BOOST 4.0 VI
PILOT : LARGE SCALE and ON-SITE TRIALS
SMART OPERATIONS and DIGITAL WORKPLACE
CONTACT NAME MR. BAS TIJSMA
PILOT PRODUCT DESCRIPTION
Philips Consumer Lifestyle Drachten (PCL), part of the Dutch multinational technology company Philips, is providing a use-case and pilot environment for testing and deploying solutions provided by the Boost 4.0 partners. It is expected that solutions developed in other pilot verticals by other Boost consortium partners will be also tested and deployed in the pilot. PCL has multiple years of experience in data collection and analytics with respect to manufacturing industry. We will feed back this knowledge and insights to the consortium. It is expected that this knowledge, together with our existing data and platform(s), will help drive (deployable) innovation within the projects.
OBJETIVE OF THE PILOT
The pilot targets to develop a scalable and robust solution, which can be used in a real production environment. The pilot location focusses on Injection Moulding processes, and it is expected that results obtained at the Drachten pilot can be exported to other Philips production locations as well as contribute to the European industry.
Injection moulding, is used in several industrial domains, and it is the main industrial process related to mass manufacturing of plastic components. Europe has also several injection moulding machine builders, all developing solutions targeting Industrie 4.0 related topics. Several of them are united in the Euromap-consortium, setting (open) standards for the plastic part making industry. Within Boost 4.0, it is to be expected to make use of these standards to accelerate deployment.
The vision is to deliver a true scalable solution, providing ‘plug-and-play’ capabilities, like data collection, semantics, analysis, visualisations and machine control based on intelligent models. Key users include the local Data Science department, Manufacturing IT department, Production managers and operators.
KPIS AND METRICS
The main KPIs of the pilot are:
- Predictive Quality – 10% improvement on Fall Off Rate (FOR)
- Predictive Maintenance – 5% less down time (OEE-A)
- Intelligent process control – Contributes to predictive quality (in process-control)
- Give operators better tools – Mean Time to Repair improvement of 5% 2-5% reduction in costs