The Significance of Machine Learning in Shipping & Maritime
August 4, 2020 Inventory of Hazardous Materials (IHM)
Machine learning (ML) is a process by which large caches of data are analyzed in order to find connections between different elements that human analysts would be unlikely to discover. In the future, all shipping companies will have access to machine learning tools to enhance their productivity. Seamlessly integrating these operations will not happen quickly. But developments in the technology and its adaptability quotient would trigger the adoption of ML in a larger scale in maritime soon.
With machine learning the seamen can detect and diagnose engine faults sooner. This in turn optimises the voyage. Early detection of malfunction prevents further damage and increases the life of a marine engine. By preventing further damage of an engine, the fuel consumption is also optimised which in turn reduces pollution. This is a chain of advantages which can be achieved by implementing machine learning technology in shipping.
Machine learning has made its own niche in maritime and supply chain management. Through it, the sectors are looking to improve their operational efficiencies and at the same time, reduce risks. While in shipping it is being used for network planning, container demand forecasting and the un-/pairing of container flows, the logistics and supply chain management are utilizing it to draw patterns and insights that are proving crucial for the evolution of these sectors.
Machine Learning and Maritime
PSA Marine, a Singapore based marine service provider partnered with Ernst and Young Solutions and began the development of a technology based on machine learning and AI – ‘Blue 5.0.’. Through it, the company plans to predict pilotage transit durations along with planning and allocating terminal resources more efficiently. With technologies like ‘Blue5.0.’ being developed, the maritime sector is also investing in some quality research work to develop machine learning models that could heavily revolutionize the sector.
A new approach incorporating the use of machine learning algorithms in developing a shipping emission inventory model has been suggested in a research paper titled ‘An application of machine learning to Shipping Emission Inventory’. The paper written by Vikram Garaniya (University of Tasmania), Rouzbeh Abbassi (Macquarie University) and Shuhong Chai (Australian Maritime College), published in the December 2018 edition of The International Journal of Maritime Engineering, extensively discussed and identified 5 machine learning models that can be utilized to predict shipping emissions based on engine parameters like engine load information.
It also suggested that a vast scope of further research and development lies ahead where better pollutant monitoring can be achieved through machine learning algorithms, hence increasing the relevance of estimated emissions.
In addition to the shipping emission inventory, machine learning is also finding its place for maritime surveillance using ASI data streams. Development of a multi-task deep learning architecture model has been proposed for trajectory reconstruction, anomaly detection, and vessel type identification. Although the research is still underway, according to some reports, the on-going work in the field of introducing deep-learning, a sub-field of machine learning, to the maritime sector can revolutionize the maritime surveillance to a great extent.
Apart from the above-mentioned domains, the shipping sector is looking to improve on the following fronts by using machine learning algorithms along with sister technologies like IoT and Artificial Intelligence.
Maintenance: By deploying machine learning algorithms, a better schedule for maintenance work can be developed and thereby improving the liner services in the long term, especially during the times when a ship may need to be out of operations temporarily for maintenance work.
Freight Rates: Machine learning can help in handling the deficits and offer more reliable container capacity utilization; hence more consistent freight rates would prevail.
Sailing schedules: By using machine learning, better and reliable sailing schedules can be achieved as more accurate calculations would be there to predict the delays or estimate the time of arrival of the cargo.
Logistics: Machine learning will have potential impact on the global logistics chain. Machine learning can predict accurately on arrival of container shipments. Using information from a variety of sources across the supply chain—including live demand and pricing data. A more accurate demand forecast can also help to scale up capacity of the existing fleet.
Supply Chain Management
The supply chain management is utilizing machine learning algorithms to locate new patterns in supply chain data almost on a daily basis and use those patterns to improve the supply networks’ success. Improved demand forecasting and production planning, better supplier delivery performance, minimized supplier risk, improved supplier chain and transportation management, physical inspection and maintenance tasks, lower inventory and operation costs, quicker response time, extended life of supply chain assets are the key evolutions happened within supply chain management by the introduction of machine learning algorithms.
Platforms like Nautilus Labs, We4Sea and ClearMetals are working in the direction where ML driven data science is being used to provide effective solutions for voyage optimization, supply chain visibility, and sustainability.
The Way Forward
Although at present, the full potential of machine learning is still to be realized and implemented, machine learning algorithms are capable of churning through different data points and derive key relationships between variables that can assist in improving operations and networking of sectors like shipping, logistics, supply chain management, thereby making it a technology to watch out in the future.
The key to Machine Learning lies around the integration, implementation, and manipulation of data infrastructures as well as machine learning approaches designed for chemical and materials datasets. Machine learning approaches and capability has already revolutionized world’s major industries and shipping industry is next in the queue to take a giant leap towards digitalization and accelerate its working to a great extent.
Machine Learning is an interesting technology that has multiple applications in the maritime sector. The need of the hour is to carry out additional researches to uncover its full potential and harness its benefits to optimize efficiency, safety, productivity and skills of seafarers across the globe.
(References: www.blog.flexis.com; www.researchgate.net; www.forbes.com; www.porttechnology.org; IDTechEx; www.arxiv.org; www.bigdata-madesimple.com)