Uncertainty has become a constant in industrial production. Demand variability, operational constraints, and constant urgencies challenge companies’ ability to manage processes effectively. Today, planning means navigating complexity, aligning decisions, and adapting quickly to change.

In this scenario, more advanced decision-making models are needed—models that integrate data, expertise, and operational logic. Having advanced tools is no longer enough: what’s needed is a process that combines method, vision, and analytical capability. When applied within a well-designed decision-making framework, artificial intelligence can enhance the effectiveness of existing tools: it supports analysis, accelerates operational evaluations, and boosts system responsiveness.

This is the perspective behind the event organized by QUIN on May 27 at LEF in San Vito al Tagliamento (Pordenone)—an afternoon dedicated to rethinking planning not as an isolated function, but as a cross-functional strategic lever.
A concrete opportunity to explore models, organizational approaches, and technological tools—starting from the real-world experience of those already embracing this transformation.

Following an overview of the industrial context and today’s most pressing organizational challenges, the event will present models and solutions for more integrated, dynamic, and data-driven planning. A roundtable discussion with entrepreneurs and managers from manufacturing companies will offer an open dialogue on real challenges, ongoing changes, and operational visions to tackle production complexity in a systemic way.

Participants will also have the chance to attend a demo experience of the multi-scenario planning system developed by Quin and integrated into the LEF environment, simulating real production dynamics.

The event will close with an aperitif, offering further opportunities for discussion and networking.

An action-driven event for those who want to transform planning into a smarter, more effective, and reality-based decision-making process.