From Grease to Gold: How AI Transformed Container Terminal Maintenance
- Henry Harel
- Jun 25
- 3 min read

It's hard to overstate the number of superlatives associated with AI applications and the concerns their adoption raises across various industries. On the other hand, many who experiment with these new capabilities are simply filled with joy and delight from the unexpected, and sometimes spectacular, results that open up before them.
This post is precisely about that kind of joy—a capability that didn't exist before, and now does. But, for a change, it's not about generating a stunning image of a fictional landscape or creating an emotional piece of synthetic text. It's about something much more mundane, entirely blue-collar. Born from the meeting point of manual labor, grease, hydraulic oil, and salt spray.
Picture this: 500 massive transport machines orchestrating a symphony of steel and cargo at one of the world's largest container terminals. Each day, these mechanical giants move containers weighing thousands of tons, carrying the dreams and commerce of nations.
Yet behind this impressive ballet of logistics lies a constant battle against mechanical failure—100 to 200 malfunctions occurring daily, each demanding immediate attention from a dedicated maintenance team.
Recently, we enhanced our Opsima AI system and deployed it to analyze these malfunctions in real-time operations. The challenge we faced was both simple to understand and complex to solve: recurring and induced failures, where technicians fix the symptom but miss the underlying root cause.
The Hidden Pattern Problem
To detect such failures, a technician must check the logs of all previous failures for that specific machine and similar machines leading up to the current malfunction. Imagine being a detective trying to solve a case, but your evidence is scattered across thousands of reports written by dozens of different investigators over months or years. Identifying these patterns improves maintenance efficiency, shortens diagnostic time, and prevents identical issues from plaguing operations in the future.
In such a large fleet, with rotating shifts, even reviewing 10 days of failure history would mean reading around 1,500 failure records daily. It's simply unfeasible. So usually, it's just not done. The failure gets fixed, and everyone hopes for the best—until the same ghost in the machine strikes again.
Enter the Language Model Revolution
Our enhanced Opsima AI system leverages the power of Large Language Models (LLMs) in a uniquely practical application. These sophisticated neural networks, trained on vast datasets of human language, possess an remarkable ability to understand context, identify patterns, and extract meaning from unstructured text—exactly what we needed for maintenance logs.
The LLMs work by analyzing the textual descriptions that maintenance technicians naturally write when documenting failures. Unlike traditional keyword-based systems that might miss nuanced descriptions, the language models understand that "hydraulic pump making unusual noise" and "abnormal sounds from pump assembly" likely describe related issues. They can recognize synonyms, technical variations, and even informal language patterns that human technicians use in their daily documentation.
The deployed system combines these language models with dedicated algorithms specifically designed for maintenance analysis. Together, they analyze the textual data already being entered into the organization's maintenance management systems, identify past failures in real time, examine the likelihood of recurring or induced issues, and notify maintenance personnel with surgical precision.
Time required for analysis: seconds. Annual cost for all analyses: negligible compared to maintenance costs. This tool transforms every technician into an expert technician, transferring the accumulated wisdom of generations to younger hands. It makes algorithmic and textual analysis just another tool in the technician's arsenal—like a wrench, only infinitely smarter.
The Proof is in the Performance
The results speak for themselves with the clarity of well-oiled machinery.
About 10–15% of daily failures are recurring, and when we presented lists of such failures to content experts with decades of experience, they confirmed that the system achieved accuracy rates exceeding 90% in its diagnoses.
These aren't just statistics, they represent prevented downtime, avoided emergency repairs, and the difference between reactive scrambling and proactive precision.
The Golden Thread
Today's organizations sit atop mountains of data, most of it as overlooked as yesterday's newspaper. The magic happens when you transform that data into actionable information. It's worth its weight in gold, and more than anything, it brings tremendous joy to watch artificial intelligence elevate human expertise rather than replace it.
In a world where AI often captures headlines for its creative prowess, there's something deeply satisfying about watching it excel at the fundamentally human task of learning from experience, one maintenance log at a time