Today, the logistics landscape is shifting at an unprecedented pace: the EU is systemically pushing for sustainability on the regulatory front, carriers are balancing the new requirements with high initial investments and infrastructure gaps, and customers respond to rising transport costs with higher efficiency and service quality expectations. As multiple case studies will demonstrate below, success in navigating such circumstances increasingly depends on carriers’ ability and willingness to leverage innovative solutions in search of unified, seamless solutions. This is where digitalization and AI come into play, offering a future-proof approach to business optimization in internal processes and beyond.

A pivotal factor in unlocking the potential of these innovations lies in co-creation and the collective commitment to sharing and using data. Modern logistics thrives on integrated ecosystems where carriers, suppliers, manufacturers, and customers collaborate to streamline operations. This requires stakeholders to not only share data but also to analyze and act on shared insights collectively, unlocking mutual benefits. By co-creating solutions through open communication and data sharing, businesses can optimize their supply chains, improve service delivery, and foster resilience in the face of disruptions.

From fleet management and route optimization to customer service, digital solutions have become a cornerstone for small, growth-driven companies and global-scale carriers alike. Adding another layer to this progression, AI integration in particular has also been building momentum for its overarching application potential in data processing, especially with reference to personalized user experience, performance monitoring, and predictive analytics. Autonomous vehicles present another opportunity for future growth, promising to reduce human error and increase safety and efficiency[1].

The global digital logistics market is expected to almost double by 2028 compared to 2024 numbers (from $24.53 billion to $46.09 billion)[2]. This is solid proof that digital advancements are transforming the field of logistics and have the potential to reshape the very foundations of how it operates.

The role of digital twins in data-backed decision-making

In the general context of digital tools and their application, the emergence of digital twins was a significant leap forward for the logistics industry. This model entails virtual representations of physical objects, systems, or processes in the supply chain that mirror real-time conditions and behaviors of their physical counterparts[3]. Essentially, digital twins are a fool-safe way to simulate a wide range of scenarios, investigate their impact, and use them as a foundation for data-driven decision-making in the physical realm.

As more and more successful use cases emerge, digital twins are expanding beyond single-use applications. The development of more comprehensive systems also opens new opportunities for incorporating and simulating a broad scope of processes and decisions within logistics operations. In connection with supply chain management, for example, digital twins bring the whole picture into focus, capturing every aspect from production and inventory to customer demand and supplier procurement[4].

For instance, by integrating shared data into digital twins, companies can optimize their logistics networks holistically, from production planning to end-customer delivery. This interconnected approach reduces inefficiencies and ensures decisions are made with a clear understanding of their impact across the entire supply chain.

Using digital twins to obtain real-time data and insights provides a reliable base for better decisions and expands forecasting capabilities. Another proven use case lies within fleet management: by allowing carriers to visualize, monitor, and simulate their fleets in real time, this approach can bring significant gains in terms of efficiency and cost reduction[5].

Digital twins are also increasingly used for predictive maintenance purposes. Equipment downtime is one of the biggest challenges in logistics, particularly due to its influence on delays and operational costs. In this case, digital twins offer a preventive approach by utilizing real-time vehicle data such as engine performance, fuel efficiency, and temperature to minimize disruptions and identify potential issues before they result in breakdowns[6].

As the technology matures, digital twins offer significant potential for boosting organizational efficiency and future-proofing logistics businesses at their core. This potential has already materialized through success stories like that of Tetra Pak, a $13 billion packaging company. Tetra Pak pioneered the adoption of digital twin technology in 2019 by collaborating with a German company, DHL Supply Chain, to create a digital version of their physical warehouse in Southeast Asia[7]. The warehouse’s forklift trucks, for example, were equipped with IoT technology, and all warehouse data was consolidated and evaluated in a virtual representation. This upgrade elevated the management of stock locations, inventory, workflows, and warehouse equipment allocation, allowing the company to better utilize storage space, boost operational efficiency, and enhance workplace safety standards[8].

AI in demand forecasting and load optimization

Digital twins are just one example of how AI algorithms can be used to analyze data, lead strategic decision-making, and boost operational efficiency. Equally (if not more), the emergence of AI sharpened the need to search for patterns and other insights by sifting through historical data. It was always seen as a treasure trove of insights, though its full potential was often obscured by the limitations of traditional analytical methods. The latest development of genAI revolutionized data analysis by offering a deeper dive than ever before to guide businesses in understanding patterns, identifying trends, and making predictions based on past occurrences[9].

With reference to demand forecasting in particular, AI offers a sustainable pathway to proactive decision-making. Through machine learning and data analytics, carriers can quickly adjust to demand fluctuations, predict potential delays, and address them. This approach offers long-term gains because this level of adaptability fosters resilience in dynamic markets, enhancing customer satisfaction, optimizing operational costs, and driving potential revenue growth[10].

Building on the advantages of AI in logistics, its transformative impact extends further into operational efficiency, with load planning emerging as one of the most impactful applications. Similarly, AI-based load optimization ensures efficient use of transport resources by analyzing factors like cargo weight, vehicle capacity, and route constraints. These solutions deliver immediate cost savings and sustainability benefits while promoting transparency and trust among partners. By harnessing AI for this purpose, productivity gains come into play before any vehicle even starts moving. Traditionally a time-consuming and manual process, modernized load planning incorporates AI-driven analysis of a complex data matrix, factoring in cargo weight, dimensions, fragility, as well as vehicle capacity, size, and stability to determine the most efficient way to load goods[11]. This approach directly translates productivity gains into saved costs, with major logistics companies reporting a 27% increase in route efficiency and a 19% decrease in fuel consumption after implementing deep learning methods in their operations[12].

Utilizing real-time data insights through telematics

Moving a step forward and to the journeys themselves, the adoption of AI is also drawing on telematics for promising benefits in terms of driver safety, truck maintenance, productivity, and compliance. In the big picture, most of these gains fall under one umbrella term – fleet management.

Telematics gather integral data points, such as vehicle diagnostics, fuel consumption, and real-time GPS location. In this case, a holistic approach to data analysis and the adoption of AI algorithms have the potential to provide valuable insights on multiple levels, ranging from the behavior or individual drivers to predictive maintenance to route optimization and dispatching and – at the highest level – to sustainability targets and resource optimization.

Thanks to real-time tracking and analysis, telematics can also be utilized for improved driver safety on the road, particularly through immediate alerts to drivers for specific behaviors, such as speeding or harsh braking. Alerts of this nature can produce a cumulative effect by correcting driver behavior to promote economical driving, which in turn contributes to fuel efficiency and better vehicle exploitation practices. The correlation between real-time monitoring and driver safety is especially clear in the case of Frost & Sullivan, a business consulting firm that linked the implementation of video telematics to an 80%decrease in driver distraction, a 65% reduction in speeding, 60% lower collision rates, and a 70% increase in seat belt usage[13].

The potential of AI-powered customer experience

The aforementioned benefits of AI application in logistics, such as improved efficiency and fewer delays, contribute to a promising foundation in terms of customer satisfaction. With that said, the needs of modern customers go beyond those variables. A report titled “Future Proofing the Supply Chain Through Real-Time Visibility” by Transport Intelligence and project44, for example, emphasizes real-time visibility as a crucial factor involved in the selection of a carrier, with 57,4% of respondents reporting it as a strict prerequisite[14].

Digital solutions, such as AI-driven analytics and chatbots, offer a sustainable pathway to improved customer satisfaction by fostering integrity, transparency, and better communication – particularly through real-time tracking, faster response times, and more flexible delivery options. Together, these advancements have a cumulative effect on business, linking improved customer experience maturity to 25% lower operational costs (compared to companies with a low level of customer experience maturity) and 5-10% growth in profits due to higher numbers of returning customers and the ability to charge higher premiums[15].

Automation in warehousing and last-mile delivery

Hiding beyond the reach of the customer, the inner workings of logistics – warehousing and other logistics operations – are also ripe for transformation through digitalization-driven innovation. From Cobots working alongside humans to IoT-integrated equipment, autonomous warehouse robots, and rapidly advancing self-driving trucks, emerging technologies offer long-term optimization and growth opportunities for businesses of all sizes.

Last-mile delivery is a striking example of this potential. With the final stretch accounting for an average of 53% of overall shipping costs[16], businesses offering this service are increasingly embracing a game-changing solution: autonomous vehicles. By reducing reliance on human drivers, these vehicles bring both cost savings and enhanced operational efficiency. Besides, the software that these vehicles come with also offers an additional layer of transparency and precision to businesses that lean into this solution, allowing logistics managers to monitor the status and location of each vehicle in real-time. However still there is a question if and how long-haul logistics will embrace those solutions.

A major factor in the making of such success stories is that numerous logistics automation tools are readily available for adoption, with tried and tested practices for smooth integration and optimal use. First and foremost, embracing these technologies sooner rather than later ensures competitiveness by positioning businesses to adapt more swiftly to future advancements and market demands[17]. Secondly, automation plays a pivotal role in reducing human error and fostering operational consistency and accuracy. When combined with AI-powered performance monitoring and predictive analytics, it ensures a stable and reliable operational framework. This technological synergy not only minimizes risks and inefficiencies but also creates a strong foundation for sustainable business growth and scalability.

Digital pathways to a greener future

As numerous success stories show, AI and digitalization hold real potential for business growth and resilience. Conveniently, especially with Europe’s growing focus on sustainability, they also promote greener practices – particularly by advancing alternative energy solutions such as hydrogen fuel cells and biofuels, lowering CO₂ emissions, and optimizing the use of resources.

Originally seen as a tool for improving energy management, AI is now increasingly recognized as a cornerstone of future energy systems. From data analysis to pattern recognition and trend forecasting, its widespread adoption is shaping nearly every aspect of the industry. From the carriers’ perspective, AI strongly contributes to progress in optimizing resource use and reducing CO₂ emissions across the board. For instance, DHL uses AI-powered route planning software to prioritize deliveries and create efficient route sequences, resulting in faster deliveries and less fuel consumption[18]. Other prominent use cases concerning resource optimization include using machine learning for predictive maintenance, demand forecasting, supply chain management, and so on. With regard to the impact of implementing these solutions, a recent study shows that, solely by optimizing delivery routes through machine learning, logistics companies can reduce their carbon footprint by as much as 20%[19].

Eager to seek out and utilize innovative solutions in daily operations, Girteka has embraced the potential of AI-supported technologies in process optimization. With a fleet of over 6,000 trucks and multiple bases across Europe, AI-driven tools have already been adopted to support data-based decision-making on different levels, including route planning, real-time monitoring, and asset usage optimization. Together, these upgrades have allowed Girteka to improve operational response to disruptions like road blockades or accidents, minimizing delays and improving efficiency. Overall, the effect of these changes also goes on to highlight the value of AI as a supplementary tool that enhances decision-making while retaining the importance of human judgment[20].

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Paving the way for smart logistics

Without a doubt, smart technologies that promise to enhance efficiency, sustainability, and customer satisfaction are already driving a transformative shift in the logistics industry. With advancements such as AI, IoT, robotics, autonomous vehicles, and blockchain on the horizon, the future of logistics is looking more dynamic than ever. However, realizing the full potential of these innovations requires a cooperative approach. The transformation of supply chains is only achievable when all stakeholders are actively involved in discussions, willing to share data, and committed to utilizing shared insights for mutual benefits. By embracing these digital solutions collectively, carriers and stakeholders can prioritize innovation—not only to stay ahead of the curve but to remain competitive, meet evolving market demands, and ensure compliance with environmental policies and regulations. By working together, sharing insights, and co-creating solutions, stakeholders not only unlock the full potential of emerging technologies but also position themselves as leaders in a rapidly evolving industry. Ultimately, the collective embrace of digitalization and AI is key to creating supply chains that are not only efficient and sustainable but also resilient and future-ready.

[1] https://www.logisticsbusiness.com/it-in-logistics/ai-iot/ai-in-transportation-the-future-of-smart-logistics/

[2] https://www.thebusinessresearchcompany.com/report/digital-logistics-global-market-report

[3] https://relevant.software/blog/digital-twins-in-logistics-the-future-of-supply-chain-management/

[4] https://relevant.software/blog/digital-twins-in-logistics-the-future-of-supply-chain-management/

[5] https://www.shiptnl.com/post/digital-twins-in-fleet-management-real-time-optimization-tools

[6] https://theafricalogistics.com/digital-twins-are-transforming-real-time-tracking/

[7] https://www.hannovermesse.de/en/news/news-articles/in-the-tetra-pak-warehouse-in-singapore-the-twin-is-in-charge

[8] https://www.mitsloanme.com/article/unlocking-the-potential-of-digital-twins-in-supply-chains/

[9] https://blog.panoply.io/the-role-of-historical-data-in-a-data-warehouse-insights-and-strategies

[10] https://www.akira.ai/blog/freight-load-optimization-using-agentic-workflows

[11] https://www.techuk.org/resource/revolutionising-cargo-load-planning-with-ai-techukdigitaltrade.html

[12] https://acropolium.com/blog/use-cases-of-ai-in-transportation-logistics-are-they-relevant-for-your-business/

[13] https://www.lightmetrics.co/blog-posts/driving-success-how-video-telematics-impacts-driver-performance-and-behavior

[14] https://ti-insight.com/real-time-visibility/

[15] https://www.strategyand.pwc.com/de/en/industries/transport/customer-experience-transport-logistics.html

[16] https://www.loginextsolutions.com/blog/how-autonomous-delivery-vehicles-are-redefining-last-mile-delivery/

[17] https://eliteextra.com/how-logistics-automation-is-transforming-the-industry/

[18] https://spd.tech/artificial-intelligence/ai-in-logistics-transforming-operational-efficiency-in-transportation-businesses/

[19] https://fastercapital.com/content/Mastering-Route-Optimization-in-Logistics–A-Key-to-Cost-Savings.html

[20] https://www.girteka.eu/the-present-and-future-of-artificial-intelligence-ai-in-road-freight-transportation/