plus10 Success Stories

Consumer goods

Maximum cost and resource efficiency

The consumer goods industry sector includes the FMCG (fast moving consumer goods) and consumer electronics as well as home appliances and textile products.

The production of consumer goods is strongly demand- and output-oriented as well as cost-oriented. The mass production of consumer goods needs to run on a high level of productivity and technical availability. Fully automated production lines, e.g. for filling and packaging, are just as widespread as rubber and plastic production, e.g. by blow molding, injection molding or extrusion.

The plus10 tools Shannon® and Darwin actively optimize fully automated consumer goods production operating in a 24/7 mode. Also existing production lines can be equipped with our AI tools (Brownfield integration).

Benefits of Shannon® for Consumer Goods

5 – 15 % increase of OEE due to reducing downtimes and troubleshooting (MTTR)

10 – 30 % shortening the ramp-up and relocation phase in the global production network

Increasing cost efficiency by reducing unit costs

Instant problem solving support by suggesting most-relevant expert knowledge

Benefits of Darwin for Consumer Goods

Increase in output per time per machine by reducing the cycle time
Identification of performance potential regarding cycle time and transparency for production planning
Early warning system before breakdowns happen
Maintenance and production optimization can be effectively scheduled

Success story: plus10 at Vileda a brand of Freudenberg

Injection Moulding 4.0: High frequency data collection and mapping of several data sources at Vileda

Reference of client for production optimization || plus10

„plus10 established a high-frequency data infrastructure for our injection moulding machines. The collaboration also included a virtual component tracking system to continuously and automatically link all process and subsequent quality data to a product. I was very impressed by the expertise and reliability of plus10.“

Uwe Dingert

Director R&D, Freudenberg Home and Cleaning Solutions

Target

Conception and realization of a high frequency big data infrastructure and data mapping for injection moulding machines as an enabler for subsequent machine learning modules.

Scope

Implementation of several data interfaces to each controller

Connection and data mapping of sensors for raw material characterization
DEMAG injection moulding machines
In-mould sensors measuring temperature and humidity
Handling devices and buffering systems for produced parts
Quality station for individual quality checks of each part
Creation of a virtual buffer system to track and trace individual parts after injection moulding