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The Challenges and Rewards of Predictive Analytics for Modern Manufacturing

As data becomes an increasingly important asset of modern manufacturing businesses, manufacturers are increasingly relying on predictive analytics to better achieve their goals and deliver for customers.

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As data becomes an increasingly important asset of modern manufacturing businesses, manufacturers are increasingly relying on predictive analytics to better achieve their goals and deliver for customers.

As data becomes an increasingly important asset of modern manufacturing businesses, manufacturers are increasingly relying on predictive analytics to better achieve their goals and deliver for customers.

Simply put, predictive analytics is a form of machine learning that uses historical data to make predictions about future outcomes. Think for a moment about the sheer volume of data a manufacturing business needs to manage, from specific measurements to power and water consumption to maintenance requirements and delivery schedules. Through predictive analytics, calculations can be made to determine the best possible way to ensure a positive outcome, to understand more about operational data, so they may learn from it and improve.

One common area where predictive analytics is valued is with predictive maintenance, which can use compiled sensor data to determine when certain machines need maintenance. Few things are more disruptive to a manufacturing business than unexpected delays caused by problems with machinery. With predictive maintenance, this can be avoided—data is compiled and can be used to determine when certain machines need to be taken offline for repairs or upgrades. When it comes to meeting tight deadlines and customer delivery demands, this is a much-preferred system to simply hoping a machine doesn’t break down before its usual maintenance date.

A major reward of predictive analytics, particularly in the manufacturing sector when production and delivery is so essential, is it creates more understanding about your data as a result of the process. Predictive models can shorten design time between taking a custom product order and getting it built—the predictive analytics model allows the manufacturer to plug in parameters and then receive a prediction of the outcome. And the difference is that results can literally be seen in minutes rather than hours.

While predictive analytics is increasingly important, it does come with its challenges. If the right kind of data is not being collected, this makes creating a model difficult. This can be addressed with a more robust data collection and quality assurance process, which could offer a better glimpse as to why a product is or is not working. Predictive analytics depends on all relevant data being accessible and brought together into one place to train a predictive model. Equipment upgrades, including to machines with better sensors that are more conducive to a predictive analytics setting, could also help in some instances.

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Additionally, the granularity of data being measured affects what kinds of predictions are possible. For example, what if a manufacturer has orders that come in on a monthly basis? Taking a daily look at something that happens monthly is inefficient and oftentimes misleading. The solution would be to change the level of granularity and adjust accordingly to take a monthly view; at that point, predictive analytics could show its true value to the business.

The key to making this work is the quality of the data being used, and having enough of it to make a predictive model that can truly be used to the business’ advantage. If these crucial criteria can be met, the investment of time and resources should prove well worth it. The unavoidable truth is predictive analytics is an increasingly important part of the manufacturing toolkit for those companies that wish to remain ahead of the curve.

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