By Frank Antonysamy
Frank Antonysamy is Vice President of Cognizant’s Global IoT and Engineering Services
A digital transformation revolution in manufacturing is underway, and data is the primary currency paving the way for more efficient ways of doing business. Gone are the days when data analysis was left to Monday night quarterbacking by poring over static results. Today, thanks to a central wireless ecosystem which links relevant mobile devices, Internet of Things (IoT) connected machines and connected employees, data gathering and analysis in a smart factory is immediate and real-time optimization drives significant efficiencies.
But not all smart factories are created equal.
Given that enterprises are all on different points on the path to complete digital maturity, it helps to take stock of the state of IoT intelligence — where we are now and where we are headed — and what industrial organizations need to be successful.
Laying the foundation for intelligence
One of the key advantages of Industry 4.0 is the promise of the Internet of Things (IoT) or Industrial Internet of Things (IIoT). Sensors connected to raw materials, factory floor equipment and final products can relay information, over a Wi-Fi connection, about their health and usage patterns to wider enterprise asset management software and enterprise resource planning systems.
Equally important, stakeholders can access this data in real-time and on-demand. Companies can leverage this data to deliver insights across three channels:
- Product intelligence
- Service intelligence
- Operational intelligence
There is significant overlap across these three pillars but their power to deliver a smart factory with new avenues for monetization, is revolutionary.
Here’s an overview of the IoT landscape with respect to its three core pillars of intelligence.
Product intelligence shakes up traditional PLM
The smart factory runs on smart products whose intelligence can be leveraged to read the tea leaves of market demand. At its core, product intelligence is defined as intelligence derived from an intelligent (read…IoT-enabled) product. In the IoT world, traditional rules of product lifecycle management (PLM) no longer apply. Gone are the rinse-and-repeat cycles of concept, design, manufacture, marketing and sales. In traditional manufacturing, the ideation-sale stage took years if not decades and slight changes in market demand had a whiplash effect on the process.
IoT has rebooted the PLM conversation to move it away from the product and make it more about the customer. IoT-enabled products can now deliver intelligence post sales about how the product is being used (or not), how it is being disposed of, and a whole host of other downstream information. Such product intelligence is useful in two primary ways: as a method of refining the product to make it more agile and responsive to consumer needs (thereby leading to potentially more sales) and as new avenues of monetizing such product intelligence.
The future of product intelligence is a complete “closed-loop” product development, with real-time customer feedback woven into the process. It bears stressing that while customer focus groups and behaviors have always been part of the design and manufacture process, IoT has effectively compressed that time cycle and expanded the scale of parameters that might be considered — and monetized.
Service intelligence delivers customer-focused monetization
Monetization in the new smart factory landscape need not be restricted to product intelligence alone. Service intelligence, for example, is about delivering aftermarket intelligence in the form of added services to an existing or expanding customer base. A customer who buys Widget A from a manufacturing company might also be interested in understanding how to optimize the use of that widget for their own tailored environments.
While aftermarket services are not entirely new, the addition of IoT has the capability of delivering service intelligence on steroids. In the future, service intelligence providers will use IoT to tailor measurements of key performance indicators (KPIs) and delivery of data insights depending on exactly what the end customer is looking for. Tailoring service intelligence to the customer potentially leads to greater client stickiness. What’s more, IoT is capable of slicing and dicing intelligence for each and every customer, making the net results that much more insightful and leading to more bountiful monetization opportunities.
Operations intelligence squeezes the most out of machines
Monetization also comes from picking the low-hanging fruit in production processes. Arguably one of the best ways to squeeze the most out of IoT is to use it to increase manufacturing uptime. IoT is also favorably impacting the ability to fine-tune production processes by being able to connect, visualize and analyze data from a whole host of new players such as machines on the plant floor. RFID and computer vision layers also add to such intelligence.
IoT-embedded devices on the plant floor can spit out data that measures machine health, which can be fed into machine learning algorithms for predictive maintenance. If a rotor heats up past a preset temperature setting, for example, it can trigger the algorithm to send an alert to a plant worker or even proactively shut the machine down. Machine learning capabilities derived from IoT enhance KPIs such as manufacturing uptime.
In the future, expect a move toward increasingly segmented manufacturing, possibly sliced and diced into ever smaller batches. Operations intelligence will allow manufacturers to segment the production process — and fine-tune each — to fulfill a variety of specialty orders at the same time.
What it takes to deliver on the promise of IoT
While IoT intelligence in its various forms promises a truly smart factory with a wealth of monetization opportunities, it needs a robust infrastructure to truly deliver. Elements of this winning infrastructure include, among others: a C-suite willing to address negative attitudes of incumbency; standardization of data aggregation and analytics processes such as machine learning; and future-proofing technologies through increasing reliance on open-source models.
Since data is the lifeblood of IoT, enterprises need to ensure that they don’t get mired in the data lake — that the data they’re working with is clean and structured, relevant to the KPIs they want measured, and fed to algorithms in a consistent format. Once data is clean and uniform, smart factories can leverage IoT to feed machine learning algorithms that learn from the data and eventually deliver an almost lights-out production stream.
Since the future of intelligence also involves its monetization — vendors up and down the digital supply network will pay for insights — it will be important to connect stakeholders to the central nervous system of the smart factory in new ways. Customer service agents (or even customers themselves) for example should be able to see where product orders are in the production process and fine-tune their forecasts accordingly. IoT delivers transparency to all stakeholders — within reason, keeping intellectual property concerns in mind.
IoT in manufacturing is not limited to the production floor either. IoT sensors in warehouses can detect when supplies are going bad, when inventory is low and beef up accordingly. Remote weather events that can affect vendor delivery can trigger automated backups. The IoT-driven smart factory touches many processes and products much beyond the plant floor.
Until true digitization from start to finish is a total reality, companies are figuring out stop-gap measures that will leverage the promise of IoT. A “nerve center,” which serves as a central repository for data gathering and analytics can serve to overcome the problem of data connectivity across locations and devices.
The ripple effect from IoT intelligence is not limited to the manufacturing floor alone. By placing the digital core at the center, it reshapes processes up and down key constituencies such as supply chain and asset management.
How tomorrow’s tech might impact IoT intelligence
IoT is already being incorporated in the smart factory of today. Tomorrow, expect acceleration with respect to monetizing closed-loop product intelligence, an increased focus on the customer through service intelligence and using operations intelligence by improving businesses processes on the way to a truly smart factory.
The road is expected to get even smoother with the advent of 5G technology which will decrease latency of IoT for edge computing devices. 5G will deliver even faster access to data in real time which will make real-time analysis even more accurate. The technology has special ramifications for production processes where time is of the essence. Devastating machine shutdowns can be averted in split seconds by machine learning algorithms fed through 5G connections from IoT-enabled equipment. This means smart factories of faster computing speeds and greater agility. The state of the union for IoT intelligence is strong and only expected to grow stronger as new technologies such as 5G make data competencies that much more robust.
Cognizant (Nasdaq: CTSH) is dedicated to helping the world’s leading companies build stronger businesses — helping them go from doing digital to being digital.
Improving working conditions with blockchain
Blockchain is more often spoken about as an external tool for businesses to help secure supply chain. In a new pilot, blockchain is to be used to help improve health and safety within the workplace – at a Levi Strauss factory.
The testing out of blockchain as an internal health and safety auditing tool is being run as a collaboration between Harvard University’s public health graduate school, U.S. think-tank New America and the U.S. denim jeans company Levi Strauss & Co. The three have declared a project to design, build and operate a blockhain-based system for health and safety at work.
The new technology will be designed to augment outside auditors of factory health and safety with a system that will allow factory workers to self-report issues of concern. The factories that will test out the technology are based in Mexico, where three manufacturing sites in total employ 5,000 workers.
Mexico’s regulations for health and safety laws are exclusively federal in content. Under this legislation employers must obey standards, maintain safety programs, maintain compliance systems, ensure proper equipment and hazardous substance control. However, the level of safety is often subject to criticism (as with the International Labor Organization), such as in terms of accident rates and occupational illnesses like respiratory diseases.
The new project is designed to provide an alternate avenue for worker health and safety to be addressed, outside of periodic audit, and the mechanism enables a U.S. based company to ensure that clothes manufactured for the U.S. market are produced under conditions that are safe for workers.
The aim of the scheme is to input an annual worker survey on the blockchain. Once inputted the company’s site-based managers will be unable to alter it, and the findings will be made available to the workforce. The findings will be available for Mexican authorities to review as well as U.S.-based Levi Strauss managers. The blockchain will be provided by ConsenSys, the blockchain company founded by Joseph Lubin, once of Ethereum.
Tesla wants its factory workers to wear futuristic augmented reality glasses on the assembly line
- Tesla patent filings reveal plans for augmented reality glasses to assist with manufacturing.
- Factory employees has previously used Google Glass in its factory as recently as 2016.
To cut down on the number of fit and finish issues — like the “significant inconsistencies” found by UBS— Tesla employees on the assembly line could soon use augmented reality glasses similar to Google Glass to help with car production, according to new patent filings.
Last week, Tesla filed two augmented reality patents that outline a futuristic vision for the relationship between humans and robots when it comes to manufacturing. The “smart glasses” would double as safety glasses, and would help workers identify places for joints, spot welds, and more, the filings say.
Here’s how it works:
And here’s the specific technical jargon outlining the invention (emphasis ours):
The AR device captures a live view of an object of interest, for example, a view of one or more automotive parts. The AR device determines the location of the device as well as the location and type of the object of interest. For example, the AR device identifies that the object of interest is a right hand front shock tower of a vehicle. The AR device then overlays data corresponding to features of the object of interest, such as mechanical joints, interfaces with other parts, thickness of e-coating, etc. on top of the view of the object of interest. Examples of the joint features include spot welds, self-pierced rivets, laser welds, structural adhesive, and sealers, among others. As the user moves around the object, the view of the object from the perspective of the AR device and the overlaid data of the detected features adjust accordingly.
As Electrek points out, Tesla has previously been employing Google Glass Enterprise as early as 2016, though it’s not clear how long it was in use.
Tesla has a tricky relationship with robotics in its factory. In April, CEO Elon Musk admitted its Fremont, California factory had relied too heavily on automated processes. Those comments, to CBS This Morning, came after criticism from a Bernstein analyst who said “We believe Tesla has been too ambitious with automation on the Model 3 line.”
Still, the company seems to be hoping for a more harmonious relationship between human and machine this time around.
“Applying computer vision and augmented reality tools to the manufacturing process can significantly increase the speed and efficiency related to manufacturing and in particular to the manufacturing of automobile parts and vehicles,” the patent application reads.
This article was originally published on Business Insider. Copyright 2018.
Dow Chemical envisions the future of manufacturing
Dow Chemical, one of the world’s biggest chemical producers, is taking a leadership role in the digital transformation of its industry.
Despite its foundation in the pure science of chemistry, the chemicals manufacturing industry doesn’t exactly conjure high-tech images when people think of what goes into making chemical products.
And yet, the chemicals industry is poised to be the poster child for the very high-tech Industry 4.0 revolution, which takes existing manufacturing processes, and infuses them with digital DNA, thanks to the IIoT.
Dow Chemical, one of the world’s biggest chemical producers, is already taking a leadership role in the digital transformation of its industry. “We have significant amounts of data from our instrumentation and process sensors to use with the new analytics and deep-learning technologies,” Billy Bardin, Dow’s Global Operations Technology Center director, told Chemical Engineering.
Dow, like many other chemical companies, has been using sensor tech for decades, but the IIoT represents an entirely new model for how data from these sensors becomes part of the company’s end-to-end process. Not only does the IIoT offer optimization of the production process, it can improve efficiency, while reducing both energy consumption, and operational cost.
Safety — a key consideration given the stakes — can also be improved. Many chemical producers, including Dow, are still manufacturing at facilities that date back 50 years or more. Modernizing these plants is a constant effort, but with the advent of the IIoT, gains in situational awareness accompany the gains in efficiency and productivity.
Recently, the company enlisted the help of Schneider Electric to digitize its Carrollton, KY processing plant, giving teams better data visibility for pumps, valves and motors. The roadmap also includes the addition of Schneider’s HART devices to enable operations and maintenance teams to remotely view equipment health or thresholds for valves in order to manage them better, according to Automation World. The improvements in preventative maintenance this data enables are key to better employee safety, as well as protecting the environment.
Better efficiency, cost savings, and greater safety? Strong arguments for better chemistry through digitization.
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