A virtual factory model cut modeled product-disassembly time by 78%

A digital twin adapted disassembly plans in real time and raised profit in an illustrative factory scenario.

Editorial illustration — not from the study.

Disassembly systems must cope with uncertainty about a product’s condition and whether particular steps will work. The researchers created a modular digital twin that connects data collection, decision-making and automated planning so that disassembly plans and schedules can change when conditions deviate from expectations.

They deployed the system in a learning-factory disassembly setup to demonstrate that the modules could operate together from end to end. In an illustrative scenario, the digital twin reduced lead time by 78% and increased profit by 2% compared with operating without reactive planning. Profit was 529% higher than with a rule-based strategy for ending the process.

What the digital twin changed

The digital twin supported automated, real-time reactive disassembly planning: it used current information to adapt disassembly processes and schedules when conditions changed. In the representative scenario, it achieved a 78% reduction in lead time and a 2% increase in profit compared with a process operating without reactive planning. Compared with a rule-based process-termination strategy, the digital twin produced a 529% increase in profit.

Why adaptive disassembly matters

Disassembly is important for recovering products through reuse, remanufacturing or recycling, but the condition of end-of-life products and the feasibility of individual steps can vary. A system that can revise plans as new information arrives could help disassembly operations respond to those uncertainties. The reported results suggest that data-driven planning with a digital twin may improve the efficiency and business case of circular-factory operations.

Evidence and limits

The researchers presented a modular architecture and instantiated it in a learning-factory disassembly system, demonstrating the feasibility and end-to-end operation of its modules and connectors. The reported performance figures come from a representative, illustrative scenario rather than evidence across a large set of commercial factories or products. The abstract does not establish how the system would perform under broader industrial conditions or whether the reported profit changes would generalize beyond this scenario.

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