Tenders

OUR DEVELOPMENTS CARRIED OUT WITH EUROPEAN UNION SUPPORT

Data-driven, intelligent manufacturing scheduling at Spiller 2000 Ltd.

One of the key directions of industrial digitalisation today is no longer limited to automation, but increasingly focuses on data-driven, intelligent decision-making. The ATAS-AI initiative, implemented as part of the AIRISE project, represents a practical example of this shift: the development of an AI-supported scheduling system aimed at improving the organisation of manufacturing processes. For Spiller 2000 Ltd., this marks an important step towards further enhancing daily operational processes.

The starting point of the development was a fundamental challenge in production planning: aligning tasks, resources and capacities in a dynamically changing environment. Traditional, manual solutions often struggle to manage the complexity of manufacturing processes, which can result in delays, underutilised capacities and less transparent workflows. The ATAS-AI system addresses this challenge through an approach that supports decision-making based on real-time data within the operations of Spiller 2000 Ltd.

The solution is capable of managing key elements of the manufacturing environment – such as worker competences, machine availability and production priorities – within a single system and generating optimised scheduling proposals accordingly. The system developed within the framework of the project not only considers the current state but is also able to adapt to changes, enabling more flexible and balanced operations.

Practical testing has shown that AI-based scheduling provides stable and reliable support for production planning. Manufacturing processes become more structured, disruptions are reduced, and resource utilisation improves. The system is particularly effective in handling situations where aligning the right expertise with the right capacity is essential to ensure continuous operation.

The optimised operation not only results in efficiency gains for Spiller 2000 Ltd. but also contributes to more sustainable production. Improved resource utilisation reduces unnecessary energy consumption and material losses throughout the manufacturing processes.

The development aligns well with broader European efforts to promote both digital and green transitions among small and medium-sized enterprises. The results of the project demonstrate that intelligent, AI-based solutions in manufacturing are not only applicable in experimental settings but can also be successfully implemented in real operational environments, delivering tangible benefits.