Achieving net-zero is a long and costly journey, especially in the building materials industry. Yet logistics and transport offer a powerful, often overlooked opportunity to reduce emissions, cut costs, and improve customer service. At INFORM, we’ve been applying artificial intelligence to this industry for decades. Our Hybrid AI approach combines methods from operations research and machine learning, always tailored to the use case and centered on customer value. 
 

AI in Concrete and Cement Logistics: A Proven Track Record 

Digital decision-making and logistics software powered by algorithms isn’t new to our industry. After all, INFORM’s software has been helping building materials producers around the world reduce cost and drive-up efficiency for 25-plus years. We are fortunate to have some of the big and medium size players put their trust in us. In the mid-90s, Redlands in France (now part of the Holcim Group) was the first company in the aggregates and ready-mix industry to use our solution. In 2001, Hanson Australia (now part of Heidelberg Materials) integrated our software into their supply chain. They all have been using algorithms, real-time information, and automated decision-making to manage and optimize their fleet of trucks ever since with amazing results. When switching from manual to AI-powered planning, our customers typically achieve  

• a cut in logistics unit costs by up to 20%, 
• an increase in loads/truck/day by up to 37%,  
• a reduction in truck fleet size by up to 30%, and 
• a reduction in empty mileage by up to 19%. 
   

Lowering Costs and Carbon Emissions with AI 

What is comparatively new, is our industry’s focus on environmental sustainability. In a bold move to reach net-zero, many cement producers are currently on a “moonshot” mission to reduce the carbon footprint of their products. A quick win on this mission, however, is often neglected: logistics and transport. On the road to net-zero, CO2 reductions in logistics are a low-hanging fruit that allows producers to reduce their overall carbon footprint and save money. Best practices in in our industry have shown that fleets can be downsized by anything from 10 to 30% - while increasing customer service at the same time. That is not only significantly less trucks on our road, but also less idling, less trucks that have to be manufactured, maintained, repaired, and scrapped at the end of their life cycle. For producers running their own fleet of trucks, this also represents a huge Capex reduction. And for those producers using haulers for their logistics, it may represent a most welcome relief for the current driver and transport cost pressure they may find themselves under.  
 

Fig 1: Decarbonization Contributors 
 

Beyond Savings: Elevating Customer Service through Smart Planning 

While the environmental benefits of our AI optimization software will certainly vary from producer to producer given their unique operating conditions, the end results will remain – there are both cost savings and environmental benefits to software-based optimization.   

But besides all these substantial benefits, there’s something on top that building material producers will get for free. By applying AI and data-driven transport planning software, dispatchers are free to focus on higher-level tasks that no robot or algorithm can replace, enhancing customer service. What’s more, our AI-Powered solution will give them the right instruments at hand that allow them to play the full scale of digital transformation. Here are two examples.  
 

Real-Time Order Visibility: Keeping Promises with Confidence 

Customer service starts at the point of order taking. And what many do not realize, logistics costs are also generated at the point of order taking. There are many challenges in placing an order that satisfies the needs of your customers and the needs of your logistics team. And the costs of not perfecting these processes can be enormous. Here’s a simple example: Tomorrow afternoon is a peak period, and your logistics assets are already overbooked or close to being overbooked. Accepting further orders is critical – service level-wise as well as cost-wise. So, you need the right tool to manage or avoid overbooking.  

The order-taking view of our AI-powered software gives you a consolidated real-time view of the current order and transport planning situation. During the order taking process, it provides all necessary information to avoid an overbooking of resources. It is constantly being updated and allows your order takers to find a suitable delivery time for your customers and your available transport resources.  
 

Dynamic Pricing: Balancing Demand, Capacity, and Customer Satisfaction 

 One step further up the scale of digital transformation is dynamic pricing. Often referred to as demand-based pricing or surge pricing, dynamic pricing is a strategy that involves adjusting the prices of products or services in real-time based on various factors like demand, supply, market conditions, and other variables. One goal is often to increase profit generated from a specific customer segment.   

Optimizing the utilization of logistical assets and capacities, however, is another goal that is more relevant to our industry. Or, in simple terms, increase prices when demand is higher than loading capacity (for example, long truck queues in front of the gate) and/or use incentives to increase demand when loading capacity utilization is low (i.e. idling loading stations). In essence, it is about breaking the old trade-off between upgrading plants to cope with peak demands and facing customer complaints for poor service. And again, the consolidated real-time data of the current order and transport planning situation from our AI software provides the necessary input to implement such a dynamic pricing use case.  

When introducing a dynamic pricing scheme, it may be very tempting to use the complete repertoire of influencing factors. However, a step-by-step approach is more advisable, as no one wants to be seen to “nickel and dime” their customers. Producers should start with a simple price differentiation approach for different weekdays and times of the day. Customer trust is a key element in any business. The driving factors behind dynamic pricing should therefore always be explainable and transparent. Once customers are used to differentiation and fluctuating prices, cement producers can slowly expand their dynamic pricing strategy, for example by using predictive algorithms or Machine Learning tools.  
 

Trustworthy AI: Aligning Innovation with Responsibility 

The capabilities of AI models and their applications in business and everyday life are developing at a fast pace. The tremendous progress on Large Language Models like GPT-4 and other AI applications fascinates people and humanity. At the same time, it raises concerns on the reliability and safety of the use of AI for some specific cases, and their impact on society, businesses, and work life. Software companies providing AI can no longer treat algorithms as a purely technical matter but need to frame the deployment of AI with questions on impact, ethics, and responsibility. INFORM's commitment to "trustworthy AI" revolves around six pivotal principles. You can read more about them here. 
 

How far will you go on AI?  

The best way to find this out is a like-for-like comparison between your manual/legacy transport plans and our AI-Powered transport plans. How does this work? You’ll give us real-world data of a recent transport plan. Three days are sufficient, but they should be a good mix of all operational scenarios: a busy day, a typical day, and a quiet day.   

Once that data has been cleaned and data inconsistencies and gaps have been clarified with your logistics team, we’ll feed our software with it and come up with an optimized delivery schedule and fleet configuration. A side-by-side comparison of all optimized KPIs will give a clear indication of how far AI will drive your financial, environmental, and service level performance.