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Seeing robots and digital advancements through someone else’s eyes

When they run the highlight reel of my greatest dad moments, this weekend’s dinner conversation with my kids will definitely be left out.

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By Randal Kenworthy

When they run the highlight reel of my greatest dad moments, this weekend’s dinner conversation with my kids will definitely be left out – the look of terror in their eyes, the curious and confused look of my wife that said it all: “What are you thinking?!”  At that moment, I realized the future of robots, AI and the latest digital technology can be a scary concept – if poorly explained.

It started nicely enough, talking about how Mother Nature and natural selection are things of beauty. But I strayed a little off topic when I explained that in the not-so-distant future, parents could apply emerging technologies to design their babies – and that this was not necessarily a good thing.  When asked why, I described a future where all babies were basically all programmed preconception, and eventually we would all look like engineered humans – not unlike robots.  That’s when the tears started.

My 10-year-old daughter provided me with an escape hatch when she asked, “Are we all going to become robots?” At that point, I channeled my inner Malcolm Frank (a top Cognizant exec and co-author of Code Halos and What to Do When Machines Do Everything) to help address her fear.  I explained that robots were actually a good thing – that they weren’t going to actually replace us but rather supplement our day-to-day activities.  We talked about examples like autonomous cars.  She built on my point that not only will self-driving cars enable us to do higher-value activities but they’ll also make driving a lot safer.

[Read more: The State of the Union for IoT Intelligence]

Personalizing the Pursuit of Digitally-Enabled Productivity

This dinnertime exchange sums up what those of us at the intersection of business and technology deal with every day, whether we know it or not. Because not everyone is comfortable with advances in digital technologies, it’s essential to explain the value of technology in personal terms.  The work we do is often complicated and technical, but when you peek under the covers at the value organizations are achieving, even a 10-year-old would nod in approval.

By telling compelling stories about demonstrated business results, our industry can make the latest digital tools and techniques a lot less scary for the people who need to invest in and implement them. Consider:

  • Product intelligence: By integrating data and applying intelligent algorithms, we helped a multinational consumer goods company create a 360-degree, omnichannel product view.  Doing so helped increase customer conversations by 15%, significantly improve customer satisfaction and boost agility of global product launches by 40%.
  • Connected factories: We also worked with a global pharmaceutical company to build a predictive maintenance model for its distributed and connected manufacturing plants. This capability harmonized processes across multiple systems and provided visibility into potential process interruptions. By reducing downtime, the business realized a 20% increase in throughput while increasing safety, enabling patients to get their medications more quickly.
  • Intelligent process automation: We used machine learning models to help a global insurance provider expedite its worker’s compensation claims process. The solution determines bodily injury information with 90% accuracy, aided by human validation. It’s also integrated with existing robotic process automation (RPA) tools to navigate multiple mainframe and web applications and apply hundreds of business rules to enable timely and accurate registration of claims. The business has achieved greater claims accuracy and accelerated claims processing, enabling workers to get the money they need to achieve a speedy recovery and return to work, which improves productivity.

[Download]: Advancing Smart Manufacturing Operations Value with Industry 4.0

The ABCs of Clear Communication

We can all benefit from remembering some basic talking points when we engage in discussions about AI, machine learning and other digital technologies – whether it’s with our business peers and colleagues or our families. In short:

  • Keep it simple: Speak in plain terms.
  • Tell stories: Use examples and stories to explain a topic and gain alignment.
  • Stay practical: Business people often talk about technology in mythical proportions. Be pragmatic about what technology can do; avoid pie-in-the sky illustrations.
  • Don’t assume: This is a two-way street. Your own assumptions may need validation, and don’t assume your listener knows what DevOps means.
  • Repeat as needed: Technology can be complex, so repetition can help ensure that complex concepts are truly understood.
  • Break down an explanation: The human mind can better understand when information is provided in manageable, logical buckets. Minto’s Pyramid Principle is built on the concept of chunking information in manageable pieces.  The same applies here.  Take a message and break it into logical components.

With all that AI and other digital technologies have to offer, it’s essential for those with insights into its potential to diminish the fear, uncertainty and doubt that often accompanies the topic – rather than inadvertently emphasizing it. Believe me – that’s what I’ll remember the next time I bring up current events at the dinner table.

[Download]: Designing Manufacturing’s Digital Future

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Manufacturing

The State of the Union for IoT Intelligence

A digital transformation revolution in manufacturing is underway, and data is the primary currency paving the way for more efficient ways of doing business.

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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.

Case Study: Fast-tracking innovation with a PLM platform

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.

[Download]: Designing Manufacturing’s Digital Future

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.

[Download]: Creating a Digital PLM Platform to Promote Collaboration and Increase Efficiency

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.

[Download]: Advancing Smart Manufacturing Operations Value with Industry 4.0

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.

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Improving working conditions with blockchain

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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.

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Tesla wants its factory workers to wear futuristic augmented reality glasses on the assembly line

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  • 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.

Tesla‘s Model 3 might have “biblical simplicity” according to one Wall Street analyst, but building any car still involves hundreds of nuts, bolts, and welds.

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:

Tesla/USPTO

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.

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