Manufacturing
analytics: How to
make the most
of your smart
factory data

Smart manufacturing is on the rise, expected to represent a global market of $658.41 billion by 2029. As the transition to Industry 4.0 accelerates, manufacturers are investing in technology that produces and collects vast quantities of smart factory data. However, successfully leveraging this information is trickier than it may first appear. To maximize the value of their smart factory insights, manufacturers must first understand what types of data are most useful for their business purposes and what kind of technology infrastructure enables smart manufacturing analytics.

According to the Gartner® information technology glossary, a smart factory involves "the application of different combinations of modern technologies to create a hyper flexible, self-adapting manufacturing capability."¹ These technologies include but are not limited to artificial intelligence (AI), machine learning (ML), the cloud, big data analytics, augmented reality, virtual reality, digital twins and the Industrial Internet of Things (IIoT). IIoT technologies will play an especially central role in the smart factory setting—200 million connections for smart manufacturing are projected to come online between 2018 and 2025.

For example, a manufacturer may roll out autonomous mobile robots as part of a warehouse automation initiative to streamline workflows, alleviate labor shortages, improve safety and reduce operational costs. These IIoT devices are equipped with sensors that continuously gather key information, such as environmental conditions on the factory floor, the location of equipment and operational parameters for specific machines. Not only does this data prove useful for the use cases listed above, but manufacturers can also use them to further improve internal processes and enhance strategic decision-making on an ongoing basis.

Manufacturers can tap smart factory data to advance their business goals in several ways. For example, they can use predictive modeling software with AI and ML capabilities to analyze data generated from the IoT sensors on their equipment, identify anomalous trends in performance, and flag potential issues before they result in disruptive and expensive downtime. When surveyed, 24% of companies said they planned on implementing predictive maintenance within the next one to two years.

Once they've become proficient in applying manufacturing analytics, manufacturers can leverage smart factory insights to achieve even more ambitious business outcomes. Digital twins present a particularly compelling opportunity, allowing manufacturers to create virtual replicas of specific manufacturing assets, processes or even entire factories so that they can optimize and fine-tune their performance even further. For example, aircraft manufacturers could use digital twin technology to simulate a new engine design's performance under various real-world conditions before it's physically manufactured, proactively spotting performance problems and improving safety as a result. When polled, 76% of manufacturing respondents said digital twins will be important or extremely important to their firm's priorities in the next 12 months.

Manufacturers require a solid technology foundation to unlock the full potential of their smart factory insights. They need advanced manufacturing analytics software that includes robust AI and ML capabilities to properly interpret trends and patterns in their IIoT sensor data. They must also have a strong network infrastructure to achieve near real-time insights into their processes and optimize their operations. IIoT devices, when deployed at scale, can collectively consume a considerable amount of bandwidth—and if they're placed in far-flung locations with intermittent or unreliable connectivity, they may not be able to share data promptly enough for it to be actionable.

Verizon's 5G Edge is edge computing technology that when combined with 5G, can give manufacturers abundant bandwidth and low latency, enabling data collection support for the growing numbers of 5G IIoT sensors used in smart manufacturing facilities. Crucially, 5G networks can do so at scale, growing to support manufacturers' evolving business requirements. By quickly gathering and analyzing key IIoT data from across all of their locations, manufacturers can identify when equipment is either broken or must be replaced soon. This, in turn, allows them to reduce costs associated with aging equipment while adhering to compact business and product life cycles. In the case of digital twins, they can be confident the data they're relying on is both up to date and accurate, enabling them to correctly project how a particular product or process will perform under certain circumstances.