How smart manufacturers bring Industry 4.0 principles to quality

Quality matters. Many manufacturers will tell you that poor quality affects both the top and bottom line. They will tell you that the consequences of poor quality are on the rise and that social media can make quality issues devastating to an OEM’s brand. They will also tell you that they are struggling to respond. The problem? While the consequences of poor quality are rising, so too are margin pressures. Margin pressures means that budgets are tight and few can respond in traditional ways. Smart manufacturers need to think differently.

Cost of quality is on the rise

Beyond brand impact, poor quality also has a real impact to the bottom line. We must scrap or rework defective products which can eat into overall equipment effectiveness (OEE) measures and lead to plant inefficiency. Challenges only increase once a defective product leaves the factory. Growing supply chain complexity means that products are increasingly costly to find and recall.

Flex – one of the world’s largest electronics makers – estimates that for every $ 1 spent in product creation, they spend $ 100 creating resolutions to quality problems.

Flex on quality for smart manufacturers

 

That is incredible. Solutions may include tracing root cause analysis to identify problems upstream with raw material inputs or downstream with the manufacturing process. It may also mean going back to product development to understand how product design contributes to quality.

Different industries; different impacts on quality

The implication of poor quality changes depending on the industry. For example, volume matters in the electronics industry. OEMs produce dozens of products per minute which means that a defect in a cell phone casing or circuit board assembly can be replicated hundreds of times before they identify and resolve the issue. That is a lot of potential scrap or rework. Smart electronics OEMs look for solutions that impact inspection speed. They need to quickly identify a quality issue, understand the root cause, and implement a resolution before hundreds of defective units are created.

Automotive companies have a different challenge. Here, OEMs are focused on manufacturing precision. Millimeters matter and a high degree of automation means that poorly calibrated equipment can cause small but meaningful variances. Sometimes these variances can be small – so small that only highly trained human inspectors with sophisticated testing tools can spot the difference. Smart automotive OEMs look for solutions that can help human inspectors identify these very small deviations with more accuracy.

Traditional inspection methods can be costly, slow, ineffective, and sometimes dangerous

Traditional manual inspections can be problematic. Quality inspection is a high-pressure job and the sole reliance on humans without new tools or methods can be slow and ineffective. Humans make mistakes. We get tired and have bad days. We require extensive training to spot defects and retraining to keep pace with new models. All this can hinder agility – a problem which intensified as our labor force ages and retires.

Some OEMs are getting smarter

Increasingly smart OEMs across a range of industries are approaching IBM about ways to get smarter with their quality inspection process. These firms are looking for help bringing technology – specifically machine learning and AI to bear on the problem. Fortunately, IBM has a number of solutions that can help.

Some firms are seeking to better understand the factors that contribute to quality. Have we exceeded quality thresholds? Does temperature or humidity play a role? What about equipment age and maintenance cycles? IBM has a statistical-based solution – called Prescriptive Quality – that dynamically weighs variables that might contribute to issues. This is a great solution when inspectors cannot identify quality based on an image or sound.

One of the hottest areas of interest from OEMs is how AI technology can identify visual or acoustic patterns related to quality defects. Can an image be used to identify a scratch on a cell phone casing or car paint job? Can acoustic sensors “hear” a poorly functioning dishwasher before the product is released from testing? The answers are yes and yes. IBM has two solutions – Visual Insights and Acoustic Insights – that use sophisticated AI to spot defects. What is even more impressive? These solutions can start with a small number of defective images or sounds and can learn over time to get smarter.

Does this mean we don’t need quality inspectors?

It is easy to position many of these AI-based solutions as replacing the jobs of quality inspectors. Yet this is rarely the case. Smart companies see these solutions as tools that help quality inspectors improve throughput and effectiveness. Put simply, technology like Visual Insights or Acoustic Insights help inspectors inspect products more quickly, with fewer misses, and fewer false positives. Rather than replacing inspectors, these technologies become important aids that help OEM respond better to the rising cost of quality without sacrificing margins.

Take the next step

Want to learn more about how IBM views quality and how we can help OEMs address quality challenges?

Learn more about quality management in the era of AI here.

Get more detail on specific quality solutions for your business:

 

 

 

 

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Entrust Datacard and Schneider Electric Bring New Innovation to the IoT Security Ecosystem

Entrust Datacard and Schneider Electric Bring New Innovation to the IoT Security Ecosystem

Entrust Datacard and Schneider Electric Bring New Innovation to the IoT Security Ecosystem

The new partnership enables customers to choose identity-based security to support their Internet of Things (IoT) projects.

Entrust Datacard, a leading provider of trusted identity and secure transaction technology solutions, today announced it will join Schneider Electric’s Collaborative Automation Partner Program (CAPP).

The collaboration will allow Schneider Electric, the leader in digital transformation of energy management and automation, to offer its industrial customers Entrust Datacard’s ioTrust™ Security Solution to accelerate the adoption of identity based security solutions within their industrial operations.

The ioTrust Security Solution creates a trusted infrastructure for the secure flow of M2M data, thereby mitigating risk and enabling greater visibility in industrial environments. The technology-agnostic software solution is designed to achieve a high degree of integration and scalability, and is compatible with industrial protocols and standards, enabling Schneider Electric to optimize its existing infrastructure and accelerate IoT deployments.

Josh Jabs, Vice President, Office of the CTO and General Manager – PKI and IoT, Entrust Datacard, said:

“To successfully transition to the digital economy, companies must secure not only their own operations, but also be confident in the security and authenticity of services and physical components which are used in the construction of their solutions.”

“This collaboration with Schneider Electric ensures the convergence of application, devices, and user security requiring a breadth of tools that all interact seamlessly and securely. The Entrust Datacard ioTrust Security Solution enables businesses to remain flexible in choosing devices, backend applications, and data analytics platforms best suited to their specific environments. The secure-by-design approach provides robust visibility in real time, with easy to manage data functions and access control to sanction productive and profitable decisions.”

CAPP enables Schneider Electric to offer complete business solutions by integrating innovative technologies developed by its partner ecosystem into its own offerings. The partnership with Entrust Datacard adds a key component to Schneider Electric’s cybersecurity solutions that are delivered through the company’s EcoStruxure™ for Industry architecture.

About the partnership, Dan DesRuisseaux, Director Cybersecurity, Industry Business, Schneider Electric, commented:
“In the past, industrial users often implemented self-signed certificates or physical attributes to identify devices, but these are not as secure as identities issued by a certificate authority.”

DesRuisseaux continued: “In addition, it is essential for critical infrastructure to be provisioned for local and cloud based identity enrollment and life-cycle management capabilities in order to deliver authentication and authorization services that work within the protected network or connect ecosystems via global distributed and hosted infrastructure.”

“Certificate authority solutions require a proven partner, and in this instance Entrust Datacard’s added expertise in both internally networked and publicly connected certificate authorities is also of value to our customers,” says DesRuisseaux. “We are excited to have Entrust Datacard bring its significant expertise to the industrial market.”

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Microshare and Senet Partner to Bring Vertical IoT Data and Networking Solutions to Facilities Management and Logistics Markets

Microshare and Senet Partner to Bring Vertical IoT Data and Networking Solutions to Facilities Management and Logistics Markets

Microshare and Senet Partner to Bring Vertical IoT Data and Networking Solutions to Facilities Management and Logistics Markets

Senet today announced a partnership with the IoT data sharing and governance firm Microshare to deliver IoT data-centric solutions to the facilities management and logistics markets.

This partnership extends Microshare’s Smart Facilities Management (SFM) and Logistics Management Solutions (LM) to Senet customers, allowing for IoT sensor data to be broken out of silos and enriched with more context to create new efficiencies and revenue opportunities. Through the combination of Senet’s cost-effective LoRaWAN™ network and Microshare’s approach to data storage, governance, and micro-contracts, customers can optimize operations at facilities and across logistical networks by controlling the flow of information and creating new business models based on actionable granular data.

Microshare, based in Philadelphia, provides Smart Facilities Management and Logistics Management solutions that give logistics professionals, property owners, managers, facilities management providers, tenants and supply chain coordinators a turnkey solution to controlling costs and issues across a portfolio of properties or assets. These solutions are quick to deploy, inexpensive to maintain, and constantly able to adapt to changing needs.

The Microshare facilities suite enables managers to leverage data to better manage staff and subcontractors in ways that control costs, avoid liabilities, improve relations with vendors and tenants, and help shape future lease and contract terms. Microshare’s logistics solution enables granular tracking, maintenance, depreciation, and other leveraging of transportation vehicles, physical plant, infrastructure and other elements of your supply chain, transforming inert assets into networked sources of intelligence and revenue.

“This is the kind of partnership that is helping move IoT from the drawing board to reality in many companies,” said Ron Rock, CEO of Microshare.

“Our goal is to help companies go beyond merely saving a bit of operational budget and to unleash the revenue potential of the data they can generate internally. That means partnering with leaders like Senet to help define the data standards of the emerging IoT economy.”

“To identify the strongest insights and make IoT data truly actionable, the right technologies and processes must be in place to collect, store, enrich and share it,” said Senet CEO, Bruce Chatterley. “We are excited to partner with Microshare to help customers unlock new IoT revenue streams and contribute to ensuring IoT realizes its full potential of delivering economic, environmental, and social improvements.”

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Iris Automation gets funding for plan to bring AI to commercial drones

Iris Automation gets funding for plan to bring AI to commercial drones

Collision avoidance provider Iris Automation has received $ 8 million funding to bring its AI technology to autonomous drones and other flying vehicles.

In October 2015, Iris Automation’s founders put fingers to keys and wrote the company’s first lines of code. Two months later, they had a prototype for a system that aims to tackle the three big challenges facing the industrial drone industry: reliability, safety and autonomy. Their collision avoidance system for commercial drones promises to unlock the potential of beyond line-of-sight use cases.

After humble beginnings, the San Francisco-based company was accepted into the Y-Combinator start-up accelerator program in 2016 and, just yesterday, announced $ 8 million of Series A funding, led by Bessemer Ventures, to bring their AI-powered technology to life.

“Iris’ exceptional team has unlocked a $ 100 billion global industry by ensuring aviation safety in a world serviced by drones. No other technology comes close to their system in providing situational awareness in a feasible package for the flying robotics industry,” said David Cowan, the partner at Bessemer who led rounds in aerospace innovators like Skybox, Rocket Lab and Spire Global.

The operation of UAVs beyond line-of-sight without suitable sense and avoid systems is largely forbidden by regulatory bodies. That’s why, if drones are to become more widely useful, autonomous tools for industry, they need intelligent situational awareness technology.

Read more: Can drones and commercial aircraft safely share airspace?

The case for autonomous drones

Regardless of how skilled a drone pilot may be, it can be difficult to see hazards, due to the reliance of onboard cameras at longer ranges. Collisions can have expensive, or even tragic, consequences. When a company can’t demonstrate adequate mitigation of operational risk, regulators must limit drone applications.

Iris’s solution combines computer vision and deep learning algorithms to allow the drone to see the world much like a human pilot does – identifying potential hazards and intuiting speed and distance. The plug-and-play system interweaves basic deterministic algorithms with more advanced, non­deterministic algorithms and neural networks, which allow the system to be fault tolerant.

Harnessing this technology, the platform can detect distant objects, identify it (as a light aircraft, for example) and estimate its distance. A sophisticated logic core then autonomously manoeuvres the drone (or other flying robot) out of a collision trajectory.

Read more: Drones offer a new perspective on British Columbia wildfires

Rising above the competition

“Iris Automation’s approach to sensing is unlike anything ever attempted in the autonomous vehicle space,” said Alexander Harmsen, CEO at Iris Automation. “Our team of experts in computer vision, machine learning, and traditional aviation have built a product that will provide the level of safety necessary for pushing the boundaries of what is possible with drones, at a size factor and price point unheard of in the world of aviation.”

For all the artificial intelligence and machine learning power behind of its software, it’s somewhat surprising to learn that the hardware employs smartphone camera technology – but given the economies of scale of such devices, the cost, weight and power-intensiveness of radar, and the low resolution, unproven potential of LiDAR, it’s a shrewd choice.

“With a range of over 1,500ft, our system is 50x more powerful than the ‘bumper solutions’ that some current drone companies are using today with a mere 30ft detection range. Furthermore, the product is a standalone unit, agnostic to all platforms and can be integrated into any commercial drone in the world,” revealed Alejandro Galindo, Head of R&D.

Iris Automation

The Iris Automation team in its early days, working out of a basement (Credit: Iris Automation)

Read more: Parrot revamps consumer drones for commercial market

Autonomous drones: the business applications

Industrial drone operations using pilots would not be economically viable for many companies. The more cost-effective ability to fly autonomously and Beyond­Visual­Line­of­Sight (BVLOS) would pave the way for new methods of pipeline inspection, package delivery, large agribusiness, mining exploration, and much more besides.

Currently in open beta, Iris Automation is looking for partners in the UAV and drone space for its Early Adopter Program. Regulatory exceptions for BVLOS flights in the US Federal Aviation Administration’s (FAA) UAV rules (referred to as Part 107) allow drone companies to trial new technologies. There’s an opportunity to apply for BVLOS operations through the waiver process by using the Iris System before it is commercially available.

With its latest funding, Iris Automation is looking to expand its team (which already boasts expertise from the likes of NASA and Boeing) and scale its technology to participate in the upcoming White House UAS Integration Pilot Projects. Given, their product, talent and increasing financial backing, we could soon see myriad Iris-equipped autonomous drones rising out of Silicon Valley.

Read more: DJI launches FlightHub for drone fleet management

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Three technology players bring their best to form QodeNext

To provide value to the customers, Intellicon, Essae Technologys and Intercode Solutions have announced joining of their forces. They are coming together to exploit the strengths of the individual companies. The merged entity is named as QodeNext.
The new Rs 100 Cr QodeNext would position itself to serve the requirements of many of its existing customers in the markets they operate. The new products will serve the Internet of Things (IoT), Manufacturing 4.0 and Artificial Intelligence needs of their existing and new customers.
Intellicon can offer Track and Trace, Warehouse Management, Asset Management solutions to existing customers of Intercode and Essae. Essae will offer consumable solutions in the form of self adhesive labels and ribbons to the combined client base.
Intellicon has strengths in providing software solutions, Essae in self-adhesive label manufacturing and Intercode in ribbons manufacturing. And, all the three players are associated with Zebra Technologies Corporation.
Combining these strengths is going to provide a huge cross selling opportunity for the combined entity and provide the customers with a total automation solution under one roof.

The AIDC market in India is only at start of hyper-growth stage, there is a lot of potential for growth in this space. Solutions offered under QodeNext has a great potential to move into markets outside India as well. There is certainly competition from about 150 companies but the value offerings of the new entity (QodeNext) would put them in a very good stead.

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