Big Data and Reman: Takeaways from WRC 2019
By Christopher Whitebell
Industry 4.0 is the name for a broad range of technologies and processes that are revolutionizing what manufacturers can do and how they can do it. When you strip away the jaw-dropping innovations and applications, what you’re left with is data. In other words, Industry 4.0 is about making sense of (increasingly) vast amounts of data in ways that benefit business growth. Read on for insights and best practices that trendsetting companies shared at the 2019 World Remanufacturing Conference so you can confidently make Industry 4.0 work for your business.
Start slow, then build up momentum
Jim Wetzel is strategic advisor for the CEO of the Clean Energy Smart Manufacturing Innovation Institute (CESMII) and a veteran of smart manufacturing. His advice to companies who are just starting out in Industry 4.0 is to remember that tons of data, in itself, won’t result in magical productivity gains. He tells them to, instead, hone in on what they want to learn and where they want to improve. His message is simple: Pilot, test, and, if it works, scale up.
Any use of data in manufacturing should begin with a careful evaluation of existing assets and processes. This should include active feedback from the people on the shop floor. According to Wetzel, “the people in your organization most likely know what the issues are—use their insights as guard rails for where data can add value.”
Kiel Ronning, manager of manufacturing controls and analytics at John Deere, echoes this view, advocating for “small wins” that deliver incremental insights. Ronning stresses that Industry 4.0 is not about having an abundance of data; it’s about putting data to use, interrogating it to identify opportunities for improvement.
Getting value out of data
The Internet of Things (IoT) and the Industrial Internet of Things (IIoT) are making product and manufacturing data much more cost effective to collect, but the actual business value is found when that data is analyzed.
The European-based engine remanufacturer F2J is exploring how visual data can be used with artificial intelligence (AI) to improve effectiveness of its core inspection process. Using a vision system, engineers at F2J developed a set of key metrics for sorting the incredible variety of parts that are returned to their facility for remanufacture. Prior to using AI, F2J did what most other remanufacturers did: Humans assessed each component by hand, which required highly skilled operators to ensure consistent results.
F2J is using data to optimize core assessment. That means taking into account a very wide range of reman components— a daunting undertaking for most. The firm’s engineering team is exploring how AI algorithms can be trained to not only recognize and distinguish components, but also how they can learn patterns to recognize damage. F2J’s system uses a camera to collect image data to train its algorithms to identify parts and features, correlating what it records with a library of images.
Even as AI becomes more of an industry norm, when it comes to analyzing data for useful business insights that may have more global impact within a company, humans are still needed to guide the analysis process. Caterpillar Inc. has invested in training analytic specialists who can translate data into language that business decision-makers can understand and make use of. This has fostered a data-centric culture throughout the whole organization. Kathleen Monson, global core manager at Caterpillar, is interested in geographic data and how it can be used to visualize the location of cores internationally. Using sensors within components, mobile devices, and other reporting methods, Monson hopes to give Caterpillar an immediate picture of its global supply chain of cores. Such a view would not only support logistics internationally, but also create strategic opportunities, like incentivizing dealers and suppliers when flows slow in a certain region.
New ways of applying data
Companies are putting data to use in ways that go beyond just boosting productivity and efficiency—they’re creating entirely new ways of manufacturing. But that hasn’t come easy.
Data and disruptive innovation
Entrepreneur and disruptive innovator Kevin Surace believes that technology’s next major disruption will use data to tie together AI, IoT, and digital communications in a new way. Whatever comes next, data will be its principal medium. But that won’t come without taking risks and, yes, failure.
Many firms have successfully deployed complex telemetric systems using IoT technologies. A telemetric platform records data from sensors that are remotely located on equipment. Information is then transmitted to a dedicated station for analysis.
WABCO, a technology firm working within the commercial transport sector, leverages telemetrics to deliver immediate condition-based maintenance (CBM) data. Each vehicle in a company’s fleet is fitted with a device—a black box—that generates real-time data that WABCO analysts can immediately access, even when it is on the road hundreds of miles from the company’s headquarters. Not only does this help sustain the health of long-haul, commercial vehicles, it also gives WABCO engineers insights that inform their development of a fully automated, electrified fleet.
The more that data can be integrated into the design process, the better the outcome will be. California-based Autodesk is a pioneer in the field of generative design. Their AutoCAD software seamlessly enhances traditional CAD (computer-aided design) processes using AI.
Autodesk is flipping design for manufacturing on its head. The traditional design process starts with a concept, then it is engineered, validated, and finally manufactured. Vik Vedantham, senior manager of Fusion 360 and manufacturing business lines at Autodesk, sees three types of loss in this process: data, experience, and technology. Ultimately, this means that 30% of an engineer’s time is spent on unproductive tasks. Autodesk aims to capture these losses by unifying the entire design process into a single workflow. The idea is to focus the engineer’s time on what humans are best at—creativity—and leaving AI to handle data. The result is what Vendantham calls an “integrated intelligent design process.”
Finding the digital thread
For Luke Kelly, SVP of Adidas and International at Carbon, Industry 4.0 is about creating a “digital thread” that is woven through the whole of a manufacturing process. Carbon is advancing the use of 3D printing within manufacturing. The result is a highly flexible digital factory—a single line can be set to produce completely different products within just one day. This innovation is set to disrupt centralized production plants, not to mention the injection-molding process. One way in which Carbon connects manufacturers to its digital fabrication process is through a subscription model. A client’s proprietary data is streamed directly into a 3D printer, allowing hyper-customization of a highly automated process.
The cases outlined here show just what’s possible through a strategic use of data. Perhaps that can seem intimidating if you are considering Industry 4.0 for the first time,yet it’s important to remember that, above all, any successful use of data begins with planning, collection, and careful consideration of what data-analysis approaches you will use.
Each year, the World Remanufacturing Conference puts the trends, challenges, and opportunities facing industry into a remanufacturing context. Through conversation and instruction, participants focus on how best to advance remanufacturing, especially as a uniquely sustainable and innovative manufacturing pathway.