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Achieving the Impossible

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How can AI contribute to your terminal?

Terminal operators make the big decisions while the system already integrated with AI technology completes the mundane and complex issues. Therefore, TOS users can achieve a higher task turnover rate.

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The different kinds of AI analytics

Various data methodologies derive from AI. Each of its kind possesses different functionalities and capabilities. Take a look and learn the difference to see what best suits your terminal needs.

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What you need to advance AI to unseen levels

AI will likely be the most remarkable development of our time. With the world progressing in its digital transformation journey, learn the steps and resources necessary to support its infrastructure that reap its full benefits.

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Unlike many other technological innovations, Artificial Intelligence (AI) is one of the few that can contribute significantly to operations and alleviate burdened terminal areas. More commonly, errors in planning, yard congestion, and insufficient use of available resources can evolve into more complex issues beyond human capacity to resolve independently. Especially in this uncertain period where technology has seamlessly integrated itself into our daily lives, it will be difficult to ignore its unexplored potential. However, TOS users must first overlook the misconceptions of AI generating mass unemployment or losing control over AI as it achieves super-intelligence. Instead, a critical perception of AI application in the terminal environment can institute a more structured strategy in advancing the fourth industrial revolution.

The magnitude of operational efficiency and productivity drill down to two factors – the time it takes to complete the task and the resources it consumes to achieve the outcome. An imbalance between the two can instigate hotspots that can become too time-consuming to resolve without incurring massive costs that compensate for the delays. As a result, various AI applications have developed to assist executives in decision-making while fueling and elevating other technological enhancements like automation and the Internet of Things (IoT). Real-life AI applications have abated redundant workloads, helping terminal employees on all planning, scheduling, and execution levels to achieve a faster task turnover rate.

The responsibilities and pressure to deliver during this time of fluctuations in demand have become frequent calls for AI technologies to rapidly create strategies and facilitate the decision-making process. In retrospect, a thriving supply chain relies on its counterparts such as suppliers, carriers, and ports, operating cohesively and effectively. Therefore, any disruption inflicted in these environments will cause a chain reaction of issues impacting the overall economy, highlighting AI as critical for forecasting and prevention.

Prevalent AI applications commonly exist as an additional add-on; however, systems already embedded with the technology can provide even greater value at no extra charge. Having AI already integrated within the software allows real-time data information flow, which is vital for the reliability of decision-making. AI evaluates from historical and real-time data and then automatically conceives the best-fit solution while factoring in user set conditions. Therefore, users still maintain control as they establish the parameters, curating a specific path for AI to garner only necessary and relevant information. As a result of AI already integrated into the system, speed is unparalleled, and decision-making always adheres to your requirements. Software embedded with AI of this kind will generate results never seen before and not at the expense of your control over operations and decisions.

Understanding the constructs of AI reveal its benefits and how it truly works, differentiating from its misconceptions. AI nurtured to its full potential can incite stark increases in task turnover rates, attributing to the terminal’s overall productivity and operational efficiency. Therefore, AI naturally embedded within a TOS streamlines daily tasks, automatically completing the mundane and often redundant tasks so TOS users can focus on other areas of concern and complete them faster. The user selects parameters and conditions that essentially shape the TOS brain, granting control over the configurations and operational spectrum as it starts the decision-making process. As a result, introducing AI within a workspace does not generate mass unemployment but rather facilitates employees to achieve better KPIs and a higher task turnover rate.



We live in an age where data is massively abundant, and thereby introducing data analytics, we can stop scouring relentlessly to strategize and develop solutions. Instead, analytics allow terminals to explore and assess their data, then transform their findings into meaningful information that assists executives, managers, and employees make well-informed and reliable decisions. There are variations of data analytics that distinguish as descriptive, diagnostic, predictive, and prescriptive, each with different insights and purposes. The various types of data analytics and their associated benefits and applications are as follows:

  1. Descriptive – what happened?

Descriptive analytics examines the pool of historical data collected, then organizes and presents it in an easily understood way. This type of analytics contrasts against other analytics methods by solely centralizing on what has already happened rather than developing inferences or forecasting from its findings. Instead, it is rather a foundational beginning point of informing or preparing data for analysis in the latter of a process. Descriptive analytics can deduce patterns and meaning through the comparison of historical data. Therefore, this can prove very useful as a yearly revenue report, for example, may appear financially stable when viewed in isolation until it is compared with the same information from past years, revealing a downward trend. The output of descriptive analytics can also represent in Business Intelligence (BI) applications as pie charts, bar charts, line graphs, tables, or generated narratives.

  1. Diagnostic – why did it happen?

Diagnostic analytics is another derived form of data science that evaluates data or content to clarify why something occurred. It contributes significant value to a sought response by gathering relevant data, then generating insights as it learns about the situation. However, this analytic method stands more than an investigation, revealing how and why an event ensued from determining correlations in data. Integrating diagnostic analytics usually means the business has well-established descriptive analytics. These two data methods work cohesively to determine trends that drill down to reveal factors that impact and attribute to results. For example, diagnostic analytics represented as a line graph in a BI dashboard shows specific periods of a terminal experiencing surges in container volume. As the year approaches holiday seasons, the diagnostic analytics infers consumer demand skyrockets, and therefore, the time of year correlates to revenue. As a result, this analytics method helps terminal operators visualize and understand what is happening in business operations.

  1. Predictive – what will happen in the future?

Predictive analytics is a branch of data analytics that learns from historical data and develops predictions, identifying trends and informing users about future outcomes. Predictive analytics uses statistical modelling and machine learning techniques to draw several conclusions that factor in user-set parameters and conditions. The results help executives, managers, and employees foresee upcoming events and deep insights that may impact their operations. Due to its increased precision and efficacy, predictive analytics reveal various aspects of a business that relay into establishing realistic objectives, efficient planning, managing performance expectations and averting risks. By learning the potential events, for example, AI calculating the most optimal schedule and position to berth at a terminal, executives and managers institute a more proactive, data-driven approach to the overall terminal strategy and decision-making.

  1. Prescriptive – what should we do about it?

Prescriptive analytics stands at the forefront of data analysis. It combines insights developed from all former analytics and determines the optimal course of action to convey a current issue or decision. This methodology serves as the final and most advanced stage in the business analysis process, encouraging business action, and facilitates executives, managers, and operational employees to undertake the best possible, data-centric decisions which optimize performance. With its ability to strategize countermeasures that overcome the issues identified in predictive, diagnostics, and descriptive analytics while also deducing predictions, terminal operators can adapt or nimbly make changes accordingly. For instance, prescriptive analytics can automatically identify the necessary steps to avoid the potential congestion in the yard, sending Work Instructions (WIs) to all active Container Handling Equipment (CHEs), which effectively move containers with a minimum number of moves. Therefore, its capabilities to factor in many favorable conditions in its strategy on the fly automatically constitute the most sought-after data analytics type.



The only way AI can reach its full potential and operate at your expectations, like any other technological advancement, requires organizational commitment to the resources and efforts necessary for setup. Adhering to these principles will enhance AI capabilities that can genuinely impact business strategy and decision-making by improving terminal operations, customer experience, and overall terminal growth. However, before you are ready to integrate and accelerate AI benefits, you must first adopt a TOS of purely real-time architecture.

Terminal operating systems are the heart of every terminal powering operation, right from planning down to execution. However, when AI already exists embedded within the TOS, it should be unwavering 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 an integral component of the initial stages of AI learning, but having accurate and reliable data is also necessary.

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 instils inefficiencies within operations and management, mainly when changes made to data has already processed onto the next step. Static control processes respond ineffectively to changes since the redundant information will continue as it gets worked on until the retrieval of new modified data from the previous workstation operator. Changes made from the previous process may not inflict such a drastic impact on the current planning task, but what if alterations need to be made five steps prior?

Consequently, if data is not in its most updated state, poor decision-making from AI is likely to induce inefficiencies in terminal operations. The omission of core AI components will directly hamper the execution process. For example, CHEs will misplace containers in the yard or move containers in a yard bay that is no longer available, incurring additional moves and time 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 benefit 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 achieving accurate decisions.

Trust in the power of AI and have the correct facilities, resources, and trained staff to support its infrastructure to propel its capabilities further. Then, witness operational efficiency, resource optimization, and employee productivity like never seen before.