Tag Archives: Automation

Updates from Siemens

Designing large scale automation and robotic systems using Solid Edge
By David Chadwick – Precision Robotics and Automation Ltd (PARI) is a leading developer of automation and robotic systems globally. Their customers in the automotive sector include established giants like Ford, Chrysler, PSA, Daimler-Benz, Tata Motors, Mahindra, and new significant players like VinFast. PARI designs, manufactures and installs complete, automated systems including multi-station lines for machining and assembly of powertrain components and assemblies.

PARI has been a major user of Solid Edge for 15 years with 160 licenses deployed at their headquarters near Pune in India. Typical automation solutions deployed by PARI incorporate a wide variety of robots, actuators and sensors and other mechatronic items. These systems can comprise over 25,000 unique components.

Mangesh Kale, Managing Director of PARI describes their design process. “If a six-axis robot is required for a specific application then we use robots from major suppliers like FANUC, ABB and Kuka, or other makes specified by the customer. We typically receive 3D models from these manufacturers and we integrate these into our automation system designs. However, many applications demand gantry type robots that we design and manufacture ourselves. In a typical solution, about 60% of the design is using standardized commodities of PARI. However, custom parts are typically 40% of the design. For example, the gripper sub-assembly for any material handling solution is typically a custom design. This design meets specific application needs to handle components at different stages in the machining or assembly process. The customization required for assembly processes is even higher. We find that Solid Edge is a very powerful and flexible solution for designing these sub-systems.” more>

Related>

When machines think for us: consequences for work and place

The one sure way not to forecast the impact of artificial-intelligence technologies is technological determinism.
By Judith Clifton, Amy Glasmeier and Mia Gray – Will artificial intelligence affect how and where we work? To what extent is AI already fundamentally reshaping our relationship to work? Over the last decade, there has been a boom in academic papers, consultancy reports and news articles about these possible effects of AI—creating both utopian and dystopian visions of the future workplace. Despite this proliferation, AI remains an enigma, a newly emerging technology, and its rate of adoption and implications for the structure of work are still only beginning to be understood.

Many studies have tried to answer the question whether AI and automation will create mass unemployment. Depending on the methodologies, approach and countries covered, the answers are wildly different. The Oxford University scholars Frey and Osborne predict that up to 47 per cent of US jobs will be at ‘high risk’ of computerisation by the early 2030s, while a study for the Organisation for Economic Co-operation and Development by Arntz et al asserts that this is too pessimistic, finding only 9 per cent of jobs across the OECD to be

In a new paper, we argue that the impact of AI on work is not deterministic: it will depend on a range of issues, including place, educational levels, gender and, perhaps most importantly, government policy and firm strategy.

First, we challenge the commonly held assumption that the effects of AI on work will be homogeneous across a country. Indeed, a growing number of studies argue that the consequences for employment will be highly uneven. Place matters because of the importance of regional sectoral patterns: industrial processes and services are concentrated and delivered in particular areas. At present AI appears to coinhabit locations of pre-existing regional industry agglomerations.

Moreover, despite globalisation, national and local industrial cultures and working practices often vary by place. Different cultural work practices mean that, once deployed, the same technology may operate distinctly in diverse environments. more>

Updates from Ciena

Why Adaptive is the biggest story in networking
The long-desired goal of network automation is coming closer to reality. Joe Cumello explains why autonomous networking alone is not enough, and introduces Ciena’s Adaptive Network™, which combines the right mixture of automation, intelligence, and scale that allows network operators to adapt in today’s constantly-shifting ecosystem.
By Joe Cumello – Next-gen, intelligent, flexible, automated, agile, optimized, programmable, elastic.

Our industry has been using these words for years to describe the end game for networks. With Ciena’s recent 25-year anniversary, we’ve been spending quite a bit of time looking back at the early days – and it seems like the entire industry has been using these aspirational network descriptions for as long as there have been networks.

Maybe 2018 is the year “aspirational” starts to become “actuality.”

Like no other time in our industry’s history, a collection of technologies and advancements is bringing the long-desired goal of a more automated network closer to reality.

And none too soon. Make your way out of the marketing slideware and into the cold reality of real network operations, and most service providers will tell you that much of their process is still too manual, with multiple network-management systems that require spreadsheets and offline planning tools to make even the simplest changes to the network.

Network operators do need greater automation to cope with the harsh realities of today’s environment. But “full automation,” or so-called “autonomous networking,” isn’t the complete answer they are seeking, because it’s now clear that today’s environment isn’t the same one they will face tomorrow. In this constantly-shifting ecosystem, automation alone will always have to be revised and reset.

It is with this challenge in view that Ciena brings the Adaptive Network to our customers. more>

Related>

Updates from McKinsey

Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages
By James Manyika, Susan Lund, Michael Chui, Jacques Bughin, Jonathan Woetzel, Parul Batra, Ryan Ko, and Saurabh Sanghvi – The technology-driven world in which we live is a world filled with promise but also challenges. Cars that drive themselves, machines that read X-rays, and algorithms that respond to customer-service inquiries are all manifestations of powerful new forms of automation. Yet even as these technologies increase productivity and improve our lives, their use will substitute for some work activities humans currently perform—a development that has sparked much public concern.

Building on our January 2017 report on automation, McKinsey Global Institute’s latest report, Jobs lost, jobs gained: Workforce transitions in a time of automation (PDF–5MB), assesses the number and types of jobs that might be created under different scenarios through 2030 and compares that to the jobs that could be lost to automation.

The results reveal a rich mosaic of potential shifts in occupations in the years ahead, with important implications for workforce skills and wages. Our key finding is that while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging—matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.

  1. What impact will automation have on work?
  2. What are possible scenarios for employment growth?
  3. Will there be enough work in the future?
  4. What will automation mean for skills and wages?
  5. How do we manage the upcoming workforce transitions?

We previously found that about half the activities people are paid to do globally could theoretically be automated using currently demonstrated technologies. Very few occupations—less than 5 percent—consist of activities that can be fully automated. more>

Your Job Will Be Automated—Here’s How to Figure Out When A.I. Could Take Over

By Gwen Moran – Automation is increasingly making its way into the workplace, raising concerns among employees about the ways technology will change their jobs—or eliminate them entirely. A June 2019 report by Oxford Economics predicts that 8.5% of the world’s manufacturing positions alone—some 20 million jobs—will be displaced by robots by 2030.

Some tasks aren’t easy to evaluate. A 2013 paper, “The Future of Employment: How Susceptible are Jobs to Computerisation?” found that roughly 47% of jobs were at high risk of being automated with advances in artificial intelligence.

Carl Benedikt Frey, Ph.D., co-author of that paper and author of The Technology Trap: Capital, Labor, and Power in the Age of Automation says predictions around automation’s impact have become very polarized: Either you believe that the robots are coming for many jobs—leaving many with no employment—or you believe it’s going to change the nature of work. more>

Self-Driving Vehicles: What Will Happen to Truck Drivers?

By Andrew Yang – You would have to have been asleep these past years not to have noticed that manufacturing jobs have been disappearing in large numbers. In 2000 there were still 17.5 million manufacturing workers in the U.S. Then, the numbers fell off a cliff, plummeting to less than 12 million before rebounding slightly starting in 2011.

More than 5 million manufacturing workers lost their jobs after 2000. More than eighty percent of the jobs lost – or 4 million jobs – were due to automation. Men make up 73% of manufacturing workers, so this hit working class men particularly hard. About one in six working-age men in America is now out of the workforce, one of the highest rates among developed countries.

What happened to these 5 million workers? A rosy economist might imagine that they found new manufacturing jobs, or were retrained and reskilled for different jobs, or maybe they moved to another state for greener pastures.

In reality, many of them left the workforce. One Department of Labor survey in 2012 found that 41 percent of displaced manufacturing workers between 2009 and 2011 were either still unemployed or dropped out of the labor market between within three years of losing their jobs.

This is a good indicator of what will occur when truck drivers lose their jobs. Truck drivers’ average age is 49, 94% are male, and they are typically high school graduates. Driving a truck is the most popular job in 29 states – there are 3.5 million truck drivers nationwide. more>

Robots at the gate: Humans and technology at work


Barclays – Humans have often had a cautious relationship with new technology, particularly when it causes widespread disruption in the workforce. Yet historically, technological advances have not resulted in fewer jobs available to humans, but rather have led to the creation of new opportunities. Farriers and saddlemakers were hit hard when cars replaced horse carriages, but the petrol stations, mechanics, motels and related industries that sprung up created new, yet different, types of jobs.

More recently, the smartphone is a great example of technological advances creating new forms of work. Twenty years ago, mobile app developer was not a job; today, millions of such developers are at work around the world.

One of the most influential economists of all time, David Ricardo, flip-flopped on the issue. In 1821, he stated that while was a general good, he was now more worried about the substitution effect on labor. And the discussion was not always academic – the Luddite movement was an early example of workers resorting to violence to protest the use of technology in textile factories.

As the decades passed, the Industrial Revolution led to a visible, massive improvement in living standards. But the debate – on how technology affects work and whether it is an unequivocal positive – continued to wax and wane.

Machine learning represents a fundamental change. It is a subset of the much-abused term ‘Artificial Intelligence’ and is grounded in statistics and mathematical optimization. The computer is fed vast data sets and a few general parameters to point it in the right direction. Then, the machine executes simulations of how biological neurons behave, uses that to recognize recurring sequences in the data, and writes its own rules.

Suddenly, it is no longer limited to applying algorithms that
a human wrote; the machine is designing its own. more (pdf)>

Updates from Chicago Booth

15 middle-class jobs that can’t be automated—a CBR thought experiment
By Howard R. Gold – A much-publicized 2013 study by Oxford University researchers Carl Benedikt Frey and Michael A. Osborne estimates that “about 47 percent of total US employment is at risk” from advances in computerization, particularly machine learning, robotics, and artificial intelligence. Using US Bureau of Labor Statistics data, Frey and Osborne rated 702 occupations on a scale of 0 to 100 percent for risk of displacement by emerging computer technologies. Workers in heavily blue-collar industries such as production, construction, transportation, maintenance and repair, and farming and fisheries face the highest risk, along with white-collar employees in service and sales.

The job categories at lowest risk, according to Frey and Osborne: management; computer, engineering, and science; education, legal, arts, and media; and, of course, health care. The latter accounted for half of the 20 occupations to which Frey and Osborne give the lowest probability of replacement by computerization.

Core skills such as “originality,” “social perceptiveness,” “assisting and caring for others,” “persuasion,” and ”negotiation” are the most difficult for computers to replicate, Frey and Osborne determine. (For more, see “If robots take our jobs, will they make it up to us?” July 2017.) more>

Related>

The Wealth of Humans: Work, Power, And Status In The Twenty-first Century

BOOK REVIEW

The Wealth of Humans: Work, Power, and Status in the Twenty-first Century, Author: Ryan Avent.

By Ryan Avent – The digital revolution actually is probably going to be as transformative as the industrial revolution and the big technologies like electricity and steam that we saw then were. I think this transformation has already begun, and ironically, the evidence of that is in the struggles that we’re seeing across lots of countries that workers are facing in terms of limited growth in wages, in terms of rising inequality.

What my book tries to point out though is that in fact the biggest effect is not going to be mass unemployment. The biggest effect of the digital revolution is not going to be massive numbers of workers who just can’t find any work; it’ll be that the work they find ends up being very low-paying, because the displacement effect of these new technologies is so great, and the economy is asked to absorb so many new workers, that that’s just going to put an incredible amount of downward pressure on wages. That’s the real short-run challenge, I think.

.. The difficulty I think, again, comes in deciding who is entitled to a share of that ownership. If you’re socialising the gains, is that limited to citizens of the country, and then are any immigrant workers second-class citizens? If you don’t limit it, then suddenly you probably have social pressure to shut out immigrants, and then that leaves people on the outside of the country all the poorer. more> https://goo.gl/1iz2EU

Is Productivity Growth Becoming Irrelevant?

By Adair Turner – As we get richer, measured productivity may inevitably slow, and measured GDP per capita may tell us ever less about trends in human welfare.

Our standard mental model of productivity growth reflects the transition from agriculture to industry. We start with 100 farmers producing 100 units of food: technological progress enables 50 to produce the same amount, and the other 50 to move to factories that produce washing machines or cars or whatever. Overall productivity doubles, and can double again, as both agriculture and manufacturing become still more productive, with some workers then shifting to restaurants or health-care services. We assume an endlessly repeatable process.

Or suppose that 25 of the surplus farmers become criminals, and the other 25 police. Then the benefit to human welfare is nil, even though measured productivity rises if public services are valued, as per standard convention, at input cost.

The growth of “zero-sum” activities may, however, be even more important. Look around the economy, and it’s striking how much high-talent manpower is devoted to activities that cannot possibly increase human welfare, but entail competition for the available economic pie. Such activities have become ubiquitous: legal services, policing, and prisons; cybercrime and the army of experts defending organizations against it; financial regulators trying to stop mis-selling and the growing ranks of compliance officers employed in response; the huge resources devoted to US election campaigns; real-estate services that facilitate the exchange of already-existing assets; and much financial trading.

Much design, branding, and advertising activity is also essentially zero-sum. more> https://goo.gl/qpxGRb