According to recent research by the Artificial Intelligence Observatory of the Politecnico di Milano, Italy is also experiencing significant growth in the artificial intelligence market, both in terms of revenue and the number of companies involved. 57% of companies have already implemented AI-based initiatives, and among them, 25% have reached full operational capacity.
AI is not just a passing trend — it is the result of a technological evolution that began in the 1950s and has now reached a crucial point of transformation.
Today, artificial intelligence is taking on an increasingly central role in the supply chain, thanks to favorable technological and market conditions, including:
- The widespread presence of data centers
- Greater processing speed and computing power
- The development of supercomputers through public investments
The accessibility of these technologies has drastically reduced costs, making AI adoption more sustainable than in the past.
Executives in the manufacturing and logistics sectors view artificial intelligence as a key resource not only to automate low-value operational tasks but also to support more complex and decision-making activities traditionally entrusted to humans.
Despite growing media attention and enthusiasm, there is still much confusion about what AI actually is, how it differs from other technological tools, and what its real potential is.
What Artificial Intelligence Is and How It Works
The OECD defines an AI system as a mechanism capable of processing inputs and, thanks to autonomous adaptive capabilities, generating outputs such as predictions, recommendations, or decisions. These systems influence physical or digital environments according to predefined goals, which can be programmed or learned through algorithms.
AI systems can be either software or hardware and are capable of:
- Detecting what is happening in the environment
- Analyzing collected data
- Transforming data into information and actions
- Reasoning and, if necessary, making autonomous decisions
Just as humans acquire and interpret information through the senses, AI uses specific functionalities — or capabilities — to simulate these processes. The main “abilities” of artificial intelligence include:
- Machine Learning: automatic learning from historical data to make predictions or decisions
- Deep Learning: use of neural networks that mimic the human brain, including:
- Natural Language Processing (NLP): understanding and generating human language (e.g., chatbots)
- Computer Vision: interpreting images and videos
- Robotics: autonomous physical interaction with the environment (e.g., intelligent mobile robots)
- Automated Reasoning: autonomous analysis and deduction based on data
- Knowledge Representation: structured organization of information from raw data (numbers, images, tables)
How Artificial Intelligence Is Transforming the Supply Chain
In the manufacturing sector, AI is taking on a strategic role. Teams of AI and Data Science experts are working on AI-based projects to enhance ASP and MES/MOM solutions — the true engines of the Digital Supply Chain — to manage, optimize, and digitize production operations.
In MES (Manufacturing Execution System) software, AI is used to monitor and analyze production in real time, detect process anomalies, predict equipment failures or downtimes through predictive maintenance models, and analyze accurate data for proactive and intelligent factory management.
Through advanced analysis of data collected from machinery, it is possible to improve product quality, reduce waste, and increase operational efficiency. Moreover, special attention is given to factory operators through constant technical support provided by an AI Agent, which works alongside them by offering additional information, video tutorials, troubleshooting answers, and operational instructions — everything needed to make their job easier.
Thus, chatbots, robots, AI Agents, and Predictive Analytics are the daily bread in AI research supporting the production process.
In APS (Advanced Planning & Scheduling) software, AI enables even more precise and responsive planning and scheduling, taking into account numerous variables such as material availability, production capacity, delivery times, and order priorities.
AI algorithms analyze complex scenarios and propose optimized plans to minimize bottlenecks and dynamically adapt to demand changes or production issues.
All of this enhances and strengthens the role of the planner, allowing for faster and smarter planning.
In this way, advanced AI-based software not only supports decision-makers but also becomes a true intelligent ally, capable of transforming vast amounts of raw data into strategic and predictive actions.
How AI Is Used in Industrial Production
There are numerous AI-based projects aimed at improving the efficiency of warehouses, yards, and management software such as WMS, YMS, and TMS.
- TMS with AI: optimizes transport planning to reduce costs and CO₂ emissions. Thanks to machine learning, route calculation has been improved — both within warehouses and on the road. A carbon footprint measurement tool has also been developed to reduce the environmental impact of transport.
- Intelligent Chatbot: integrated into company software, it provides first-level customer support and can execute voice or written commands (e.g., “Enter a new order”). It recognizes images and documents to speed up data entry, automatically importing data into management systems (for example, from a photo of a delivery note or driver’s documents).
- AI-enhanced WMS:
- RTLS: real-time tracking of forklifts, pallets, and operators in warehouses using laser, UWB, or Bluetooth beacon technologies.
- AMR: autonomous mobile robots for goods handling, capable of adapting to pallet types and recalculating routes in real time. The management system assigns missions to the most suitable robot based on distance and battery life.
In transportation, there are also several projects simulating trip planning for self-driving trucks, which are expected to be introduced in the near future.
Finally, both in manufacturing and logistics, work is being done on labor management or workforce management solutions to better manage human resources engaged in operational activities in factories or warehouses.
How to Use AI for Managing Manufacturing and Logistics Personnel
Supply chain software can integrate advanced AI-based features for:
- Automatic shift optimization: AI algorithms generate shift schedules in seconds, considering regulatory constraints, vacation requests, skills, availability, and company priorities.
- Data-driven forecasting: by analyzing historical production data, expected volumes, seasonality, and absenteeism, the system proactively suggests the ideal number of resources to plan, anticipating activity peaks or slowdowns.
- Dynamic skill matrix: competence mapping is automatically updated upon project completion or after training programs, making it easier to assign the right person to the right task.
Artificial intelligence is not just a conference “buzzword” — AI is already operational and embedded in the logic of transport, warehousing, and production.
Companies that are experimenting with and investing in AI today are already reaping tangible benefits in terms of efficiency, accuracy, and sustainability. Of course, it’s not magic: it requires know-how, quality data, and a clear vision.
AI is not the future — it is the present.
