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Lessons from Hannover Messe: 5 Ways to Leverage IT for Your Industrial Ops
At the 2017 Hannover Messe this week, I talked with dozens of manufacturing execs trying to figure out how to move forward their platforms, teams, and philosophies to enable Industrie 4.0 outcomes at scale.
Our customers are already connecting thousands of intelligent devices and demanding new transparency from their vendors: They want to liberate plant floor data from operational silos and proprietary technologies. Only by doing this will they be able to adapt to the latest advances in automation, sensor technology, and machine learning… not to mention the economic imperative to get in front of tightening competition due to globalization.
To take full advantage of new technologies now hitting manufacturing – like predictive analytics, cloud computing, mobility, and collaboration – it’s critical to align operation technology goals with company IT teams. It is time to unite the Operational and the IT tribes so we can succeed together in enabling Industrie 4.0, beyond paper projects.
As I learned at Hannover, there are 5 major issues that manufacturing execs are thinking about as they work to integrate their various industrial systems with each other and with IT’s.
Here they are:
We Need Open Data Platforms
Contemporary machine learning technology means that we are getting new capabilities to see patterns and signals in data that we used to discard as noise, that we can use to improve operational efficiency.
Currently, many shop floors run legacy protocols designed for command and control; they were not designed to feed bits into a data warehouse. Without a way to collect, collate, and store data from different systems, the opportunity to harvest this data is lost.
(This is why we build switches like the IE 4000 line that support multiple protocols and standards which is the first step is bringing the data streams together. CAM for IOT Intelligence helps to connect and organize all the new streams of IOT diagnostic data for business improvement whether from sensors, machines, IP cameras, or asset and people location tags.)
We Must Move Beyond Computing Silos
The massive increase in the data flows we will be processing means that we also need to look at where and how we are doing that processing. The old client/server model, with designated machines working on particular projects, isn’t manageable and does not scale. We’ve got to get our data and our processing out of silos. We need processing at the “edge,” the interface between shop floor machines and the data centers that are archiving their information.