PILOT: PREDICTIVE MAINTENANCE AND QUALITY CONTROL ON AUTONOMOUS AND FLEXIBLE PRODUCTION LINES

In the pilot are collected production related data from different company sources, namely mobile robots, production machines
and production management softwares, storing then them in a datalake infrastrucutre with the main objectives to, through
applications developed by external or internal providers, enable optimal production planning and management of incoming
events, perform a production quality control and determine the planning of the maintenance activities

The general objective of the pilot is to support the automation and itemization of automotive industrial processes. More in detail,
the trial will enable the optimal planning of production missions, evaluating the incoming events (delays, failures) in an
innovative autonomous production environment, in which the traditional linear process is removed and mobile robots permit a
higher level of autonomy, re- configurability and robustness to external events. The control of the AGV fleets, their availability
and reliability respect to cycle time and leadtime is crucial to ensure the stability and throughput of the production systems.

From a connectivity point of view we expect to define the standard technology and the best practice for data collection, data
transfer and data storage.

From a functional point of view the aim of the trial is to define procedure and best practice and best procedure for predictive
maintenance and also to define a closed loop innovation process.

The trial will prove the maximum flexibility to potential changes in the demand or to issues/delays/changes in the logistics or
productive systems by means of using available and new datasets (such as flows of components in the plants and their precise
localization) ensuring business continuity, at the same time the over-3 dimensioned fleet of robots is reduced and the (big-)data
are shared among the whole value chain (providers, maintenance services, etc.).

– Close-to-zero defect manufacturing
– Production increase (∼2%)
– Total cost reduction (∼1%)
– Enviromental impact reduction (∼4% of water/energy consumption)