Logistics 4.0 and AI: excellence in the supply chain [Europe]

Logistics 4.0 and AI: excellence in the supply chain [Europe]

Logistics 4.0 refers to a new management model within the framework of the fourth industrial revolution, where enabling technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics (Big Data), among others, have the potential to completely transform logistical activities as we know them today.

Understanding Logistics 4.0

There are five design principles that guide this model and are aligned with a series of economic trends that will increasingly become important. Each principle also presents significant associated challenges:

  1. Efficiency: Increasing automation and process optimization aim to reduce waiting times, minimize errors, and synchronize supply and demand so that companies can plan and execute their operations more precisely through information technology. The greatest challenge is the development of models based on innovation, flexibility, and excellence. A significant cultural change is required, especially in family businesses where the management model is still very traditional and not process-oriented.
  2. Transparency: Future value chains will be transparent. That is, at any given moment, it will be possible to consult any type of information related to a specific product to find out its origin, under what conditions it was produced, the location of a particular shipment, or obtain delivery certification. All this in real time, with the goal of improving decision-making and response capacity to problems. Many organizations have obsolete technical solutions that prevent integration with emerging technologies and block the adoption of the model.
  3. Experience: Enhancing customer experience is another main focus, where different trends converge, on one hand, omnichannel with the possibility for the customer to choose the communication channel, for example through an app, email, website, indistinctly; hyper-personalization of deliveries to choose location (home, physical store, or pickup point) within a precise time window; in addition to automation, notifications with shipping updates and return management are the new standard. The challenge here is not only to invest in emerging technologies but to go further and integrate them into frontend solutions with an excellent user experience to generate brand loyalty.
  4. Sustainability: Implementing more sustainable and environmentally friendly practices aligns with a new consumer consciousness that considers these issues as a purchasing criterion, plus in terms of legislation the standards will increasingly be higher, as the Western economic model is shifting towards sustainability as a central axis of business. In Europe, the implementation of the Digital Product Passport (DPP) is expected to start in 2027, where the first affected sectors will be batteries and energy products, the metallurgical sector (iron, steel, and aluminum) and other sensitive sectors such as chemicals and textiles. This will force companies to adopt sustainable production models and circular economy, promote informed decision-making by consumers, and also be key when choosing the best suppliers to ensure that products can be reused once their lifecycle ends.
  5. Scalability: Companies need to improve their capabilities to adapt to work volumes that may increase during certain seasonal periods and adapt to changes in market demands. There is a wide range of technologies available, robotics stands out in this area as we can scale processes such as picking, packaging, or product storage without directly increasing staff. A very neglected aspect tends to be investing in training and adaptation processes for the workforce, in the logistics field there is often a lot of personnel turnover and it is crucial that an employee can be trained in the shortest possible time to maintain efficiency levels in a company.
Logistics 4.0 as a Driver of Innovation and Growth

Currently, in the geopolitical landscape, we are in a transitional phase to a new industrial paradigm. On one hand, we come from an intense period of globalization that lasted exactly three decades (1990-2020), where the expansion of emerging markets with China leading the way and the free flow of goods and capital was the main feature.

Following the COVID-19 pandemic, a new model emerged, prompting a rethinking of supply chains upon realizing our dependence on other regions for basic materials that impacted the material security of citizens. Ultimately, we are moving towards a much more selective globalization process, where strategic sectors are being relocated locally (such as energy and raw materials) while others that do not pose a strategic risk can be outsourced.

In Europe, we enjoy a privileged geographical situation in the logistics field. Globally, we are a central node in the flow of goods, bridging several continents due to our proximity to the Atlantic and the Mediterranean corridor, connecting North Africa, the Americas, and Asia, and boasting one of the largest networks of maritime ports and airports. Added to the vast distances within our continent, which has led us to develop one of the largest networks of rail and road infrastructures, we have significant growth potential to capitalize on in the realm of Logistics 4.0.

To cite some figures, the sector currently represents 14% of the Eurozone's GDP, totaling 11 million jobs. Additionally, 6 of the top 10 global logistics companies are European. This, combined with the fact that internet purchases continue an upward trend, with growth estimated to be close to 10% in the coming years. All these figures speak volumes and reflect a potential growth engine in economic terms and employment that must be leveraged through improved logistical performance to be fully aligned with the goal of reducing CO2 emissions by 2030.

Key Enabling Technologies
  1. Internet of Things (IoT): This technology enables interconnectivity between physical products and digital systems. Examples include incorporating temperature sensors in vehicles or using RFID tags on products to automatically collect data, which has several advantages over QR codes in the specific context of the supply chain. Logistics companies must analyze in which cases there is potential ROI from automatically extracting data.
  2. Big Data and Data Analysis: Analyzing large volumes of data can boost various areas in the supply chain such as better understanding market trends, demand forecasting, and auditing and optimizing operational performance through KPIs. A data culture must be developed so that organizations can truly leverage these tools.
  3. Blockchain: Offers a secure and transparent platform for recording and sharing information across a network of companies. Its potential is very broad within the supply chain, as its decentralized architecture is useful in contexts where it is necessary to verify documents and formalize contracts among multiple parties in international trade. It is interesting to explore its possibilities in local supply chains formed by multiple companies creating innovation ecosystems.
  4. Autonomous Vehicles: This is a strong trend that will begin to impact by 2030 with the arrival of autonomous vehicles and drones, which will revolutionize distribution networks by developing algorithmically automated logistics infrastructures. Companies must start using algorithms in their operations to not be swept away when this new technological wave arrives.
  5. Digital Twins: This promising technology involves creating a virtual replica that can be applied to processes, products, and services within a supply chain. It allows us to simulate changes in the virtual model to verify its potential benefits before implementing them in the physical model. Its application is interesting to simulate how potential changes in aggregate demand and logistics infrastructure would affect operations.
Practical Use Cases with Artificial Intelligence

Artificial Intelligence will be the differential technology in Logistics 4.0, which will rely on previous technologies to collect and extract the necessary data, creating algorithms that will learn to make autonomous decisions in various areas: bottleneck prediction, optimal workflow planning, and logistics automation.

AI is particularly interesting because it allows any logistics company to optimize operations without the entry barrier of making significant investments in robotics and industrial hardware.

A wide range of machine learning algorithms can start optimizing KPIs by feeding on historical data.

Smart Inventory Management

AI has vast potential in this area where we can optimize inventory levels. Using historical data, we can predict the optimal stock levels for each season, developing an intelligent rotation policy that prevents stock shortages and excess storage and maintenance costs.

Also, AI can be applied in the order picking phase where the correct placement of products and grouping of orders can significantly reduce the distances traveled in the warehouse. This problem is known as the Order Batching Problem, and AI is opening new optimization possibilities by applying machine learning techniques.

The correct combination of digital twins and AI allows simulations within the warehouse to better decide which storage and order preparation policies would be most optimal, considering various restrictions such as available space, order volume, and worker availability. It's like having a personal assistant within our industry.

Optimization in Logistics Routes

This field also offers a wide range of possible improvements. With AI, we can find the most efficient routes for a fleet of vehicles that must deliver a series of goods, taking into account various variables: distances, driver availability, fuel consumption, etc., to meet specific delivery time windows. This problem is known as Vehicle Routing with Time Windows.

Moreover, AI can also be applied in other areas such as the optimal location of facilities, that is, once a distribution network with a series of established routes has been built: Where could I locate a new warehouse or distribution center to minimize my total logistics costs? This opens up a vast range of possibilities for deciding the architecture and evolution of any type of logistics network.

Finally, the Last Mile

There is also significant potential in the last mile where AI can help boost efficiency and sustainability. For example, a good business case is optimizing loads so that storage space is better utilized, achieving a higher number of orders per route, especially in urban areas where conditions such as traffic, fuel consumption, or access restrictions in low emission zones must be considered.

Being able to anticipate and prepare for changes in demand, in delivery there can be days with very high activity peaks due to changes in weather conditions that can be anticipated by making proper resource planning. Another widespread use case is validating and correcting delivery addresses; AI can help detect erroneous patterns and complete missing information in an address.

Ready to Make the Leap to Logistics 4.0?

Do not miss the opportunity to innovate, optimize, and stand out in today's competitive market; we are fully aware that developing a transition process to Logistics 4.0 can be challenging.

At Ettnia, we provide you with the tools, knowledge, and support needed to make this transition a creative and transformative process. Now is the time to enhance your logistics through a consulting service fully adapted to your business reality.

Start boosting your adaptation to Logistics 4.0 with Ettnia!