Quality and efficiency are hallmarks of a great manufacturing company. From your enterprise infrastructure to your production machines, all of your systems must be running synergistically on all cylinders. Since everything rides on product quality for manufacturing companies, they’re always seeking ways to optimize operations.
Manufacturing analytics has wholly revolutionized the manufacturing process, whether it’s how companies design products, manage personnel, manage supply chains, or schedule machine maintenance. Large and small manufacturers are finding ways to incorporate data analytics into their business strategies. Continue reading to learn some of the many ways manufacturing analytics could help set your manufacturing enterprise apart from your competition.
If you’re not a tech insider or are still running legacy systems in your facilities, you may have no idea what manufacturing analytics is. Data analytics is the application of data science principles to find cryptic insights from multiple data sources and finding trends through analysis. Manufacturing analytics is merely the application of data analytics to the manufacturing industry.
Through manufacturing analytics, manufacturers can make significant adjustments to their product lines and shop floor practices in real-time. As an integral part of the Fourth Industrial Revolution, colloquially known as Industry 4.0, manufacturing companies automate key processes via the internet of things (IoT). If your company isn’t applying manufacturing analytics solutions to its production strategy, you should continue reading to see some of the many incredible features it’s missing.
When your machines go on the fritz, it halts output and significantly cuts into your bottom line. Not to mention, if you have an extended downtime period, you may have to pay personnel for not working, which means you’re paying to lose money.
One of the ways big data has impacted the manufacturing industry is predictive analytics, which uses historic data and predictive algorithms to forecast future events. This analytics technology is so effective that some of the top law enforcement agencies in the United States employ this feature to determine where a criminal is most likely to strike next.
With predictive analytics, manufacturing companies can predict when a machine will break down. With that data, they’re able to schedule predictive maintenance on everything from accumulation conveyors to hydraulic pumps before they’re able to halt production. Conveyors are hugely important to any manufacturing enterprise that takes itself seriously. By making sure that conveyors are up to speed and your conveyor systems don’t break down, you can eliminate bottlenecks and optimize output.
The manufacturing industry is affected by changes in demand like no other. Once an increase or drop in demand hits a retailer or even wholesaler, they already possess the product and will put it on the floor regardless of its demand. Manufacturing companies have to make constant changes to their production to keep up with the market and ensure they don’t end up with a logjam of inventory.
One of the most powerful tools data analytics lends to the manufacturing industry is forecasting demand increases and drops to maintain an ideal production flow. Using metrics that pinpoint when products become hot and not allows them to schedule increases in production and downtime in advance. The ability to make production line adjustments based on real-time data prevents unplanned downtime and maintains consistency across distribution networks.
One of the most frustrating things for manufacturers to have problems with product quality they can’t solve. Sometimes, it can be hard to tell whether the quality of the raw materials, the machinery, the conveyor, or human error that’s causing the issue, and time is of the essence.
Manufacturing companies use data analytics to pinpoint faults in production and find solutions on the fly. Whether the change needs to be in your machinery or the procurement of raw materials, advanced analytics can help you find the trouble spots. If your company isn’t using data analytics yet, the only real question is, why?