What value can cloud computing deliver to the maritime industry?
Majority of the global trade happens at sea: the ocean shipping industry is at the core of the global economy, yet still enjoying a lower level of digitization. In this post we will review how companies in this vertical can take advantage of cloud computing to transform their business.
Cloud computing can deliver value to maritime companies and organizations in three ways. First, it can reduce IT costs. Companies like Matson, Seaco, and Deutsche Bahn moved all its IT workloads on the cloud and achieved savings up to 50%. Second, a cloud architecture comprising of a data ingestion layer, a data lake, and a client layer can enable cheap, reliable, and efficient collection, storage, and analysis of data at scale. This enables maritime companies to break data siloes and create a single repository for their data from multiple sources. This fully managed, pay-per-use, and scalable solution takes away the complexity and costs of managing and synchronizing on-premises databases, establishing a unique source of truth for the business. Finally, advanced technologies can be delivered on top of this ingestion and storage layer: raw data can be turned into actionable knowledge with cross-querying and business intelligence services, disseminating insights across the organization and the ecosystem that the organization is part of. Machine learning-based predictive and prescriptive analytics can enable predictive maintenance for vessels and port equipment, reducing downtime and failure rates. Key metrics such as vessels ETA can be predicted to optimize workforce and assets utilization, while digital logistics twins can simulate large-scale facilities helping to improve their throughput.
What is cloud computing?
Cloud computing provides a framework of technologies and solutions ideal to establish a global ecosystem of collaboration for the maritime industry. Cloud computing can enable actors across the global supply chain to contribute to, consume, and benefit from an uninterrupted digital thread, bridging the gap between the digital flow of data and the physical movements of goods/assets. Cloud computing will establish end-to-end visibility and proactive control on transportation and logistics activities, leveraging machine learning, Internet of Things (IoT), and artificial intelligence at a fraction of the cost of traditional, on-premises technologies.
The term “cloud computing” refers to the on-demand delivery of IT resources via the internet with pay-as-you-go pricing. Instead of buying, owning, and maintaining their own data centers and servers, organizations can acquire technology such as compute power, storage, databases, and other services on an as-needed basis. It is similar to how consumers flip a switch to turn on the lights in their home, and the power company sends electricity. With cloud computing, providers manage and maintain the technology infrastructure in a secure environment and businesses access these resources via the internet to develop and run their applications. Capacity can grow or shrink instantly and businesses only pay for what they use. There are five reasons companies are moving so quickly to the cloud.
The first is agility. Cloud computing lets customers quickly spin up resources as they need them, deploying hundreds or even thousands of servers in minutes. This means customers can quickly develop and roll out new applications, and it means that teams can experiment and innovate more quickly and frequently. If an experiment fails, you can always de-provision those resources without risk.
The second reason is cost savings. Cloud computing allows customers to trade capital expense for variable expense, and only pay for IT as they consume it. And, the variable expense is much lower than what customers can do for themselves because of cloud computing economies of scale. For example, Dow Jones has estimated that migrating its data centers to the cloud will contribute to a global savings of $100 million in infrastructure costs.
The third reason is elasticity. Customers used to over provision, buying upfront hardware and maintaining their own data centers, to ensure they had enough capacity to handle their business operations at the peak level of activity. However, this is not efficient since during non-peak time these expensive assets are underutilized. With cloud computing, customers can provision the amount of resources that they actually need, knowing they can instantly scale up or down along with the needs of their business. In this way, customers also reduce cost and improves their ability to meet users demands. This is very important for an industry like ocean transportation, which is cyclical in nature and experiences steep fluctuations in transactional volume.
The fourth reason is that the cloud allows customers to innovate faster. Instead of the undifferentiated heavy lifting of managing infrastructure and data centers, companies can focus highly valuable IT resources on developing applications that differentiate their business and transform customer experiences. For an industry like ocean transportation, which is highly conservative and relies predominantly on legacy application, cloud computing would allow a substantial leverage to innovate quicker.
The fifth reason is that cloud computing enables customers to deploy globally in minutes.
A data-driven transformation
Customers in the maritime industry today can leverage the massive amount of data that they’ve accumulated in order to transform their business. But, they need new tools to easily store, process, and analyze all that data – reliably, cost effectively, and at scale. Cloud computing enables this data-driven transformation: it has never been easier to collect, store, analyze, and share data than it is today in the cloud. And that’s because it’s not only much more cost effective in the cloud, but also the analytics services available today change the possibilities.
The foundation of a data-driven transformation is storage. Over the years, customers have accumulated so much data, and a lot of that data lives in different silos, which makes it hard to do anything with their data, including analytics. So customers pull that data together in a data lake. Once a data lake is built, the next step is the creation of data warehouses or databases. There are two trends we are seeing with databases. First, over the last few decades, companies have felt constrained by their commercial-grade database options and have been unhappy with their old guard database providers—these offerings are expensive, proprietary, have high-lock-in, and punitive licensing terms. The second interesting trend is that the days of using a relational database to solve all database requirements are over. Over the past 20–30 years, companies have run most of their workloads using relational databases. Organizations are moving towards microservices architectures with composable building blocks and purpose-built tools, for instance graph databases like , ideal to optimize complex logistic networks such as the ones run by ocean carriers.
A robust cloud architecture, comprehensive of a data lake and a data warehouse enables transportation companies to get value out of their data with advanced analytics, blockchain, machine learning and Internet of Things (IoT).. We think about machine learning in three layers of the stack, and we believe in the fullness of time, most organizations that have significant technology capability will use all three of these. The bottom layer is for expert machine learning practitioners–including advanced developers and data scientists–who are comfortable building, tuning, training, deploying, and managing models themselves, and working at the framework level.
At the middle layer of the stack, fully managed services remove the heavy lifting, complexity, and guesswork from each step of the machine learning process, empowering everyday developers and scientists to successfully use machine learning.
At the top layer of the stack, we have services that people call artificial intelligence (AI) because they mimic human cognition. And, the services make it easy to incorporate AI (for example, turning text to speech, transcribing audio into text, or translate text into multiple languages) into applications without having to build and train algorithms. This last layer fits well into organization that do not have ML-specific capabilities and want to consume such services in a fully managed way.
Connecting assets and devices to the cloud and collect data in real time is the value provided by coupling cloud data lakes with edge services. Over the next 10–20 years, it is likely most companies’ on-premises footprint will not be servers—those will virtually all be in the cloud—their on-premises footprint will be connected devices. Billions of these connected devices will be onboard vessels, attached to shipping containers, installed into trucks, ship to shore and gantry cranes. These sensors already are everywhere and they are typically small, with a limited amount of CPU and disk space: they are cheap and leave the heavy lift workloads into the cloud, where complex calculation tasks are centralized. The days when smart trucks, containers, vessels, cranes, and chassis will exchange information with the cloud and between themselves to self-optimize their utilization and repositioning, are not far away.
At AWS, customer obsession and working backwards is fundamental to how we approach solving complex business challenges. Our team of industry veterans with solid cloud proficiency can help maritime companies to make the best of their assets by leveraging on their data more .