Smart Manufacturing Experience 2018: IIoT, VR/AR, 3D Printing, and Whatnot

Posted by Vivek Murugesan on Fri, Jun 08, 2018 @ 10:29 AM

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Smart Manufacturing Experience, 2018The Smart Manufacturing Experience launched its first edition of manufacturing event on April 30, 2018 at the Boston Convention Center. The 3-day event, which focused mostly on Smart Manufacturing, the Industrial Internet of Things (IIoT), additive manufacturing, and augmented reality/virtual reality (AR/VR), witnessed several hundred presenters and attendees from an assortment of companies including IIoT Platform start-ups, industrial analytics providers, 3D printing companies, and big technology players like Microsoft, PTC, among others.

The IIoT

Sensors. Connected products. Cloud connectivity. Real-time data. Last year, they were just buzzwords. Today, they are actual capabilities. The message was loud and clear - the IIoT has moved past hype, it’s ready for industries today. Which was not surprising, given that LNS’ research on Analytics That Matter indicates that 15% of the companies have implemented an IIoT platform and 19% more have budget allocated for one.

One of the reasons for this boom is how economical it has become to implement homegrown IIoT solutions, says Professor Thomas Kurfess from Georgia Institute of Technology. During a panel discussion on the transformation of manufacturing, he described how low-cost processors like Raspberry Pi and Arduino boards, a couple of inexpensive sensors, accelerometers, and an AWS instance can be used to measure an equipment’s uptime (which can be used to measure OEE) and send it all the way to your ear through Amazon’s Alexa.

None of this would have been economically justifiable a few years ago. While this accelerated innovation has been a catalyst for a lot of home-grown pilot IIoT programs, there are many questions to be answered and a lot more ground to be covered when it comes to long-term scalability and sustainability of IIoT platforms.


Most of the industrial analytics solutions displayed in the event had out-of-the-box capabilities to provide real-time information from structured and semi-structured data sources through small Edge devices and had an ethernet connection to the cloud. The analytic capabilities included metrics that can be sliced and diced, visualizations, and interactive dashboards that could be drilled right down to individual part numbers.

Most of the vendors with an enterprise solution were running robust diagnostic and predictive analytics on them, though there is still a considerable room for improvement on their prescriptive analytics capabilities. Looking ahead, both predictive and prescriptive analytics are primed to go hand-in-hand with manufacturing operations. LNS’ Analytics That Matter research also corroborates this - 55% of companies running predictive and 36% of those running prescriptive analytics have implemented digital tools for continuous improvement (CI).

Drilling deeper, we find that the predictive analytic models used today mostly leverage existing statistical and 1st principle models and not artificial intelligence (AI)/ machine learning (ML) models. It will be intriguing to see how most of the analytics providers gradually transition to these advanced AI/ML algorithms to digitize CI.

Strati, 3D printed carAdditive Manufacturing

There was much activity around additive manufacturing (3D printing) as well. Strati, the first 3D printed electric car, displayed at the event gathered quite some attention (a marketing gimmick well done). The current additive manufacturing market needs to focus more than just trying to reduce cycle times and implementation costs. There must be innovation right from design stages (design for 3D printing), in materials (metal 3D printing), etc. With most of these innovations still in its initial or pilot stages, there is undoubtedly a lot of expectations on additive manufacturing to deliver at a mass industrial scale, and not only focus on spare parts.


One of the more crowded sections in the event was the mixed reality section, which featured AR/VR solutions leveraging the IIoT.

PTC showcased a product that enables the user to control and monitor a Digital Twin of an asset in real-time either by wearing a VR headset or using an AR-enabled tablet device. Right next to it, Mechdyne, a VR technology provider had a product that the user can use to navigate and inspect machinery in a virtual environment. Users wear a VR headset and use a joystick to navigate through the virtual space and can also pick up individual (virtual) parts that can be inspected in 360 degrees.

Digital TwinAR/VR technology has seen its share of evolution by becoming less expensive, more accessible, and reducing latency. However, the interesting question lies in adoption - the race to reach mass adoption between the industrial and consumer market. Given the fact that until now, the consumer market is leading in AR/VR adoption, it will be a big surprise if the industrial market reaches mass adoption before the consumers. Bottom line - there still is a long road ahead before AR/VR captures industrial use cases like maintenance, assembly, training, and more.

Apart from these areas, there was some activity around automation, robotics, and other startups trying to cash in on the Cloud buzz. Up LLC, a cloud-based software company claims to be like Uber for machine tool service, provides an online marketplace where manufacturers can request bids for machine/tool service based on their location. Nexonar, another start-up has wearable products that perform 3D motion capturing which can be used during assembly and picking operations.


The availability of smart and economical manufacturing technologies has opened the gates for vendors and manufacturers. However, with all the excitement around the IIoT, it is easy for companies to get sidetracked and fall for marketing hype while trying to hop on the Digital Transformation wagon. To get most out of technology, companies need to see the bigger picture and find proper business cases that align with their strategic objectives, and then seek a technology that seems fit, and not the other way around.

Tags: Cloud, Industrial Internet of Things (IIoT), Digital Transformation, Industry 4.0 / Smart Manufacturing, Artificial Intelligence / Machine Learning (AI/ML)