Jul 17, 2021
Jeff Nygaard

Seagate Technology: Putting Data to Work on Factory Floors

SmartManufacturing
Data
Technology
DigitalFactory
Jeff Nygaard, Executive Vice President of Operations and Technology at Seagate Technology

It’s been an eventful year for all industries, but the pandemic has been especially disruptive for the manufacturing sector, which relies on delicate balances of supply chains to function effectively. You might think that manufacturers would be jumping at any opportunity to give their operations a boost. The sector has a rich history of technological innovation as one of the first business sectors to embrace robotics, automation and IoT. Manufacturers have a wealth of data at their disposal. 

Yet ReThink Data, a recent report carried out by Seagate and IDC, suggests a rocky relationship between the two. Manufacturing lags other industries in exploiting the value of data, due in part to infrastructure challenges and a lack of investment in training. How can manufacturers turn this around and start putting their data to work on the factory floor?

How manufacturing lags other industries in data management 

IDC surveyed C-level and senior IT professionals in manufacturing and identified several roadblocks that prevent companies from getting value from their data. A fundamental one is a lack of integration within IT infrastructure. For example, there has been a rapid growth in IoT devices at the edge of manufacturers’ data networks, but these devices are disconnected from headquarters’ systems—and big data transfers over corporate networks are expensive and slow. Data is often scattered across uncoordinated data collection points. If factories are flooded with tens of thousands of random IoT devices deployed on different platforms then it can be difficult to use data to accomplish basic goals such as, for example, improving throughput. 

Another roadblock is data storage. Manufacturing lags other industries in adopting multi-cloud and hybrid cloud and the industry is also less likely to have a central data hub than other industries. This leads to data being discarded rather than being transferred to a core environment for long-term storage. In this, manufacturing is not alone, in fact, managing the storage of collected data was the second greatest challenge identified by the report, after making collected data usable. 

Why is manufacturing lagging? 

For some organisations, having internally managed enterprise data centres makes it difficult to keep up with the growth of their data. The manufacturing sector has the highest on-premises enterprise data centre footprint of any industry surveyed by IDC for Rethink Data. This makes it difficult to expand capacity due to physical constraints and costs, especially when compared to scalable cloud infrastructure. 

IDC research in Rethink Data also suggests underlying legacy infrastructure is unable to keep pace with other parts of the business. Manufacturing technologies change fast. For example, it can be hard for data management to include all the new varieties of sensors on various factory machines. In many cases, the ageing infrastructure can’t process the number of connected assets entering the plant. As a result, too often plants implement ad hoc processes to connect and manage assets without an underlying infrastructure for comprehensive management. 

In addition, there is also a skills gap. Manufacturers are dealing with ageing workforces and challenges in finding new skilled employees willing to work on the plant floor. This lack of new talent means manufacturers can only invest in upskilling existing staff – no bad thing on the face of it, but not a sustainable long-term strategy on its own. If skilled workers represent manufacturing’s future in developed economies, the lack of adequate skills is one of the toughest barriers that companies need to address. 

What’s the solution? How manufacturers can get more value from their data

Ignoring your data means missing opportunities to improve the bottom line, to innovate, to grow. Investing in data management has a potentially transformative ROI, but where to begin? Activating data—putting it to work—starts with data capture. 

Businesses do not currently capture all the data they have available to them. In fact, ReThink Data found two-thirds of business data goes to waste. To reduce this wastage need to take a long hard look at how they approach data management across the production line. 

Your data storage setup needs to be a part of this process. Having outdated or ineffective storage can undermine your efforts by slowing down access to data or by simply being unreliable. The decision to hold data either in the cloud or on-premises is one that should reflect a business’s circumstances and there is no one-size-fits-all answer, but it’s not an area you should cut corners on. 

Your data storage solution can even be an active contributor to the process of sorting and analysing information. The latest generation of hardware and software tools allow real-time reporting of stored data to improve workflows, security, and resource management initiatives.

Finally, data capture should then feed into data analysis using specialized data analysis software. This software can be used to mine information from captured data and provide insights to decision-makers. For example, predictive analytics could be used to identify inefficiencies in your production line, or spot degrading machinery before it breaks entirely. Not every company needs to have a full team of data scientists on the payroll – just a handful of trained technicians with the right tools can pay dividends. 

The race to data supremacy 

Getting value from data isn’t something that just happens. While it appears the UK manufacturing sector has been slower in realising this than many other sectors, that’s not to say there aren’t some incredibly successful manufacturers who are ahead of the pack. Those organisations able to master their data will have an edge over the competition. Savvy business leaders will see that there’s no time to waste in putting their data to work.  

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