Monthly Archives: June 2017

This Axios headline is problematic: Summers: Automation is the middle class worst enemy.

The accompanying piece doesnt actually quote economist Larry Summers making that declaration. Rather it summarizes an interview in which Summers indeed points out the challenge automation poses for workers. Hes right. Of course thats been the case for the past 200 years and will likely be the case for the next 200. But in exchange for a degree of instability and disruption, technological progress has dramatically raised living standards for workers.

Automation is kind of like alcohol, which, as Homer Simpson putsit,is the cause of, and solution to, all of lifes problems. Its the job of policymakers to make sure workers are ready to climb to the next footholdor ledge as the waters of automation continue to rise. Its also their job to make sure policy is as supportive as possible of innovation. Indeed, we need more tech progress, not less. The U.S. economy currently suffers not from too much automation, but rather from too little investment in the sort of technology that would raise the countrys lackluster productivity, writesDerek Thompson in an excellent new piece at the Atlantic.

Technology will erase jobs but also create them. Unfortunately, as Kevin Kelly writes, we cant see those jobs from here because we cant yet see the machines and technologies that will make them.

The piece also includes this chart, which shows lower US labor force participation than other advanced economies:

But I doubt whether Summers blames automation vs. the lack of USpolicies that center-left economists see as supporting workers, such as paid leave, and high USincarceration rates. And here is economist David Autor on the net impact of automation on jobs this century:

A final observation is that while much contemporary economic pessimism attributes the labor market woes of the past decade to the adverse impacts of computerization, I remain skeptical of this inference. Clearly, computerization has shaped the structure of occupational change and the evolution of skill demands. But it is harder to see the channel through which computerization could have dramatically reduced labor demand after 1999. My suspicion is that the deceleration of the U.S. labor market after 2000, and further after 2007, is more closely associated with two other macroeconomic events. A first is the bursting of the dot-com bubble, followed by the collapse of the housing market and the ensuing financial crisis, both of which curtailed investment and innovative activity. A second is the employment dislocations in the U.S. labor market brought about by rapid globalization, particularly the sharp rise of import penetration from China following its accession to the World Trade Organization in 2001.

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Is Automation Really the Worst Enemy of the Middle Class? - Ricochet.com

BERKELEY Advances in artificial intelligence and robotics are powering a new wave of automation, with machines matching or outperforming humans in a fast-growing range of tasks, including some that require complex cognitive capabilities and advanced degrees. This process has outpaced the expectations of experts; not surprisingly, its possible adverse effects on both the quantity and quality of employment have raised serious concerns.

To listen to President Donald Trumps administration, one might think that trade remains the primary reason for the loss of manufacturing jobs in the United States. Trumps treasury secretary, Steven Mnuchin, has declared that the possible technological displacement of workers is not even on [the administrations] radar screen.

Among economists, however, the consensus is that about 80% of the loss in US manufacturing jobs over the last three decades was a result of labor-saving and productivity-enhancing technological change, with trade coming a distant second. The question, then, is whether we are headed toward a jobless future, in which technology leaves many unemployed, or a good-jobless future, in which a growing number of workers can no longer earn a middle-class income, regardless of their education and skills.

The answer may be some of both. The most recent major study on the topic found that, from 1990 to 2007, the penetration of industrial robots defined as autonomous, automatically controlled, reprogrammable, and multipurpose machines undermined both employment and wages.

Based on the studys simulations, robots probably cost about 400,000 US jobs each year, many of them middle-income manufacturing jobs, especially in industries like automobiles, plastics, and pharmaceuticals. Of course, as a recent Economic Policy Institute report points out, these are not large numbers, relative to the overall size of the US labor market. But local job losses have had an impact: many of the most affected communities were in the Midwestern and southern states that voted for Trump, largely because of his protectionist, anti-trade promises.

As automation substitutes for labor in a growing number of occupations, the impact on the quantity and quality of jobs will intensify. And, as a recent McKinsey Global Institute study shows, there is plenty more room for such substitution. The study, which encompassed 46 countries and 80% of the global labor force, found that relatively few occupations less than 5% could be fully automated. But some 60% of all occupations could have at least 30% of their constitutive tasks or activities automated, based on current demonstrated technologies.

The activities most susceptible to automation in the near term are routine cognitive tasks like data collection and data processing, as well as routine manual and physical activities in structured, predictable environments. Such activities now account for 51% of US wages, and are most prevalent in sectors that employ large numbers of workers, including hotel and food services, manufacturing, and retail trade.

The McKinsey report also found a negative correlation between tasks wages and required skill levels on the one hand, and the potential for their automation on the other. On balance, automation reduces demand for low- and middle-skill labor in lower-paying routine tasks, while increasing demand for high-skill, high-earning labor performing abstract tasks that require technical and problem-solving skills. Simply put, technological change is skill-biased.

Over the last 30 years or so, skill-biased technological change has fueled the polarization of both employment and wages, with median workers facing real wage stagnation and non-college-educated workers suffering a significant decline in their real earnings. Such polarization fuels rising inequality in the distribution of labor income, which in turn drives growth in overall income inequality a dynamic that many economists, from David Autor to Thomas Piketty, have emphasized.

As Michael Spence and I argue in a recent paper, skill-biased and labor-displacing intelligent machines and automation drive income inequality in several other ways, including winner-take-all effects that bring massive benefits to superstars and the luckiest few, as well as rents from imperfect competition and first-mover advantages in networked systems. Returns to digital capital tend to exceed the returns to physical capital and reflect power-law distributions, with an outsize share of returns again accruing to relatively few actors.

Technological change, Spence and I point out, has also had another inequality-enhancing consequence: it has turbo-charged globalization by enabling companies to source, monitor, and coordinate production processes at far-flung locations quickly and cheaply, in order to take advantage of lower labor costs. Given this, it is difficult to distinguish between the effects of technology and the effects of globalization on employment, wages, and income inequality in developed countries.

Our analysis concludes that the two forces reinforce each other, and have helped to fuel the rise in capitals share of national income a key variable in Pikettys theory of wealth inequality. The April 2017 IMF World Economic Outlook reaches a similar conclusion, attributing about 50% of the 30-year decline in labors share of national income in the developed economies to the impact of technology. Globalization, the IMF estimates, contributed about half that much to the decline.

Mounting anxiety about the potential effects of increasingly intelligent tools on employment, wages, and income inequality has led to calls for policies to slow the pace of automation, such as a tax on robots. Such policies, however, would undermine innovation and productivity growth, the primary force behind rising living standards.

Rather than cage the golden goose of technological progress, policymakers should focus on measures that help those who are displaced, such as education and training programs, and income support and social safety nets, including wage insurance, lifetime retraining loans, and portable health and pension benefits. More progressive tax and transfer policies will also be needed, in order to ensure that the income and wealth gains from automation are more equitably shared.

Three years ago, I argued that whether the benefits of smart machines are distributed broadly will depend not on their design, but on the design of the policies surrounding them. Since then, I have not been alone. Unfortunately, Trumps team hasnt gotten the message.

Continued here:

Labor Markets in the Age of Automation by Laura Tyson - Project ... - Project Syndicate

The Digital Twin concept is steadily gaining groundin the product development worlda means of creating a virtual representation of physical assets, including modeling behaviorfor validation and test purposes. This process, which promises to reduce reliance on costly prototypes while accelerating time to market, is now starting to take root in the plant floor environment as a way to garner efficiencies for production and, in some cases, set the stage for predictive maintenance.

Unlike the product development space, where the definition is more universal, the concept of a digital twin varies among automation providers, depending on where their offerings fit in the automation stack. Some companies with deep roots in 3D CAD modeling like Dassault Systmes and Siemens see the digital twin as a way to define and optimize factory floor layout and production processes in a virtual world prior to putting physical assets in place and flipping the switch on production.

Other companies like Emerson Automation and Beckhoff consider the digital twin as a tool for validating and optimizing control systems and automation processes in the virtual worlda tactic that lends itself to a variety of use cases, including operator training and virtual commissioning. Other companies, like GE Digital, have a broad and ambitious game plan for the digital twin, leveraging it for everything from asset performance management to predictive and prescriptive maintenance, the latter combining a digital model with Industrial Internet of Things (IIoT) connectivity, real-time operational and historical data, as well as machine learning and analytics.

In an entirely different interpretation, some players conflate the concept of a digital twin with virtualization, the now mainstream IT technology that abstracts operating systems, applications, networks and storage from the underlying hardware or software so the process is no longer dependent on a specific physical platform, allowing for greater flexibility and scale. For example, Wind River Titanium Control is an open standards-based, on-premise cloud infrastructure that creates a digital twin of a plants legacy equipment, allowing it to become a full participant in IIoT by creating a real-time data channel between the traditional physical controllers and automation systems and its virtual representation.

Despite the murkiness of the digital twin concept, one thing is clear: It doesnt constitute any one idea, any single set of simulations or analytics or even a specific product category. A digital twin is a whole set of analytics that look at different aspects of how an asset performs, says Matt Wells, general manager of automation software at GE Digital. What we found is not one analytics model has all the answers.

Twinning thesmart factory Currently, one of the more prominent use cases for the digital twin is validating plant floor layouts and simulating logistic processes as part of a digital manufacturing portfolio. At Dassault, the concept of a connected production digital twin is a virtual 3D replica of an actual physical structurerobots, conveyors, CNC machines and other plant floor assetsalong with a simulation of the actual production processes of a smart factory, according to Prashanth Mysore, portfolio technical director of digital manufacturing at Dassault.

Under pressure to improve quality and responsiveness, reduce costs and strive for continuous improvements, manufacturers have an opportunity with the production digital twin to react more responsively to various internal and external disruptive events driven by mass customization needs. Specifically, Mysore says, manufacturers embracing a digital twin strategy can increase productivity and manufacturing efficiency by reducing variability and synchronizing material. They can also improve quality and compliance by validating processes virtually to ensure they are right the first time in addition to running multiple what-if scenarios to analyze production options and ensure worker safety and productivity.

A 3D replica of the actual physical plant is not the end of the road for the digital twin, Mysore says. The digital twin is also used to connect production with materials management, quality processes, and labor and maintenance processes.

As part of its 3DExperience platform for global industrial operations, Dassault makes its version of the digital twin come to life through its Delmia digital manufacturing portfolio, which includes numerous simulation tools as well as manufacturing operations management (MOM) capabilities resulting from its Apriso acquisition and operations planning and optimization functionality from its buyout of Quintiq.

A digital twin or dynamic software representation of an entire plant or an offshore oil rig, for example, can serve a variety of use cases across an entire automation and process control project lifecycle, according to Ronnie Bains, business manager for dynamic simulation and process optimization at Emerson Automation Solutions. Emerson customers are leveraging digital twins to support the initial design of a facility, to build actual processes and control systems, and to understand whether what is being built can function at the proper throughput.

If youre doing something incorrectly and you dont have such simulation, you dont find out about the potential impact until much later in a projects process, Bains explains. With a digital twin, you can identify areas of concern and design flaws early on and fix them as opposed to when things are built out and its more expensive.

As part of its Multi-Purpose Dynamic Simulator systems, Emersons DeltaV Simulate capabilities allow companies to test control logic and operator graphics in a virtual commissioning scenario, minimizing potential errors and streamlining the startup process. The same technology can also be leveraged to assist in training operators in unique processes. The level to which the digital twin is applied varies from customer to customer, Bains says. For some, its just for training; for others, its the full lifecycle.

For its part, Beckhoff has assembled a set of tools for its TwinCAT automation suite that extends into the realm of digital twin, specifically for upfront virtual testing and commissioning. Via support for the vendor-neutral Functional Mock-up Interface (FMI), Beckhoff has created interfaces between its platform and popular model-based design and simulation tools like MathWorks Matlab and Simulink and Maplesofts MapleSim to allow for acquisition and visualization of real-time parameters while creating a closer connection between physical and digital models. The ability to import simulated code and run it directly on a physical system enables machine builders to test before setup, aiding in reliability and shortening time to market, according to Daymon Thompson, an automation specialist at Beckhoff.

At Rockwell Automation, the whole premise of the digital twin is to remove the need for the physical asset, whether its to test the actual hardware or control systems, notes Andy Stump, business manager for the companys design software portfolio. Rockwells Studio 5000 Logix Emulate software enables users to validate, test and optimize application code independent of physical hardware while also allowing connectivity to third-party simulation and operator training systems to help teams simulate processes and train operators in a virtual environment.

In this context, a digital twin can be employed to provide a safer, more contextualized training environment that focuses on situational experience. It helps with emergency situations, starting up and shutting downthings you dont encounter ever day, Stump explains.

A digital twin of a control system created in the Logix Emulate tool could also be tapped for throughput analysis, Stump adds, ensuring, for example, that a packaging machine could handle a new form factor without having to actually bring down the machine to test the new design. Any time you take a machine out of production, its expensive, he says. If you can estimate that a machine is going to be down 60 percent of the time running what-if scenarios in a digital twin, theres a lot of money to be saved.

Moving forward, Rockwell will leverage new technologies such as virtual reality (VR) and augmented reality (AR) to enhance its vision for a digital twin. At the Hannover Fair in April, the company demonstrated a next-generation, mixed-reality virtual design experience using its Studio 5000 development environment with the Microsoft HoloLens VR headset.

For Siemens, the concept of a digital twin straddles both product design and production. In a production capacity, the digital twin exists as a common database of everything in a physical plantinstrument data, logic diagrams, piping, among other sourcesalong with simulation capabilities that can support use cases like virtual commissioning and operator training. Comos, Siemens platform for mapping out a plant lifecycle on a single data platform, and Simit, simulation software used for system validation and operator training, now have tighter integration to support more efficient plant engineering and shorter commissioning phases, says Doug Ortiz, process automation simulation expert for Siemens. In addition, Comos Walkinside 3D Virtual Reality Viewer, now with connectivity to the Oculus Rift Virtual Reality 3D glasses, enables a more immersive experience, allowing plant personnel to engage in realistic training and virtual commissioning exercises, he says.

Customers want to get plants from the design stage to up and running in the shortest period of time and these tools are paramount for that, Ortiz says. The digital twin is great to use for any plant for the lifecycle of that unique plant.

Improvedmaintenanceopportunities While most companies in the automation space are settling in with the digital twin for roles in operator training, virtual commissioning and optimization, there is still not a lot of activity leveraging the concept for predictive and preventive maintenance opportunities. The exception might be GE Digital, which is clearly pushing this use case as its long-term vision.

GE Digital sees four stages of analytics that will be impacted by digital twin and IoT:

GE Digital showed off a digital twin representation of a steam turbine to showcase what is possible in the areas of predictive and prescriptive maintenance at its Minds + Machines conference last November.

A digital twin is a living model that drives a business outcome, and this model gets real-time operational and environmental data and constantly updates itself, said Colin J. Parris, vice president of software research at GE Globals research center, during the presentation. It can predict failuresreduce maintenance costs and unplanned outages, andoptimize and provide mitigation of events when we have these types of failures.

Though the digital twin is certainly making headway in production, its still in its early days. Digital twin is definitely hot right now, but it really depends on what the customer is trying to achieve and what they are trying to model, says Bryan Siafakas, marketing manager in Rockwell Automations controller and visualization business, adding that its just a matter of time. There is a huge upside in terms of productivity savings and shortened development cycles.

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Digital Twin Spawns Automation Efficiencies - Automation World