Why are transparency, integration and trust becoming decisive in logistics technology? Peter MacLeod speaks to an expert.
At this year’s LogiMAT, if there was a theme that cut through the noise more clearly than most, it would be speed. Not just speed of operations, but speed of deployment, speed of innovation, and ultimately The Big One: speed of return on investment. For Inform Software, that discussion increasingly leads to a broader question: how can logistics organisations adopt more intelligent systems without losing transparency, control or trust?
Speaking to me on the busy show floor in Stuttgart, Inform’s SVP Inventory & Supply Chain, Dr. Bernd Heinrichs outlined how the company sees artificial intelligence developing in supply chain and intralogistics environments.
Extending the Optimisation Layer
Inform has long been associated with optimisation in complex, data-driven environments. But as markets become more volatile, optimisation systems are being asked to react faster, incorporate more signals and support more dynamic decision-making.
That shift is particularly relevant in environments where decisions are interdependent. A change in demand planning may affect inventory, transport capacity, labour allocation or service levels. A recommendation made in one part of the operation can create consequences elsewhere, which makes transparency essential for day-to-day use.
For Heinrichs, this is where AI in logistics must prove its practical value. “I don’t talk about AI. I talk about explainable AI,” he says. “Everything we do, everything we propose, has an explanation. Otherwise, people don’t trust it.”
Trust as a Practical Requirement
In conversations with customers across different industries, he says the same question comes up repeatedly: “Why did the system pick that option and not another one?”
The question matters because logistics decisions are rarely made by technology alone. They involve planners, managers, operations teams and, in many cases, customers or external partners. If these stakeholders cannot follow the reasoning behind an AI-supported recommendation, they are less likely to act on it.
For Heinrichs, this could become a meaningful point of differentiation for European technology providers. “We can build AI as good as anyone, but we can add something different,” he says. “It should not be a black box.”
As companies look to embed AI applications into established business processes, that difference becomes increasingly important. Systems need to be technically strong, but they also need to be understandable enough for users to challenge, validate and improve them over time.
Managing Less Predictable Environments
Operational environments are becoming harder to plan with historical data alone. Demand patterns shift, external factors intervene and market conditions can change quickly, often before those changes are clearly visible in the numbers. “You need to gather real-time data and not rely on historical data alone,” he says. “You have to react to volatility and integrate signals from different sources into your decisions.”
This marks a shift from more static optimisation models towards responsive systems that continuously take new information into account. “It is getting more dynamic,” he adds. “The next step is making it more agentic – reacting on its own to changes in the environment.”
From News to Forecast
One example Inform presented for the first time at LogiMAT is a new AI-based approach designed to bring external events directly into forecasting and scenario planning. The starting point, Heinrichs says, was a simple question: why do forecast models so often ignore what is happening in the world around them?
“If you run a classical forecast today, it is based on historical figures,” he explains. “But in reality, demand is constantly influenced by events such as geopolitical conflicts, supply chain disruption, new regulation or market trends. This information exists, but usually as news, not as numbers.”
The new solution is designed to close that gap. Users provide a time series, such as sales figures or a market indicator, and briefly describe the context. The AI then researches relevant news events, analyses historical relationships and generates several possible future scenarios. The result is a forecast accompanied by an evidence-based explanation of why a market may develop in different directions.
Human in the Loop
For Heinrichs (pictured, below), the discussion about AI also leads directly to the role of human expertise. AI can identify patterns, process large volumes of information and produce scenarios at speed. But its value increases when people can add the experience, context and judgement that data alone cannot provide.
“AI is only as good as the data it works with and the people who are able to give that data meaning,” he says. “That is why the human remains an essential part of the loop.”
In practice, that means planners and decision-makers are not removed from the process. They remain central to it. Their role is to validate scenarios, question assumptions and refine outputs based on operational knowledge or market intuition.
“If people understand why the system recommends something, they can decide whether to trust it, question it or improve it,” Heinrichs explains. “That is where collaboration between human judgement and machine intelligence becomes really powerful.”
Integration and Interoperability
Another consistent theme in customer discussions is integration. As logistics operations become more interconnected, the ability to link AI-driven applications with existing systems is becoming essential. “We always get the question: how do I integrate with my ERP system, my other solutions?” Heinrichs tells me. Inform’s response has been to standardise connectors and align with major platforms such as SAP and Microsoft. The result is a more straightforward integration path, reducing both cost and implementation time.
“It makes a big difference,” he adds. “And it also makes it easier for us to expand internationally.”
This is a crucial point in the adoption of AI. Even the most advanced application will struggle to create value if it sits apart from the systems where business processes are actually managed. Logistics companies already operate with established IT landscapes, and new solutions must fit into those environments without creating additional complexity.
Data Responsibility
With increased connectivity and data usage comes heightened scrutiny around security. Heinrichs’ background in cybersecurity informs a strong stance on this issue. “Every product has to have a security stamp before it goes out,” he says. “It is mandatory.”
As AI models draw on wider data sources – including external feeds such as news and market information – the complexity of managing and securing that data grows. “The amount of data we are tapping into creates a huge demand in terms of data security,” Heinrichs notes. “You have to stay on top of it.”
A Market Ready to Move
Perhaps most striking is Heinrichs’ assessment of market sentiment. Rather than caution, he sees a growing appetite for experimentation and rapid progress.
“Customers are asking us to come with ideas,” he says. “They are willing to win fast, fail fast.” That openness creates fertile ground for intelligent solutions that can deliver tangible improvements without the inertia of large-scale transformation projects.
For many companies, the next phase of digitalisation will not be defined by AI alone. It will be defined by AI that explains itself, connects cleanly with existing systems and supports decisions that people can trust.