BOOST 4.0 V
PILOT : REAL TIME MONITORING AND ANALYSIS IN AUTOMOTIVE PRODUCTION
PREDICTIVE MAINTENANCE AND QUALITY CONTROL ON AUTONOMOUS AND FLEXIBLE PRODUCTION LINES
PILOT PRODUCT DESCRIPTION
This pilot regards a shopfloor equipped system of mobile robots (AGVs) linking different production cells, connected through factory IoT or managed through cyber physical systems. The production related data are collected from different company sources (e.g. real-time data form the field, MES, PDM), standardizing their connectivity. The data are then directly sent to Mindsphere, the cloud platform, where new applications permit to tranform data in value (i.e predictive maintenance, reporting analytics etc.). Their development can be done by internal or external providers, so the data access has to be disciplined. To ensure data privacy and security, open datamodels are developed and IDS technology (e.g. IDS connector) is implemented. Since normally factory data are high sensitive and considered confidential, the data are splitted into hot data (and mark as confidential), intermediate data (e.g. anonymized) and cold data that may be shared with the equipment provider.
OBJETIVE OF THE PILOT
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-dimensioned fleet of robots is reduced and the (big-)data are shared among the whole value chain (providers, maintenance services, etc.).
KPIS AND METRICS
The main KPIs of the pilot are:
– Close-to-zero defect manufacturing
– Production increase (2%)
– Total cost reduction (1%)
– Environmental impact reduction (4% of water/energy consumption)