Visit
Official Website

Fictron Industrial Supplies Sdn Bhd
No. 7 & 7A,
Jalan Tiara, Tiara Square,
Taman Perindustrian Sime UEP,
47600 Subang Jaya,
Selangor, Malaysia.
+603-8023 9829
+603-8023 7089
Fictron Industrial
Automation Pte Ltd

140 Paya Lebar Road, #03-01,
AZ @ Paya Lebar 409015,
Singapore.
+65 31388976
sg.sales@fictron.com

Latest News

No Programming, No Problem: Chinese TV Makers Join 8K Club

Sep 19, 2019
No Programming, No Problem: Chinese TV Makers Join 8K Club
View Full Size
Chinese manufacturers will soon market their own 8K high-definition televisions, regardless of a dearth of content at this moment available for the next-generation technology. Haier will put its 8K TVs on the market in China by the end of this year, next in Europe and various other regions opening as soon as next year. Hisense will also roll out 8K models in Europe as early as next year, and TCL said it will release its own offering that same year.
 
The players are trying to gain a very early foothold in an ever growing market. The market for 8K TVs worldwide will increase to 8.26 million units in 2023, up from just 28,000 last year, according to the Japan Electronics and Information Technology Industries Association.
 
The Chinese TVs were featured at the IFA electronics expo held in Berlin through Wednesday. Haier launched its 75-inch product while Hisense showed off how the dual-layered LCD screens can produce rich contrasts and depth. The 8K TVs showcased by TCL utilize quantum dots, or nano-sized particles engineered to spark and emit certain colors. The quantum dots reproduce brilliant imagery in 65-inch, 75-inch and 85-inch models.
 
But, 8K TVs accounted for less than 1% of all flat-panel TVs worldwide, and the ratio will only inch up to 3% in 2023. On the other hand, the market for 4K TVs exceeded 90 million units last year, constituting just about 40% of all TVs. Limiting demand for 8K TVs is a lack of programming. In Japan, the public broadcaster NHK began airing 8K shows in 2018, but no TV network abroad has followed suit, according to NHK.
 
Sharp, the earliest company to release 8K TVs, is determined to help expand the market. The Osaka-based company debuted a small 8K video camera and a notebook computer that can edit footage. Sharp plans to sell the equipment to video production companies. 4K TVs started to take off after Netflix and other video distributors began to put out content in the high-definition standard. But Netflix says there are no plans at the moment to release 8K programming.
 
'We will immediately demonstrate that the technology to make [8K models] is available,' said a representative at Chinese TV maker Konka Group.
 

Hyundai Motor To Deploy Self-Developed Center Side Airbag In New Cars

Sep 19, 2019
Hyundai Motor To Deploy Self-Developed Center Side Airbag In New Cars
View Full Size
Hyundai Motor Group announced on Wednesday new passenger car releases will be built with self-developed center side airbag to divide the space between driver and passenger and stop head injuries.
 
The center side airbag situated alongside the driver’s seat will spread in 0.03 second by the time the impact is sensed. Lab test results showed the center side airbag can lessen head injuries caused by passengers colliding with each other by about 80 percent, the Korean conglomerate said.
 
Hyundai Motor Group said it used newly patented technology to the airbag to be certain system stability, while achieving the industry’s smallest and lightest airbag. A new technology was created to streamline the design and reduce the weight of components by about 50 percent compared to competing products, it said.
 
The smaller size of the airbag means the group’s design team has way more flexibility in the type of seat design for future mobility products. Hyundai Motor Group will roll out the new center side airbag in impending automobiles.
 

ABB Builds $150m Robotics Plant In Shanghai

Sep 19, 2019
ABB Builds $150m Robotics Plant In Shanghai
View Full Size
Swedish-Swiss engineering group ABB has initiated construction of a $150 million plant here to build automation equipment, eyeing the sector's growth opportunity in China and the rest of Asia. The 67,000-sq.-meter facility, when completed in 2021, will substitute a site that is one of the group's three global production factories. ABB outlined its new plant in Shanghai as the 'factory of the future where robots will make robots.'
 
Distinct from the convention linear system, production will be automated with robots moving flexibly from station to station for greater customization. It will feature cutting-edge technologies including digital twin, a feature that allows the analysis of data by connecting the physical and virtual worlds to ease production. The factory will also host a research and development center to boost innovation in artificial intelligence, ABB said.
 
The construction of the Shanghai plant happens shortly after a recent dip in ABB's order book for robotics in China, reflecting the group's optimistic view of the sector. 'Despite short-term market challenges, China's development as a global manufacturing hub, the ongoing trend toward mass customization and a rising shortage in skilled labor will continue to create strong and lasting demand for automation solutions in the region,' Sami Atiya, head of ABB's robotics business, said on Thursday. 
 
ABB's order book for robotics declined 14% per annum to $883 million in the second quarter, mostly due to weaker demand in China, the group said in a July statement. Its robotics solutions serve industries that include electronics, food and beverage, pharmaceuticals and automotive.
 
China is the world's biggest market, churning out 133,200 units of industrial robots in 2018, based upon the International Federation of Robotics in Germany, exceeding rival Japan's 52,400 units and America's 38,100 units. ABB projected that the global robotics market will grow from the current $80 billion annually to $130 billion in 2025.
 
'In the years ahead, we estimate the breadth and depth of our portfolio will nearly double,' Atiya said.
 
The group has spent over $2.4 billion in China since 1992, among them a $300 million manufacturing hub for electrification products in the southern city of Xiamen. The 425,000-sq.-meter site initiated operation in November. In China, ABB competes against the likes of Siemens, which opened its first AI lab outside of Germany in Beijing in May, as the German engineering group hastened the adoption of AI-related solutions. ABB also operates robotics factories in Sweden and the U.S.
 

Why IIoT Is A Priority For Manufacturers

Sep 18, 2019
Why IIoT Is A Priority For Manufacturers
View Full Size
The Internet of Things — exclusively in an industrial capacity — is reaching a primary tipping point in terms of accessibility, capabilities and usefulness.
 
Industry-wide, use cases have been tested, IIoT platforms have shown their effectiveness and potential, and roadmaps to success have been developed in comparison to the mere visions imagined just a few years ago. Generally, this means that manufacturers who have not formerly invested in IIoT will be empowered to quickly catch early entrants and fast followers in IIoT, while still finding comparable value. For manufacturers who have not harnessed the power of IIoT yet, the environment will not be better than it is now.
 
Why IIoT Deployment Is So Vital
 
Year after year, manufacturers are faced with the unrelenting need to decrease operating costs through operational improvements. While many of the same operational cost-reduction strategies may continue to work, IIoT initiatives present new ways to obtain more benefits. 
 
As an example, having real-time insights into operations helps manufacturers to more quickly react to manufacturing chain complications and also allows for the mitigation of time- and cost-consuming shutdowns or other problems. On top of that, manufacturers can create new products and iterate on existing lines based on data collected from devices connected to the IIoT. 
 
Those deploying IIoT products or investing in IIoT platforms for the first time may possibly not have the expertise of those already leading the way, but they aren't too far behind. Manufacturers who purchase and deploy the latest IIoT products can still maintain parity with their early-adopter competitors by moving rapidly. But the window is closing. Manufacturers who do not invest in IIoT will significantly find it to be an uphill climb to catch up to those who have embraced the new technologies because of having to navigate a maturity curve.
 
Investments have been made and projects have been created and implemented in a variety of industrial and manufacturing capacities. Those who have already implemented IIoT into their business and those who do so over the next year will have an unique advantage due to the institutional knowledge flowing through the organizations. As that knowledge gap grows, it will be even more difficult for other manufacturers to catch up with the leaders, early-adopters, and those who are just starting to embrace IIoT.
 
How To Invest In IIoT
 
Those who are taking their first foray into IIoT need to have the right strategy when putting money into new products and platforms. The very first investments should be made with IIoT device connectivity and basic real-time analytics. This makes manufacturers to not merely deploy new technologies but to have a complete understanding of what is needed to generally bring devices onto the platform and then track what is happening with them once they are on the platform.
 
The first investment in IIoT in connectivity and analytics is critical not simply for getting a platform deployed but likewise for closing the knowledge gap. Time and time again, manufacturers have invested in platforms without also emphasizing connectivity and analytics. Sadly, those businesses have had to spend one to two years creating simple analytics and trying to find ways to securely connect devices. This is why there are still opportunities for those deploying IIoT for the first time catch up. Selecting the correct, all-encompassing platform for their requirements can close the knowledge gap and the technology gap.
 
Manufacturers Must Implement Smart Strategies With Their Smart Technologies
 
Manufacturers must be intelligent not only how they invest in IIoT, but also how they deploy the solutions they want to use to optimize their business. Many organizations who deploy new technologies, products, or software do so in a fragmented, improvised manner and have to fix costly errors down the line. The best advice for manufacturers who decide to deploy IIoT is to begin with small while thinking big. Simply put, understand your starting point but always have an end target to serve as a beacon.
 
Original projects should be co-used across teams and operational processes and carefully scoped out in order to deliver the highest impact and value over the shortest amount of time possible. Additionally it is important that manufacturers deploying IIoT for the first time are aware that they should be ready to take action immediately if it is not working with their business as predicted. If tangible value takes more than three months to obtain then either the project is too large in scope or the IIoT platform has a major deficiency. Adjust accordingly.
 
As for instance, if an industrial equipment manufacturer wants to offer product contracts based on outcomes rather than the just selling the product, this will require an enormous amount of capabilities including predictive analytics, machine learning, and automating a sizeable number of processes. As opposed to focus on this implementing the IIoT solutions as THE objective, use it as a target with an initial objective of connecting existing equipment and providing condition-based alerts to drive service actions. Creating and launching the least possible viable product for the latter should take between four to six weeks which gives a long lead time to observe whether or not value has been obtained inside the three-month threshold.
 
To conclude, manufacturers should be smart and assess their IIoT deployment. Doing this the right way can have an incredible impact on the bottom line of the business immediately and distance manufacturers from competitors for many years.
 

Using Slack on the Shop Floor

Sep 18, 2019
Using Slack on the Shop Floor
View Full Size
Since its origin in 2013, Slack’s collaboration platform has turned business communication, with more than 10 million daily active users. Adoption has been confined basically to office workers, but Slack can also help manufacturers expedite problem resolution on the shop floor, support continuous improvement activities, and even improve customer relationships.
 
That has been the experience for us at MBX Systems, a build-to-order server hardware manufacturer and integrator. Slack has helped us reduce production bottlenecks and secure valid delivery of finished product in its extreme high-variability environment. All MBX personnel including plant employees were onboarded at the Slack rollout three years ago to enhance cross-company collaboration about operations, including production and supply chain issues. MBX has since added customers to Slack conversations, enabling near-real-time discussion among line workers, supervisors, platform engineers, account managers, customers and other key players.
 
In addition, MBX has rolled out a Slack app that allows customers to integrate hardware supply chain-related discussions into their company’s own Slack instance. Customers can now get real-time notifications of MBX engineering changes, work in progress and order updates in their Slack data stream for fast information access.
 
Carl Nothnagel, MBX vice president of operations, says that Slack has made it very easy to communicate both internally and with customers, “whether we're asking a question about a component, troubleshooting an assembly or software imaging issue, or discussing an unexpected production delay.” All stakeholders can see all the messages, keeping everyone in the loop. It also allows one person to pick up where another left off, and makes it easier to track a conversation as three or four different people respond throughout the day.
 
Communicating by Channel
 
Most Slack communication takes place in persistent chat rooms called channels that are created by each organization based on their unique needs and searchable for quick knowledge-sharing. Each channel functions as a workspace for team members involved in that particular project or area of business. 
 
On top of establishing conventional Slack discussion channels to expedite business communication on issues such as company announcements and project collaboration for front and back office personnel, MBX has established manufacturing-related channels dedicated to each work center, specific customers, new product introductions, production issues, corrective actions and other matters relevant to plant operation and optimization.
 
With this plan, questions about build instructions, component changes, supply chain issues, production problems, line interruptions, and other manufacturing concerns are routed through the relevant channel for smoother cross-functional communication.
 
System builders can promptly consult with platform engineers, manufacturing supervisors or sales personnel to resolve issues without leaving the plant floor. Managers can organize team improvement events without the back-and-forth of group email. Builders can communicate with the product documentation technician to update a discrepancy in the build instructions. Abilities like these save time and expand agility.
 
Bringing Customers on Board
 
Adding customers to relevant Slack channels has yielded additional benefits for system troubleshooting, delivery, and operational transparency that helps increase customer trust and satisfaction. One example is a customer that supplies cutting-edge simulation solutions for military aviation training. Custom hardware systems ranging from single rackmount servers to wall-sized racks with up to 36 integrated servers are manufactured and loaded with site-specific aerial images based on the end purchaser’s aircraft and geographic training needs.
 
On one recent full-rack build, technicians found a possible cooling issue on the manufacturing line. The manufacturing team had to have a timely resolution that would require collaboration between engineering and the customer’s team.  They reached out to the customer through an open Slack channel they’d set up for them and could actually collaborate exclusively with the customer to solve the problem quickly.
 
Accelerating Information Access
 
The app that MBX developed for Slack presents even more efficiencies to customer interactions with the manufacturer. These stem from the app’s integration with MBX Hatch, the company’s manufacturing orchestration software toolset.
 
Now in its first iteration, the integration makes engineering change notices and additionally shipment updates documented in Hatch to be automatically pushed to the customer’s Slack channel. This eliminates the need to check email or open the Hatch toolset. Other Hatch-based integrations that will bring manufacturing program insights into the Slack environment are on the MBX roadmap. We have only scratched the surface of capabilities and already realized major benefits for ourselves and our customers.
 

Universal Robots Introduces Its Strongest Robotic Arm Yet

Sep 18, 2019
Universal Robots Introduces Its Strongest Robotic Arm Yet
View Full Size
Universal Robots, undoubtedly the dominant force in collaborative robots, is warming up its muscles in an effort to further expand its reach in the cobots market. The Danish company is introducing today the UR16e, its strongest robotic arm yet, with a payload capability of 16 kilograms (35.3 lbs), reach of 900 millimeters, and repeatability of +/- 0.05 mm.
 
Universal says the new “heavy duty payload cobot” will enable customers to automate a broader range of processes, such as packaging and palletizing, nut and screw driving, and high-payload and CNC machine tending.
 
At the beginning of 2015, Universal introduced the UR3, its smallest robot, which joined the UR5 and the flagship UR10, offering a payload capability of 3, 5, and 10 kg, respectively. Now the company is going in the other direction, announcing a bigger, stronger arm. “With Universal joining its competitors in extending the reach and payload capacity of its cobots, a new standard of capability is forming,” Rian Whitton, a senior analyst at ABI Research, in London, tweeted.
 
Like its predecessors, the UR16e is part of Universal’s e-Series platform, which features 6 degrees of freedom and force/torque sensing on the tool flange. The UR family of cobots have stood out from the competition by being versatile in a variety of applications and, most important, easy to deploy and program. Universal did not release UR16e’s price, saying only that it is about 10 percent higher than that of the UR10e, which is about $50,000, depending on the configuration.
 
Jürgen von Hollen, president of Universal Robots, says the company determined to launch the UR16e after studying the market and talking to customers about their needs. “What came out of that process is we understood payload was a true barrier for a lot of customers,” he tells IEEE Spectrum. The 16 kg payload will be mainly useful for applications that require mounting specialized tools on the arm to perform tasks like screw driving and machine tending, he explains. Customers that could benefit from such applications include manufacturing, material handling, and automotive companies.
 
“We’ve added the payload, and that will open up that market for us,” von Hollen says.
 
The difference between Universal and Rethink
 
Universal has grown by leaps and bounds since its starting in 2008. By 2015, it had sold more than 5,000 robots; that number was close to 40,000 as of last year. During the same period, revenue more than doubled from about $100 million to $234 million. Some time later when a string of robot makers have shuttered, including most notably Rethink Robotics, a cobots pioneer and Universal’s biggest rival, Universal finds itself in an respectable position, having amassed a commanding market share, estimated at between 50 to 60 percent.
 
About Rethink, von Hollen says the Boston-based company was a “good competitor,” helping disseminate the benefits and possibilities of cobots. “When Rethink basically ended it was more of a negative than a positive, from my perspective,” he says. In his view, a large difference between the two companies is that Rethink focused on delivering full-fledged applications to customers, whereas Universal focused on delivering a product to the market and letting the system integrators and sales partners deploy the robots to the customer base.
 
“We’ve always been very focused on delivering the product, whereas I think Rethink was much more focused on applications, very early on, and they added a level of complexity to their company that made it become very de-focused,” he says.
 
The collaborative robots market: massive growth
 
But still, despite its success, Universal is still tiny when you compare it to the giants of industrial automation, which include companies like ABB, Fanuc, Yaskawa, and Kuka, with revenue in the billions of dollars. Although some of these companies have added cobots to their product portfolios—ABB’s YuMi, for example—that market represents a drop in the bucket when you consider global robot sales: The size of the cobots market was estimated at $700 million in 2018, whereas the global market for industrial robot systems (including software, peripherals, and system engineering) is close to $50 billion.
 
Von Hollen notes that cobots are expected to go through an impressive growth curve — nearly 50 percent year after year until 2025, when sales will reach between $9 to $12 billion. If Universal can maintain its dominance and capture a big slice of that market, it will add up to a nice sum. To get there, Universal is not alone: It is backed by U.S. electronics testing equipment maker Teradyne, which acquired Universal in 2015 for $285 million.
 
“The amount of resources we invest year over year matches the growth we had on sales,” von Hollen says. Universal currently has more than 650 employees, most based at its headquarters in Odense, Denmark, and the rest scattered in 27 offices in 18 countries. “No other company [in the cobots segment] is so focused on one product.”
 

Silicon Takes Center Stage At The AI Hardware Summit

Sep 18, 2019
Silicon Takes Center Stage At The AI Hardware Summit
View Full Size
With the appeal of AI flourishing in the last few years, so too have the volume of conferences either dedicated to machine learning and artificial intelligence or at least discussing the topics as a major portion of the agenda. Nevertheless, the inaugural AI Hardware Summit stood out from the crowd last year. At the conference, not just did attendees hear from the major technology players, some start-ups like Habana emerged from stealth mode with innovative solutions. The second annual AI Hardware Summit presented by Kisaco Research assures more of the same.
 
This year’s conference promises to deliver numerous types of solutions for AI, not simply at the chip level but also some of the supporting products and services for AI solutions. Additionally, there will be presentations on training and inference, the edge and the cloud, and driving applications, such as autonomous vehicles. The conference will also offer a diverse set of solutions demonstrating how AI is pushing the boundaries of technology with everything from innovative digital architectures to analog, photonics, and neuromorphic computing.
 
One of many companies scheduled to present is Cerebras, which just came out of stealth mode a few weeks ago at Hot Chips with the world’s largest processor. Other relative newcomers include GrAI Matter Labs, Rain Neuromorphics, and SambaNova Systems. More recognizable start-ups include Flex Logix with scalable FPGA solutions, Mythic with its unique analog computing architecture, Graphcore with its Intelligent Processing Unit (IPU) with massive amounts of on-die memory, and Habana with its Goya inference and Gaudi training processors.
 
Not to be surpassed by the start-ups, the more established technology leaders Alibaba, Cadence, IBM, Intel, Google, Facebook, Nvidia, Microsoft, Qualcomm, Mentor (now a division of Siemens), Synopsys, and Uber will also be touting their technology. Other manufacturers include those enabling the AI platforms, such as compiler and OS provider Applied Brain Research (ABR), power delivery supplier Vicor, ASIC chip designer and manufacturer eSilicon, and memory and storage providers Crossbar, Pure Storage, and Rambus. And companies developing solutions around AI like Optum for medical services and Medallia for customer experience.
 
In all, the AI hardware Summit promises to be a thrilling conference. As part of Tirias Research’s coverage of AI, will be attending the conference and delivering more details on some of the announcements that will be shaping AI technology in the near future.
 

The Used of Computer Vision in Business

Sep 17, 2019
The Used of Computer Vision in Business
View Full Size
It turns out that something most humans take for granted — the ability to see, process and then act on visual input — is immensely hard to replicate in machines. That is specifically what computer vision (CV) aims to do. While perhaps not as advanced as human sight, computer vision has advanced to the point it is very useful in business today. Here’s more about what computer vision is and how it is used in business.
 
What is computer vision?
 
Computer vision describes the process when a computer using artificial intelligence algorithms can recognize and process images (photos, videos, etc.) and then create an appropriate output from the analysis because the computer can basically 'understand' the content. Especially, computer vision can classify, identify, verify, and detect objects. The developments with computer vision in recent years were facilitated by machine learning technology—in particular, the iterative learning process of neural networks—and significant leaps in computing power, data storage, and high-quality yet inexpensive input devices.
 
There are three main components to computer vision.
 
1.   Acquire the image: When a digital camera captures an image, it creates a digital file composed of zeros and ones.
 
2.   Process it: Algorithms are used to determine basic geometric elements to build images out of the binary data.
 
3.   Analyze and understand: In this final step of computer vision, the data is analyzed. High-level algorithms are used to then make decisions based on the images.
 
Since the very youngest of humans can process images and understand them, the challenges of replicating that ability in machines were underestimated. What first seemed like a simple problem to solve, turned-out to require decades of research. There is so much complexity in the visual world, and there is still many unknown about how human vision works and how the brain perceives visual input.
 
Although the field of computer vision has overcome many challenges so far, there are still hurdles to overcome based on what the computer vision is being used for and the data it’s able to acquire. Computer vision requires loads of data processing power and memory, plus its results can be impacted by the quality of the images/data. Computer scientists are still working on optimizing computer vision for all applications.
 
How is computer vision used in business?
 
There are unlimited applications where the ability to extract meaning from “seeing” visual data is useful. Computer vision combines with other technologies such as augmented and virtual realities to enable additional capabilities.
 
Facial recognition, powered by computer vision, is used for surveillance and security systems as well as the technology behind Facebook that identifies people to 'tag' in photos. China uses facial recognition technology in police work, payment portals, and more. Even retail stores use the technology to monitor inventory, track customers through the store, and allow customers to bypass the cash register by paying virtually when facial recognition technology puts the items on their bill.
 
Numerous car manufacturers from Ford to Tesla are scrambling to get their version of the autonomous vehicle into mass production. Computer vision is an essential technology that makes autonomous vehicles possible. The systems on autonomous vehicles constantly process visual data from road signs to seeing vehicles and pedestrians on the road and then determine what action to take.
 
Computer vision in medicine helps in diagnosing disease and other ailments and extends the sight of surgeons during operations. There are now smartphone apps that allow you to diagnose skin condition using the phone's camera. In fact, 90 percent of all medial data is image-based—X-rays, scans, etc. and a lot of this data can now be analyzed using algorithms.
 
Digital marketing: By using computers to sort and analyze through millions of online images, marketers can bypass traditional demographic research and still target marketing to the right online audience and do this work dramatically quicker than humans could. Marketers even use computer vision to ensure ads are not placed near content that is contradictory or problematic for its audience. Financial institutions use computer vision to prevent fraud, allow mobile deposits, and display numerical information visually.
 
In manufacturing, computer vision makes things more effective, effective, and safe. It is used in predictive maintenance to decide an issue before any breakdowns occur as well as in quality control measures. The quantity of items a machine can verify outpaces human's ability to do the same substantially.
 
The agriculture industry uses computer vision to make operations more productive by monitoring fields looking for signs of disease or pests so swift action can be taken to eliminate it. John Deere introduced a semi-autonomous combine harvester that can find the optimal route through crops after analyzing the quality of grains that are harvested. Handwriting extraction and analysis: Computer vision can translate handwritten meeting notes or creative brainstorming into digital formats which make it easier to share with others in the company. The applications of computer vision are so varied that it is hard to imagine a business that couldn't benefit from it.
 

The Ultimate Lean Workforce

Sep 17, 2019
The Ultimate Lean Workforce
View Full Size
Industry 4.0 and the digital age were induced by an avalanche of technological advances, such as for example the Internet of Things (IoT), the Cloud, and Artificial Intelligence (AI). These technological advances are welcoming in the emergence of a new type of workforce that leverages the availability of new tools, devices and gadgets. 
 
While it is part of human nature to always look for, and benefit from, whichever new tools are created in order to make life and work easier, it's not merely technological advances that have brought about this new era. There are other elements that also contribute to the emergence of a new type of workforce, for example the sharing economy, that promote the culture and ethos of teamwork, collaboration and synergy.
 
Collectively, all of these points demand a workforce that excels and thrives with the advent of new technologies. It can be viewed as the “ultimate lean workforce” or, as I like to call it, the “Autonomous Workforce.” Members of the Autonomous Workforce are masters of the new technologies and the backbone of Smart Manufacturing, Smart Factories and Smart Cities.
 
The implications of such a workforce are many — from management delayering to new attitudes toward continual improvement and learning. Although the idea of a lean workforce has been in existence since the 1980s, the definition of the lean organization has changed considerably due to new technologies, the speed of work, communications and cultural changes.
 
Present and future generations of employees will demand decentralized, team-based organizational structures, compared with traditional pyramid structures. This newer thinking envisions people working together as self-directed work teams to make a better world in contrast to following a boss just to increase company profit. Peer-to-peer teaming relationships will be the ultimate method to get a lean system to be as quick and agile as possible.
 
This dynamic dynamic doesn't mean bosses or supervisors will not be needed. On the other hand, their duties will change into those of planners, coaches, facilitators, problem solvers, trainers, etc. Management will still need to provide strategic plans, reports, etc., on the status of the organization.
 
The truth is, autonomous workers have been around for a long period. Smaller companies have always had little choice but to allow their workers the autonomy to make their own decisions due to lack of resources. What is different is that this thinking  is now taking hold at larger enterprises. Those companies are asking “why” and “how” they can advance their autonomous workforce.
 
Why an Autonomous Workforce?
 
As the lean value stream quickens, information is now available immediately and processes are more consistent, controlled and standardized. Under these circumstances, a traditional organizational structure is not anymore cost competitive and is too slow. Even with technology, communications up and down the management chain are too cumbersome. Plus, why pay for supervision? Supervision is a non-value-added expense. We no longer can afford people whose only mission is to direct others.
 
Under these new conditions, employees should be trained to operate as self-directed work teams. Teams are small groups (5 to 12 employees) that work together inside of a product family, along a supply chain, within and outside the organization. Processes and/or product information that requires action needs to be clear, available and visible. If you find that teams are not able to make decisions on their own, this is an opportunity for improvement that should be addressed.
 
How to Develop an Autonomous Workforce
 
Switching from a traditional organizational model to an autonomous one involves a formal understanding of how to get there, as well as support and commitment from senior management backed by a shared strategic plan.
 
As an organization matures on its lean journey, it will get to the point that an autonomous workforce seems sensible. It’s the ultimate lean organization. During this transformation, an autonomous implementation team needs to look at the total supply chain and determine what parts are ready to evolve into an autonomous work team. Start with pilot areas. Learn, adjust, and roll out to the suppliers or customers of that first team.
 
To make it work, team members will have to take on additional responsibilities earlier shouldered by supervisors. They will volunteer and rotate to take on scheduling, reporting, safety, quality, leadership, discipline, continuous improvement, etc. This will require training in both business and human understanding that will result in a very knowledgeable and supportive workforce.
 
Essential to the success of an autonomous workforce initiative is employees knowing strategic objectives, having a clear and attractive reward (and discipline) system, belong to an appropriate team, and being accountable to company and supply chain results. A culture of continuous training/improvement that focuses on using visual systems and developing and maintaining real trust is also a must. The Autonomous Workforce is mostly about the need to be globally competitive. It hastens the delivery of value, reduces costs, improves quality, and results in happier workers and ultimately customers.
 

Network Upgrade Insights

Sep 17, 2019
Network Upgrade Insights
View Full Size
Like many technologies in the industrial automation sphere, networking technologies are generally installed for the long haul. In essence, if it ain’t broke, don’t fix it.
 
But with the advance of Industry 4.0 and Industrial Internet of Things initiatives bringing IT and OT (operational technologies) closer together, a move toward more regular upgrades of industrial network technologies looks to be underway - at least in some verticals.
 
According to Schaffer, the frequency of network upgrades across industries does, of course, tend to deviate - due to “the nature of the work in a particular vertical and the amount of capex involved. On the low end, it tends to be every five years, but typically ranges from 10-20 years. In the IT space, where I’ve spent much of my career, the standard is to upgrade every 3-5 years to keep up with technology changes.”
 
Speaking about upgrade practices in particular industry verticals, Schaffer said the water/wastewater and electric power industries tend to have longest intervals between upgrades cycles. In these industries, going “20 years between upgrades is not uncommon due to specialized network design” and the relevant attitude around NTAR, i.e., never touch a running system.
 
On the contrary, the oil and gas industry refreshes a lot more regularly, particularly over the past a few years with the upsurge in this industry’s profits. “They’re also embracing a much more data centric model of operation,” said Schaffer. “To get access to that data, they need to upgrade more frequently. They’ve also seen crippling effects of cybersecurity attacks - like the one impacting Saudi Aramco (in 2012), which is making them much more proactive. On the discrete side of industry, automotive is leading the charge because they’ve been actively embedding IT into their OT ranks. So, they have more of that three- to five-year upgrade mentality.”
 
Beyond the technological benefits, Schaffer said one of the biggest business advantages of a network upgrade is that it provides the perfect excuse to update, validate, and clean up documentation. “Too many times I’ve been in plants asking about what devices are connected to the network and what they’re connected to on the network only to find that the documentation is out of date. No one knows the answer—so it’s difficult to manage the network from an operational and cybersecurity vantage point. I’m a big believer in knowing your network. Whenever you do an update, it gives you the perfect opportunity to re-acquaint yourself with the infrastructure that makes your plant tick.”
 
The biggest impacts to be obtained from a network upgrade will take place on the higher end, where IT and OT meet, said Schaffer. “The closer you are to the high end of network - where data is going to edge or cloud - that’s where you see a change in the mindset in the past couple of years. If you want to take advantage of these new capabilities, you need to upgrade regularly here.”
 
Schaffer also suggests taking security into account as part of your network upgrade. He suggests three best practices here:
 
  • Follow the principle of least privilege (or least authority). A device should only be allowed to communicate with what it needs to communicate with. Give it the connections and access rights it needs and nothing more.
  • Proactive defense in depth.Layer your defenses with different and various techniques and technologies. Having just one firewall with no defenses behind it is not ideal.
  • Know your network. Logging, auditing, monitoring, performing baselines, and understanding what your network should look like normally is a huge benefit when something goes wrong. For example, if your network normally sees 7 mbps traffic levels and you see it spike to 27 mpbs, you can focus on the devices generating the extra traffic.
 
When it's about answering the reader question about how often industrial networks should be upgraded, Schaffer noted that, “while mileage may vary, I suggest patching once per year at least, with once per quarter being best, and doing a full technology refresh every 5-7 years.”
 
TRONSERVE

Key Industrial IoT Terms Every Manufacturer Should Know

Sep 17, 2019
Key Industrial IoT Terms Every Manufacturer Should Know
View Full Size
As a manufacturer, you don’t necessarily need to be an expert on the technology behind IIoT. You are most concerned with precisely how the technology will probably enable you to deliver quality products on time, keep costs contained, and improve productivity. It helps to be as informed as possible so you know enough to be an informed consumer of this technology.
 
With that in mind, here are key Industrial Internet of Things (IIoT) terms that will be helpful for you:
 
Data Terms
 
The majority of Industrial IoT terminology revolves around data - mainly, the massive amounts of data that it generates:
 
  • Big data. A very large data set that can be analyzed for patterns and trends.
  • Streaming data. Data that is continuously generated by different sources.
  • Sensor data. The output of a device that detects and responds to some type of input from the physical environment. The output may be used to provide information or input to another system, or to guide a process.
  • Time-series data. Data that collectively represents how a system, process, or behavior changes over time.
 
Business Process Terms
 
You’ll also hear significant amounts of terminology that defines special business processes:
 
  • Predictive maintenance. Techniques that are designed to help determine the condition of in-service equipment to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted.
  • Operational intelligence. A category of real-time, dynamic business analytics that delivers visibility and insight into data, streaming events, and business operations.
  • Overall equipment effectiveness (OEE). A measure of how well a manufacturing operation is utilized (facilities, time, and material) compared to its full potential, during the periods when it is scheduled to run.
  • Asset monitoring. The process of monitoring all activity associated with a particular machine. Including but not limited to production, performance, quality, health, etc.
 
Technical Terms
 
This is where the terms get somewhat technical so it’s good to educate yourself on these:
 
  • Application Programming Interface (API). A set of functions or procedures that allow one application to access / interact with the features or data of another application or service
  • Programmable Logic Controller (PLC). An industrial digital computer that continuously monitors the state of input devices to make decisions (to control manufacturing processes and equipment) based on pre-programmed logic. 
  • Radio Frequency Identification (RFID). A wireless communication technology that uses radio frequency to power passive tags (small circuit antenna) to uniquely identify people or objects.
  • Supervisory Control and Data Acquisition (SCADA). A control system architecture that uses computers and networked data communications to monitor and control factory floor equipment.
 
Security and Standards Terms
 
When your data is being stored in the cloud, security comes to be so very important. That’s why it is good to familiarize yourself with these terms:
 
  • Identity and Access Management (IAM). A framework of business processes, policies, and technologies that manage digital identities (for e.g. used for authentication and access management)
  • Message Queuing Telemetry Transport (MQTT). A messaging protocol that works on top of TCP/IP. Designed for use cases with a low code footprint or limited network bandwidth.
  • Transmission Control Protocol/Internet Protocol (TCP/IP). The language used to access the Internet.
  • Ethernet IP. One of the manufacturing communication protocols used for transmitting information between electronic devices. Ethernet IP was originally developed by Rockwell Automation.
  • Hyper Text Transfer Protocol (HTTP). The underlying protocol used by the World Wide Web. HTTP defines how messages are formatted and transmitted, and what actions Web servers and browsers should take in response to various commands.
 
With an understanding of these terms, you are well prepared to keep researching and determining your options.
 
TRONSERVE

Why Every Business Owner Should Adopt An AI Approach

Sep 13, 2019
Why Every Business Owner Should Adopt An AI Approach
View Full Size
Artificial intelligence is the most transformative business trend in the world today. Hold on, you say — AI is not a new idea. In the 1940s the great mathematician and code breaker Alan Turing predicted that digital computers in the future would be capable of logical reasoning. Commercial interest in AI began in the 1960s and waxed and waned over the next several decades.
 
Why is AI a big deal now? Partly, it’s because of the rapid advances in computer and communications hardware, says Pat Gelsinger, CEO of VMware, a provider of cloud computing solutions based in Palo Alto, California. Gelsinger knows hardware. In the 2000s he was the chief technology officer at Intel.
 
“The faster pace of change in AI today is because you now have data at scale and computing at scale,” he says. Data at scale, he says, comes from the 30 billion or so computerized sensors in the world that are constantly gathering information. Computing at scale comes from cheap rentable supercomputers offered by Amazon, Microsoft, Google, Alibaba and others.
 
These, along side the coming 5G wireless speeds, are superpowers, says Gelsinger. If your company doesn’t tap into these powers, your competitor will. CEOs and boards, take note. Stop relegating your company’s IT challenges to a 30-minute discussion within the audit committee. The superpowers that drive AI will accelerate business evolution.
 
Envision two rival companies: A and B. Company A has invested in AI across its organization, at times a painstaking process, and is getting 10% smarter per year. That is 10% smarter about its customers, its opportunities, its costs and its risks. Company B, which is old school and run by penny pinchers, thinks technology is a commodity and not a strategic weapon. Company B’s cheap approach saves money, but at a steep cost. Company B is basically getting 2% smarter per year. Which company do you want to be?
 
This begs the question: Which countries are ahead in AI? The question often comes up these days at highest levels of governments world wide. I asked Silicon Valley venture capitalist Jim Breyer, who is an investment advisor in IDG’s China funds, which has about 200 investments in China, including some of the country’s most interesting AI companies, for his opinion.
 
“The innovation in China is extraordinary,” Breyer says. “There really is a space-like race going on in AI between the U.S. and China. Both have deep capabilities. There are areas in China where some of the facial-recognition AI companies are the most advanced in the world. There are other technologies in the U.S., such as IoT and machine learning, where AI is more advanced. But it is a race.” In coming columns I will write about the Silicon Valley-Asia connection, and why — whether you are a CEO, entrepreneur or investor — you’ll fall behind quickly if you don’t tap into this amazing pipeline of innovation.
 

US-Japan Trade Deal Allows for Antitrust Actions on Big Tech

Sep 13, 2019
US-Japan Trade Deal Allows for Antitrust Actions on Big Tech
View Full Size
Japan and the U.S. will agree not to require technology companies to divulge software secrets under their bilateral trade deal, except in cases of possible antitrust law violations, Nikkei learned Thursday. The in-principle ban on forced disclosures, part of the section of the draft agreement pertaining to digital trade, shows an endeavor to hit a balance between high-tech competition and government's role in intervening to stop data from becoming concentrated in the hands of a few companies.
 
Japanese Prime Minister Shinzo Abe and U.S. President Donald Trump are poised to sign the trade agreement on the sidelines of the United Nations General Assembly meeting in New York later this month. Details on farm and factory good tariffs are still being hammered out following a basic agreement reached by the two leaders on Aug. 25.
 
The rules on data, an important determiner of competitiveness for tech companies, are among the most closely watched parts of the trade deal's digital provisions. Japan's competition regulator recently published new enforcement guidelines meant to circumvent abuses of consumer data by platform companies - a category that includes U.S. tech giants Google, Amazon.com and Facebook.
 
The risk of government seizures of software source codes, proprietary algorithms and other tech secrets poses a barrier to business expansion. Japan and the U.S. have pushed for international rules on this front at the World Trade Organization and other forums, harboring particular concerns over China. But Tokyo and Washington will leave room in their trade deal for exceptions to the ban on forced disclosures, according to the draft document. Companies could be required to hand over data in cases in which consumer safety is at risk or in possible violation of competition or privacy laws.
 

Samsung Unveils Its First 5G-Integrated Mobile Processor Exynos 980

Sep 13, 2019
Samsung Unveils Its First 5G-Integrated Mobile Processor Exynos 980
View Full Size
Samsung Electronics Co. Wednesday introduced its mass production of 5G-intergrated mobile processor Exynos 980 powering 5G modem and intelligent processing performance in a single chip. The Exynos 980 is Samsung Electronics’ first artificial intelligence (AI) mobile processor with an integrated 5G modem. Rather than being coupled with a separate 5G modem, the new mobile processor not only helps lessen power consumption but also enhances the space efficiency within a device.
 
The new mobile processor’s powerful modem supports 5G to 2G networks, offering a fast gigabit downlink speed in 4G LTE and up to 2.55-gigabits per seconds in sub-6-gigahertz 5G. The modem also supports E-UTRA-NR Dual Connectivity (EN-DC), which combines 2CC LTE and 5G connectivity to improve mobile downlink speed of up to 3.55Gbps, the company said.
 
The neural processing unit (NPU) features elevated performances of up to 2.7 times compared to its predecessor and is built into the Exynos 980 to provide new levels of on-device intelligence. With the NPU readily available on-chip, AI tasks are processed right from the device instead of off-loaded to a server, thereby providing better data privacy and security, the company explained.
 
The NPU offers enhancements to applications such as secure user authentication, content filtering, mixed reality, intelligent camera, and much more. Samsung Electronics plans to start mass producing the Exynos 980 within this year ahead of rival system chip makers such as Qualcomm and Media Tek that have also developed their own 5G-inetergrated mobile processors.?
 

7 Types Of Artificial Intelligence

Sep 13, 2019
7 Types Of Artificial Intelligence
View Full Size
Artificial Intelligence is certainly the most complex and astounding creations of humanity yet. And that is disregarding the fact that the field stays largely undiscovered, which means that every amazing AI application that we see today shows purely the tip of the AI iceberg, as it were. While this fact may have been stated and restated numerous times, it is still hard to adequately gain perspective on the potential impact of AI in the future. The reason for this is the revolutionary impact that AI is having on society, even at such a relatively early stage in its evolution.
 
AI’s rapid growth and powerful capabilities have made people paranoid about the inevitability and proximity of an AI takeover. Moreover, the transformation brought about by AI in different industries has made business leaders and the mainstream public think that we are close to achieving the peak of AI research and maxing out AI’s potential. However, understanding the types of AI that are possible and the types that exist now will give a clearer picture of existing AI capabilities and the long road ahead for AI research.
 
Understanding the types of AI classification
 
Since AI research purports to make machines emulate human-like functioning, the degree to which an AI system can replicate human capabilities is used as the criterion for determining the types of AI. Which means, subject to how a machine compares to humans in terms of versatility and performance, AI can be divided under one, among the different types of AI. Under such a system, an AI that can perform more human-like functions with equivalent levels of proficiency will be considered as a more evolved type of AI, while an AI that has limited functionality and performance would be considered a simpler and less evolved type.
 
Based on this criterion, there are two ways in which AI is normally classified. One type is based on classifying AI and AI-enabled machines based on their likeness to the human mind, and their ability to “think” and perhaps even “feel” like humans. According to this system of classification, there are four types of AI or AI-based systems: reactive machines, limited memory machines, theory of mind, and self-aware AI.
 
1. Reactive Machines
 
These are the oldest forms of AI systems that have extremely limited capability. They emulate the human mind’s ability to respond to different kinds of stimuli. These machines don't have memory-based functionality. This implies such machines cannot use previously gained experiences to inform their present actions, i.e., these machines do not have the ability to “learn.” These machines could only be used for automatically responding to a limited set or combination of inputs. They cannot be used to rely on memory to improve their operations based on the same. A prominent example of a reactive AI machine is IBM’s Deep Blue, a machine that beat chess Grandmaster Garry Kasparov in 1997. 
 
2. Limited Memory
 
Limited memory machines are machines that, in addition to having the capabilities of purely reactive machines, are also capable of learning from historical data to make decisions. Nearly all existing applications that we know of come under this category of AI. All present-day AI systems, such as those using deep learning, are trained by large volumes of training data that they store in their memory to form a reference model for solving future problems. For instance, an image recognition AI is trained using thousands of pictures and their labels to teach it to name objects it scans. When an image is scanned by such an AI, it uses the training images as references to understand the contents of the image presented to it, and based on its “learning experience” it labels new images with increasing accuracy.
 
Almost all present-day AI applications, from chatbots and virtual assistants to self-driving vehicles are all driven by limited memory AI.
 
3. Theory of Mind
 
While the previous two types of AI have been and are found in abundance, the next two types of AI exist, for now, either as a concept or a work in progress. Theory of mind AI is the next level of AI systems that researchers are currently engaged in innovating. A theory of mind level AI will be able to better understand the entities it is interacting with by discerning their needs, emotions, beliefs, and thought processes. While artificial emotional intelligence is already a budding industry and an area of interest for leading AI researchers, achieving Theory of mind level of AI will require development in other branches of AI as well. This is because to truly understand human needs, AI machines will have to perceive humans as individuals whose minds can be shaped by multiple factors, essentially “understanding” humans.
 
4. Self-aware
 
This is the final stage of AI development which currently exists only hypothetically. Self-aware AI, which, self explanatorily, is an AI that has evolved to be so akin to the human brain that it has developed self-awareness. Creating this type of Ai, which is decades, if not centuries away from materializing, is and will always be the ultimate objective of all AI research. This type of AI will not only be able to understand and evoke emotions in those it interacts with, but also have emotions, needs, beliefs, and potentially desires of its own. And this is the type of AI that doomsayers of the technology are wary of. Although the development of self-aware can potentially boost our progress as a civilization by leaps and bounds, it can also potentially lead to catastrophe. This is because once self-aware, the AI would be capable of having ideas like self-preservation which may directly or indirectly spell the end for humanity, as such an entity could easily outmaneuver the intellect of any human being and plot elaborate schemes to take over humanity.
 
The alternate system of classification that is more generally used in tech parlance is the classification of the technology into Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).
 
5. Artificial Narrow Intelligence (ANI)
 
This kind of artificial intelligence represents all the existing AI, including even the most complicated and capable AI that has ever been created to date. Artificial narrow intelligence refers to AI systems that can only perform a specific task autonomously using human-like capabilities. These machines can do nothing more than what they are programmed to do, and thus have a very limited or narrow range of competencies. According to the aforementioned system of classification, these systems correspond to all the reactive and limited memory AI. Even the most complex AI that uses machine learning and deep learning to teach itself falls under ANI.
 
6. Artificial General Intelligence (AGI)
 
Artificial General Intelligence is the ability of an AI agent to learn, perceive, understand, and function completely like a human being. These systems will be able to independently build multiple competencies and form connections and generalizations across domains, massively cutting down on time needed for training. This will make AI systems just as capable as humans by replicating our multi-functional capabilities.
 
7. Artificial Superintelligence (ASI)
 
The development of Artificial Superintelligence will probably mark the pinnacle of AI research, as AGI will become by far the most capable forms of intelligence on earth. ASI, in addition to replicating the multi-faceted intelligence of human beings, will be exceedingly better at everything they do because of overwhelmingly greater memory, faster data processing and analysis, and decision-making capabilities. The development of AGI and ASI will lead to a scenario most popularly referred to as the singularity. And while the potential of having such powerful machines at our disposal seems appealing, these machines may also threaten our existence or at the very least, our way of life.
 
At this point, it is hard to picture the state of our world when more advanced types of AI come into being. However, it is clear that there is a long way to get there as the current state of AI development compared to where it is projected to go is still in its rudimentary stage. For those holding a negative outlook for the future of AI, this means that now is a little too soon to be worrying about the singularity, and there's still time to ensure AI safety. And for those who are optimistic about the future of AI, the fact that we've merely scratched the surface of AI development makes the future even more exciting.
 

Vietnam's Viettel shuns Huawei 5G tech over cybersecurity

Sep 12, 2019
Vietnam's Viettel shuns Huawei 5G tech over cybersecurity
View Full Size
Viettel Group, Vietnam's largest mobile carrier, has decided not to use Huawei Technologies equipment for its 5G wireless network, citing security concerns with the Chinese company. The military-run telecom will depend largely on hardware supplied by Finland's Nokia and Sweden's Ericsson, according to local media reports.
 
Viettel joins an increasing list of carriers and nations to reject Huawei over allegations of cybersecurity vulnerabilities. 'Many other countries, including the U.S., have found evidence that showed using Huawei is not safe for the security of the national network,' Viettel CEO Le Dang Dung told Bloomberg in an interview. 'So we need to be more cautious.'
 
The U.S. has spearheaded an attempt telling allies and other nations to blacklist Huawei. But Dung insisted that Viettel reached its decision independently. 'We decided not to use Huawei, not because of the U.S.'s ban on Huawei - we just made our own decision,' he said.
 
Two other big Vietnamese telecoms appear to have distanced themselves from Huawei as well. Vietnam Telecom Services, the second-ranked player known as Vinaphone, has opted to procure Nokia equipment for its 5G network. MobiFone, ranked third, is working with Samsung Electronics. Neither of them has confirmed that it will exclude Huawei.
 
Like Viettel, Vinaphone and MobiFone are state-run enterprises. 'It's possible that considerations were made regarding the U.S.,' a diplomatic source said. Vietnam is bucking the trend in Southeast Asia, where various nations have embraced Huawei. Smart Axiata, Cambodia's largest mobile carrier, has joined with the Chinese provider to launch what could be the region's first fifth-generation wireless network, with service set to go live by the end of the year.
 
Nevertheless Vietnam possesses strong trade ties with China, Hanoi is cooperating with the U.S. on the security front. Vietnam is also at odds with China over territorial rights in the South China Sea, which has fueled a strong anti-Beijing sentiment among Vietnamese.
 

Turning The WorldĄŻs Waste Problems Into Energy Solutions

Sep 12, 2019
Turning The WorldĄŻs Waste Problems Into Energy Solutions
View Full Size
Each day, the world produces some 5.5 million tons of waste. During the period of a year, that adds up to more than 2 billion tons of solid municipal waste, much of it taking up space in landfills and producing carbon emissions as it rots away. By 2050, the World Bank estimates that the problem will be even worse, reaching 3.4 billion tons of annual waste — a 70 percent increase. 
 
As nations around the world confront all of that household refuse and unused industrial material, new technologies are turning this liability into an energy asset. Power is being generated from semisolid waste discharged from urban incinerators or industrial plants, liquid waste such as domestic sewage and excess gas produced in refineries.
 
Solar, wind and hydro power may grab the headlines as clean energy solutions, but steadily, waste is a source of renewable, low-carbon energy. With 6% annual growth, the global waste-to-energy (WTE) market is expected to exceed $35.5 billion by 2024, led by the Asia-Pacific region where adoption of these novel technologies is projected to expand rapidly.
 
Asia’s leading role
 
Today, incineration is the most widely used technology in waste management to prevent the costly transport of refuse to landfill sites. But throughout Asia, governments are extremely adopting greener and more innovative approaches.
 
Japan was a very early adopter of WTE technologies and proceeds to lead the way; it processes about 70 percent of its municipal solid waste in WTE facilities. But China is making big strides as its economy and population continue to develop and its government transitions to cleaner energy. The power demands of the world’s most populous country are increasing rapidly, along with mounting domestic and commercial waste. Heavy investment in renewable energy technologies, buoyed by government policies that encourage sustainable development, is driving China’s WTE sector.
 
In 2020, the southern city of Shenzhen is scheduled to start operating the world’s largest waste-to-energy plant, with an expected daily incinerating capacity of 5,000 tons of the city’s trash. The incinerator’s residual heat, once captured, is used to drive a turbine, generating electricity. According to the project’s architects, the process should halve the amount of carbon dioxide typically released from landfill sites.
 
The Shenzhen facility is just the opening for China, currently the fourth largest WTE user after Japan, Europe and the United States. China’s growing awareness of its waste’s environmental impact is spurring policies that promote efficient, cost-effective solutions. By 2022, the Chinese government plans to build 300 dedicated WTE plants throughout the country — double the number of current facilities.
 
Converting a variety of waste
 
While the U.S. continues to lag in WTE, the European market is growing. Producers in Europe are adopting initiatives to process different types of waste, including solid or semisolid, excess gas and heat. In Turkey, for example, the country’s largest egg producer has installed an Organic Rankine Cycle (ORC) system from Turboden, a company within Mitsubishi Heavy Industries Group. The system converts chicken manure into electricity and hot water, creating clean energy out of unwanted waste.
 
A similar system is in operation in Russia but with a different source. Rather than solid or semisolid waste, international oil and gas company Lukoil uses a Turboden ORC system to process residual heat from flare gas to provide electricity and hot water for the oil refinery. This is usually burnt off at the top of a torch. During each hour of operation, the system prevents 720 kilograms of CO2 emissions from reaching the atmosphere.
 
And at ORI Martin’s steel plant in northern Italy, ORC turbines capture residual heat from the manufacturing processes, producing enough energy to power and heat part of the industrial city of Brescia. Such innovative projects represent a continuing shift in attitudes toward cleaner, more sustainable production. As more governments and private operators acknowledge the need for alternative waste management, creative solutions like these might become the norm around the world, combining the future of waste and the future of energy.
 

Huawei's Rapid Rise is Driven by a Global Talent Hunt

Sep 12, 2019
Huawei's Rapid Rise is Driven by a Global Talent Hunt
View Full Size
Four years ago, headhunters started out calling a top engineer at Taiwanese smartphone maker HTC. They were persistent, calling three times a week - but the engineer, who asked to be identified only as Emily, turned down their overtures.
 
Others could not resist the opportunity. For the coming few months, dozens of her colleagues - including the engineers who made up the 'Magic Lab,' HTC's most important research and development team - took up offers from recruiters to join Huawei. Some were offered as high as twice their existing salaries to make the switch, as the company, at that time just a producer of low-end smartphones, snapped up the talent it needed to challenge market leaders Samsung Electronics and Apple.
 
'Huawei was the most aggressive company when it came to attracting us to work for them,' Emily told the Nikkei Asian Review. 'Three out of five people I knew leaving HTC at the time were going to Huawei, and they were all the crucial talents that knew how to build premium phones.'
 
Huawei's drive to become the world's largest smartphone maker has been fueled by an aggressive global hunt for science and engineering talent. The company made its first smartphone, running on the Android operating system, a decade ago. In 2016, the year after it tempted dozens of staff from HTC, Huawei shipped 30% more smartphones than it did in 2015. In 2017, it sold more than 150 million handsets; in 2018, it shipped more than 200 million.
 
The company states that it now has 800 physicists, more than 700 mathematicians and 120 chemists working for it. Among those hires are high-profile people like the award-winning engineer Tong Wen, and the Italian microwave technology expert Renato Lombardi, who runs a global research facility for Huawei in Milan. Eighty-five thousand of Huawei's employees - 44% of its total headcount - work in R&D, Huawei spokesperson Joe Kelly told Nikkei, including 20,000 working on fifth-generation technology alone.
 
Huawei also sponsors universities to the tune of more than $300 million per year, and maintains a specialist research unit, called the '2012 Laboratories,' which focuses on developing technologies that will give it an edge decades in the future - from DNA data storage to atomic-scale manufacturing and optical computing. The company said that it invested $1.5 billion in developing artificial intelligence last year. Some in the industry say that Huawei poaches more than talent from its competitors. The company has been engaged in lawsuits with the U.S. telecom carrier T-Mobile, telecom equipment makers Cisco Systems and Motorola, and semiconductor startup Cnex Labs, all of which allege that their Chinese rival stole technology. Huawei declines all allegations of wrongdoing.
 
Even some of Huawei's own suppliers are suspicious. 'To be honest, although Huawei is an important client for us, we have to stay vigilant when their people visit our facilities,' one executive at a company in Huawei's supply chain told Nikkei. 'Their staff are often quite eager to know the parameters of our equipment, the materials we use and details of our processes.'
 
Fears of corporate espionage, combined with Huawei's close links to the Chinese government, contributed to the U.S. government's increasing crackdown on the company. In May, the U.S. Department of Commerce put Huawei on its 'Entity List,' a trade blacklist that restrains American companies from selling technology to Huawei. In August, the U.S. government added 11 Huawei research institutes, including the Milan research institute that employs Lombardi, to the Entity List.
 
Huawei has hit back, launching an even more ambitious talent hunt with even greater incentives for R&D talent, including 2.01 million yuan (around $282,000) starting salaries for newly graduated young employees with doctorates. Its average salary of $300,000 for AI specialists is now higher than some senior engineer positions at companies such as Apple and Google, according to job recruiting platform Glassdoor.
 
Those large sums is likely to be tempting, but further crackdowns could still limit the company's ability to bring in researchers. Alex Capri, senior fellow in the Business School at the National University of Singapore, said that Huawei's aggressive expansion into R&D has made it a magnet for talent, but 'this is all coming under increasing scrutiny and will likely be the subject of further technology licensing and controls.'
 

Huawei and Samsung Show Off Rival 5G Ambitions in Berlin

Sep 12, 2019
Huawei and Samsung Show Off Rival 5G Ambitions in Berlin
View Full Size
Huawei Technologies was among the Asian tech giants showing-off its latest 5G and foldable smartphones at the IFA trade show in Berlin, focusing on innovation over politics even as the China-U.S. trade war remains to cast a shadow over the company. Richard Yu, CEO of Huawei's consumer business group, presented the opening keynote speech for the event. The Chinese company is accused by the U.S. government of being unduly close to Beijing and has been targeted by a number of measures, including a ban aimed at blocking its access to American technologies.
 
Yu, however, avoided any mention of politics in his speech. He concentrated rather on Huawei's technological achievements, revealing the Kirin 990 5G smartphone system-on-a-chip that will power the Huawei Mate 30 smartphone slated for release later this month. The Kirin 990 5G is the first SoC that supports 5G - rival offerings from Qualcomm and Samsung Electronics use a 4G SoC with a 5G modem. Yu said the Kirin 990 5G is the fastest and the most power-efficient SoC on the market.
 
'We are leading the mobile AI in the world with the 10.3 billion transistors on our Kirin 990 5G,' Yu said. 'Since we launched the Kirin 980 here at IFA last year, the number of mobile APPs using AI, including related to photography, online shopping and education, has been rising, and today 5G is coming.'
 
Smartphone makers are competing to win over users with 5G and foldable devices, betting that cutting-edge features will rekindle excitement in a market that has struggled with slowing growth recently. Samsung Electronics and LG Electronics, both of South Korea, also showcased their latest smartphones and prototypes at the IFA, while Chinese electronics maker TCL punctuated its push into smartphones by showing its first own-brand smartphone at the Berlin event.
 
The closest that Huawei's Yu came to touching on politics was when he presented two new color variants of the existing P30 Pro smartphone, which comes with Google's latest smartphone operating system, Android 10, pre-installed. The move was significant because Washington's ban on Huawei accessing U.S. technology means the company is going to be not able to launch new phones running the latest Google-certified Android systems.
 
The U.S. Commerce Department imposed the trade embargo on Huawei in May, though it granted a 90-day grace period for certain companies, including Google, to cover the interests of American consumers. Google was not included in the latest 90-day extension, which runs to late November, however, and Huawei plans to launch the Mate X without popular Google apps such as Google Maps and Gmail. While Huawei highlighted its 5G progress, foldable and dual-screen smartphones also grabbed attention at the IFA.
 
Samsung lastly relaunched the Galaxy Fold after a recall and subsequent five-month delay over reliability issues following its initial debut in April, and the device went on sale in South Korea last Friday. Huawei presented a prototype of its first foldable handset, the Mate X, scheduled for release in October.
 
Fellow Chinese player TCL presented its own foldable prototype, to go on sale in the second half of 2020, and also introduced the TCL Plex, a milestone in its attempt to expand beyond its traditional focus on TV-making. 'In the previous decade, we globalized by moves such as acquiring Alcatel, Thomson and BlackBerry, and in this decade, we have been investing heavily in verticalization,' said Bill Jiang, TCL's general manager for Europe.
 
'The current focus is the strengthening of the TCL brand, as well-reflected in this year's IFA with the releases of TCL Plex, as well as the showcasing of the 8K TVs under the TCL 8K QLED X-Series,' he added. Jiang described 8K resolution as an irreversible trend in TVs, as it provides an increasingly immersive experience by allowing viewers to sit much closer to the screen without being able to pick out individual pixels.
 
LG Electronics, on the other hand, presented the G8X ThinQ, a dual-screen smartphone in which the two screens are connected via hinges, at the event. I.P. Park, LG's chief technology officer, explained that the first screen can be used to watch a baseball game, for example, while the second screen can display the game's stats. According to Park, an LG 5G dual-screen smartphone will be out in the fourth quarter of this year.
 
Like TCL, LG has also been focusing on 8K televisions. Park said the company plans to rollout the LG Signature OLED 8K beginning this month in global markets, with the rollable LG Signature OLED R to be launched later this year. The OLED panels are paper thin and resilient enough to be rolled and unrolled thousands of times, Park said.
 
'Consumers clearly prefer ever-bigger TVs, but the size of one's apartment doors, windows and staircases will always be the limit unless it's a rollable screen,' Park said. 'And also the beginning of the 5G era will push demand for bigger and flexible screens, as the wider bandwidth and faster transmission speed will lead to the emergence of new content forms that allow for a much great immersion experience.'
 
But while smartphone makers are betting on foldable and dual-screen smartphones to revive sales, Anshul Gupta, senior director at Gartner, said the popularity of these handsets will depend upon the creation of content created to maximize these features, something he does not see happening yet. 'The operating systems and applications need to be optimized, so that when the phone is folded and unfolded, the application scales up and down accordingly and support multiple display and multiple window form factors,' Gupta said.
 
'It will probably take a second generation of these phones to overcome any teething problems, but once that one is there, the affiliated ecosystems are going to grow fast,' he added.
 

Xiaomi Unveils World's Fastest Wireless Phone Charger

Sep 11, 2019
Xiaomi Unveils World's Fastest Wireless Phone Charger
View Full Size
Xiaomi has developed wireless charging technology that can power up even the largest devices in around an hour, the fastest among comparable systems, the Chinese smartphone maker said Monday. The Mi Charge Turbo will be introduced shortly coupled with Xiaomi's new 5G flagship smartphone.
 
'The speed of the new smartphone charging system is the fastest among wireless charging worldwide,' Vice President Cui Baoqiu told a news conference Monday. 'It does not fall short when compared to wired charging.' Beijing-based Xiaomi retains the distinction of being the first major Chinese phone maker to announce a wireless charging system, in March of last year. That feat was followed by the addition of a 20-watt system this past February. The new system announced Monday has a maximum capacity of 30 watts.
 
The company is testing a more powerful 40-watt charging system with the aim of commercializing it at an early date. 'Wireless charging will enter the age where it will become superior to wired charging,' said Cui. Xiaomi also announced plans for a corporate alliance that will permit smartphone users to charge devices wirelessly in automobiles, hotels and restaurants.
 
Xiaomi is the world's fourth-largest smartphone maker by volume after Samsung Electronics, Apple and compatriot Huawei Technologies, data from IDC shows. As well as growth in China and other Asian markets, Xiaomi's sales are also on the rise in Europe.
 

You have 0 items in you cart. Would you like to checkout now?
0 items