AI-based predictive maintenance to reduce unplanned downtime of printing machines

Case Study

Background

The printing industry is known for its tight margins and the need for efficient production. Printing machines are central to this process. Any unplanned downtime can result in significant financial losses. Not only does unplanned downtime affect efficiency, but it can also erode clients’ trust in the printing company. Furthermore, increased production waste escalates costs, squeezing the already tight margins.

Objectives

The aim is to harness AI technology to develop a solution capable of detecting potential production downtimes in advance. This way, proactive measures can be initiated to prevent breakdowns or at the very least minimize their impact.

Solution

An AI-driven Predictive Maintenance solution was developed that evaluates the performance data of all HDM machines connected to the IoT Platform. By analyzing this data, the solution can identify potential production failures early on and notify customers in real-time. Furthermore, solution-oriented reports and evaluations are provided to customers to assist them in addressing the identified issues.

Results

Introducing the AI-driven Predictive Maintenance allowed customers to take proactive steps to prevent downtimes. If a breakdown is inevitable, they can detect it within minutes and adjust their production processes accordingly. Moreover, customers can now better align the need for consumables with any production delays, resulting in additional cost savings.

Our role

Our team was involved right from the start, assisting in the conception, development, and implementation of the AI-based Predictive Maintenance solution. With our expertise in AI and machine learning, we were able to create an efficient and reliable solution that significantly boosts the efficiency of print production.

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