Industry 4.0. Big Data. IIoT. The Grid View. Industrial Internet of Things: The Next Frontier for Manufacturing

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1 Industry 4.0 IIoT Big Data The Grid View Industrial Internet of Things:

2 Contents Building on IoT Big Data, Big Results How VGVs Play in IIoT Title of the book

3 Building on IoT Machines on the plant floor will soon actively identify problems, determine a course of action, execute, and measure those results - all without human interaction. It s not the Jetsons - it s the Industrial Internet of Things (IIoT), and it s already beginning to reshape manufacturing. Though many manufacturers are just starting to explore the implications of IIoT, the Internet of Things (IoT) has been generating buzz for years and already represents a booming market opportunity. The Internet of Things refers to a network of physical objects equipped with sensors that collect data and communicate t hat data t o other machines (or ba ck to humans) insta ntaneously. Built on cloud computing, t his ma chine-t o-machine intera ction is set to make everything in our lives smart, from thermostats that set themselves to refrigerators that re-order our milk. One McKinsey report estimates that IoT will have a potential economic impact of up to $6.2 trillion by Wit h numbers like t hose, ma ny manufact urers are embracing the transition to IIoT as a nat ural next step in the innovation lifecycle. A recent study by PwC found that 34% of manufacturers believe it is extremely critica l that US ma nufact urers a dopt an Internet of Things strategy in their operations. As those manufacturers ahead of the Industry 4.0 curve are already seeing, IIoT has important applications on plant and distribution center floors. Connected to either the cloud or in-house servers a nd equipped with unique IP a ddresses, ma nufact uring equipment in the modern factory is collecting data about when a machine is in use and how it is working, among many ot her potential applications. The result? Manufacturers now have more raw data than ever before, and sometimes more than they know what to do with. 3

4 Big Data, Big Results Increasingly, manufact urers are using big data and advanced analytics to scrutinize the massive amounts of data being collected by their smart machines. These evaluations are yielding valuable insight s into pla nt workflows, including predicting maintena nce needs before equipment breaks and tracking pa rts as they move through t he supply chain. By remotely monit oring product performa nce, compa nies are able t o prevent unplanned maintena nce and part s loss, dra matically reducing dow ntime and enhancing productivity. They re then able to pa ss these insight s onto t heir customers, improving customer service and even some st udies suggest creating new revenue strea ms. In surveying more tha n 1,400 C-suite decision ma kers, Accenture found t hat t he va st majority (84 percent) believe their organizations have the ca pability t o create new, service-based income strea ms from t he IIoT. Similarly, Pw C predict s that ma nufact urers will continue to unlock new product-as-service models based on their ability to gather big data and provide their customers with actionable recommendations for improvement based on their findings. 4

5 How VGVs Play in IIoT Ahead of many ot her machines on the factory floor, VGV sensors accurately and consistently ca pt ure performa nce data. E mploying vision technology, VGVs gather thousands of important data points per second, including dista nce tra veled, t otal errors, obstruction hours, follow hours, motion hours, and powered hours. Argua bly as important as collecting these data points is conveying them to t he end-user (in this case, pla nt managers) in a way t hat is bot h comprehensive and intuitive, whether through fleet data reports or easy-touse dashboards. Importantly, this allows the pla nt manager to identify inefficiencies and potential problems before they affect throughput productivity. Increasingly, companies like Seegrid that are invested in IIoT will continue to release dynamic metrics to improve enterprise-wide global operations. In the future, VGVs will use big data to not only identify problems, but to execute a solution, and mea sure the results--a ll without human interaction. Widespread adoption of IIoT will take time, but innovative ma nufacturers a re already investing in data analytics, and they re seeing improvements in improved productivity, dow ntime, and even new revenue streams. Manufacturing is at a crossroads, and the companies that understand and embrace the power of big data will be the ones to thrive in the future of manufacturing. 5