Surveyor 2018: Volume 1

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2018 VOLUME 1


The Digital Evolution of Asset Management



Robots that Know Their Way Around


Inside Digital Transformation


At the core of the digital revolution, a heartbeat Embedded within this image is a very important truth about the digital revolution now occupying the minds and movements of many maritime organizations. Rolls-Royce made the image to illustrate its futuristic concept of a command center for intelligent marine assets, but, in expressing the advanced technologies involved in the evolution of smart shipping, it also places people squarely at the center of that transformation. The importance of people in the digital revolution, or in any technology-based upheaval, cannot be overstated. Although technology is often a vehicle for great change, it is always built and steered by human beings – guided by the vision of leaders and realized by the actions of staff. Today, new digital technologies are enabling people to work more efficiently, make more informed decisions and respond with greater agility to changing circumstance. At the same time, enlightened industry leaders recognize these fantastic systems and devices as just sophisticated tools, and are providing their people with the education, training and support needed to understand, absorb and make best use of them.

Š Rolls Royce


COVER The future takes shape today. The cover image, created by PSA International to illustrate the digital transformation of the Port of Singapore, hints at the high level of connected technologies now employed to bring new levels of safety and productivity to leading ports and terminals around the world. Through use of new digital technologies, businesses across the maritime industry are discovering new ways to boost efficiency, new methods of solving problems, new operational philosophies, and even new ways to cultivate staff talents. This issue of Surveyor looks at some of the ways digital transformation is being realized around the industry.



The Digital Evolution of Asset Management

Crowley Maritime Corporation pilots data analytics onboard ship-assist tug, Guide, to take its reliability culture to the next level.


Robots that Know Their Way Around

The Perceptual Robotics Laboratory (PeRL) at the University of Michigan develop algorithms by which robots process perceptive data.


Inside Digital Transformation

Insight from Wärtsilä on transitioning from a traditional industrial company to an as-a-service, smart technology industrial company.


Ports and Terminals in the Digital Era

Published by ABS ABS Plaza 16855 Northchase Drive Houston, TX 77060 USA Tel: 1-281-877-6000 Fax: 1-281-877-5976 Email: Web: For permission to reproduce any portion of this magazine, send a written request to: Editorial Joe Evangelista

Fundamentally changing transport logistics for the better — big data, analytics, and automation making ports and terminals faster, cheaper, and smarter.

Graphics Sharon Tamplain


Copyright © 2018

Viewpoint: The “People Part” of the New Norm

Susan Lundgren, Manager

New skillsets, new mindsets, new leadership and a culture of continuous learning will fuel the digital revolution.

Photo Credits: Cover: PSA International; IFC: Rolls-Royce; 2-5: Crowley Maritime; 6: ABS; 7-9: Perceptual Robotics Laboratory, University of Michigan; 10-12: Wärtsilä; 13-14: Port of Rotterdam; 15-17: ABS The opinions and conclusions contained in this publication are solely those of the individuals quoted and do not reflect, in any way, the position of ABS with regard to the subjects raised. Although every effort is made to verify that the information contained in this publication is factually correct, ABS accepts no liability for any inaccuracies that may occur nor for the consequences of any action that may be taken by parties relying on the information and opinions contained herein.

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Crowley Maritime Corporation outfitted the Guide with a monitoring system to test the impact of data analytics on operational efficiency.



undits and proponents of big data analytics argue that this latest evolution of computerized data analysis can improve every aspect of a business’ life, from operating efficiency to workforce health. Many early adopters, however, have discovered that bigger isn’t necessarily better. There is so much data available, about so many things, and so much one can do with the information – that, without a strong set of priorities and a hard focus – one can very easily fall down a rabbit hole of data hoarding, deluded speculation and dubious correlation. That said, no company is known to have died of a data overdose, yet; the big question about big data many organizations are asking right now is, how to use it to best advantage. Today, a number of companies in the maritime sector are testing various aspects of the big data promise and finding

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that, while the nature of data means there is no onesize-fits-all answer to their questions, there is an encouraging reality behind the hype. Such is the experience of San Francisco, Californiabased Crowley Maritime Corporation, a marine transportation leader with global activities that operates a fleet of over 150 vessels ranging in size from small harbor tugs to Aframax Tankers. In early 2016, Crowley installed a remote monitoring software system on a harbor tug operating out of the Port of Seattle, to determine whether big data analytics could have a positive impact on the organization and its operations, and what the extent of that impact might be. The custom-built vessel monitoring and diagnostics system was supplied by the Marine Asset Intelligence (MAI) division of US engine maker Caterpillar. It monitored the vessel’s main engines, generators,

thrusters and other critical systems around the clock for six months feeding the data to MAI analysts – who scrubbed and parsed it to generate advisory and management reports. The reports included maintenance recommendations for specific pieces of equipment as well as suggestions on ways to lower costs and increase operational efficiencies. “We’re looking at it as the next level of management and optimization, and we want to see how it can help us increase reliability, safety and efficiency onboard our vessels. It’s the next logical step in marine technology, and we want to be early adopters if it adds value,” Bill Metcalf, Crowley Maritime’s Vice President of Strategic Engineering, said at the outset of the experiment. The test proved to be an eyeopener for all involved, indicating that data analytics could bring a range of benefits to the fleet and help the company figure out how to turn those indications into fruitful reality. Purely by coincidence, the vessel chosen to help guide the company towards a digital future through its role in the experiment was a ship-assist tug named Guide. “We selected the Guide, an 18-year-old harbor tug, mostly for reasons of availability – harbor boats work eight to eighteen hours a day, and the ships they handle typically come in during the morning and go out during the afternoon,” Metcalf says. “That gave us a good opportunity to go onboard and to pull cable, install equipment and run tests, etc., as needed. We wanted to experience data analysis, see the kind of data that comes off a vessel, and find out if it could help us with operations and maintenance – fuel management, electrical load analysis and so on – and determine how it could fit in with our existing maintenance program,” he adds, noting that the prime mover for the experiment was the company’s reliability culture. “At Crowley, we sell safety and reliability; that’s why our customers choose us, and how we build durability with them,” Metcalf says. “Now we want to take this reliability culture to the next level, which includes big data.” That evolution involves a cultural shift in maintenance, from a visual, calendar-driven approach to an analytical, data-driven approach, he explains. For example, instead of mandatory visual inspection or replacement of a pump after, say, 20,000 hours of operation, sensor data from the equipment would be analyzed for indications that maintenance or intervention is needed. Constant, live equipment monitoring and data crunching are giving vessel operators new

Bill Metcalf, Vice President of Strategic Engineering, Crowley Maritime

opportunities to manage their assets differently and better. This is particularly true in maintenance and repair, where data analysis is helping reduce the frequency of invasive inspections and the associated risks to sensitive equipment. “Years ago, with the big slow-speed diesels, every so often you’d have to drop the main bearing for inspection,” Metcalf says. “There’s a risk of damage when you do that, from dirt or other contamination, or from just not reinstalling the bearing correctly. Now, through sensors, you can do oil and temperature analyses and prove the bearing is running fine. You can also do wear-down analysis of a tailshaft by analyzing performance data and measuring how much it is dropping,” he adds. “This data could also be used from a classification perspective – if you could provide the class society your test data, and they accept it, you wouldn’t have to drop the tailshaft. That means you would not only save the cost and downtime of the visual inspection, but also eliminate the risks that stem from opening it up.”


Data analysis can also bring to light invisible inefficiencies. This happened during the Guide’s engine performance analysis, when it was discovered that one engine was running at a higher load than the other, and that one bank of injectors was not firing as well as it should have. From the perspective of a single boat, these accounted for marginal differences in efficiency, but from a fleet-wide perspective indicated the potential for big data to make a big difference to the bottom line. Live performance monitoring and data analysis can also make a big difference in maintenance scheduling, guiding companies on how to get the most out of both their equipment and their maintenance budgets. By

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spotting the earliest stages of failure, performance data analytics let operators test and weigh their options regarding management or replacement of critical parts. Take, for example, a fuel pump to the main engine that is supposed to be running at 100 psi. If it starts running at 98 psi, the monitoring system would detect the change and alert the analysts, who would inform the operator that that the pump is starting to go, but that it doesn’t need to be replaced immediately because the unit is good to 80 psi. This allows the operator to schedule replacement for a time that is convenient, and cost-effective, for the vessel. Or, it might be discovered that, although a bank of injectors has been operating for 10,000 hours, the cylinder pressures and temperatures are still good and the engine is still running well; the analysts might then suggest the operator try to get another 2,000 hours out of them. In the end, the company will spend the same money on maintenance, but can push those expenses into the future, so over time it saves money and operates more efficiently, Metcalf says. A good data analyst crunching large, diverse data streams – such as position, trim, engine and propulsor operations, sea state, weather and fuel consumption – can suggest ways to improve voyage economics,

whether the vessel in question is an oceangoing tanker or a harbor tug. A ship-assist tug like Guide, for example, is paid a one-way fare, into or out of port as the client requires; the other leg of the trip, called dead-heading, is an operating expense. Any money the tug can save while dead-heading, then – say, by optimizing fuel consumption through a combination of speed, trim and propeller pitch – represents a significant economic gain over time. The broad diversity of data that can be gathered for analysis, from both the vessel and its environment, was one of the factors that drew Crowley to Caterpillar for its experiments aboard the Guide. The engine builder acquired an asset intelligence firm in creating its MAI division, making it one of about 20 marine sector vendors currently offering general data analysis services. By contrast, many original equipment manufacturers (OEMs) see digital asset management as a form of customer support. How these two approaches ultimately fit into the business of big data is a question the marine market has yet to settle. “We talked with a number of OEMs, but most wanted to monitor only the equipment they sell,” Metcalf says. “It’s true that, whatever solution you choose for your data and analysis, you’re going to need vital input from the OEM during the life of your engine, but it’s also

© donvictorio/Shutterstock

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true that there’s a lot of other data that can be used to enhance vessel efficiency – hull roughness, propeller roughness, thrust, torque, energy management and so on. In addition, if you want to do voyage management, you have to look at the tides, currents, weather, etc., to determine optimal vessel routing vessel and trim,” he explains. “We didn’t want multiple systems from multiple vendors onboard our vessels. We sought a single, whole-vessel solution from a single vendor that would gather all this diverse information, process it and produce useful recommendations for our operation and maintenance teams.”


The tugboat test also raised important questions for Crowley regarding data ownership, access and management. Recognizing that each instrumented vessel would generate a vast amount of information, not all of which should be shared outside the company, the group running the experiment recommended that a new dedicated data team be created within shoreside management to curate the accumulating data bank and oversee data analysis for the fleet. “This was a small-scale test, the goal of which was to understand big data analysis and find out how we might be able to use it at Crowley. In the process, we determined that we should have an internal owner of our data, and have processes in place whereby the

data is housed, evaluated and disseminated within the fleet through maintenance processes and clear advice or instructions for the engineers and vessel managers,” Metcalf says. “I believe in big data analysis and do see us adopting it, but how we adopt it is critical,” he adds. “We want to do it right, and the keys to doing it right include prioritizing the equipment and the vessels for monitoring, developing our own key performance indicators, and identifying and evaluating the returns on investment we can achieve. We will, most likely, prove the technology one vessel at a time, probably starting with high-risk/high-revenue vessels so as to enhance reliability on important runs,” he says. “For us, the trial on the Guide was largely about learning the processes surrounding big data, understanding how they could work within our culture and figuring out how to make them robust for us. We want to help our crews through data analysis and improve service for their vessels, but not overload them with spreadsheet after spreadsheet. In other words, we don’t want to go out and buy something just because it’s on sale; we want something that will work for us and add to our culture of safety and reliability,” Metcalf says. “We embrace technology here, but we want to embrace it with our eyes open.” n SURVEYOR | 2018 VOLUME 1 | 5

© posteriori/Shutterstock



reat strides have been made in development of self-driving automobiles, marine vehicles and aerial drones, but the day is still far off when, say, computer-controlled cars commonly roll down busy suburban streets, with children playing in yards behind vehicles parked on both sides of the road. Autonomous robots require perception (of sight, sound, depth and so on) to be able to navigate realworld spaces, to localize objects within those spaces, to recognize the objects related to their goal, and to pursue the goal safely and efficiently. Their tasks become more complicated when the space includes other autonomous objects, for example people and their vehicles. What will ultimately bring self-driving machines success and acceptance, as part of everyday life, is research known as perceptual robotics. A decades-old area of study with roots in diverse fields ranging from biology and neurology to mathematics and computer vision, perceptual robotics is essentially concerned with having robots use sensory or ‘perceptive’ data – input from visual, auditory or tactile sources – in conjunction with a vast memory bank of associated information to ‘understand’ their environments and determine how to move

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and function correctly within them, without human assistance or oversight. Autonomous robots are already capable of very impressive activities. Self-driving cars, for example, have been using camera, LIDAR (laser) and radar equipment for a decade to independently navigate mock urban environments, obey the rules of the road and perform such complex tasks as merging into traffic, and crossing four-way-stop intersections in the proper order. These capabilities were first demonstrated in the 2007 Urban Challenge from the US Defense Advanced Research Projects Agency (DARPA), whose Grand Challenge prize competitions have spurred much of the significant progress to date with autonomous robotic land vehicles. As one might expect, that progress has been made under controlled conditions in both laboratory and natural environments. For robots to function flawlessly in real-life situations, cluttered by fast-moving objects and unpredictable players, many complex problems remain to be solved. The answers will come from new inventions and evolution of existing technologies, and are being pursued by researchers in academic and industrial laboratories around the world.


One group working to make autonomous mobile robots an everyday reality is the Perceptual Robotics Laboratory (PeRL) at the University of Michigan. Led by Dr. Ryan Eustice, the lab focuses on the piece of the puzzle dealing with navigation and mapping, functions fundamental to autonomous movement. The faculty-student teams at PeRL develop algorithms and programming by which robots absorb and process perceptive data to model their environment, locate themselves accurately within that map, and determine how to move, act and react as situations require. Their efforts typically result in mathematical papers and programming. The papers essentially present algorithms that capture the processes that allow robots to perform certain functions, and the coding is the implementation of those algorithms. Published on the lab website (, most of the work is openly available to other researchers to learn from, build upon, or copy and use in their own research and development efforts. Current PeRL projects include: a self-driving car, in cooperation with Ford Motor Company; a freeswimming hull inspection robot for the Office of Naval Research (ONR); developing active safety “situational awareness” technology to give robots the ability to detect, assess and handle dangers; and, in partnership with the Naval Engineering Education Center, a variety of efforts to improve the autonomy of unmanned land and air vehicles. While the projects focus on very different robots and pursue very different goals, they also share significant common threads. “Even though the applications seem very different, from self-driving cars to underwater robots and aerial drones, there is a lot of commonality between them when it comes to mapping, navigation and also what we call perception – in which the robot builds a meaningful model of the environment and kind of ‘understands’ the world around it through perceptive data,” says Dr. Eustice. “Much of the mathematical work we do in my lab is actually a general framework that, essentially, can be applied across different domains.” The self-driving car and the underwater vehicle provide a good example of this connection. Some ten years ago, PeRL began work for the ONR on a fully autonomous hull inspection robot, the prime mover for the project being the Navy’s desire to replace human divers in such dangerous tasks as inspecting warship hulls for limpet mines. The development platform for this work is a free-swimming underwater vehicle on loan from the Navy, which PeRL has brought to the point of being able to function in environments about which it has no prior information.

Dr. Ryan Eustice Head of Perceptual Robotics Laboratory (PeRL), University of Michigan

Since its job is to locate hull surface issues, the inspection robot necessarily uses the vessel under survey as its position reference – a logical approach, but challenging in that the robot doesn’t know where it is the first time around, nor does it know what the vessel looks like. So, like an ancient explorer mapping an unknown coastline a mile at a time, on its first survey the robot collects ship imagery bit by bit and knits it all together to form a picture of the whole. In a process named simultaneous localization and mapping (SLAM), the robot collects this imagery with underwater camera and sonar equipment, supplemented by a periscope camera looking abovewater, to build a map of the vessel in real time. This map is the robot’s memory of the ship, and allows it to determine its location, to store a properly organized visual record of the hull surface, and to recognize the vessel in the future. The robot notes all distinguishing features of the surface under inspection – including names and numbers, which it can read – and uses them collectively as a fingerprint to identify the ship. In this way, it can distinguish even sister ships that appear to be exactly alike. When it returns to survey the vessel at a later time, it will match the new camera and sonar imagery to its database and retrieve the map made during its previous visit; the existing map provides reference points the robot uses to determine its position along the hull. The new data acquired on the subsequent survey is used to update and refine the existing map, accurately noting the type and location of any differences from the last inspection, such as dents or marine growth or attached mines. It also notes exactly the distance and area it has covered, so that its client (‘operator’ being an outdated word for this new human-robot

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REPLACING HUMAN DIVERS WITH FREE-SWIMMING ROBOTS In the simultaneous localization and mapping (SLAM) process, the hull inspection robot collects imagery with underwater camera and sonar equipment, supplemented by a periscope camera looking above water,to build a map of the vessel’s surface in real time.

relationship) can be certain that every inch of the hull has been seen.

the reconstructed geometry of the environment, and of where the robot has been.”

The potential uses of this technology did not escape notice in the commercial maritime sector. For two years, PeRL worked with ABS on a pilot project to apply the technology to underwater inspection in lieu of dry docking (UWILD).

The self-driving car uses quite a similar matching, verification and correction process, but its position is referenced to a detailed map containing road geometry and area imagery. The present level of technology is such that today’s autonomous land robots can position themselves with an accuracy far greater than possible with the publicly available Global Positioning System (GPS) data used by conventional automobiles.

“In the ABS project, we were able to build dense, photo-realistic models of the hull that let the surveyor zoom in and get a close-up view of the hull surface,” Dr. Eustice recalls. “We would have liked to further explore this by adding ultrasonic equipment, or another technology, that would allow the robot to characterize hull plate thicknesses. We didn’t do so then, primarily because the vehicle we used was borrowed from the US Government and we didn’t have authority to modify it; but it is certainly promising to explore in the future.” Self-correction is an important element of autonomy, particularly for a robot generating massive amounts of map, measurement and positioning data. To keep itself correct, the hull inspection robot uses what Dr. Eustice characterizes as “a lot of probabilistic math” and a verification process that involves matching camera and sonar imagery to the vessel map and accounting for such sources of error as its own motion. “Each measurement has some error or uncertainty associated with it, so the way this problem is formulated is such that we very rigorously track all the different sources of error associated with these different measurements,” Eustice explains. “By rigorously accounting for that error in a probabilistic way, we are able to look at all the various sources of measurements used and develop a best estimate of

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“The commercial GPS that cars use today is able to tell you what road you’re on, but not what lane you’re in; the error can be on the order of several meters,” Dr. Eustice says. “One of the bedrocks of the technology that goes into self-driving cars today is to use extremely detailed maps of road environments; matching what the vehicle’s cameras and lasers see with the maps, the robots are able to know their position to centimeter-level accuracy at any given time. The GPS gives the car a very coarse estimate of its location relative to the earth and an initial guess as to where it is in that map, but once it matches its camera and laser data with the map, it no longer needs GPS to know where it is,” he explains.


Today’s autonomous vehicles can be thought of as having completed the first half of a long journey – they know where they are, they can see their environments, and they can move about freely and do their jobs within those spaces. They are now entering the second half, moving up a steep incline towards true integration into society. This integration requires giving them a kind of reasoning capability to deal with external moving objects, and make rational predictions about the movement of

the things and people around them. Answering this challenge is one of the most active areas of robotics research at present, and is helping further the revolutionary science of machine learning.

self-driving cars and ships may combine to produce far-reaching, disruptive effects on the supply, logistic and economic chains that have long defined the ways world does business.

In machine learning, computers write their own software, using not the rigid if-then questions of traditional programming, but a more open, abstract process that seeks to emulate human reason and understanding.

“I believe the self-driving car will bring as disruptive and transformative a change as did the transition from horse-and-buggy to automobile, and has potential for even greater impact on how we think about mobility,” Eustice says. “We can now imagine a future in which you can call a robo-taxi to your door; this alone could cause disruptive change in the economic model behind car ownership. Most people own a car, which is typically their second-largest asset behind their home, but a car sits unused 95 percent of the time. By allowing people to always have a car on demand, instead of owning one, the robo-taxi could change that economic model and make transportation much more cost-effective,” he explains.

“Much of the work in robotics to date has involved algorithms and software written by humans, which have nice mathematical structures. In machine learning, we don’t actually write much of the software by hand any more. Instead, we write what amounts to a generic algorithm that essentially teaches the machine how to learn its own model; given a sequence of inputs – training data, if you will – the machine constructs a representation of reality such that it is able to predict what it should expect to happen in real time, based on what it sees,” Eustice explains. Those fearing a robot revolution need not worry, yet. Computers are not at the point of posing their own problems. Humans still pose the problem, give the robot any needed associated data sets and tell it the structure of the model; the advance is that the machine itself learns the parameters of that model and works to make the best fit, in the input/output relationship (that is, the machine equivalent of a human trying to harmonize ideals with reality).

“There are still many hard problems and open challenges ahead. With the self-driving car, for example, the robot has a detailed map and can know where it is in terms of road geometry, but now we have to get it to the point where it can handle changes – for example, how to deal with other moving vehicles and pedestrians, and to understand their intent,” he adds. “Much of our development in mapping and navigation is the bedrock that allows us to work on this next set of problems, which is all about how to make this technology work in the real world.” n

“Machine learning is a very powerful tool, particularly when aided by computer vision, in terms of being able to look at an image and having the robot automatically recognize it,” Eustice says. “Have you noticed how well Google Photos deals with facial recognition, how the system can spot you and your friends in photos across the Internet? Much of that process is powered by machine learning.” One reason for the rapid pace of development in autonomous robotic vehicles by industry right now is that, after some three decades of research in universities around the world, the technological basics are quite well established and ripe for application. When perfected, the various technologies behind

© LEONARDO VITI/Shutterstock

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or many organizations, ‘digital transformation’ is both a buzzword and a mystery. It implies a high-tech form of self-improvement, but, like many such terms, represents a concept more than a process and leaves many people wondering what the words mean, what such a transformation could accomplish and what a ‘digitally transformed’ company might look like. The meaning of ‘digital transformation’ is about as open for discussion as are the notions of ‘success’ and ‘happiness’; one must look to the street to see how it is shaping up as a practical matter. One good example of how the concept is being realized can be found at Wärtsilä, an engine manufacturer and technology leader in the global marine and energy sectors, which describes its digital transformation initiatives as – a change in corporate culture affecting how the organization brings new products, services and solutions to market. “For us, digital transformation means a transition from being a traditional industrial company to an as-a-service, smart technology industrial company,” says Marco Ryan, Wärtsilä’s Chief Digital Officer. “This involves transforming from a product-led model – selling a product and servicing it – to a much more data/insight-led model in which we partner and collaborate differently across what’s known as ecosystem thinking.”

Marco Ryan, Chief Digital Officer, Wärtsilä

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‘Ecosystem thinking’, another fairly new term, essentially refers to a broad point of view that sees questions not as isolated problems with singular answers, but as elements of a network of causes and effects and interrelationships (the ‘ecosystem’) that allows for many possible solutions – based on the interplay between those elements. In a way, it’s the Digital Age version of what a previous generation called ‘getting the big picture.’ So, for a ‘digital business’, ecosystem thinking on a customer service issue might look at the activities of related manufacturers, vendors, transporters and regulators to find a solution that brings benefit to all parties; or, for an equipment service matter, might look at the operation of the entire plant in which the equipment is installed, to find secondary or tertiary causes affecting the unit’s function. An example from the maritime world would be the way the engine manufacturers service programs have changed in recent years. Historically, it was sufficient to focus on optimizing an engine to get from Point A to Point B at greatest efficiency; today that is no longer the case, and many manufacturers now offer expanded digital asset management services. “There’s little sense in going from A to B efficiently if the ship wastes time and effort going from B to C, or if it ends up sitting outside Point D burning off all the savings it just achieved because it got there too soon for the port’s scheduling,” Ryan says. “It is much better to optimize the whole path from A to D, which means looking beyond fuel consumption to vessel speed and routing, trim, readiness of the destination port and other aspects of the voyage, to yield better value for money overall. That’s ecosystem thinking, and companies like Wärtsilä are trying to think up and down the value chain to find where we can do things differently to add value for our customers. Our transformation as a business is very much about transformation into a service company, which means how we build, maintain and update products, how we develop solutions, how we collaborate between product divisions, and so on. It’s all part of as-a-service thinking, which is about trying to find solutions and products that deliver value to customers. As our customers’ markets and needs change, we need to make sure our solutions, products and services remain relevant and drive value for them. This represents quite a lot of change in some of our ways of working together and in some of our underlying competencies and business models.”


The Digital Acceleration Centers (DACs) that Wärtsilä recently opened in Helsinki and Singapore offer good examples of such change. Part of a new initiative to take promising ideas and turn them into workable service products – usually through close collaboration with internal or external clients – the DAC efforts are organized into four phases: Ideation, in which ideas are gathered; Incubation, in which ideas and the vision behind them are evolved; Transformation, in which a project is developed into a prototype form called a minimum viable product (MVP); and Growth, in which the MVP is rolled out globally or accelerated if it represents a new business idea. After each stage, a stakeholder panel reviews the product and decides if it is worth further investment, until the project fails or becomes a marketable product. Despite the way that sounds, the centers are not aimed at creating new technology. “The Digital Acceleration Centers are very much focused on creating new business models, new ways of using our existing expertise to drive different outcomes – but not on developing new technology,” Ryan says. “We still have our traditional R&D environment around technology and products. An idea for a new piece of equipment, for example, would proceed through traditional R&D, but an idea to install a sensor in that equipment, to gather data or create a value-adding outcome we might be able to sell to a client, would be brought into a DAC to develop the business model.” The centers run on a ‘start-up mentality’, under which new business ideas are tested much more rapidly than traditional corporate processes would allow, often getting results in a few weeks, he says. This speed comes from removing barriers between departments and collaborating with customers on issues important to both parties. That’s how Wärtsilä successfully tested remote-control operation of a Dynamically Positioned (DP) platform supply vessel in September 2017. The 80-meter PSV was offshore Scotland, but the fourhour trial was controlled from an office in California. Its Wärtsilä bridge system had been fitted with new software that allowed data to be routed over its regular satellite link using standard-bandwidth onboard communications. Wärtsilä developed the technology in early 2016, but this first onboard trial only came to fruition because the manufacturer and vessel owner worked closely together on the project. “Although we had the capability to remotely control DP vessels, we hadn’t thought about running a live test with a customer,” Ryan says. “By adopting the rapid ideation process we use in the DAC innovation acceleration environment, we were not only able to complete that test, but also do it at very low cost and in a matter of weeks.”


While digital transformation is an experience unique to each company that undertakes it, Ryan can offer some advice on how one might get off on the right foot as it starts the journey. “I would say the first step in a digital transformation is to figure out where you stand, what your starting point is, and where your value is created. That’s not as silly as it might sound,” he says. “Many people get seduced by the hype around the technology. It’s important to understand that technology is an enabler, not the destination. Transformation is not about technology; you can’t do it without technology, but technology is not the be-all and end-all.” The next step, he says, is for the company to identify its biggest pain, its biggest value, and the ease with which it can get to them. For example, since fuel cost is a common pain point in ship operations, one place an operator could begin a transformation would be by investigating whether data and analytics, or sensors and the Internet of Things, etc., could be used to optimize fuel consumption. Such analysis helps achieve the ‘quick wins’ Ryan says can engender management buy-in and help pay for future steps in the process.

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“Once you know your starting point, it’s extremely important to understand what is relevant to your business specifically,” Ryan says. “Knowing what’s relevant to your business and finding the quickest win go hand-in-hand, because, together, they help you understand your opportunities. It’s never a good idea to simply copy the digital transformation that worked for one of your competitors, even if you are similar companies operating in the same segment. That company will have differences from yours – size and age of fleet, customer base, corporate structures, etc. – and different priorities in its value chain. If you have a good understanding up and down your value chain about how data can be shared and what you can do with it, and how technology can make things more efficient or more scalable, then you can start creating value out of your transformation.”


Possibly the most important item on the transformation to-do list is to remember that, while the outcome is called digital, it is, in fact, personal. “Companies are all about people,” Ryan reminds. “From the outside, many competitors appear quite similar, with similar products and a similar focus on the industry, but what often differentiates them is the leadership, the culture and the way they work. That’s why a large part of digital transformation is about culture change. It is absolutely about the human component; it’s a people journey, not just a technology journey or a process journey.” That’s why Wärtsilä recently initiated a culture change program aimed at driving awareness of the behaviors, and ways of working, in a smart technology company. The program provides training and support for staff to develop skills and competencies that make digital concepts, like data analytics, relevant to their daily work.

emotional reaction to change,” Ryan says. “People tend to buy in to change if the experience is good, but resist if the experience is bad. A fundamental enabler of digital transformation, then, is creating an environment in which the experience of change, while not always comfortable, is meaningful. When people understand the value being created, and its relevance to them, they tend to take the changes in stride.” Some changes are harder to absorb than others. One shock to the corporate culture often brought about by digital transformation is a leveling effect on management structures, which brings its own set of challenges. Because the digital mindset relies on collaboration across traditional boundaries, trust and empowerment are key factors in its success; and for trust to thrive, he points out, management’s behavior must match its messaging. “In digital transformation, when you go from a very hierarchical to a much flatter organization, you end up with increased transparency, and, hopefully increased empowerment and trust as well,” Ryan says. He also cautions that, if management maintains a silo mentality, – that is, by talking the horizontal but keeping the vertical – staff will see that pretty quickly and lose trust in the leadership. “This is all very much about trust,” he says, “and, because of that, you have to work very hard on communications. If you don’t communicate clearly, if you approach a transformation disjointedly, if it isn’t part of your day-to-day working, and if you don’t make it relevant to the person on the shop floor, the journey will be very, very difficult” he warns. “Ultimately, how well you are organized, how well you work, how well you collaborate and how well you incentivize will either accelerate or retard your transformation.” n

“We’re trying to provide a very bottom-up approach to changing the company, providing opportunity and a safe learning environment, creating challenge but not mandating how things and structures must change. Rather, it is very much about enabling the organization to change,” Ryan says. One cultural change involves cultivating what Ryan calls the ‘digital mindset,’ a way of thinking that “starts with customer need and focuses ruthlessly on the experience.” This mindset is concerned with understanding needs and creating experiences, through software, service design or other approaches, that make these new concepts easier to absorb. “Service design thinking is very much focused on creating compelling experiences that create a positive

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Using ecosystem thinking helps Wärtsilä optimize ship efficiency, looking beyond fuel consumption to vessel speed and routing, trim, and readiness of the destination port to yield better value for money overall.



round the world, ports and terminals are taking steps to make digital technologies part of daily operations, hoping to improve efficiency, boost profitability and revolutionize user experiences. To do this, some are employing systems that rely on big data analytics, the Internet of Things and cutting-edge technologies like blockchain. Others, such as the Port of Los Angeles, are taking a simpler path into a hightech future. In November 2016, the Port of Los Angeles announced the launch of a pilot project with GE Transportation to make maritime shipping data available to key stakeholders up to two weeks ahead of a vessel’s arrival at the port. The idea was to create a single online portal which allowed users to access all information about their shipments – improving port efficiency and streamlining user experiences. The program used a cloud-based platform and data from US Customs and Border Protection, two shipping lines and one terminal operator to populate the portal – and granted secure access for cargo owners, freight forwarders, truckers, rail lines and others involved in moving cargo through the port and beyond. The participants hoped to refine the concept to digitize shipping information for the entire port and, ultimately, the larger supply chain. They were not disappointed. Whereas most companies only receive information about the placement and content of containers 48 to 72 hours before a vessel’s arrival in port, users in the pilot portal had that data

10 to 14 days in advance. Besides the early notice, the secure portal also brightened the land transporters world by removing several layers of Internet frustration from the shipping process. At present, according to the Port Authority, a truck dispatcher in southern California has to visit up to 20 different websites to get complete information about containers moving through the port. Having a single website as the data clearinghouse promises a big efficiency boost to local truckers – and to the port as well. Based on initial pilot results, the Port of Los Angeles anticipates efficiency gains of between eight and twelve percent once the full solution is implemented. In November, less than a year after the pilot began, GE and the Port of Los Angeles announced that they were expanding the portal to include all of the port’s container terminals and shipping lines. The new agreements, worth up to $12 million, continue the relationship for five years. Subject to review by the Los Angeles City Council, the deal will support some nine million TEUs (20-foot equivalent) shipping containers, more than 15,000 truck providers and thousands of cargo importers. Calling the portal “critical to our future success,” the executive director of the Port of Los Angeles told the press at the outset of the pilot that “Digital solutions that enable supply chain partners to receive a ship’s cargo information, well in advance of arrival, are a critical key to optimizing US cargo efficiency and trade

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competitiveness. We believe this project will not only move the needle but could be a game changer.” Europe’s third largest port, Hamburg, has similar hopes for the Internet of Things (IoT). Its port authority has focused on making the most of that technology for the past six years. For example, some 300 sensors presently track traffic conditions and bridge status (open or closed) in the port, allowing the Road Management Center to direct traffic flow to minimize congestion. Crunching this data with asset-monitoring sensors identifies available warehousing, while the control system directs traffic and storage operations to make most efficient use of the facilities at all times. Communications connect the port to approaching ships, telling them where to dock and raise drawbridges in time to let the vessels pass. At the same time, the system adjusts traffic to the bridges, tells trucks which parking spaces or cranes have been allotted to them, and then sends the vehicles out on the best route possible. The port’s 2025 Development Plan envisions transformation into a smart logistics port, distinguished by smart infrastructure, intelligent traffic flow and intelligent trade flow.

One country basing a big piece of its future on technology as a game-changer is Singapore, which over the past decade has made an increasingly intense effort to become a maritime technology hub for Southeast Asia. Its PSA International, a holding company that manages ports around the world with Singapore as its flagship, has a long high-tech history; it was among the first organizations to computerize port operations and connect them to the shipping and logistics communities. PSA’s first big tech upgrade came in 1988 with the Computer Integrated Terminal Operations System, which coordinates port operations between containers, movers, quay and yard cranes. A decade later, the organization introduced the FlowThrough Gate, a fully-automated entry system that, within 25 seconds, verifies the identity of both driver and truck, checks container weights and numbers against the manifest and clears a truck for entry. The system currently handles some 9,000 trucks per day. That throughput may get a bump up once PSA finishes installing its latest gadgetry. The Automated Rail Mounted Gantry (aRMG) cranes, just being introduced, use laser guidance for precise container handling control and improve productivity with proprietary job scheduling systems. The port will eventually have 186 of these units in operation. The aRMGs already installed are capable of handling almost 90 per cent of all container moves without human intervention. Planning its digital future, in August 2017 PSA inked a deal with IBM and Pacific International Lines to test a new supply network based on blockchain technology, with the goal of automating document flow between trading partners. Blockchain – literally, a continuously updated “chain” of “blocks” or groups of bundled transactions – is best known as the technology behind the Bitcoin cryptocurrency system, but is also applicable to custodianship of document records or sale-andpurchase trails. The reputation of the blockchain as a trustworthy record keeper is founded in the prohibitively complex and lengthy calculations and processes that protect the blocks from forgery like a mathematical suit of armor. In the case of Bitcoin, the blockchain replaces trusted third parties like banks in financial transactions, using a database that contains the payment history of every Bitcoin in circulation and provides proof of ownership at any given moment. This “distributed ledger” is replicated on thousands of computers (called nodes) around the world and is publicly available, but nonetheless appears to be extremely robust against fraud. For that reason, the shipping industry is beginning to test the potential of blockchain as a business tool. IBM, for example, has several projects focused on its use in the supply chain. In 2017, the tech giant partnered

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© naulicreative/Shutterstock

The shipping industry is beginning to test the potential of blockchain as a business tool to replace expensive paper-based records for cargo delivery. In 2017, IBM partnered with Maersk in a live blockchain trial that sought to automate documentation processing, and Zim Lines completed the first commercial test of a blockchain-based digital bill of lading. In Bitcoin transactions, blockchain’s distributed ledger technology replaces trusted third parties like banks in financial transactions.

with Maersk in a live blockchain trial that sought to automate documentation processing, investigating its use in creating a reliable digital bill of lading. Typically, a bill of lading today takes a traditional paper-based route to record delivery of a cargo shipment, even if digital methods are used along the way. Processing the paperwork for cargo movement is expensive, with many stops on the supply chain factored in, such as customs, freighters and truckers. Proponents of digital bills of lading say there can be as many as 100 stakeholders for a single cargo movement, and that a single, reliable, continuously self-updating and verifiable digital document chain could revolutionize the movement of goods. The idea of a blockchain revolution has found receptive ears in Europe’s two largest ports. Antwerp, the second largest port in Europe in container capacity, is now running a pilot blockchain project focused on logistics automation. It hopes to use the technology to automate and streamline logistics operations, accelerating interactions between port customers and preventing malicious manipulation of data. The fundamental goal is to securely digitize supply chain processes involving multiple parties, such as carriers, terminals, forwarders, drivers and shippers. According to the Port Authority, getting a container from point A to point B today frequently involves more than 30 parties, with an average of 200 interactions between them. Given that many of these interactions are carried out by e-mail, phone and fax, the Authority says paperwork accounts for up to half of the cost of container transport. In September this year, Rotterdam, Europe’s largest port, made its first steps towards the digital frontier

with the launch of a research effort called BlockLab. The port has already begun making use of the IoT, with all critical equipment – such as gantry cranes, container tractors and bridge cranes – connected via its own 4G broadband communications network to an automated port management system. Now, through BlockLab, a collaboration between the port authority, the municipality of Rotterdam, academia and industry, it will investigate blockchain-based logistics solutions. The latest advance in commercializing blockchain for shipping came in November, when container shipping company ZIM announced a successful experiment employing blockchain to carry out a shipment using paperless bills of lading. For the first time, blockchain-based software was used to send documentation acknowledging receipt of cargo for shipment. The cargo in this case was containers moving from China to Canada; the participants issued, transferred and received original digital documents. ZIM reported that the containers were delivered without incident and stated its conviction that blockchain technology is “the solution that will drive trade into the digital era.” If there’s one big lesson of the Digital Age, it’s that nothing in cyberspace is 100-percent secure over the long term. With quantum computing and other such advances on the (admittedly distant) horizon, whether blockchain’s mathematical armor will remain secure enough to be the foundation of a new global era of business transactions remains to be seen. For now, however, it stands with the Internet of Things, data analytics, augmented reality and other exciting technologies as a promising tool for building a digitally transformed industry. n

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© Siv Dolmen




igital transformation” is far more than a buzzword. It is a vision sparking disruptive change in virtually every sphere of industrial activity. In the maritime sector, technologies enabling new levels of data analysis, not previously possible, are beginning to open the door on a true knowledge revolution, in which companies have the opportunity to obtain an unprecedented depth of understanding of the industry—ranging from business operations to the condition of marine assets.

Christopher J. Wiernicki, Chairman, President and CEO, ABS

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The foundation for this revolution has been in place for some time—onboard sensors already stream an ever-increasing amount of data about engine performance, hull structural response, tailshaft and bearing wear, oil temperature and condition, machinery operation, vibration and the functioning of all control systems. What’s new is our industry’s growing ability to derive insight from that data. Properly and fully analyzed, this limitless ocean of information will render vessels ‘functionally transparent’, visible

to a level of technical detail that was once only imagined in science fiction. Then, just as financial transparency revolutionized the corporate side of the shipping sector, so will vessel transparency revolutionize its maintenance and repair side. This revolution clearly influences the future of survey.

effort, in which a qualified data scientist will be in the background, providing a risk-based advisory assessments that will assist the fulfillment of the surveyor’s mission. This, in turn, will bring a new era of collaboration on safety between class and industry.

It is now possible to interpret data generated by onboard sensors and systems to develop a technical intelligence report about a vessel long before a survey is scheduled. Combining that information with the asset’s digital documentation, and maintenance and repair records, we can run models that make reliable predictions about corrosion, fatigue and failure. Armed with this intelligence, owners will be able to develop better-focused maintenance plans and surveyors will be able to focus their efforts on areas needing special attention.

In the digitally transformed industry, clients will share operational data that, in the past, class would never see, setting in motion a virtuous circle wherein class, having a truly comprehensive view of vessel risks, helps operators make betterinformed decisions about their assets, which as a result will be in better physical condition and operate more efficiently. Therefore, reflecting positively on both class and the industry as a whole.

Taking that capability a step further, through a process we call anomaly detection, we can now use data to look for precursors to failure. In one of our pilot programs, for example, a customer is sharing quite a lot of maintenance data, including critical failure history, which allows us to develop survey plans that focus on higher-risk areas. The surveyor still needs to examine everything onboard over time, of course, but this will help identify developing issues that, in the past, might have escaped notice until they become an actual problem.

Ultimately, digital transformation will encourage an alignment of objectives between class and its clients. We will never defer our responsibilities or our values, but in this newly transformed world, we can align our rewards with clients rewards in the service of meeting our mission to protect the safety of life, property and the environment at sea. n

The data revolution will also help reduce the surveyor’s administrative burden onboard. By having vesselspecific information and answers to surveyor questions ahead of a survey, both sides can be better prepared for the visit and even agree in advance on the scope of work to be performed. Altogether, the new data services we are developing will increase efficiency on both sides of the survey equation, through more intelligently planned work orders and more efficiently executed surveys. Amid the excitement of all this change, it is important to remember that data and digital technologies, amazing as they are, are merely tools. They will inform the surveyor in new and as yet undreamt-of ways, but will never supplant the surveyor’s judgment or authority. They will, however, enhance the surveyor’s job through a new team SURVEYOR | 2018 VOLUME 1 | 17

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