and storing energy, and we measure success in terms of energy efficiency, which we call Eʹ (E prime). Those that are efficient at capturing and converting energy survive. Every organization and industry need to raise its Eʹ in order to create a sustainable society. Energy expenditure is a component of nearly every cost of business. Eʹ is the invisible fuel, and the resulting savings go directly to the bottom line and enhance sustainability.
Every organization is a capital creation system and has a recipe for converting one of six types of capital (economic, human, natural, organizational, social, and symbolic) to another form or enhancing one form of capital. The transport industry, composed of multiple value-producing organizations, creates economic capital by moving goods from producer to consumer. In so doing, it relies on, for example, human capital (a ship’s crew), economic capital (a ship), and social capital (connections to shippers and shipping agents/port operators). Successful capital creation is measured by capital productivity or Cʹ (C prime), and in the shipping industry this is influenced by Eʹ because ships require large amounts of energy.
Managing a ship requires balancing these two primes. To maximize Cʹ you want to spend the least amount of time traveling between ports and in ports and carry the largest possible cargo on each leg. In other words, you want to maximize port visits so you can carry more cargo with fewer ships. However, as you are aware, this not a trivial management task, as exemplified by the following challenging questions:
Answering these questions requires a shipping company to have insights into the needs of its customers and the capabilities and plans of its service providers. It also means understanding how the relationship between E’ and Cʹ varies for major decisions. Raising Eʹ by green steaming between ports might lower Cʹ as a ship will be able to make fewer port turnarounds. On the other hand, a higher speed over water could raise Cʹ and lower Eʹ. Furthermore, when a ship cannot immediately enter a port for cargo handling, then there is no loss in Cʹ if ship green steams to arrive just-in-time.
You can raise Eʹ by green routing between ports so that a ship takes the path of least resistance in terms of wind, currents, and depth under keel to minimize energy consumption. Combining green routing and steaming (2) will generate a higher Eʹ.
Overall, the critical metric is Cʹ and its value is determined by initial capital costs for building, buying, or leasing a ship and operational costs, which are mainly determined by Eʹ. We are evangelists for developing Maritime Informatics (3) to give ship owners or ship charters the wherewithal for the dynamic short-term management of Cʹ and Eʹ, say between three or four ports, to maximize the lifetime Cʹ of a ship.
Astute captains and shipping owners intuit what you have just read, but they likely use different terms. Our goal is to work with the shipping industry to generate the data needed to compute Cʹ and Eʹ and develop the data analytics to maximize Cʹ for a shipping fleet or each voyage. We first need to start with a common understanding of the fundamentals, which we argue are Cʹ and Eʹ, and then we can build the digital infrastructure to manage them.
Importantly, the values of C’ and E’ are dependent on the actors participating in the co-production of transport between manufacturer and consumer. They must continually inform each other of their progress and plans to minimize delays and raise capital utilization. As the maritime industry is a self-organizing ecosystem, this means the various parties must share standardized digital data(4) as needed to enable tight integration of operations when required. Collaboration greases Cʹ. This is particularly true in the current economic and maritime situation, where many shipowners are extending their operations to, cover the entire multimodal logistic chain. In this respect, digital data sharing is essential for the effectiveness of this entire chain. By improving E’ for the different modes (e.g., optimizing loading/unloading trains or optimizing truck payload). Such improvements will be reflected in a higher C’ of the different actors in the chain.
The optimization of Eʹ and Cʹ is enabled by data sharing and data analytics. Without standards for digital data sharing about all aspects of transport, such as the status of every container (5), optimization is infeasible. Once a foundation of data sharing standards is established, then data analysts can combine real-time digital data streams and historical databases to apply techniques such as artificial intelligence, machine learning, and digital twins (6) to raise the quality of decision making in the pursuit of higher values for Eʹ and Cʹ. Maritime and logistics companies are already working on problems such as the optimization of vessel operation and predictive and prescriptive solutions to various critical aspects of the logistic chain.
The Energy Efficiency Operational Index (EEOI) is a current measure of E’ appropriate for the shipping industry. It refers to GHG emissions per unit of transport work, such as grams of CO2 per ton-mile (gCO2e/tnm). In practical terms, this KPI recommends that ship operators maximize the number of fully laden holds for the fewest number of legs on each voyage. As well, one could measure Megajoules per ton-mile, which would measure energy usage directly.
From an operational standpoint, managing to reduce EEOI begins by collecting relevant and accurate data. These data include vessel type, size, its route, reports from the ship, engine/equipment performance, weather conditions along that route, port congestion, availability of berths at each port and of course, speed and bunker consumption. Each of these data sets helps to paint a picture of a vessel’s performance while steaming and while conducting activities in a port, which also requires using fuel of some type.
For each of the needed data sets —and there are more—there is some inherent level of inaccuracy, and they are generated or updated at different times and frequency, from real-time, to quarterly. Some data feeds are subject to the availability of the Internet and can, in fact, go missing for days. AI and machine learning can enrich, validate, correct and present data in an actionable way to maritime decision-makers. When there are missing entries, machine learning can interpolate based on historical data.
As an energy and capital-intensive industry, shipping needs to build a digital foundation spanning from data standards to new analytics tools to digital twins that makes feasible effective measurement and management of Eʹ and Cʹ to improve decision quality. To have some influence over the future, you need to participate in making it. You want to prejudice your future by shaping today’s key decisions. Maritime Informatics is an emerging discourse joining practitioners and researchers to explore the opportunities enabled by enhanced digital data sharing, digital collaboration, and data analytics to optimize Eʹ and Cʹ. Thus, we argue that a body of industry innovators and scholars need to coalesce around the theme of Maritime Informatics (7) to create an overarching set of forums for addressing the full range of issues relevant to Eʹ and Cʹ. The captain steers the ships between ports, and Maritime Informatics can chart the industry’s voyage between the present and its digital future.
Mikael Lind is Associate Professor and Senior Strategic Research Advisor at RISE and has a part-time employment at Chalmers University of Technology, Sweden. He serves as an expert for World Economic Forum, Europe’s Digital Transport Logistic Forum (DTLF), and UN/CEFACT.
Richard T. Watson is a Regents Professor and the J. Rex Fuqua Distinguished Chair for Internet Strategy at the University of Georgia. He has written books on Data Management; Electronic Commerce, Internet Strategy, Energy Informatics, and Capital, Systems, and Objects and has published nearly 200 journal articles.
Chye Poh Chua is founder of the ShipsFocus group and a venture partner at Quest Ventures. His interest is in maritime digitalization and inclusivity, helping MSMEs narrow the digital divide.
David Levy is Chief Marketing Officer of OrbitMI, a maritime software company headquartered in New York City with offices in Sweden, Norway, and Serbia, whose purpose is to unlock the value in data to help maritime become more efficient, profitable, and sustainable.
Socrates Theodossiou is the owner and co-CEO of Tototheo Maritime, a maritime technology company specialised in optimizing vessel and fleet performance. His experience includes designing and rolling out integrated software solutions for data analytics as well as communications’ and secure network solutions.
Omer Primor is a specialist in maritime predictive intelligence and has collaborated with and advised organizations worldwide on optimizing operations and mitigating risk. He is a former Intelligence Officer and currently Head of Marketing at Windward.
Alexio Picco is Managing Director at Circle Group, providing IT solutions for ports and logistic actors. He was involved in digital transport projects since 1996. Currently he is the coordinator of the project on the port of the future (DocksTheFuture) and the leading consultant to support the EU Coordinator for Motorways of the Sea. He works also as IT transport specialist for the European Commission (e.g., EMSWe, TAF TSI).
1. The concepts of Eʹ and Cʹ are introduced in Watson, R. T. (2019). Capital, Systems and Objects: The Foundation and Future of Organizations. Athens, GA: eGreen Press.
2. Watson R.T., Holm H., Lind M. (2015) Green Steaming: A Methodology for Estimating Carbon Emissions Avoided, Thirty Sixth International Conference on Information Systems, Fort Worth 2015
3. Lind M., Watson R., Hoffmann J., Ward R., Michaelides M. (2020) Maritime Informatics: an emerging discipline for a digitally connected efficient, sustainable and resilient industry, Article No. 59 [UNCTAD Transport and Trade Facilitation Newsletter N°87 – Third Quarter 2020] (https://unctad.org/en/pages/newsdetails.aspx?OriginalVersionID=2456)
4. Lind M., Becha H., Simha A., Bottin F., Larsen S. E. (2020) Digital Containerisation, Smart Maritime Network, 2020-06-18 (https://smartmaritimenetwork.com/wp-content/uploads/2020/06/Information-transparency-through-standardized-messaging-and-interfacing.pdf)
5. Becha H., Lind M., Simha A., Bottin F., Larsen S. E. (2020) Standardisation in container shipping is key to boosting economies of scale – importance of data collaboration between shipping lines, Smart Maritime Network, 2020-05-14 (https://smartmaritimenetwork.com/wp-content/uploads/2020/05/Standardisation-and-the-importance-of-data-collaboration-between-shipping-lines.pdf)
6. Lind M., Becha H., Watson R. T., Kouwenhoven N., Zuesongdham P., Baldauf U. (2020) Digital twins for the maritime sector, Smart Maritime Network, 2020-07-15 (https://smartmaritimenetwork.com/wp-content/uploads/2020/07/Digital-twins-for-the-maritime-sector.pdf)
7. Lind, M., Michaelides, M. P., Ward, R., & Watson, R. T. (Eds.). (2020). Maritime Informatics: Springer. https://www.springer.com/gp/book/9783030508913