How Big Data is Shaping the Future of Manufacturing

Leveraging actionable big data insights may be the key to higher time and cost savings.

Fast Radius has submitted this post.

Written by: Charlie Wood, Senior Director of Research & Development, Fast Radius

(Image courtesy of Fast Radius.)

(Image courtesy of Fast Radius.)

“Big data” is a focus across industries, referring to the massive datasets collected from connected devices that are analyzed to generate data-driven insights. Using big data, businesses can identify patterns and consumer behaviors, analyze historical trends and predict future needs in order to optimize operational efficiency and improve business practices.

Part statistical analysis and part consumer research, big data has become a popular topic because of its ability to drive value. In the manufacturing sector in particular, leveraging actionable big data insights may be the key to higher time and cost savings. A joint study conducted by Honeywell and KRC found that effectively harnessing big data analytics can reduce breakdowns by up to 26 percent and cut unscheduled downtime by nearly a quarter.

Forty-four percent of industry leaders believe big data analytics creates new avenues for innovation and disruption. By collecting and analyzing data, businesses will be able to better understand their operations, customers and pain points, and data analytics enables new, innovative approaches to improve operations and performance. At Fast Radius, this data is part of the learning engine in our Cloud Manufacturing Platform, designed to codify centuries of engineering knowledge in order to optimize designs and production for new parts. Collecting and leveraging data throughout the production process is critical for engineering and manufacturing to improve design, production and fulfillment.

The following infographic breaks down how big data features in manufacturing.

(Image courtesy of Fast Radius.)

(Image courtesy of Fast Radius.)

Big Data and Manufacturing Today

According to the same Honeywell-KRC study, 67 percent of manufacturing executives have plans to invest in big data, even though many are facing pressure to reduce costs. Most global manufacturers already have real-time shop-floor data at their disposal for statistical assessments—so it is just a matter of aggregating, analyzing and applying that data effectively. Big data analytics has three primary advantages for manufacturers:

  • Improved operational efficiency: Manufacturers rely heavily on maximizing the value of their tools and machines to increase productivity, reduce inefficiencies and stave off breakdowns. IoT-connected machines can measure, record and transmit real-time data, enabling manufacturers to uncover insights that can improve performance, create more efficient production runs and maximize uptime for machines.
  • Optimized supply chain and production processes: As supply chains grow more complex, manufacturers need to build effective data structures to track and measure their supply chains to identify and measure inefficiencies and opportunities for improvement. Precise tracking helps manufacturers pinpoint specific opportunities to streamline and optimize processes by eliminating redundancies, automating wherever possible, optimizing vendor selection and more. Data-driven supply chain insights can also reveal dependencies within the chain, enabling manufacturers to build more flexible and resilient supply chains.
  • Risk identification and mitigation: Big data is also useful for pinpointing potential vulnerabilities within a manufacturer’s operations. By analyzing data about equipment wear and past failures, for instance, manufacturers can more accurately predict the lifecycle of their machines and plan maintenance accordingly. According to a report from PWC and Mainnovation, big data-powered predictive maintenance reduces costs by 12 percent, extends equipment lifetime by 20 percent, improves uptime by 9 percent, and helps manufacturers create a recovery plan in the event of an unanticipated failure.

Preparing for the Future of Big Data in Manufacturing

Many manufacturers use big data to optimize internal operations, but manufacturers can push their big data capabilities further by exploring a wider variety of use-cases.

Traditionally, manufacturers have focused more on mastering production at scale than product customization. Now, the quality of a company’s consumer experience can make or break its future success—and 90 percent of consumers are willing to offer up their personal information if it means unlocking a more personalized experience. Big data can help manufacturers detect minute changes in consumer behavior, which in turn helps them give customers the personalized experiences and customized products they want. Having a big data cache capable of updating in real-time allows manufacturers to create customized products ahead of time with the same degree of efficiency as normal large-scale production.

What’s more, big data can help manufacturing companies make greater strides toward safer working environments. Widespread predictive maintenance adoption, powered by big data, can cut health and safety risks to workers by 14 percent. Plus, leveraging data-driven control processes can decrease quality costs and improve output.

Big data analytics can also be leveraged to improve energy efficiency and sustainability in manufacturing. When a prominent European metalworking company used big data techniques and discovered that variances in carbon dioxide flow were reducing their overall yield, they reduced raw material waste by 20 percent and energy costs by 15 percent. If more manufacturing companies incorporate big data into their day-to-day operations to elevate energy efficiency, the industry’s carbon footprint could shrink significantly.

Go Big with Big Data

Manufacturing companies can use big data to operate more efficiently, make better products, reduce waste and save energy. Still, industry stakeholders should be wary of jumping onto the big data bandwagon without doing their due diligence by researching and testing. Apply big data analytics to a small project first, measure the results, and then roll out larger projects in phases.

At Fast Radius, we’re focused on building Industry 4.0 practices into our daily operations and embracing the possibilities of big data and cloud manufacturing at every step of the way. The future possibilities are infinite for big data analytics in manufacturing, and in the short term we can create better engineering and manufacturing workflows, safer manufacturing environments and smoother operations.

A similar version of this article was originally posted on