January 20, 2025

Supply Chain Trends 2025: expert insights and recommendations

In 2025, the Supply Chain continues to deploy "Logistics 4.0", in reference to Industry 4.0, two of whose main features are automation with the deployment of robotized solutions and prediction orienting a world hitherto highly focused on reactivity towards the era of planning and simulation, all boosted by data and Artificial Intelligence algorithms.

Deciphering trends observed by Generix through case studies

Artificial Intelligence - AI - and Gen AI are more than ever tools for operational excellence and anticipation, thanks to high-performance prediction algorithms that enable simulation.

For their part, optimization calculations based on Operational Research benefit from the power of IT infrastructures, available on demand in the Cloud, to deliver exceptional results in record times, fully compatible with the pace of logistics and transport operations.

Let's share some 2025 trends through case studies of the use of these technologies in the supply chain:

Automated systems and robots in the warehouse

They are packed with AI and data sensors (IoT - Internet of Things) so that they can reproduce, without ever getting tired or bored, highly repetitive tasks that humans can't perform as reliably over time. Automation, in whatever form, continues to be deployed in warehouses, bringing flexibility and productivity to logistics operations. Robots are becoming better at reproducing human gestures and decision-making processes, are less costly, and are quicker and easier to set up.

Computer vision

Here's another technology that, like robots, replaces humans for tedious, repetitive, non-value-added tasks. Examples include inventory counting, or conformity and quality control of incoming goods or shipments. Although not yet widely deployed in logistics, AI-based control is one of the most frequent use cases in industry. It considerably reduces the cost of non-quality and lowers the risk for the company.

Advanced analysis

This field aims to cross-reference all available or specifically collected data to understand the phenomena that impact performance. Advanced analysis models are trained to learn appropriate behaviors, then monitor execution data toprovide early warnings of potential deviations by comparing them to a standard. Only AI can monitor this huge volume of data, deduce operational risks in advance, and warn managers in good time to limit the impact of behavioral drift on performance. Currently, analyses use 3D graphical interfaces to represent operations and alerts in the form of HeatMaps. A second phase will involve teaching the models to apply corrective actions themselves, and modifying the parameters of the execution software.

Planning and anticipation

Logistics 4.0 also means moving on from the era of hyper-reactivity, which is costly and exhausting for teams, to a mix with planning and anticipation, and thus apprehending the discipline of forecasting. AI algorithms make it possible to predict workload volumes: warehouse receiving or preparation, transport flows. These predictions form the basis of 2 levers for improving logistics and transport performance: planning and simulation. These simulation algorithms, connected to 3D graphic representations and a large number of parameters describing operational constraints, form the basis of the digital twins, which will certainly be implemented on an industrial scale after 2025. In the meantime, they can be used toplan resources, and that's already a great deal, especially in geographies where recruitment and retention are difficult.

These are just a few examples of the technologies that will be deployed in 2025 and beyond. We could also mention autonomous vehicles and a number of warehouse operations optimization topics such as slotting, task interleaving, parceling, truck filling and delivery round organization. These are just some of the applications that promise unprecedented efficiency.

For its part, GenAI will help to accelerate the development of logisticians' skills, both in terms of their profession and the use of IT solutions.

Women in the supply chain discuss artificial intelligence

Tips to avoid missing the "train" in 2025

But before we all head off to the beach and let AI handle the operation for you, we'd like to add a couple of observations.

We're seeing a rise in corporate maturity when it comes to AI-related topics, among both users and solution providers.

In particular, there is a growing awareness of the need to be organized in order to maintain algorithm performance over time. Machine Learning models must continue to be trained to adapt to behaviors or phenomena unseen in their previous learning phases, at the risk of seeing them "hallucinate", which means they start suggesting anything... really anything. Between model training phases, you also need to monitor a number of indicators to measure the quality of the results delivered by the AI.

With GenAI in particular, we have also identified the need to set up data governance and training for people using these tools. This will enable them to make the most of these technologies, to become aware of the ethical issues surrounding data and AI, and to prevent corporate data from ending up freely available on the Internet, or being used free of charge to drive models that could benefit the competition.

Faced with the current slowdown or crisis in certain sectors, many companies are seeking to transform themselves, either out of necessity or in anticipation.

The digitization of business processes, or digital transformation, is a lever for cash generation and frees up the skills needed to establish and implement corporate strategies. In this way, it contributes to the company's long-term viability.

So, in 2025, let's not wait for the crisis...