Integrated Manufacturing Analytics Platform for IoT Enabled Predictive Maintenance

Purpose and goal:

Predictive maintenance is one of the major thrust areas for many global manufacturing companies. However, as more companies adopt the related technologies, several key challenges have emerged hindering the progress towards complete digitalization of maintenance operations.

The aim of this project is to develop an Integrated Manufacturing Analytics Platform (IMAP) that combines core Industry 4.0 technologies of industrial IoT, digital twins and analytics to realize the full potential of predictive maintenance and pave the way towards prescriptive maintenance. Expected results and effects:

The project is expected to bring the following targeted improvements to production and maintenance KPIs: - increase in equipment uptime, availability and OEE - reduction in unplanned downtime of equipment, - reduction in time required to plan maintenance, - reduction in spare parts inventory, and - reduction in overall maintenance cost.

Industry-wide studies have shown that predictive maintenance can increase equipment uptime and availability by 10-20%, reduce the time required to plan maintenance by 20-50%, and reduce overall maintenance costs by up to 25%. Approach and implementation:

The project consortium is composed of (i) University of Skövde as the project coordinator, (ii) Chalmers University of Technology as the academic partner, and following five industry partners who will provide use cases and support various project-related activities, (iii) Scania CV (iv) Volvo Car Corporation, (v) Volvo Trucks, (vi) Automotive Components Floby, and (vii) Jernbro Industrial Services.

The project will be executed over a period of 3-years, from 01 December 2021 to 30 November 2024. This period consists of six work packages, each involving all project partners.