eMoldino

Predictive Maintenance

Enhance your mold maintenance to peak condition

Keep global tooling assets healthy and ready for production

Maintain high standards for tooling assets by gaining end-to-end visibility and real-time performance data.

With eMoldino’s Predictive Maintenance, data can be analyzed to foresee potential component failure, which leads to unnecessary repair cost reduction, shorter maintenance turn times, and shortened unexpected downtime.

Whenever there is a need for tooling maintenance that involves tooling component refurbishment or replacement, our auto-linked system will allow an agile component delivery process and expedite the overall maintenance lead time.

Maintenance Data Hub

The maintenance data hub stores and analyzes all data regarding the tooling assets (i.e. maintenance history, tooling asset specification, operational status, maintenance best practices, tooling component details, 3D blueprint).

Most of the data will require data integration or input from various parties(OEM, supplier, toolmaker), which will be seamlessly done with the help of eMoldino’s expert integration and on-boarding team. A centralized data hub for all relevant data on tooling maintenance provides a complete view on the maintenance status for OEMs’ global production assets and further supports advanced features like predictive maintenance and third-party add-on services.
Predictive Maintenance Planning

Based on maintenance history, tooling usage, and real-time performance metrics, users are notified of the optimal maintenance schedules for their tooling assets.

Coupled with the daily, weekly, or monthly production schedule for a plant or a single tooling, the solution helps users plan for future maintenance activities that will lead to reduced production downtime for the facility.
Solution Integration

eMoldino’s Predictive Maintenance is designed to be flexible and scalable in terms of services it can provide to the users. With users’ discretion, a wider range of capabilities can be unlocked by collaborating and integrating with third party add-on services and solutions.

For example, OEMs and suppliers can get detailed data and instructions regarding the best maintenance practices that will be embedded into the system by collaborating with a partnered toolmaker.

When predictive maintenance is scheduled, the system identifies what type of maintenance is required based on the recorded data and will pair it with an appropriate maintenance action.