From Reactive to Predictive: What It Really Takes in Manufacturing

A practitioner conversation on data, downtime, and what changes when you stop guessing
Every unplanned stoppage has a cost. In most manufacturing environments, that cost is tracked but the stoppage itself isn't predicted. Machines collect data around the clock. Maintenance teams have years of experience. And still, the line goes down without warning.
The gap between data and prediction is rarely a technology problem. It's a data readiness problem, a trust problem, and an organisational problem - in that order.
In this session, we talk to Marta Łukasik, Delivery Manager at Astral Forest, who has worked hands-on inside a manufacturing analytics project. We'll cover:

• Why manufacturers invest in sensors and platforms and still react to failures instead of preventing them.
• What machine data actually looks like before it's ready for any predictive model and what has to change first.
• How to build trust between a maintenance team with 20 years of experience and a system they've never relied on.
• Where most PdM projects stall and what the first working steps actually look like in a production environment.
• ⁠Where manufacturing stands today in terms of data maturity compared to other industries and where it's heading over the next decade.

This is not a vendor overview. There are no platform recommendations or architecture slides. It is a 30-minute conversation with someone who has been inside the problem.
Recommended for:
Plant Directors · Heads of Operations · Maintenance Managers · Manufacturing Excellence Leaders · Data and Digital Transformation Leaders in manufacturing