The manufacturing industry is no stranger to the advent of Big Data. It is quite impressive that most manufacturers have been embracing disruptive technologies to stay ahead of their competition and thrive. One of the most powerful capabilities that plays a vital role in the manufacturing world is the application of predictive analytics capabilities—which allows them to move their businesses forward.
Specifically, the ability to extract meaningful insights about products, processes, production, yield, maintenance, and other manufacturing functions, as well as the ability to make decisions and take proactive action—when it matters—can deliver tremendous growth and profitability results. Of course, the data environment and predictive analytical requirements vary across different manufacturing organizations. However, there are some broad areas of predictive analytics that can benefit most manufacturers, according to an article in Toolbox.com.
Improving Quality
Databases and data storage improvements, complemented by easy-to-use, analytical software are the biggest changes when it comes improving the quality of a company’s products, says Studio B analyst David Gillman, author of the article.
“Standard quality improvement analysis is being pushed toward less technical analysts using new software that automates much of the analytical process,” Gillman notes.
Additionally, companies that store more data about their products and manufacturing processes, will be able to analyze more factors to help them improve the quality of those products and processes, he says.
Forecasting Demand
Understanding the changes in demand for their products enables manufacturers to better determine how to allocate their resources, according to Gillman. Predictive analytics help manufacturers forecast future sales based on past sales.
“Good predictive analytics modelers find additional factors that influenced sales in the past and apply those factors into forecasted sales models,” he notes.
Maximizing Equipment Value
Manufacturing engineers spend a lot of their time ensuring their companies get the most value out of the equipment in their factories. Predictive analytics help them do this by automating much of the analysis processes so even people without high-level skills can perform various analyses, according to Gillman.
In the past, engineers had to turn to spreadsheet-based analytics that were subject to considerable assumptions made by the people who created the spreadsheets, Gillman notes.
However, using predictive analytics apps for machine scheduling lets manufacturers look at the demand forecast for their products to ensure they make the best use of their equipment, according to Gillman.
“In reality, that may only increase the utilization for many machines by a few percentage points, but that still looks good to the finance people when it comes time to buy new production equipment,” he adds.
Increasing Equipment Uptime
Predictive analytics can also help manufacturing companies ensure that their production equipment keeps running by comparing past machine failures to sensor data from the machines to identify patterns before breakdowns occurs, Gillman says.
Having that information allows a manufacturer to perform the necessary maintenance on a machine in nonemergency conditions without having to shut down production.
“The equipment manufacturers use this type of predictive analysis as an add-on service because they can collect data from all their machines across many different factories,” Gillman notes. And more data as to why their machines stopped working means more accurate predictive models, he says.
These are just some of the ways predictive analytics can help manufacturers improve their products and operations, Gillman adds.
