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To digitally transform, think like Clive Davis

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By: Ben Pring

If you’re a music fan, you probably know the name Clive Davis. If you’re not though – and heaven help you – Clive Davis is one of the most successful music producers and record industry executives of all time. He’s worked with a who’s who of rock and pop musicians, from Janis Joplin to Rod Stewart to Whitney Houston, over the last 50 years. Now 85, he’s still in the game as the chief creative officer of Sony Music Entertainment. By any measure of success and longevity in what is, after all, an extremely precarious and fickle business, Davis has earned his place in the Rock and Roll Hall of Fame.

What, you may be wondering though, does the archetypal A&R man have to do with “digital transformation?” Well, let me explain …

The “digital” alarm bell has been going off (literally and figuratively) now for over 20 years. The transition to the cloud, the slow decline of ERP, the rise of Google and Apple and Amazon, the primacy of “consumer IT,” the move to Agile and containers, the awakening to the power of data, the importance of design thinking – none of these are new. And yet, in the second half of 2017, many, many organizations still struggle to master them, let alone leverage them, to thrive in markets changing all around them faster than ever.

Related: Designing Manufacturing’s Digital Future

The question is, why? In my humble opinion, it’s because the executives running these organizations don’t think like Clive Davis.

It’s Not About You

Clive Davis’s success can be attributed, in no small measure, to his ability to separate his own personal tastes from those of the market. As an octogenarian, Davis probably favors Frank Sinatra or Tony Bennett when he’s doing the dishes or mowing the lawn (as if). But when he’s working, he’s listening like an 18-year-old and can hear the magic in Lil Uzi Vert or Rex Orange County – music that to his contemporaries must sound like the aural equivalent of a dislocated shoulder. Or at least the decline and fall of Western civilization.

Davis recognizes that he is not the target audience, that the music is not aimed at him and has nothing to say to him. He knows he wouldn’t buy the music. But yet, he can still make judgments about its quality and its commercial appeal. And he can do this precisely because he knows the music isn’t being made for him.

[Download]: Designing Manufacturing’s Digital Future

This is the mistake that is hampering so many executives in so many businesses facing the onslaught of change being rendered by digital technology. They don’t personally like the new generation of technology and technology mediated solutions, and they don’t appreciate that the new technology/solutions aren’t aimed at them.

Twitter is ridiculous. Facebook is for egotistical blowhards. What even is Snap? Why do my kids spend so much time on it? Social media is destroying a generation. We can’t do this transaction online because of the threat of hackers. Pokémon Go? Give me a break. Virtual reality? What are these guys on? The cloud? But we’ve got a data center. Monetize our customer’s data? Why? Isn’t that illegal? How does this Slack thing even work? What’s wrong with e-mail?

How to Love What You Don’t Love

To the average 50-year-old, running an insurance company, a bank, an airline, a retailer, contemporary technology, contemporary business approaches and contemporary norms are the commercial equivalent of Lil Uzi Vert – terrible, ugly, ridiculous, not nearly as good as the things we listened to, aka, the technology solutions we built and used.

These executives fail to see they are not the target audience. That new solutions shouldn’t be built for their contemporaries but for their kids. They fail to separate their own personal tastes from the tastes of where the market is going.

Doing this – separating your own personal judgments from those of the market – is terribly hard (hence why so few executives can do it). It’s tough for people who have ascended slippery career ladders to admit they don’t know something. It’s tough for them to even contemplate that they are “aging out,” that they are no longer “hip to the hop,” in touch, on fleek. But mostly, it’s hard to admit – privately to yourself, let alone publically to your staff/boss/board – that you’re no longer that interested in something and that you don’t really like X or Y.

[Download]: Designing Manufacturing’s Digital Future

To truly grasp the promise of the Fourth Industrial Revolution, you’ve got to really love it, and everything about it. Or, if you can’t, you’ve got to surround yourself with people who do. In Clive Davis’s case, this means A&R people who trawl the clubs and SoundCloud and YouTube and Spotify and SXSW. In your case, it could be a youth mentor or a digital whisperer you trust in the industry.

So next time you’re in a meeting with your team trying to inch forward with your digital transformation initiative, remember to think like Clive Davis. It’s not about you – it’s about the next generation and the stupid things they’re interested in. Play your Sinatra or Costello or Counting Crows tunes all you like at home. But don’t pretend that, now that you have the turntable (aka the digital transformation budget), the kids are going to dig what you all say. They ain’t lit with that.

This article originally appeared on the Cognizant Center for the Future of Work site.

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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.

Cognizant

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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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Manufacturing

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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Manufacturing

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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