Manufacturing is an industry that has been facing severe pressures for a number of years. Raw materials have become more expensive and difficult to source, growth has slowed significantly and economies the world over are facing uncertain futures. Productivity growth for industrial companies in the European Union fell from an average of 2.9% between 1996 and 2005 to just 1.6% from 2006 to 2015, according to the Organisation for Economic Co-operation and Development. The result is fairly obvious. Manufacturers have had to streamline and squeeze every ounce of productivity out of their businesses just to survive. To do more, with less is the industry’s focus.
Beyond simple changes in supply chain efficiency which the industry has already capitalised on, deeper insights are needed to stay ahead of the competition. One rich resource which many manufacturers are still slow to exploit is data. Manufacturing processes are complex, and by their nature generate a lot of it. Deploying robust analytics systems to monitor, analyse and even predict future outcomes is the next step for manufacturers.
Being able to measure and record a process, in detail, is central to fully understanding it. Analytics tools, that were previously too expensive to be deployed full-scale for many manufacturers, now grant this ability at ever-decreasing costs. Real-time data can be collected from multiple sources across the supply chain and factory, and combined with machine learning and visualisation tools can deliver insights across the business. Using effective analytics tools can help manufacturing companies to improve both production efficiency and product quality.
New ways to optimise the manufacturing process can be found, delivering greater value for the business. This is particularly relevant for businesses that operate globally where real-time developments in the economy can have drastic effects on source and sale pricing. Keeping on top of this means low wastage and no lost opportunities, as well as ensuring the business is as prepared as possible for any upcoming obstacles.
Another big benefit for manufacturers when they invest in data analytics is that they’ll uncover previously hidden insights and solve problems that they might not even realise they had. Modern visualisation tools allow users to manipulate the data in ways that simply aren’t possible with traditional methods. It is striking how often a change in perspective will uncover new patterns or trends. Previously unnoticed bottlenecks or unprofitable production lines are just two examples of how this could work.
These deeper insights can be transformative for manufacturing companies who feel they have already exhausted every avenue in the pursuit of production efficiency and improvements to the bottom line. They also offer an element of competitive advantage in an industry that has traditionally been slow in updating IT systems. Furthermore, these deep insights are available to everyone across the business.
The always-on nature of modern manufacturing requires that insights are delivered to where they’re needed most in near real-time. That can only happen with democratic approaches to data, possible with modern tools that can be accessed cross-device, cross-platform and even in areas of poor internet connectivity.
As we discussed in our recent blog, every company would benefit from being able to predict the future, and this is particularly relevant for the manufacturing industry. The applications for predictive analytics in this industry are many and the benefits potentially significant. Operating in a global economy, we have already said, is difficult for manufacturers when struggling to balance source and sale prices. Predictive analytics, applying machine learning to historical data, can identify seasonal and event trends and alert the manufacturer on not only when to account for the change, but also suggest the best course of action. The increasing power of AI will mean this becomes far more common.
Also central to manufacturers is keeping their machinery online. Machine maintenance is costly and incredibly time-consuming, especially when it is reactive. Predictive maintenance analyses the historical data of the performance of machines and uses models to predict when one is likely to fail and coinciding with data on supply demands can suggest times when it would be most cost-effective to have them out of action. Many manufacturers are already experimenting with some carnation of predictive maintenance, but few are grounded in the reliable and highly-accurate analytics and machine learning platforms available on the market.
The drive for efficiency within manufacturing means no stone will be left unturned. That in itself is a good metaphor for the benefits of data analytics, where every source of data is monitored and turned into valuable insights. Whether this identifies and suggests resolutions for bottlenecks, highlights unprofitable production lines or even plans the maintenance routine for every machine in the plant, the benefits are far-reaching.
If you’d like to integrate these benefits of analytics into your manufacturing business, get in touch today to see how we can help.