And that’s how we arrive at Industry 4.0, an era where sensor technology and the interconnectivity of machines – the Internet of Things – is driving the industry forward. This is an evolutionary journey of analytics capabilities that begins with today’s Agile Data Warehouse and culminates in a future state where you have a Sentient Enterprise and manufacturing processes at optimal capacity. Industry 4.0 promises new levels of process efficiency, “zero unplanned downtime” of machinery, and the ability to manufacture “batch size 1” – highly customized products produced at reasonable cost to the consumer.

While manufacturers are in agreement that Industry 4.0 brings beneficial advancements, there is a tendency to overlook what should be a crucial cornerstone of any company’s strategy when it comes to Industry 4.0 – data.  For it is data that is at the heart of Industry 4.0. The more devices that talk, the more sensors that are fitted, the more data is generated. Certainly, the volume and variety of data sources generated is not something that the manufacturing community has had to deal with before.

These then are the four key areas of consideration for any manufacturer in the process of establish a data strategy to meet Industry 4.0 objectives: 

1.Acquiring data.  Modern production and OEM equipment comes equipped with a multitude of sensors, all generating data.  However, on its own, sensor data does not do the trick – the magic happens when sensor data is brought together with ERP, maintenance management data, and financial data.  For example, if you get data from a vibration sensor alone, what you get is pure technical information.  But if you combine this with data from the maintenance management system, you will be able to link that vibration pattern to performed or missing maintenance activities or specific parts that have been changed. This enables you to map potential dependencies or root causes of an issue and even predict what might happen. If you then add financial data – you can predict costs of future maintenance activities that might arise if a specific vibration pattern happens.

2.Transferring data.  Within the manufacturing environment, data tends to be generated in geographically dispersed production sites, in OEM equipment in remote locations, sometimes even in mobile assets.  How can we transport this data to a centralized location, securely and in a timely manner?  Also, data transfer costs.  So manufacturers need to have a clear strategy about their plans for the data, so that decisions can be made about the kinds of data to be transferred and when.

3.Storing data.  Sensors throw up a huge amount of data, not all of which will be imminently useful.  Manufacturers need to make decisions on the appropriate storage technology and philosophy (which data is needed, when, where, and how quickly, as these factors impact the cost of storage).

4.Getting insights from data.  How can we analyze the data and ensure that we can run that analysis as and when it is needed by the business, to drive better decisions? The value of data to the business is intrinsically linked to cost savings or increased efficiency through improvements in a production process, a maintenance procedure, or system behavior.

This new data strategy demands a new way of working in order to be successful.  Engineers have to let IT into their domain, share information about their IP and how things are done.  IT has to be prepared to go much deeper in forging new partnerships with their engineering counterparts, while respecting established processes.  Instead of delivering a turnkey IT project based requirement specifications, Industry 4.0 projects need a much more collaborative approach – IT and engineers working side by side, sharing insights and working together.  A great opportunity for engineers and IT to come together to deliver on the Industry 4.0 vision!