Gathering data is easy, it is making sense of it that poses the challenge.
What Value Does AI Offer?
5 AI Capabilities to Look for in a TOS
WHAT VALUE DOES ARTIFICIAL INTELLIGENCE (AI) OFFER?
Existing processes and resources unable to efficiently or immediately meet operational demands has become the incentive for port and terminal operators to expand their field into alternative technologies. The courage to adopt technological innovations heighten with authorities realising issues are no longer viable to resolve manually or independently. Introducing some level of AI technology will facilitate faster decision-making, enhance operational flow, and achieve cutting costs. The value that AI offers:
+ Pattern Detection
When data is collected, organised, and then processed, it feeds valuable information for AI to comprehend, which assists in determining trends. Detecting patterns, especially earlier on, will inherently mitigate the spur of bottlenecks and port congestion. Gathering the data is easy but making sense of the information is what proved challenging. That is why AI has the deft capabilities to facilitate well-informed and better decision-making.
+ Streamlined Processes
AI operates as the brain of the TOS, calculating strategic moves which mandate the minimal number of steps involved in monitoring and container planning. By removing unnecessary stages, AI enables the streamline of processes. Cutting back on additional steps can shorten the timeframe of moving a container from location to location, achieving more efficient operations.
+ Efficient Operations
True AI could detect signs of bottlenecks in real-time, as well as offer the next best route if an issue were to arise. Being able to respond automatically to dynamic situations in real-time relieve time constraints that may lead to delays, and hence incur costs. Introducing intelligence into TOS operations will not only prepare for potential impediments but also drive on-demand and accurate decision-making.
+ Optimised Resource Use
AI technology embedded in the architecture of a TOS is capable of strategically allocating resources to ensure minimal wastage and redundancy. Decisions derived by AI bases on real data factoring in potential upsets to the workflow. By declaring the necessary labour time, amount of CHEs, and moves required to complete an operation, achieves optimised resource and prepares for changing situations.
+ Reduced Costs
As operations, processes, and resources optimise, the margin for wastage in fuel, equipment wear-and-tear, and idle activity mitigates. Massively reduced costs because of employing AI capabilities is vital when confronted with continually changing labour costs, logistics costs, adjacent costs, and compliance costs, which are affecting the ability to allocate resources economically while sustaining revenue flow.
5 AI CAPABILITIES TO LOOK FOR IN A TOS
Artificial Intelligence (AI) and its limitless capabilities left unexplored, could possibly be the most important technology of our lifetime. Although the world is still in the early stages of perfectly emulating human interactions and behaviors in technology, appearances of machine learning in tangible software and applications have increased across different industries. The maritime and port industry are the few that will experience exceptional benefits from true AI capabilities. AI capabilities offered in the market that can be of use to ports and terminals include:
✔ Berth Optimisation
Vessel sizes are only becoming larger to meet the increasing consumer demand, making it difficult for terminals to maintain berth productivity. It is extremely complicated to manually devise an efficient berthing strategy without omitting valuable factors or encountering port congestion. Embedding AI within the TOS will offer autonomy, accuracy, and precision in calculating the optimal time schedule to berth, while maximising on the available pool of resources.
✔ Strategic Vessel Scheduling
Port and terminals are yet to fight another elaborate problem – the number of exports amassing at gates, yards, and warehouses. With limited space on-premise in conjunction with limited capacity on vessels, TOS users will confront challenges of managing vessel scheduling effectively or timely. Vessel scheduling can easily facilitate performance and strategic targets by incorporating AI technology. With AI, optimal vessel times and berthing arrangements are determined to maximise the limited space, time, and resources.
✔ Automatic Berth Planning
Berth planning grows in complexity when more factors like delays, CHE downtime, etc. are incorporated. Sometimes, yielding a berth plan without omitting favorable elements like cost efficiencies are almost impossible to achieve manually with the human brain. Time is an invaluable resource that cannot afford to waste when AI can do it automatically, so you can leverage control and concentrate on other objectives vital to the business vision.
✔ Resource Optimisation
As COVID-19 persists in threatening port authorities with resorting to a reduction of current resources to combat the crisis, optimisation poses as a better alternative. Optimisation capabilities are a valuable tool for operators to leverage economic benefits since calculating and deploying the exact number of CHEs required will save costs on fuel, staff, and time. It also prevents a spur of errors from occurring, decreasing the number of container re-handles. Due to current conditions weakening the economy, port and authorities cannot afford to let any of their resources go to waste.
✔ Dynamic Real-time Work Instruction Sequence Execution
AI becomes inherently valuable if its ability to conjure responses and develop steps which proactively respond to dynamic situations is in real-time. There is no use if AI is only programmed to respond to a pre-defined set of circumstances. Instead, it should develop agility where dynamic real-time work instructions navigate through unexpected disruptions and execute WIs according to current conditions. As a result, workflows streamline and operations complete at an optimal level of efficiency.
Terminal operating systems are the heart of every terminal powering operations right from planning down to execution. However, when AI is integrated into a TOS, it must be unwavering of the centralized server architecture. Before we continue to expand on this, it is crucial to understand the constructs of AI which follows a process that collects pools of data, then learns from it, makes algorithms, and applies this knowledge to make independent decisions automatically. As a result, Big Data is not only an integral component of the initial stages of AI learning, but having accurate and reliable data is also another necessity.
Your conventional TOS requires multiple servers to load information, which not only instigates longer wait times but inherently pulls fragmented data. Data becomes redundant when flowing through a TOS with a decentralized architecture that cannot support real-time control and planning. A non-true real-time environment instills inefficiencies within operations and management, mainly when changes made to data has already been processed onto the next step. Static control processes respond ineffectively to changes since the information which is now deemed redundant will continue to be worked on until the new modified data is retrieved from the previous workstation operator. Changes made from the previous process may not inflict such drastic impact upon the current planning task, but what if alterations need to be made from five steps prior?
Consequently, if data is not in its most updated state, poor decision-making from AI is a strong likelihood, inducing inefficiencies in terminal operations. The omission of core AI components will directly tamper the execution process such that CHEs will misplace containers in the yard or move containers in a yard bay that is no longer available, incurring additional moves and time for it to be placed in its correct location. These events will snowball and will inevitably yield a reduction in ROI. Therefore, for AI within a TOS to be beneficial for a terminal, it must devise an architecture that sustains real-time control planning to ensure that CHEs are always making the optimum number of moves and achieve accurate decisions.