INFORM GmbH has supported quarrying and aggregates operations to embrace artificial intelligence solutions to make tangible improvements to their business.
Artificial Intelligence (AI) is reshaping industries worldwide, but in the aggregates sector, its role is often misunderstood. Beyond futuristic promises and flashy demos, AI is making tangible, bottom-line improvements in transport logistics today. With the right application, it’s not about replacing dispatchers or revolutionising operations overnight. It’s about getting more done, with fewer resources, and far less stress.
For over 30 years, INFORM has worked with aggregates, concrete, and asphalt producers to optimize logistics workflows. That experience has shaped a new generation of AI tools designed not just for innovation’s sake, but for measurable, operational gains.
The daily realities of dispatch planning are demanding. Planners face unpredictable challenges: drivers calling in sick, unplanned equipment downtime, bottlenecks at plants, traffic delays, and mounting customer expectations. Most logistics systems simply don’t cope well with that level of variability. As a result, many teams operate in a near-permanent state of reactivity, battling issues as they arise.
What sets modern AI-based systems apart is their ability to evaluate countless setup scenarios before the first truck leaves the yard. The result isn’t just a better transport plan. It’s the ability to start each day with confidence, knowing that resources have been allocated in the smartest possible way.
This kind of advanced planning frees up dispatchers to deal with exceptions rather than reacting to every shift in the schedule. Instead of juggling phone calls and manual adjustments, they can focus on customer satisfaction, efficiency, and continuous improvement.
Crucially, these systems are not built with Large Language Models (LLMs). They are crafted using a mix of optimization techniques, operations research, and machine learning, engineered for the specific complexities of bulk logistics. These algorithms dynamically integrate order intake, fleet status, customer demand, and production data, providing a continuously updated decision framework.
The practical results speak for themselves. One European producer running 580,000 cbm of ready-mix concrete annually saw productivity gains of nearly 25 per cent after implementing INFORM’s AI-based dispatch optimization. Without adding vehicles or infrastructure, the company achieved annual savings of €2.2 million. That’s nearly a 23 per cent drop in logistics costs.
Such improvements are not outliers. The ability to deliver more with the same (or even fewer) resources is one of the most sustainable ways to improve margins in an industry where price volatility and tight budgets are the norm.
AI’s role doesn’t end at dispatch planning. It also extends to how orders are taken and scheduled. In many cases, the seeds of inefficiency are planted when customer service teams make commitments without a full view of transport capacity. Intelligent systems close this gap, connecting sales and operations through real-time data. When order takers know which delivery windows are viable, they’re less likely to overpromise or cause avoidable strain on resources.
And while tools like ChatGPT may not be dispatching trucks anytime soon, natural language AI has a supporting role to play. Real-time translation for multilingual driver communications or AI-assisted input validation for order times and durations are just a few ways large language models are enhancing user interaction and data quality in logistics software.
Ultimately, the question isn’t whether AI belongs in the aggregates industry. It’s whether companies are ready to leverage it thoughtfully. The good news is that implementation doesn’t require a leap of faith. INFORM, for example, offers simulation-based assessments. By analysing a few representative days (one busy, one average, one quiet), logistics teams can see the before-and-after impact of AI-based planning on KPIs like fleet utilization, on-time performance, and cost per ton delivered. This hands-on, data-driven approach removes the hype and provides clear insight into whether a logistics AI system will pay off.
In the end, the most effective use of AI in cement and aggregates logistics is not flashy or disruptive. It’s practical. It helps companies deliver more loads, with fewer vehicles, under tighter deadlines, and without compromising service levels. For an industry built on pressure, that’s a future worth embracing.
