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Lenovo has launched an Experience Store opens at Singapore’s Funan mall, offering an online-to-offline experience. The 4500sqft store has specific spots for product showcases, workshops and pop-up events.“The opening of our new flagship store, together with the launch of the four new devices, embodies our Intelligent Transformation strategy to bring a new experience to our customers in Singapore,” stated Eddie Ang, Lenovo Singapore’s country GM.“We hope this Lenovo Experience Store will be a platform, where we can continue to build meaningful relationships with our customers and provide them with new and engaging ways to experience our brand.” During the launch, Lenovo revealed four new gaming laptops. Smart Retail is applied by utilizing solutions to track footfall analytics and optimisation. The data captured can map areas frequented by customers and determine the general amount of time spent at each. This can certainly help in planning the positioning of store employees to provide assistance.In-store cameras and product webcams that take facial expressions can help in realizing customer choices and their reactions to the various products. Studying the data can likewise showcase demographic trends among users and assist stores to push focused content that is crucial.Self-service kiosks will be offered in store to drive automation and free up employees to provide personalised services, where needed. Products are also labeled with radio-frequency identification tags, supplying store employees with inventory information right at their fingertips through their hand-held tablets.INSIDERETAIL

Your salesforce is dealing with some growing — and shrinking — pains. By 2020, millennials will represent the most important generation in the workplace at 35 percent. While waiting, the portion of baby boomers, the generation that comprises the majority of manufacturing sellers, will slide to 9 percent.So here's the good news: There couldn’t be a more suitable time to attract millennial sellers. Eighty-five percent of current sales agents suggest they would recommend a career in sales to a young person. For the time being, the world wide salesforce is estimated to remain fairly stable through 2020, with a 46 percent compound annual growth rate — a positive sign for millennials, who are potentially to prioritize job security over other considerations.So what do manufacturing sales teams would like to always remember to attract, hire and retain potential millennial sellers?Tell your company story the right wayDespite the fact that compensation is the No. 1 factor that affects millennials’ assessment of a possible job, they also plan to work at a place that recognizes and fosters their passions. Manufacturing sales careers offer the opportunity to develop expertise in cutting-edge technology. Play up the exciting features behind your company’s robotics program or the new software running your automated sensors.In the same way, millennials are interested in a forward-thinking company culture which makes them appear like a person rather than a faceless asset. They prefer companies with a strong work-life balance, adaptive and remote work environments and transparent parental leave and vacation policies.Be clear about the personality and skill set you’re seekingAs with any role, your sales and human resources departments should form a thorough definition of the characteristics, skills and personality traits that are likely to make a millennial applicant a good fit. Talent and skills assessments during the interview process can certainly help ensure a cultural fit and define where their strengths can best serve your company — and how you can develop their skills once they are on board.Don't discount a millennial candidate just because he or she fails to have a certain background in selling. As the traditional definition of sales swings more toward a proof-based practice, graduates with a science, technology, engineering and mathematics (STEM) education are getting into sales. In 2017, sales were the most popular non-computer-related job for STEM graduates.Develop training programs that go beyond the technicalMillennials matured as a digital-first generation and usually possess intuitive knowledge of the technical aspects of manufacturing sales. Though with the increase in the number of stakeholders in the manufacturing sales process, it is critical to train and coach them on connecting with customers — mainly for STEM graduates who carry a reason-based approach. Young sellers have to diagnose and speak to the value of solutions based on valid business needs.Another fact STEM-grad millennial sellers will appreciate: There's evidence to back up the success of this training. Companies that blend sales education with technical and product training achieve greater success; organizations with reps that exceed client expectations in providing insights and perspectives have win rates 12.4 percent more than those who simply meet expectations.Invest in millennial sellers to cement a long-term relationshipWhen millennials see a pathway to advancement that sets them up for success at your manufacturing company, they're going to feel connected to your mission and future. Be specific about how they can improve their careers through benchmark goals and provide them with the appropriate resources. Millennials genuinely want to become great in their careers; 87 percent say professional development represents an important part of their jobs.Tech-savvy millennials have the actual possibility to contribute to your manufacturing sales team from day one. Ask about their career priorities, just like partaking in the launch of new technology, and give them opportunities to showcase their strength to help and lead others in these initiatives from the beginning. Millennials who believe their companies exhibit a high-trust culture are 22 times likely to want to work there for a long time.With a freshly, energized perspective, millennial sellers have the potential to make a difference in your manufacturing sales team. Furnish them with the job benefits, technology, data, and training to succeed, and they will reward you in spades.This article is originally posted on manufacturing.net

PARIS — Daimler’s chief executive Ola Källenius earlier this month revealed plans to scale back the automaker’s investment in robotaxis. Although the comment did not come as a big shock to the automotive industry, Daimler’s decision exposed a hard and important reality: the development track for assisted driving is different from the development track for autonomous driving, and car OEMs are going to have to pick a lane.Vehicle manufacturers are under tremendous pressure to develop advanced driver-assistant systems (ADAS) and to create autonomous vehicles (AVs). The assumption that the former would lead to the latter persisted even as evidence accumulated that though ADAS and AV have parallels, the two are not directly related. “ADAS and Autonomous have been, of necessity, on very different development paths, typically with different teams,” noted Ian Riches, executive director for the Global Automotive Practice at Strategy Analytics.And it is becoming apparent that most carmakers cannot afford to keep these parallel projects alive at the same time. The sorts of sensor suites, processing power and vehicle architectures required for autonomy are still way too expensive for mass-market ADAS features, Riches explained. “There is no easy way to scale-down an L4 architected-system to meet the cost points needed for standard-equipment, NCAP (New Car Assessment Program)-required features at present.”“The reverse is also true,” stressed Riches. “The typically stand-alone, discrete architectures, modest processing power and limited sensor suites required by ADAS do not easily scale up to an L4 solution.” Does ths mean that no auto companies should plan to upgrade their ADAS cars of today to be L3, L4 and L5 AVs in the future?“I’m not sure anyone has truly been thinking that ADAS can evolve into autonomous. There have typically been completely separate development teams,” Riches reiterated — so far.In the chart above, Daimler talks about a "step-by-step" gradual transition from ADAS to AV, even though there isn't such a singular path for product development. In the current ADAS/AV development environment, leading automotive chip suppliers, whether Intel/Mobileye or NXP Semiconductors, have been pitching a variety of chips designed to proliferate ADAS features. Additionally, many of these semiconductor companies are promoting separate silicon solutions with beefed up processing power to target AVs. For example, Xilinx, which recently announced automotive-qualified 16-nm family devices, offers Zynq 7 (for L2 and L2+) and Zynq 11 (a single-chip domain controller designed for L2+ and above). The FPGA company is positioning both programmable chips to address everything from ADAS to in-cabin monitoring and automated driving, all of which are evolving rapidly.Incremental advances?The question, then, becomes: Will ADAS development ever intersect with AV innovation? If it ever does, where and when?Phil Magney, for example, told EE Times that he doesn’t believe that ADAS and robotaxis are mutually exclusive. “Incremental automation is what Tesla is doing. You build up the fundamentals and collect as much data as you can.  Eventually you will have gained enough knowhow to apply it to robo-taxis,” he said. Magney is referring to Tesla’s third-generation “full self-driving car computer” equipped with two AI processors designed internally by Tesla.Riches is hopeful that “the cross-over point where a combined development team and approach may make sense is perhaps coming into sight.” In Riches’ opinion, “the growing interest in so-called ‘L2+’ features could be the point that these two worlds start come together.” He added, “This could emerge as an L4-style architected vehicle, with centralized control — but without the redundancy across sensors, processing and actuation to make it fully robust for L4 operation.”But this still remains just a theory.Riches said, “The optimist in me sees ‘L2+’as a way of bringing the maximum safety benefits to the widest audience as soon as possible.  The cynic in me sees it as a way of potentially finding some sort or ROI on ‘failed’ L4 development programs.”Two different design principlesMeanwhile, there are ongoing ADAS vs. AV debates in the industry. Safety experts and people with real-world experience running autonomous vehicles in cities claim that the two — designing a vehicle with a human driver in it and a car driven by no human driver — are worlds apart in their fundamental design principles.Carlos Holguin, Urban Mobility expert and a co-founder of AutoKAB, told EE Times, “As the famous saying goes, ‘the electric light did not come from the continuous improvement of candles.’ Better ADAS would not make a fully autonomous vehicle.” In contrast, all chip suppliers have a vested interest in portraying their solutions as universally applicable from Level 2 ADAS to Level 4 highly automated vehicles. This is especially true if chip suppliers design more generic, highly programmable solutions such as GPU, FPGA and graph streaming processors (GSP). Nvidia, Xilinx and even some startups, notably Blaize, fall into that category.As Willard Tu, senior director, Xilinx’ Automotive Business Unit, explained to us, when ADAS and AV developers keep adding sensors such as 4D radars and lidars, the race among automotive chip suppliers boils down to one issue: “How do you stay ahead of the innovation game?” Blaize co-founder and CEO Dinakar Munagala also explained, “Our clients keep coming back to tell us, ‘I wish your chip could do XYZ.” Munagala believes that’s where his GSP could shine. He said that the GSP will be applied to intelligent telematics, ADAS, driver monitoring and occupant assessment. A single GSP architecture for many automotive applications is getting Blaize’s foot in the door at OEMs and Tier Ones.As “intelligent” features keep rapidly evolving, the automotive industry wants a platform that extends from ADAS to AV.  Xilinx’ Tu noted that although the industry has not converged on a common approach for an ADAS-to-AV transition, automakers are looking for thermally efficient programmable solutions that can accommodate the continuous innovation demanded by Euro NCAP, fast changing AI algorithms and flexibility in data pipeline to reduce AI latency.Confusion among automakersThe L2+ pitch by technology suppliers rose in volume in early 2019. Rather than promoting L4 as an end game, both automakers and chip suppliers have become increasingly vocal on the need for L2+. But here’s a news flash. Riches said, “What I think is undoubtedly true is that the consumer has yet to be truly consulted and has very little conception of how ‘L2+’ could be of benefit.” He cautioned, “Automakers need to ensure that they are not implementing technology for technology’s sake but are indeed offering true user benefits at an affordable price.” In other words, regardless whether a vehicle is L2+ or L4, it is quite possible that car OEMs (including Daimler) actually have little idea what consumers want.Simply put, car OEMs can’t possibly dismiss L4/L5 right off the bat. They have been developing AVs out of the fear they could be replaced by tech companies such as Waymo or Uber. Worse, despite little evidence of a market need, OEMs have latched onto the idea of L2+ to justify their investment in L4/L5 development.Meanwhile, Magney observed, “I believe Daimler/Mercedes was never as committed to L4/L5 AVs as other OEMs. Daimler would produce ‘concept AVs’ for major shows that were never capable of self-driving other than some remote-control maneuvers, but they always lagged in developing self-driving solutions.” Magney added, “I feel as though the Germans are not willing to throw in the towel on human driving, something they take national pride in!”Bottom LineDamiler’s recent decision is best explained by the bottom line of today’s automotive business.Riches noted, “The global downturn in the automotive market is causing financial realities to hit home.  Electrification / C02 reduction *has* to be carried out: there are strict targets to be met.” In contrast, “There is still no guaranteed ROI for a robotaxi.  Uber has yet to make the business model work with a driver, and simply removing the driver doesn’t magically fix everything.  The capital investment required for a significant robotaxi fleet will also be huge.  It’s no surprise to me that some ambitions are being scaled back,” he explained.Magney concurred. “I don’t really know what to make of this other than appease the shareholders and the conservative board members.”Note, however, that Daimler and Bosch have picked San Jose, Calif., as the pilot city for trials of a highly and fully automated driving (SAE Level 4/5) on-demand ride-hailing service, using automated Mercedes-Benz S-Class vehicles. The service was scheduled to begin in the second half of 2019.Magney pointed out, “It is kind of odd as the Daimler CEO's words are a contradiction to their ‘self-driving’ pilot program in San Jose with their partner Bosch.” He added, “Daimler also entered a partnership with BMW to develop self-driving technology. Only five months ago Daimler and BMW signed an agreement for a long-term strategic cooperation, which will focus on joint development of technologies for driver assistance systems and automated driving…” However, Magney noted, “Honestly, I am not really surprised by this news. And I would not be surprised to see other OEMs doing something similar. The bean counters are not fond of spending billions on something where the economics are not clear.”Daimler and ADASMagney called Mercedes “a pioneer in ADAS” and predicted it will continue to enhance its ADAS incrementally. He noted, “Eight months ago Mercedes said they would offer low-speed Level 3 ‘Traffic Jam Assist’ in 2020.” And let’s not knock the ADAS approach. “It’s not necessarily a bad thing,” Magney said. “Sales of series production cars over the next twenty years are not going to look much different than they are now.  L2/L3 is the new ADAS in my opinion.”   Lastly, Magney pointed out. “Who says you need to own the AV stack?”There are numerous full stack providers from which to source, he observed. “At the end of the day Mercedes-Benz will focus on building better cars as they have done for 100 years. Fifty years from now when human driven cars are replaced by robo-taxis, Mercedes-Benz remains a premium coachbuilder and there is nothing wrong with that.”EEtimes

Using HPE Pointnext services to deploy machine learning at the edge, Foxconn is able to address quality assurance issues in its production of highly customizable IT servers.Customized manufacturing is a major trend across all manufacturing verticals—from food and beverage to automotive. But the industry vertical that has likely been impacted most by this trend is electronics. Think about your electronics devices—from your computer to your tablet to your smartphone—and the various configurability options offered by the supplier. From memory to processor speeds to the amount and type of connection ports, the number of commonly available options on a consumer electronics product typically translates into hundreds of possible assemblies for the manufacturer. Now consider IT electronics, such as servers, and the amount of configurability offered by suppliers of these technologies to address various business demands. That’s the level of complexity faced by Foxconn on its production lines in the company’s Kutna Hora, Czech Republic, facility which produces HPE (Hewlett Packard Enterprise) IT systems.  To give you an idea of how complex assemblies can be at this Foxconn site, consider that one HPE server model can be equipped with between two and sixteen memory modules, each of which can be 16, 32, 64, or 128 gigabytes. “These memory options allow for several hundreds of product variants—and these, in turn, must be multiplied with the number of options available for the processors, fans, disk drives, etc.,” says Norbert Reil, director of global IoT services, HPE Pointnext (the company’s digital transformation consultancy).Reil notes that quality assurance of these servers not only requires assessment of the number and position of system components, but also of their correct implementation. “For example, a cable must not only be positioned in front of the correct port, but also checked to make sure it was actually plugged in successfully during manufacturing and assembly,” he says. “In addition, other product defects, such as scratches on the surface of the server chassis, must be accounted for.” Conducting all of these quality checks can take a human several minutes each. And at the Foxconn facility, which can produce thousands of IT devices every day, this translates into “a considerable amount of cost and time added to the manufacturing process,” Reil adds.To address this issue, HPE Pointnext, together with Relimetrics (which conducts smart quality audits for Industry 4.0), deployed a video analytics for quality assurance system to automate the inspection process. “The video cameras—10 for every conveyor belt—capture high-resolution images of products on a conveyor belt and stream them to an embedded or attached IT system where the images are analyzed by a video analytics application using machine-learning (ML) algorithms,” says Reil. “ML compares the image of the actual product with reference images that display accurate and defective implementations. Thus, the machine learns if a cable is properly plugged into a port, if a memory module is properly inserted into its socket, or if there is a scratch on the chassis.” The video analytics/quality system can work with a variety of industrial camera systems, Reil says. At the Foxconn Kutna Hora plant, the Basler acA4024-8gc gigabit Ethernet camera, which delivers 8 frames per second at 12.2 MP resolution, is used.As efficient as ML technology can be at performing these types of inspections, training the analytics system can be challenging due to product variety. One approach to training an ML-based analytics system is to teach it to detect defects in configuration, implementation or any other damage to the product. This approach can require thousands of reference images that the analytics application can compare with the product image from the conveyor belt. The downsides to this approach include the weeks it takes to train the ML algorithms and the need to do a new training cycle for every new configuration, product refresh or update.To avoid these steps, HPE Pointnext and Relimetrics designed the system so that it does not require the storage of reference images of complete servers, but only of components—such as memory modules placed in the proper slots or the processor socket with its fan. In this setup, the Manufacturing Execution System (MES) provides a bill of materials to the analytics application for every product on the conveyor belt. According to Reil, this enables the system to create a complete reference picture for assembly inspection based on the relevant reference image components. “The data exchange with the video solution happens through the SCADA layer and attached MES—both of which are part of FoxConn’s custom production system called eFox,” he adds.Two key benefits of this approach, Reil says, are: “first, the ML algorithms aggregate learning much faster and more efficiently because reference image components are frequently reused. At Foxconn’s Kutna Hora factory, HPE Pointnext was able to train the ML model for new server quality assurance with roughly 1,000 configuration variants in two days, completely automating the defect detection process. Second, this approach enables increased flexibility by combining image components according to the actual product configuration as provided by the bill of materials.”Dealing with the large amounts of data created by all the cameras used in this system was another issue that had to be confronted. Considering that these cameras generate 3 GB of image data per hour, it would be impractical to transfer that data via internal or external networks to be processed on remote servers. “The latency would be too high, networks would be overloaded with these data volumes, and production systems would grind to a halt during network outages,” Reil says.To overcome this problem, HPE Pointnext deployed the ML-based video analytics system on HPE Edgeline Converged Edge Systems. “These rugged, compact systems are designed with manufacturing environments in mind and also integrate operational technology (OT) like data acquisition systems, control systems, and industrial networks to enable seamless bi-directional and deterministic communication and control of OT systems like video cameras, production machines or conveyor belts,” says Reil.Data from the video camera stream is first pre-processed on HPE Edgeline Converged Edge Systems running near the conveyor belt. “The solution extracts images of the actual product and then analyzes the data in real-time using ML algorithms to detect defects,” explains Reil. “Only a subset of the analyzed images are transferred via the network to be archived for traceability and compliance.”AUTOMATIONWORLD

The advances you hear about most when it comes to AI are the technologies once discussed in science fiction novels: nanobots that root disease out of the body; cars that navigate relentless traffic better than a human driver; scanners that detect potential skin cancer as well as a dermatologist. What you don’t hear about nearly as much, however, are the myriad ways that technologies powered by AI are transforming routine processes and common frustrations, and helping businesses handle mundane tasks so employees can focus on more interesting work.There is perhaps no better example of this than content generation software, powered by Artificial Intelligence (AI) and Natural Language Processing (NLP). Content generation software is incredibly intuitive and easy to use: most companies can start generating content like BSS reports, e-commerce product descriptions, news content like weather reports, and more within a manner of minutes by inputting basic information via an Excel spreadsheet. The natural language generation (NLG) market includes my company, AX Semantics, as well as other leaders like Arria, Narrative Science, Yseop and Automated Insights.Here are five common pain points content generation software can help businesses solve:1. Scaling and Entering New MarketsEntering a new market, particularly a foreign one, is a challenge for any business. Say you are an online retailer based in the US who sells women’s shoes. You have content in the form of product descriptions – but only in the English language. That won’t help you much if you hope to sell shoes in China. You’ll need product descriptions in Chinese if you want to enter that market, which means you now need to pay for translators. If the translation quality is poor or inaccurate, you now have other problems, such as poor SEO, low conversion rates, high product returns, and more.Building your business at scale quickly becomes problematic if you use the old method, but if you use content generation software scale is not an issue because language is no longer a barrier. You simply input basic facts via a spreadsheet or similar method and get unique content in a different language.2. Regulatory DemandsMany businesses are mandated by law to routinely change forms, contracts and documents. A bank, for example, might be required to update risk assessments for every active plan on a quarterly basis. Content generation software provides an easy way to handle these relentless demands. In the past countless writers would need to go through forms or documents, items like forms and contracts, whereas now with content generation software, they are easily updated simply by inputting updated information into a spreadsheet.3. Lack Of Fresh ContentAll companies need to think of themselves as media companies. Your presence on social media and blogs is a key way to communicate with your audience. If you let content get stale or don’t update your sites, you will lose potential customers. How do you get this done? Equip writers and content creators with software that enables them to constantly create unique content. “Hybrid” content borne from a partnership between man and machine fills the need for fresh, vital content around the clock, linked to the underlying data, and auto-updating if that data changes.4. Writing JobsIt’s a tough time to be a professional writer or be a company that wants to keep a writer gainfully employed. Writers, such as journalists or content producers are under enormous pressure to create fresh content that can be changed and updated at a moment’s notice. Working with content generation software allows them to not only fulfill but also exceed their job requirements and expectations. It also allows them to create more content so their employer can thrive.The writing jobs of the future will doubtless have a content generation software component. AI is also helping journalists do better work – a newspaper in Germany, the Stuttgarter Zeitung, won the prestigious European Newspaper Award for reporting on fine particle and nitrous oxide pollution in the Stuttgart area using AI and content generation.5. Apathetic CustomersOnline shoppers are turned off by stale content, bad photos, descriptions that don’t match what they see on their computer or mobile device, and outdated information. A recent survey indicated that 87 percent of shoppers said clear images were important to them. Content generation software provides a better, more thoughtful way to reach potential customers, particularly those looking for a specific item. Customers are better informed and are likely to shop and buy more when accurate, precise copy is generated. When customers find what they want quickly, they also spend less time on e-commerce and can be more productive in their daily lives.As we continue to look to AI to make big changes in our lives, let’s not forget the many ways it’s already being used to resolve problems that have challenged businesses for decades. Content generation software is just one way a partnership between man and machine is reimagining what is possible.CONTENT GENERATION SOFTWARE

Could all the hype around the IoT (Internet of Things) in manufacturing be premature? The breathlessness with which the topic is discussed seems to suggest otherwise.A Google search for "IoT manufacturing" turns up 97,700,000 results in 0.44 seconds. Much of the focus has been on how sensor-equipped devices can actively monitor their own health - enabling proactive maintenance and greater efficiency in the manufacturing process. As a result, “global spending on IIoT [Industrial Internet of Things] Platforms for Manufacturing is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40 percent compound annual growth rate (CAGR) in seven years, according to research from IoT Analytics.  I would be among the first to acknowledge the potential of the IoT to upend the manufacturing process. However, a degree of caution is warranted as major challenges to the effective implementation of IoT-enabled devices remain. These potential pitfalls largely fall into three areas: legacy machines, security and regulatory concerns and the availability of lower-cost options to improve efficiency. Roadblock #1: Legacy Investments Many manufacturers have massive sunk costs in the form of their existing machines. These machines were often custom-built, purchased long before the onset of the IoT age and lack the capability to integrate with IoT devices. Most of them are dedicated machines that run on (hopefully air-gapped) Windows 95 or even older systems (yes, you read that right) - an issue that remains widespread.According to Amnon Shenfeld, CEO of 3DSignals, “by some estimates there are 60 million machines in factories throughout the world and 90 percent are not connected. Meanwhile, 70 percent of the machines are more than 15 years old.” Yet manufacturers are hesitant to discard these machines because they're phenomenally expensive to replace (if replacement hardware/software even exists) and still perform their intended function. As the old adage goes: “if it ain’t broke don’t fix it,” a notion manufacturers have clearly taken to heart. While vendors like HPE and IBM have tried to push the idea that older machines can be refitted to become IoT-compatible through the use of inexpensive sensors, interface dongles are only part of the problem.Far deeper and harder to fix are the accompanying software and storage issues. Do you really want a machine running Windows 95 (for which support ended in 2001) connected to the Internet? Roadblock #2: Security & RegulationAll manufacturers in the United States are subject to export controls. Some manufacturers, particularly those in fields like aerospace or who have extensive government contracts, must comply with additional, extensive, federal regulations regarding what data is collected and where it is stored. This can make implementing an IoT strategy tricky because it's not solely a technology problem but a legal and compliance one as well.After all, an IoT-enabled machine is obviously one that’s connected to the Internet and continuously generating data that is typically stored and maintained in the cloud. Data stored online comes with the increased risk it will be accessed by malicious actors through illegal means. Should that data involve dual-use technologies (those that have a military and civilian use) or other sensitive components, the legal repercussions can be devastating.Because they’re not typically in the business of running data centers, manufacturers must rely on technology vendors to maintain and store whatever data they’re not maintaining on-premises. This understandably makes compliance teams nervous because data generated and stored by large, multinational vendors can be accessed even through legal means.Consider the Supreme Court case Microsoft Corp. v. United States, which revolved around the issue of whether law enforcement in the United States could compel American vendors (in this case Microsoft) to surrender data stored overseas (in this case Ireland). The case was ultimately never adjudicated because Congress passed (and the President signed) the 2018 CLOUD Act, which clarified that American law enforcement can indeed compel U.S.-based vendors to turn over customer data, even if stored overseas.Roadblock #3: More Cost-Effective Options Sometimes the simplest solutions are the best ones and manufacturers would be wise to consider whether there are cheaper options for improving productivity than an expensive IoT strategy. To illustrate, an aerospace manufacturer I previously worked with effectively doubled their output through simple data collection (hand written KPIs), using Six Sigma to analyze the results and applying lean manufacturing waste reduction principles. Improvements were often deceptively simple changes like arranging all necessary tools next to the technician who needed them. The results were so eye-opening that entire production lines were remodeled to implement similar lean principles across the organization. While there were certainly costs involved (in terms of both training and equipment), the capital expense was minor compared to the hardware, software subscription and training costs that a true IoT strategy would necessitate. The Road Ahead No one doubts that manufacturing will change in the coming years and that IoT-enabled devices will play a role in that change, but the degree to which IoT-enabled devices will have an immediate impact is often overstated. In particular, manufacturing leaders need to make sure they don’t overlook the critical security and compliance repercussions of the IoT.  Far more than just a challenge of technology, manufacturers will need to ensure they have a proper framework in place to deal with the corollary problems IoT-enabled devices generate, particularly in sensitive industries. Fortunately, numerous other avenues - such as the implementation of Lean Six Sigma methodology - offer a way for manufacturers to improve their processes, drive greater efficiency and take the first steps on the path to making data-driven decisions while they wait for the IoT landscape to mature.IOT IN MANUFACTURING

(REUTERS) - FACEBOOK Inc said https://bit.ly/3jjcdwy on Wednesday it is launching its dating service in 32 European countries after the rollout was delayed earlier this year due to regulatory concerns. The social media company had postponed the rollout of Facebook Dating in Europe in February after concerns were raised by Ireland's Data Protection Commissioner (DPC), the main regulator in the European Union for a number of the world's biggest technology firms including Facebook. The DPC had said it was told about the Feb. 13 launch date on Feb. 3 and was very concerned about being given such short notice. It also said it was not given documentation regarding data protection impact assessments or decision-making processes that had been undertaken by Facebook. Facebook Dating, a dedicated, opt-in space within the Facebook app, was launched in the United States in September last year. It is currently available in 20 other countries. In a blog post on Wednesday, Kate Orseth, Facebook Dating's product manager, said users can choose to create a dating profile, and can delete it at any time without deleting their Facebook accounts. The first names and ages of users in their dating profiles will be taken from their Facebook profiles and cannot be edited in the dating service, Orseth said, adding that users' last names will not be displayed and that they can choose whether to share other personal information on their profiles. (Reporting by Juby Babu in Bengaluru; Editing by Kim Coghill and Subhranshu Sahu) U.S.NEWS

Enterprises in Germany are looking for digital workplace vendors to help them improve the employee user experience and transform their business with new workplace processes and technology, according to a new report published today by Information Services Group, a leading global technology research and advisory firm.The 2019 ISG Provider Lens Digital Workplace of the Future Report for Germany focuses on technology and services that enable employees to access their work profiles, data and applications anytime and from anyplace. German enterprises want digital workplace vendors to help them deliver similar experiences and information to employees on all the devices they use, irrespective of the different interfaces.“A hassle-free work environment is a must these days, not a differentiator,” said Andrea Spiegelhoff, partner, ISG DACH. “The days of sequential work performed by humans are numbered, and enterprises need to redesign their workflows, automate tasks, and free employees’ time for more collaboration.”The report finds enterprises in Germany increasing their use of design thinking methodologies for understanding employee needs related to office layouts, organizational hierarchies, job descriptions and surrounding technologies.Digital workplace providers, meanwhile, are putting business transformation services at the core of their offerings as a way to help clients develop new and better processes and adopt new, often more mobile, technologies.Organizations that do not embrace the workplace transformation trend will see eroding profit margins as their competitors reduce costs while providing better services, the report says. Even the German midmarket, often reluctant to change, has recognized this trend and is demanding digital workplace services and solutions.German companies want vendors to help them improve worker experience and productivity, and they are looking for vendors that can provide seamless experiences across employee desktops and smartphones, the report says. Not all vendors offer this end-to-end service platform to deliver on company expectations.In addition, intelligent automation and cognitive intelligence technologies are opening up new possibilities for German companies to improve employee experience by using bots or virtual agents to act as personal digital assistants or digital twins of employees. The report also finds German companies of all sizes embracing the device-as-a-service model. Companies do not want to own and manage the hardware and are asking managed service providers to cover device lifecycle management, device app provisioning and security.The 2019 ISG Provider Lens Digital Workplace of the Future Report for Germany evaluates the capabilities of 45 providers across six quadrants: Digital Workplace Consulting Services, Managed Services – Workplace Support for Large Accounts, Managed Services – Workplace Support for the Midmarket, Managed Services – Mobility Support for Large Accounts, Managed Services – Mobility Support for the Mid-Market and Unified Communications as a Service.The report names Computacenter as a leader in five quadrants, and Atos as a leader in four. Accenture, Cancom, IBM and Vodafone are named as leaders in three quadrants, and Bechtle, Capgemini, Deutsche Telekom, Deutsche Telekom (TSI), DXC Technology, Fujitsu and Syntax are named as leaders in two. Microsoft and NTT are named as leaders in one quadrant.GERMAN ENTERPRISES

NEW DELHI (Reuters) - Alphabet Inc’s Google on Monday said it would spend around $10 billion in India over the next five to seven years through equity investments and tie-ups, marking its biggest commitment to a key growth market. The investments will be done through a so-called digitization fund, highlighting Google’s focus on the rapid pace of growth of apps and software platforms in India, one of the world’s biggest internet services markets.  “We’ll do this through a mix of equity investments, partnerships, and operational, infrastructure and ecosystem investments,” Sundar Pichai, CEO of Alphabet, said on a webcast during an annual “Google for India” event. “This is a reflection of our confidence in the future of India and its digital economy.”  The new $10 billion investment was the largest Google had done in India, Pichai said.  “We’re particularly focused on making sure the internet expands beyond English and other vernacular languages. That’s an important angle we’ve looked at,” he told Reuters in an interview. Google wants to bolster the growth of internet in India, which currently has over 500 million active users, and help get another 500 million people online, Pichai said. Beyond investments via the fund, Google, would also focus on areas like artificial intelligence and education in India, he added.  Google has already made some direct and indirect investments in Indian startups such as local delivery app Dunzo. Indian-born Pichai joined Google in 2004, and is widely credited for making the Chrome browser. He replaced company co-founder Larry Page as CEO of parent Alphabet Inc last year.  The U.S. tech group, whose Android mobile operating system powers a bulk of India’s roughly 500 million smartphones, will continue to work with manufacturers to build low-cost devices so that more and more people can access the internet, another Google executive said. REUTERS

Fifty years ago, in a café that did not employ a barista—and coffee was cheap—I’m convinced two colleagues on their lunchbreak were debating what the future of jobs would look like.“I bet one day robots will have their own employment agency,” one said to the other. Laughter ensued, and they returned to their jobs on the production line of a manufacturing plant. Well, it’s happened. Robots do have their own employment agency.In December of 2019, MusahiAI offered up its robots for hire to be paid either on an hourly rate or a task-completed salary rate. Robots wanting these jobs would need to be able to do strenuous and repetitive work.While today, robots across the U.S. are employed doing just this type of work, they have a career development trajectory that involves more advanced skills such as “thinking.” Through artificial intelligence and machine learning, robots will ultimately be able to figure out ways to improve whatever jobs they are given. Does this robot evolution threaten human evolution? It depends on which study you believe.A study by Oxford Economics predicts that manufacturing could lose 20 million jobs by 2030, making the sector 8.5% smaller than “if robots were not remaking the market.” A McKinsey Institute study predicts that by 2030, 39 to 73 million jobs that exist today – one-third of the US workforce– will be automated.Then there is the study from the World Economic Forum which predicts that the 75 million jobs lost by 2022 due to “the technological advances of the fourth industrial revolution” will in fact be overshadowed by the 133 million new jobs created.What Skills Are Needed in the Future?One thing that everyone can agree on is that whatever jobs are available in the future, they will look different and require new skills.Is this a bad thing? Well, one viewpoint is that robots are freeing us from the drudgery of both harmful and repetitive tasks, allowing us to move higher up the value chain in the organization. Jobs that involve creative thinking and problem-solving are more valuable to a company and therefore pay more and are more secure in terms of long-term employment.But that might be a large leap forward.While many reports show that jobs that require less skills have been taken over by machines, there is debate as to the scale of this. A recent report by the Brookings Institution shows that better-paid, white-collar occupations may be the most exposed to artificial intelligence. And the effects of this will be felt more by men and prime-age workers.So, the question for the future closely resembles the current questions of who will work which jobs, and what types of skills are necessary.What will happen to the population that is less skilled and currently can  find work on a factory line earning a good living?  What about those employees who are now upskilling or learning new skills?  Are they receiving the training for the value-added jobs of the future?Manufacturing companies are squarely focused on these issues and are already addressing them. For example, one East Coast manufacturing company I know of had its finger on the pulse of its workforce, understanding that workers were concerned about machines taking their jobs. The company looked at its career development path and ended up promoting a significant portion of the workers. The promotion enabled workers to move up the value chain and made them less vulnerable to job loss. This company also noted that some of its most skilled workers had come to the company from a fast-food chain, validating the idea that hiring less-skilled workers and training them in-house is a good approach.The Future of TrainingTraining has always been in the DNA of manufacturing. In-house training for continuously improving processes has evolved to in-house training of the workers themselves. In the past few years, apprenticeship programs, many based on the successful German model, have become more common in the U.S. Often, German-based companies such as VW, Siemens and Bosch are offering apprenticeship programs to their U.S.-based employees. Many companies are also working with educational institutions to provide vocational classroom training that complements company-specific training.While these programs are successful, they are limited. Research done by Johann Fortwengel at King's College London of the apprenticeship model in the US, England and Australia found that many small-to-medium-sized companies cannot either afford these programs or find them to be too complex. State and local workforce agencies are jumping in and trying to build out these programs in order to accommodate any size company.But on a policy level, it’s slow going. While the Department of Labor certifies these programs, often they are company-specific."America lacks a national strategy that prioritizes youth apprenticeship as an effective talent pipeline to boost the sector’s competitiveness and spread economic opportunity across the country,” says Brent Parton of New America, a nonprofit public policy institute.Where Will Future Workers Come From?Putting all of this together, there are a lot of questions to be answered and policy choices to make.Will the U.S. adopt the German model which tests children at a young age to understand their capabilities and then set them on a track? For example, if a 10-year-old shows an aptitude toward programming, do we line up an education track that essentially trains them toward a future job? Do we send them to robotic camps in the summer and at the high school level move them into internships with area companies with the hope that they will be employed by these companies?Many educational systems are already doing this in an effort to fill manufacturing jobs, but the larger question is how education, and vocational education will be viewed in the future.In an extreme view, robots will do all of the work, even what we consider service work today, and then how will people earn their keep? Political discussions are already underway about universal income and using social wealth funds to compensate for earned wages.“A social wealth fund would create a true ownership society, insuring the working populace against the rise of the robots by allowing each person to own a piece of those robots’ output,” explained Noah Smith, professor computer science and engineering at the University of Washington, in a Bloomberg op-ed.What humans will do for work, how we will perform our jobs and how we will be compensated is not something that will be solved anytime soon.So, it’s possible that in 2070, there will be two robots sitting in a café with a robot barista, drinking expensive coffee. One turns to the other and says, “Do you think we need to contact that human employment agency to find someone who can come up with new product lines?”INDUSTRYWEEK