Terminals have the dual duty to assess their overall business operations to depict any flaws and evaluate the best fit terminal operating system that can combat these hotspots. The era of terminals taking a crucial point in the global supply chain is fast approaching, and terminal operators may be facing circumstances that are outside their expertise and capabilities. Autonomous operations abreast the help of artificial intelligence in computing masses of data to determine reliable decisions will come to fruition by all industry experts.

A thriving terminal is reflective of terminal operations which are responsive in the scalability of container throughput while maximizing the use of their existing resources. Therefore, terminal operating systems incorporating true AI capabilities become the intrinsic option when decision-making is very time-consuming and are liable to errors. Turnkey software like TOPX Expert has cultivated the essence of AI which overturns these implications. TOPX Expert leverages greater control for terminal operators since the AI instituted automatically learns from the substantial pool of data and creates algorithms to which the knowledge is applied, and different scenarios are deduced for selection. These scenarios have been determined with the consideration of set parameters to achieve the optimum position, time, and departure for a vessel to berth. Early detections of bottlenecks are also another benefit derived from TOPX Expert as it facilitates forecasting any upcoming constraints, driving greater visibility over operations to make faster decisions. The lead up to the completion of berth planning and scheduling is a complex process that is almost impossible for the human to calculate free from errors, requiring automation and artificial intelligence to cease errors before they even appear. Not only does this indicate AI in berth optimization is necessary but requires real-time data to make accurate decisions which benefit the overall terminal.

AI is introduced to assist in complicated tasks that typically fail to address all set factors optimally, and as a result, inhibits resource-, process-, and operations optimization. All this is only made possible due to the real-time and in-memory architecture allowing for the centralization of data. An environment that provides a centralized space for information flow allows all users, independent of the type of operation, to work on data that is always maintained in its most updated state. This is exceptionally important for berth optimization since all these data fuels AI in inferring the most viable decision without sacrificing efficiency in any area. Another benefit of AI conceived on a real-time architecture is that it enables process agility, allowing operators to make changes to previous tasks without compromising the current operation or the overall process. Challenging the conventional TOS, TOPX Expert is built on a robust architecture that makes automatic changes in real-time, despite when it was made within the process timeline. Outcomes such as the elimination of congestion before and after is rendered as the fore benefit of AI in berth optimization. Therefore, decisions are always achieved with precision in TOPX Expert and remain fully functional even when exposed to drastic changes in data.

All in all, terminal operators have the responsibility to take control and define the most ideal solution that captures the entire spectrum of terminal operations which streamlines processes and delivers efficiency. Common deciding factors when searching for the right TOS derives from short-term goals which have precluded revolutionary technology offered in a quality TOS. Consequently, terminal operators miss the opportunity of meeting better capabilities which drive higher efficiency. Every functionality should always be evaluated and proved against its acclaimed level of AI inherent in the technology to understand what the TOS is truly capable of. Don’t just limit your vision to the present, look ahead, and invest for a future. Click here to find out more factors to look for when researching into AI in a TOS.

Technologies that adopt various characteristics of human intelligence and behavior are typically referred to as Artificial Intelligence (AI). It is the study of agents that analyze their surroundings to create plans and accomplish the best decision after accounting for both retrospective and current percepts. AI is a construct using two techniques, which are machine learning and deep learning. Although these derivations are closely related since deep learning is a subset of machine learning when put in simple terms, they have distinct characteristics which impact AI differently.

Machine Learning

Machine learning uses algorithms which interprets data, learns from it, and then applies the knowledge to achieve informed decisions. This term falls under the umbrella of AI as it is used to forecast and render conclusions based on the data analyzed. Rather than following the conventional route of human learning, machine learning is instead, engineered to be proactive when confronting complex and dynamic environments. It uses the provided data to perform this function and progressively improves.

Deep Learning

Machine learning and deep learning are loosely interchangeable due to its similar functions; however, the distinguishing factor lies in its capabilities. Deep learning generates algorithms which analyze data continuously using a logical structure similar to how a human would deduce conclusions, entirely on its own. The overall deep learning decision-making process is driven by a multifaceted framework of algorithms called an Artificial Neural Network (ANN). An ANN is reflective of a human brain, which by far, exceeds the standard machine learning in the aspects of capability and intelligence.

Another disparity between these two concepts draws machine learning as more dependent on engineer intervention to resolve inaccurate algorithm predictions. On the contrary, a deep learning model is independent and can identify whether their prediction is accurate or not on its own. Essentially, correctly executed deep learning is the core fundamental powering true artificial intelligence.

Categories of Artificial Intelligence (AI) – Weak and Strong

AI can further be classified as weak or strong. Weak AI is characterized by its limited design and set of rules conforming it to a particular task. It cannot act upon any information beyond the scope of their assigned rules. For instance, Apple’s Siri may hold the impression that it is very intelligent for being able to respond in conversations and make snarky remarks, but as soon as it is asked a question outside what it is programmed to answer, Siri struggles to deliver accurate results. Although the Internet is an encapsulation of the entire world’s knowledge, leveraging the information and data to power AI is not enough for it to respond in dynamic situations. Its intelligence to compute and make conclusions is capped at an extent since they are only capable of what they are wired to do, which is why Siri’s “Sorry I didn’t quite catch that” has become quite the familiarity. The key takeaway is that weak AI is very competent in executing tasks that it is programmed to do and may even become exceptional at it; however, its stream of certainty is lost when it diverges from the job it is assigned to do.

On the opposite spectrum, the limitations of strong AI are almost non-existent, as it is capable of mimicking cognitive functions without discrepancy from a human mind. Other than adopting the same skillset as humans to facilitate reasoning, problem-solving, planning, learning, and communicating; true intelligence also requires a consciousness with sentiments and self-awareness. These key attributes are crucial in the learning process, which starts with a childlike mind and matures with learning and development. After reaching this stage, it should be able to interact with its surroundings make inferences independently and even render their own creations. However, due to the complex construct governing strong AI, we have yet to witness a technology that embodies true intelligence in its entirety.

Although AI is still in the early stages, RBS has already developed upon its true principles by integrating vital AI attributes to achieve berth optimization. To ensure your terminal meets efficiency in all areas ranging from planning to forecasting, click here to find out how.

Terminal optimization is segregated into divisions that are allocated different roles and responsibilities. Optimization in the Container Handling Industry is conceived due to terminals experiencing various bottlenecks when vessels berth at the port, inefficient yard planning affecting container movement, the disarray of CHE instructions causing congestion, and poor management of resources which fail to maximize time. If an area of a terminal evokes inefficiency, it will inevitably impact another which can engender various risks that will accumulate until a complete catastrophic outbreak dismantles the entire terminal. Consequently, terminals are in pursuit of a terminal operating system that incorporates a suite of capabilities that counteract these implications.

Vessel berthing is the most prominent area where optimization has been instituted to enhance planning processes which is accountable for maximizing terminal capacity and limiting congestion. A unique function only offered in RBS, is TOPS Expert is Artificial Intelligence (AI) Berth 优化, which incorporates cutting-edge technology that mimics human behavior by gathering then understanding data to develop algorithms utilized to optimize berth planning. Not only is discharge operations executed at the highest degree of productivity such that it meets the critical vessel schedule when AI is integrated into the vessel berth planning process, but it substantially reduces the amount of human intervention. Personnel no longer must spend copious amounts of time manually analyzing calculations to determine the best date and time to schedule a berth since this conventional method of planning is simply not efficient, nor is it reliable. With AI, decision-making is achieved much faster with extreme accuracy as ETAs and ETDs are strategically and automatically depicted. Real-time visibility is inherently amplified with TOPS Expert AI Berth Optimization module as terminal operators can foresee the actual workflow regarding the vessel operation, eradicating any ambiguity. A positive correlation also emanates from this module for vessel operators as they can save fuel and energy costs due to optimized travel distances. Terminals, therefore, must re-evaluate their TOS and berth optimization functionalities if they confront bottlenecks that are impacting their profitability – let AI do the thinking so you can work smarter not harder.

Deprived throughput may be attributable by a TOS lacking in infrastructure or competency to perform and deliver optimal decisions. As a result, yard space may not be used to its maximum potential, creating chaos when a container enters and departs from a terminal. These complications are offset by deploying a TOS that can efficiently plan the allocation for containers not just when they are active or discharged from the terminal but even prior to their arrival. Strategically managing the containers limit the number of container moves, facilitating CHE optimization as the task is executed faster, and the CHE can move onto other processes. Countermeasures such as seeking for an ameliorate TOS must commence when poor yard management pose as a constraint inhibiting optimization.

The performance of CHE, processes, and resources are the key drivers determining the criticality of vessel berthing optimization and yard optimization. The ability to calculate the minimum number of CHE moves required to move a container from one location to another should be reinforced in the TOS. Like TOPS Expert, every process is carefully planned to consider every set parameter, provide alternative scenarios, and heed only the necessary resources to calculate the shortest but the best path in executing the action. Therefore, as the components in berth optimization and yard optimization are derived and refined, there is a clear inference that optimizing CHE, processes, and resources are just as pivotal since they are the principal factors in defining terminal performance. Always conduct thorough research and evaluate each TOS against its infrastructure, capabilities, and range of functionalities before deciding on which to deploy.

Terminal optimization may be easily definable as a terminal operating system that devises intelligent capabilities which facilitate more efficient operations and processes. However, this explanation provides a very basic understanding of optimization and confines it from its complex nature. All planning, instructions, and decisions made within a terminal can be optimized, and therefore, is inclusive of any activity on the operational spectrum that ranges from vessel berthing to managing containers within the yard. Optimization is not limited to the sole objective of achieving productivity, and instead, is also responsible for forecasting the emergence of any potential problem areas, depleting costs, and balancing various resources to yield transparency in all decisions.

Due to the underlying definition of terminal optimization, it is easy to slap this label onto any TOS and certify their functionalities as being able to deliver the most optimal decisions. Each TOS has divergent views of optimization, and hence renders different functionalities which generate different results. If a TOS is not maximizing the use of the available resources and achieving the desired outcome, the terminal is highly likely exposed to the risk of suffering in performance. Optimization must be supported by the correct technology and established on a robust infrastructure to ensure they are not encountering any conflict in operational priorities – being unable to achieve the optimal and most functional outcome without sacrificing a set parameter. Each decision made by the TOS should be calculated with diligence and account for all contingencies without omitting any set parameter to present terminal operators with the most viable optimal solution.

A TOS with refined terminal optimization should exhibit the following capabilities and benefits:

• Forecasting problems – warrant an early detection of upcoming issues that can constrain daily terminal operations
• Risk management – instigate proactive measures in responding to any potential risks or challenges
• Reduce costs – TOS users can leverage forecasting problems earlier to avoid investing financial resources in amending issues
• Improve process efficiency – avoids vessel waiting times and resource bottlenecks
• Improve operational efficiency – heed greater control and visibility as a result of optimized resources and forecasting abilities
• Faster Decision-Making – the timeframe for planning and exercising decisions should be significantly reduced and not at the expense of any set requirements to reflect a capable TOS
• Accurate information – TOS should have AI integrated into the system and automated data to provide accurate information which inhibits errors from ensuing, granting heightened visibility over operations
• Performance monitoring and accountability – increased traceability over all activity conducted within a terminal which is owed to optimization sharing information across departments which derives optimum results
• Transparency – every projected scenario must reinforce all the parameters set by the user to contribute a clear and refined decision

Therefore, optimization transcends the rudimentary notion of being limited to just efficiency and requires accurate and in-depth knowledge of the associated processes before it can be clarified as a TOS that can truly exhibit optimization. The term can be loosely defined which administers greater responsibility for terminal operators to take prudence when searching for a TOS. As a result, a TOS that can truly optimize all activity within a terminal should be ergonomically stable and absorb all factors without excluding any benefits.