Big data and the industrial Internet of things (IIoT) are natural partners on the plant floor, but not always considered as a source of business intelligence in the corner office.
- Connecting and analyzing ever-wider data sets from industrial machines and sensors that listen to the “voice of the factory”.
- Aggregating manufacturing and business information within data lakes to drive next generation insights across their enterprises.
- Creating high-performance industrial data pipelines to ingest and analyze manufacturing data in real-time.
Wayne Somers, Manufacturing IT Specialist at Sciemetric, did a great job talking about IIoT and Industry 4.0. He said that Industry 4.0 is characterized by highly intelligent, agile, information-driven factories that are able to respond rapidly to change, connected sensor (camera) networks and advanced data analytics, and entirely new, customized smart products and services. And, he listed the key goals of Industry 4.0 as:
- Real-time inline quality control based on big data analytics.
- Real-time visibility into process and product variance.
- Vertical integration from sensors through MES to real-time production planning for better machine utilization and faster throughput times.
- Preventative maintenance on key assets using predictive algorithms to optimize repair and maintenance schedules that improve asset uptime.
- Horizontal integration throughout the entire supply chain.
And, he summarized his section of the presentation by sharing that:
- It’s important to understand the manufacturing environment in order to use the data it generates for an enterprise data lake.
- In turn, it’s important for those within a manufacturing environment to take the time to understand what the enterprise could possibly need because if you just dump raw manufacturing data into your corporate data lake you will set yourself up for a lifetime of “explaining to do.”
- It’s important for enterprise divisions to come together and have the proper discussions regarding data needs, feeds and expectations before engaging those in IT responsible for managing the data lake.
- Once all parties agree, populating the data lake needs to be automated and frequent.
- Everything changes over time, so you need to have new communication paths to convey these changes
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