How big data can be used in manufacturing
Big data refers to massive datasets collected from connected devices that are analyzed to generate data-driven insights. Industry leaders use big data to identify patterns and consumer behaviors, analyze historical trends to optimize operational efficiency, and improve business practices.
Part statistical analysis and part consumer research, big data is key to driving 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% and cut unscheduled downtime by nearly a quarter.
The big data industry as a whole is expected to be worth $77 billion by 2023, and 44% of industry leaders believe big data analytics creates new avenues for innovation and disruption. Collecting and analyzing data allows businesses to better understand their operations, customers, and pain points and enables new, innovative approaches to improve operations and performance. Here’s a breakdown of how big data features in manufacturing, plus key considerations for industry stakeholders.
Big data and manufacturing today
According to the same Honeywell-KRC study, 67% of manufacturing executives have plans to invest in big data, even though they’re facing increased pressure to reduce costs. Most global manufacturers already have real-time shop-floor data at their disposal for statistical assessments — so it’s just a matter of aggregating and analyzing that data effectively. When manufacturers use big data to their advantage, they receive a boost in three key areas:
Improved operational efficiency
Manufacturers rely heavily on maximizing the value of their tools 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.
Optimized supply chain and production processes
As supply chains grow more complex, it can be challenging for manufacturers to track and measure their supply chains without the right data structures. Without robust tracking and data collection, businesses struggle to identify or measure supply chain inefficiencies and weak links. Big data enables manufacturers to gain greater visibility into every step of their supply chains. With this insight, they can 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 create backup plans and prepare for the future.
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%, extends equipment lifetime by 20%, improves uptime by 9%, 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 — it seemed most prudent to do so. Now, the quality of a company’s consumer experience can make or break its future success — and 90% 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%. 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% and energy costs by 15%. 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 always looking for new ways to make manufacturing faster, better, and more dynamic. If you want to lean into Industry 4.0 and learn how to use big data to your advantage, our team of experts can walk you through every step of the process. Contact us today.
For more insights into the manufacturing industry at large, visit the Fast Radius resource center.