Sarah is a sales & marketing content writer, with ten years of experience within the engineering & manufacturing industry. Working both at Qimtek and on a freelance basis, she can usually be found hammering away at a keyboard or with her head in a pile of engineering drawings.
As manufacturing undergoes constant evolution in line with the latest technological advancements, the way that manufacturing & engineering businesses operate is ever-changing. You’ve probably heard of the Fourth Industrial Revolution - otherwise known as 4IR or Industry 4.0 - which focuses on the rising prominence of automation, as well as equipment’s ability to communicate through the Internet of Things. But did you know that the Fourth Industrial Revolution is also making it easier for companies to identify potential areas for improvement?
You may have heard of the term ‘big data’ – it’s another buzzword which has become increasingly popular in the modern engineering and manufacturing sphere. But what’s the difference between plain old data and ‘big’ data? Hint: the clue is in the name.
What is Big Data?
Big data refers to huge data sets that comply with the three V’s – volume, variety and velocity. That is, there has to be an extreme volume of data with a high variety, which must be processed at a high rate of velocity. There are also three additional V’s which can be used to assess the reliability of the data: veracity – or the extent to which the data can be trusted, the value of the collected data from a business sense, and variability – the ways that the data can be used and formatted.
Big data has become more prominent with the rise of the Fourth Industrial Revolution, simply due to the fact that automation and the Internet of Things have made this data much easier to capture. There is no limit to the sources from which big data can be pulled, although universally, these commonly include sales transactions, online marketing analytics and social media. However, manufacturing offers additional opportunities for big data capture, including sensor data, which can be used to track and monitor defect rates. From here, big data affords OEMs a unique insight into larger scales of their own information and identifies areas in which potential improvements can be made.
Big Data and Manufacturing:
The manufacturing sector – perhaps more so than others – can benefit exponentially from big data, due to the ease and rate at which IOT-equipped plant can gather information. In fact, the newest generation of manufacturing plant seems to be designed with this in mind, as more and more companies recognise the potential benefits of harvesting big data for analysis.
IOT technology, such as RFID tags, has streamlined the art of data collection, giving OEMs the ability to record and weigh products against serial numbers. By scanning these tags, manufacturers are able to capture big data with ease, with anomalies illustrating defect rates amongst any number of product lines. However, consistent and accurate data is crucial to its analysis, which can only be achieved through continual investment by the manufacturer.
In fact, big data analysis – and therefore, company investment – is set to become even more critical to the way that manufacturers conduct their business. Predictive manufacturing is already making big waves throughout the industry and shows no sign of abating as the adoption of 4IR practices increases. Predictive manufacturing systems (PMS) can be used to grant machinery & equipment the autonomy to detect and diagnose faults; however, in order to do this with any context, a large amount of intelligence needs to be programmed onto the system itself. This is where big data goes from being a mere luxury to the lifeblood of manufacturing’s future, although the use of PMS systems is a long way from becoming a necessity to an OEM’s operations.
What Are the Pitfalls of Big Data?
Of course, there are caveats when it comes to the use of big data, which can affect its viability as a reliable insight tool. Firstly, it’s imperative that manufacturers are confident in the reliability of the data they’re collecting, as erroneous data sets can lead to a mis-navigated change in the company’s direction and strategies. Secondly, it requires its users to adopt a ceaseless approach to data analysis, due to the astounding rate at which the information is collated. This may not be possible to introduce overnight and therefore, a more gradual implementation may be necessary. A change in approach will undoubtedly require modifications to company culture and therefore, buy-in from others involved further down the chain.
However, possibly the most sinister disadvantage of big data is its vulnerability to potential data breaches. Whilst a large volume of data can provide valuable insights for the company it belongs to, it will also be of interest to the likes of competitors and hackers. This means that big data users have to pay particular attention to their cyber security, whilst also ensuring that the use and storage of their data complies with GDPR and other such laws.
Should I Be Using Big Data?
While the use of big data is becoming more and more commonplace within the manufacturing industry, it’s still far from being an essential requirement. Therefore, companies considering big data capture should first be confident in their own abilities to accurately and consistently analyse it, whilst realising that considerable investment is required from both a time and monetary standpoint.
When used correctly, big data is unrivalled in offering a complex understanding of a manufacturing company’s operations. However, it’s important that OEMs consider that there is a wider spectrum of advantages and disadvantages to big data usage, which need to be fully comprehended and considered before a full commitment and/or investment is made.