Digital Reverse Logistics Twin 5.0 (DigiRL 5.0) – Enabling Human-Centricity in Data-Driven Decision-Making and Flexible Automation

What if products could tell us when they should be recycled?

Every year, millions of products reach the end of their lives. Many of them still contain valuable materials. For example, electric vehicle batteries are complex, expensive, and full of valuable resources. But when a battery stops working in a car, it does not always have a clear path forward.

In my project Digital Reverse Logistics Twin 5.0 (DigiRL 5.0), I study how digital technologies can help us manage these products better once they return. The goal is simple: make it easier to bring used products back into the economy.

The problem: we don’t know where products are

Reverse logistics is the system that collects used products so they can be repaired, reused, or recycled. It is a key part of the circular economy. But there is a major challenge. Companies often do not know when products will return, where they are located, or what condition they are in. Imagine trying to plan transport and remanufacturing without knowing what will arrive tomorrow. This kind of uncertainty makes reverse logistics both expensive and inefficient.

A human-centric digital twin of the reverse logistics system

The DigiRL 5.0 project explores a new idea: a digital twin of the reverse logistics system that mirrors real operations. It combines product data with optimization models, simulations, artificial intelligence, and automated systems to support better decisions and smoother operations.

But the most important element is not technology. It is people.

The system is designed to support human-centric decision-making and operations. Workers, planners, and operators remain in control, while digital and analytical tools help them navigate complex situations and make better choices.

For example, information about returned products can help plan transport routes, reduce empty trips, and organize recycling processes more efficiently. In recycling facilities, humans and machines work side by side in flexible systems where automation handles repetitive tasks while people supervise and solve unexpected problems.

Our research suggests that empowering human intelligence with advanced technologies may be one of the keys to making this possible.