Material handling isn’t just about brawn — it’s about smart technology, and increasingly so. Innovative and data-driven technologies are driving unprecedented efficiencies and bringing new benefits to the industry.
Somewhere in the world, a team of engineers is meticulously designing a new container terminal using advanced digital twinning software. The technology allows them to test multiple layout options, predict future equipment needs, and assess operational efficiency in a virtual environment—tasks that would be impossible to achieve with traditional methods. In another region, a steel mill leverages real-time data-driven asset management, optimizing its operations dynamically and ensuring equipment is used with maximum efficiency. Still elsewhere, in a busy port, a predictive maintenance system alerts the operators to change a crane motor part before it fails, preventing what could have been a costly shutdown.
In all these scenarios IoT sensors are collecting information about everything from motor vibrations to temperature changes, while cloud-based systems aggregate and analyze this data, providing actionable insights.
This isn’t speculative fiction — it is a reflection of how material handling is evolving already today, powered by digitalization and analytics. Central to all of these advances is one common denominator: data. As it has in many industries in the 21st century, data has become the “new oil,” a valuable resource fueling innovation and efficiency in material handling.
“Emerging tech as a whole is quietly revolutionizing the industry and can offer major benefits to both small and large companies,” says Stephen Hopper, Founder of Inviscid Consulting. He has spent more than three decades helping clients optimize material handling operations and has seen smart, data-driven technology evolve from a relative rarity in the field to commonplace, used for everything from facility design to order picking and forklift operation.
“Technology is progressing and simultaneously becoming less expensive, so ‘smart’ solutions are an increasingly viable option,” he adds. “In many cases, what used to be available only for companies on the cutting edge is now something that even those with much smaller operations can consider.”
Moreover, we have entered an era where the how we use the data is improving exponentially.
“The future of material handling will be shaped by not only smart technologies but also by the standardization and convergence of data from various sources, allowing those technologies to be leveraged in more and better ways,” explains Csaba Boer, Chief Product Owner at Konecranes.
The transformative benefits of data-driven material handling
Data has always been at the heart of successful material handling operations, but for most of the industry’s history, that data has been limited in scope and quantity. Today, data is not only being generated in far greater volumes, but it’s also being transmitted and processed in real-time.
This ongoing shift brings numerous substantial benefits along with it.
“The result will be an industry that operates more efficiently, more sustainably, and more safely than ever,” says Boer.
The most immediate benefit of a data-driven approach is increased operational efficiency, and today’s predictive maintenance systems are a perfect example of this transformation. While corrective maintenance is performed on a reactive basis and preventive maintenance schedules based on time intervals, predictive maintenance uses real-time data to stop equipment failure before it happens. This means that unplanned downtime can be avoided, and costs reduced by maintaining or replacing parts only at the optimal time.
Data is also driving sustainability in material handling. Tools such as emulation and digital twinning, exemplified by Konecranes' CONTROLS emulation product, allow designers and operators to optimize facilities, systems and equipment even prior to their implementation.
“The best way to learn is from making mistakes and seeing where things have gone wrong,” says Boer. “Emulation allows us to do this without the financial, environmental, and safety risks.”
Using virtual tests to maximize efficiencies gives customers the chance to better plan their energy use, incorporating more energy from renewable sources, lowering fuel consumption and thus leading to lower CO2 emissions. It also helps identify areas to cut waste and plan the most efficient use of resources.
Telematics systems such as TRUCONNECT can provide real-time information and send alerts when equipment is being overloaded or when safety features are being overridden. This provides an additional layer of protection, helping operators and managers ensure that machinery is being used safely. In this way, data-driven asset management not only protects the equipment but also creates a safer working environment for operators.
Ongoing evolution: The role of artificial intelligence
We are already seeing data as a major force in material handling – but there is even more to come. According to Boer, there are plenty of opportunities and reasons to increase both the quantity and quality of data being generated. In doing so, he says, companies should start from the “why.”
“What kind of data do we want, and why do we want it? If we are collecting data without answering those questions first, we won’t know how to analyze or benefit from it.
“Current algorithms and systems are already quite sophisticated,” he emphasizes, “but the more data they have, and the more accurate that data is, the better they perform.”
But alongside this, there will also be an increasing need to manage data from disparate sources in ways that are streamlined and standardized, if we are to maximize the benefits from it. Companies generate data from a variety of systems — fleet management software, warehouse sensors, maintenance logs, etc. — and merging these streams into a cohesive system is no small task.
This is where artificial intelligence (AI) will be a big part of the solution, capable of analyzing massive amounts of data from multiple sources and uncovering patterns that are too complex for human analysis. These algorithms can generate insights that drive more efficient decision-making and process optimization.
“Normal software can’t learn or improve based on its own experience,” Hopper points out. “AI can, and that capability means that optimization can take place on much deeper levels. It’s unclear exactly where we are in the AI hype cycle, but as we head up the slope of understanding and adoption, it’s clear that software is going to become much faster and more effective.”
