by Gary Mintchell | Jun 6, 2018 | Asset Performance Management, Data Management, Internet of Things
Have you been wondering about GE Digital and such products as Predix Asset Performance Management since the announcements of the new GE CEO reducing the group and throwing it into turmoil?
Well, just when I realized I had not heard anything for a while, this press release appeared. I don’t usually write about the announcements that come daily about sales “wins” or about success stories. But I felt this was significant in that it was news that GE Digital is still out there and that here is a user that is not a GE company. Also it reflects a trend of collaboration among companies. Plus another trend—one of the original hopes for the Industrial Internet of Things, that is, adding ability for OEMs to monitor their equipment at the customer’s site and provide service and support.
Here, GE Digital and SIG, a leading provider of packaging systems and solutions for the food and beverage industry, announced a strategic partnership to power digital innovation in food and beverage packaging.
SIG will deploy GE Digital’s PredixAsset Performance Management (APM) and Predix ServiceMax industrial applications across more than 400 customer factories worldwide to drive new levels of efficiency, create intelligent solutions and enable new possibilities for its customers.
The food and beverage industry is ripe for digital transformation, with consumers increasingly seeking innovative, convenient products that are not only safe and sustainable but also affordable and differentiated. At the same time, producers are facing competitive pressures, supply chain complexities and ever-shorter production cycles – creating an increased need for technologies that can enable producers to quickly identify, predict and act on changing consumer and market demands.
The unique combination of GE Digital’s APM and ServiceMax applications will enable SIG to build an end-to-end digital platform that will bring a new level of insight and data-driven intelligence to its customers worldwide – helping them and SIG transform how they predict, manage and service the entire lifecycle of SIG filling lines. By automatically collecting and analyzing asset data – tapping into billions of data points across its operations globally in real time – SIG and their customers can move beyond traditional asset monitoring and predictive service models to reimagine their supply chain, enhance quality control technologies and evolve their portfolio mix.
“Our ability to harness data is central to delivering on our promise of opening up new opportunities for our customers,” said Rolf Stangl, SIG, CEO. “By tapping into information in new and innovative ways, we will be able to deliver an unmatched level of performance, security, transparency and creativity across the entire food and beverage supply chain – through to the end consumer.”
SIG’s customers fill more than 10,000 unique products into SIG packaging across 65 countries worldwide. In 2017 alone, SIG produced 33.6 billion carton packs for its customers. Through this large-scale partnership, SIG and GE Digital will co-innovate packaging solutions and technologies to address the industry’s two biggest needs today: improving asset performance and optimizing service delivery.
The new digital service model will also enable SIG to deliver new solutions and business models based on advanced performance metrics, including as-a-service delivery, performance-based and subscription solutions.
The initial deployment is expected to go live in July 2018 with the global rollout anticipated to begin in January 2019.
by Gary Mintchell | May 24, 2018 | News, Technology
Living with technology a decade from now. Dell Technologies and the Institute for the Future conducted an in-depth discussion with 20 experts to explore how various social and technological drivers will influence the next decade and, specifically, how emerging technologies will recast our society and the way we conduct business by the year 2030.
There is no universally agreed upon determination of which technologies are considered emerging. For the purpose of this study, IFTF explored the impact that Robotics, Artificial Intelligence (AI) and Machine Learning, Virtual Reality (VR) and Augmented Reality (AR), and Cloud Computing, will have on society by 2030. These technologies, enabled by significant advances in software, will underpin the formation of new human-machine partnerships, according to the IFTF.
Talk of digital transformation is virtually everywhere in Information Technology circles and Operations Technology circles. My long and varied experiences have often placed me at the boundaries where the two meet—and are now increasingly overlapping.
The take on robotics is right on target. And forget about all the SciFi scare stories that mainstream media loves to promote. The future is definitely all about human-machine partnership or collaboration. For example I often talk with EMTs about life in the rescue squad. These people are always in the gym. Our population in the US has gotten so large and obese that they often have to lift 300+ lb. people who haven’t the strength to help themselves up. Think about a robot assistant helping the EMT.
The AI discussion is also fraught with prominent people like Ray Kurzweil or Elon Musk giving dystopian SciFi views of the future. We are a long way from “intelligence.” Where we are is really the use of machine learning and neural networks that help machines (and us) learn by deciphering recurring patterns.
Back to the study, the authors state, “If we start to approach the next decade as one in which partnerships between humans and machines transcend our limitations and build on our strengths, we can begin to create a more favorable future for everyone.”
Jordan Howard, Social Good Strategist and Executive Director of GenYNot, sees tremendous promise for the future of human-machine partnerships: “Many of the complex issues facing society today are rooted in waste, inefficiency, and simply not knowing stuff, like how to stop certain genes from mutating. What if we could solve these problems by pairing up more closely with machines and using the mass of data they provide to make breakthroughs at speed? As a team, we can aim higher, dream bigger, and accomplish more.”
Liam Quinn, Dell Chief Technology Officer, likens the emerging technologies of today to the roll-out of electricity 100 years ago. Quinn argues that we no longer fixate on the “mechanics” or the “wonders” of electricity, yet it underpins almost everything we do in our lives. Similarly, Quinn argues, in the 2030s, today’s emerging technologies will underpin our daily lives. As Quinn provokes, “Imagine the creativity and outlook that’s possible from the vantage point these tools will provide: In 2030, it will be less about the wonderment of the tool itself and more about what that tool can do.”
By 2030, we will no longer revere the technologies that are emerging today. They will have long disappeared into the background conditions of everyday life. If we engage in the hard work of empowering human-machine partnerships to succeed, their impact on society will enrich us all.
Robots
While offshoring manufacturing jobs to low-cost economies can save up to 65% on labor costs, replacing human workers with robots can save up to 90% of these costs.
China is currently embarking upon an effort to fill its factories with advanced manufacturing robots, as workers’ wages rise and technology allows the industry to become more efficient. The province of Guangdong, the heartland of Chinese manufacturing, has promised to invest $154 billion in installing robots.
Buoyed by their commercial success, the adoption of robots will extend beyond manufacturing plants and the workplace. Family robots, caregiving robots, and civic robots will all become commonplace as deep learning improves robots’ abilities to empathize and reason. Google recently won a patent to build worker robots with personalities.
Artificial Intelligence and Machine Learning
Approximately 1,500 companies in North America alone are doing something related to AI today, which equates to less than 1% of all medium-to-large companies. We’re seeing this in the financial services industry already, with data recognition, pattern recognition, and predictive analytics being applied to huge data sets on a broad scale. In a 2015 report, Bank of America Merrill Lynch estimated that the AI market will expand to $153 billion over the next five years—$83 billion for robots, and $70 billion for artificial intelligence-based systems.
In addition to their ability to make decisions with imperfect information, machines are now able to learn from their experiences and share that learning with other AI programs and robots. But AI progress also brings new challenges. Discussions surrounding who or what has moral and ethical responsibility for decisions made by machines will only increase in importance over the next decade.
Virtual Reality and Augmented Reality
Although both Virtual and Augmented Reality are changing the form factor of computing, there is a simple distinction between the two. VR blocks out the physical world and transports the user to a simulated world, whereas AR creates a digital layer over the physical world.
Despite the difference, both technologies represent a fundamental shift in information presentation because they allow people to engage in what Toshi Hoo, Director of IFTF’s Emerging Media Lab, calls “experiential media” as opposed to representative media. No longer depending on one or two of our senses to process data, immersive technologies like AR and VR will enable people to apply multiple senses—sight, touch, hearing, and soon, taste and smell—to experience media through embodied cognition.
Over the next decade, Hoo forecasts that VR, combined with vast sensor networks and connected technologies, will be one of many tools that enable distributed presence and embodied cognition, allowing people to experience media with all their senses.
Cloud Computing
It’s important to recognize that Cloud Computing isn’t a place, it’s a way of doing IT. Whether public, private, or hybrid (a combination of private and public), the technology is now used by 70% of U.S. organizations. This figure is expected to grow further, with 56% of businesses surveyed saying they are working on transferring more IT operations to the cloud, according to IDG Enterprise’s 2016 Cloud Computing Executive Summary.
While the cloud is not a recent technological advancement, cloud technology only really gathered momentum in recent years, as enterprise grade applications hit the market, virtualization technologies matured, and businesses became increasingly aware of its benefits in terms of efficiency and profitability. Increasing innovation in cloud-native apps and their propensity to be built and deployed in quick cadence to offer greater agility, resilience, and portability across clouds will drive further uptake. Start-ups are starting to use cloud-native approaches to disrupt traditional industries; and by 2030, cloud technologies will be so embedded, memories from the pre-cloud era will feel positively archaic by comparison.
Human Machine Partnership
Recent conversations, reports, and articles about the intersection of emerging technologies and society have tended to promote one of two extreme perspectives about the future: the anxiety-driven issue of technological unemployment or the optimistic view of tech-enabled panaceas for all social and environmental ills.
Perhaps a more useful conversation would focus on what the new relationship between technology and society could look like, and what needs to be considered to prepare accordingly.
By framing the relationship between humans and machines as a partnership, we can begin to build capacity in machines to improve their understanding of humans, and in society and organizations, so that more of us are prepared to engage meaningfully with emerging technologies.
Digital (Orchestra) Conductors
Digital natives will lead the charge. By 2030, many will be savvy digital orchestra conductors, relying on their suite of personal technologies, including voice-enabled connected devices, wearables, and implantables; to infer intent from their patterns and relationships, and activate and deactivate resources accordingly.
Yet, as is often the case with any shift in society, there is a risk that some segments of the population will get left behind. Individuals will need to strengthen their ability to team up with machines to arrange the elements of their daily lives to produce optimal outcomes. Without empowering more to hone their digital conducting skills, the benefits that will come from offloading ‘life admin’ to machine partners will be limited to the digitally literate.
Work Chasing People
Human-machine partnerships will not only help automate and coordinate lives, they will also transform how organizations find talent, manage teams, deliver products and services, and support professional development. Human-machine partnerships won’t spell the end of human jobs, but work will be vastly different.
By 2030, expectations of work will reset and the landscape for organizations will be redrawn, as the process of finding work gets flipped on its head. As an extension of what is often referred to as the ‘gig economy’ today, organizations will begin to automate how they source work and teams, breaking up work into tasks, and seeking out the best talent for a task.
Instead of expecting workers to bear the brunt of finding work, work will compete for the best resource to complete the job. Reputation engines, data visualization, and smart analytics will make individuals’ skills and competencies searchable, and organizations will pursue the best talent for discrete work tasks.
by Gary Mintchell | May 22, 2018 | Internet of Things, Manufacturing IT, Technology
A few of us gathered for a round table discussion of Internet of Things while I was at Dell Technologies World at the beginning of the month. I arrived a little early and had a private round table for several minutes before others arrive and the discussion became broader.
Ray O’Farrell, CTO of VMware and GM of IoT at Dell Technologies, said the focus of last 6 months since the new Internet of Things organization was announced included these three points:
1. Dell is 7 companies, trying to achieve one cohesive strategy across all; one organization when facing customers.
2. Best way is to work within the ecosystem, that is history of VMWare.
3. Building technology and leverage solutions. This is a complex undertaking as not all challenges within IoT are alike—there are few cookie cutter applications.
The evolution of Internet of Things within Dell to Dell EMC to Dell Technologies constitutes an upward spiraling path encompassing the greater breadth of technologies and organization reflecting the post-merger company. When I first came along, the concept was building an ecosystem around selling an edge device appliance. Now the strategy is much broader bringing the goal of IT/OT convergence closer to reality. As I’ve mentioned before, the IT companies are attacking that convergence from the IT side after years of manufacturing/production oriented suppliers trying to accomplish the same thing from the OT side. Maybe like the old country song we’ll meet in the middle someday.
Everyone talks Artificial Intelligence (AI) these days, and Dell Technologies is not exception. However, AI is not the science fiction doom and gloom predicted by Ray Kurzweil, Elon Musk, and others. Mostly it entails machine learning (ML) from detected patterns in the data.
Or as Dell Technologies says, it is applying AI and ML technology to turn data into intelligent insights, drive a faster time to market, and achieve better business outcomes.
News summary
• Dell EMC PowerEdge expands portfolio to accelerate AI-driven workloads, analytics, deployment and efficiency
• Deepens relationship with Intel to advance AI community innovation, machine learning (ML) and deep learning (DL) capabilities with Dell EMC Ready Solutions
• Dell Precision Optimizer 5.0 now enhanced with machine learning algorithms, intelligently tunes the speed and productivity of Dell Precision workstations.
• Dell EMC uses AI, ML and DL to transform support and deployment
14th generation Dell EMC PowerEdge four-socket servers and Dell Precision Optimizer 5.0 are designed to further strengthen AI and ML capabilities.
According to the recently released update of the Enterprise Strategy Group (ESG) 2018 IT Transformation Maturity Curve Index, commissioned by Dell EMC, transformed companies are 18X more likely to make better and faster data-driven decisions than their competition. Additionally, transformed companies are 22X as likely to be ahead of the competition with new products and services to market.
“The Internet of Things is driving an onslaught of data and compute at the edge, requiring organizations to embrace an end-to-end IT infrastructure strategy that can effectively, efficiently and quickly mine all that data into business intelligence gold,” said Jeff Clarke, vice chairman, Products & Operations, Dell. “This is where the power of AI and machine learning becomes real – when organizations can deliver better products, services, solutions and experiences based on data-driven decisions.”
Unlike competitors’ four-socket offerings, these servers also support field programmable gate arrays (FPGAs)3, which excel on data-intensive computations. Both servers feature OpenManage Enterprise to monitor and manage the IT infrastructure, as well as agent-free Integrated Dell Remote Access Controller (iDRAC) for automated, efficient management to improve productivity.
Dell EMC is also announcing its next generation PowerMax storage solution, built with a machine learning engine which makes autonomous storage a reality.
Leveraging predictive analytics and pattern recognition, a single PowerMax system analyzes and forecasts 40 million data sets in real-time per array4, driving six billion decisions per day5 to automatically maximize efficiency and performance of mixed data storage workloads.
The new Dell Precision Optimizer 5.0 uses AI to automatically adjust applications running on Dell Precision workstations to maximize performance by:
• Custom-optimizing applications: Dell Precision Optimizer learns each application’s behavior in the background and uses that data to employ a trained machine learning model that will automatically adjust the system to optimized settings and deliver up to 394% improvement in application performance.
• Automating systems configuration adjustments: Once activated and a supported application is launched, the software automatically adjusts system configurations such as CPU, memory, storage, graphics and operating system settings.
Speaking of partners and collaboration, Dell Technologies and Microsoft join forces to build secure, intelligent edge-to-cloud solution featuring Dell Edge Gateways, VMware Pulse IoT Center, and Microsoft Azure IoT Edge
News summary
• Joint IoT solution helps simplify management, enhances security and help lowers cost of deployment at the edge
• Built on innovative analytics applications, management tools and edge gateways to enable network security from edge devices to the cloud
• Accelerates IoT adoption in industry verticals key to economic growth and development
The joint solution offers an underlying IoT infrastructure, management capabilities, and security for customers looking to deploy IoT for scenarios like predictive maintenance, supply chain visibility and other use cases. The solution will deliver:
• Intelligence at the edge with Microsoft Azure IoT Edge: This application extends cloud intelligence to edge devices so that devices can act locally and leverage the cloud for global coordination and machine learning at scale
• Management and monitoring of edge devices with VMware Pulse IoT Center: This provides more secure, enterprise-grade management and monitoring of diverse, certified edge devices including gateways and connected IoT devices, bios and operating systems. This ecosystem will be built over time involving deeper integration and certification to support customer requirements.
• High-performance, rugged Dell Edge Gateways: IoT devices with powerful dual-core Intel® Atom™ processors connect a variety of wired and wireless devices and systems to aggregate and analyze inputs and send relevant data to the cloud
VMware Pulse IoT Center will serve as the management glue between the hardware (Dell Edge Gateways or other certified edge systems), connected sensors and devices and the Microsoft Azure IoT Edge. Initially, Pulse will help to deploy the Microsoft Azure IoT Edge to the requisite edge systems so that it can start collecting, analyzing and acting on data in real-time.
by Gary Mintchell | Apr 19, 2018 | Asset Performance Management, Automation, Internet of Things, Operations Management
Artificial Intelligence, always known as AI, along with its sometime companion robots leads the mainstream media hype cycle. It’s going to put everyone out of jobs, destroy civilization as we know it, and probable destroy the planet.
I lived through the Japanese robotic revolution-that-wasn’t in the 80s. Media loved stories about robots taking over and how Japan was going to rule the industrialized world because they had so many. Probing the details told an entirely different story. Japan and the US counted robots differently. What we called simple pick-and-place mechanisms they called robots.
What set Japanese industrial companies apart in those days was not technology. It was management. The Toyota Production Method (aka Lean Manufacturing) turned the manufacturing world on its head.
My take for years based on living in manufacturing and selling and installing automation has been, and still is, that much of this technology actually assisted humans—it performed the dangerous work, removing humans from danger, taking over repetitive tasks that lead to long-term stress related injuries, and performing work humans realistically couldn’t do.
Now for AI. This press release went out the other day, “With AI, humans and machines work smarter and better, together.” So, I was intrigued. How do they define AI and what does it do?
Sensai, an augmented productivity platform for manufacturing operations, recently announced the launch of its pilot program in the United States. Sensai increases throughput and decreases downtime with an AI technology that enables manufacturing operations teams to effectively monitor machinery, accurately diagnose problems before they happen and quickly implement solutions.
The company says it empowers both people and digital transformation using a cloud-based collaboration hub.
“The possibility for momentous change within manufacturing operations through digital transformation is here and now,” said Porfirio Lima, CEO of Sensai. “As an augmented productivity platform, Sensai integrates seamlessly into old or new machinery and instantly maximizes uptime and productivity by harnessing the power of real time data, analytics and predictive AI. Armed with this information, every person involved – from the shop floor to the top floor – has the power to make better and faster decisions to increase productivity. Sensai is a true digital partner for the operations and maintenance team as the manufacturing industry takes the next step in digital transformation.”
By installing a set of non-invasive wireless sensors that interconnect through a smart mesh network of gateways, Sensai collects data through its IIoT Hub, gateways and sensors, and sends it to the cloud or an on-premise location to be processed and secured. Data visualization and collaboration are fostered through user-friendly dashboards, mobile applications and cloud-based connectivity to machinery.
The AI part
Sensai’s differentiator is that it provides a full state of awareness, not only of the current status, but also of the future conditions of the people, assets and processes on the manufacturing floor. Sensai will learn a businesses’ process and systems with coaching from machine operators, process and maintenance engineers. It will then make recommendations based on repeating patterns that were not previously detected. Sensai does this by assessing the team’s experiences and historical data from the knowledge base and cross checking patterns of previous failures against a real-time feed. With this information, Sensai provides recommendations to avoid costly downtime and production shutdowns. Sensai is a true digital peer connecting variables in ways that are not humanly possible to process at the speed required on a today’s modern plant floor.
About the Pilot Program
Participation in Sensai’s pilot program is possible now for interested manufacturers. Already incorporated throughout Metalsa, a leading global manufacturer of automotive structural components, Sensai is set to digitally disrupt the manufacturing industry through AI, including those in automotive, heavy metal and stamping, construction materials, consumer goods and more.
Porfirio Lima, Sensai CEO, answered a number of follow up questions I had. (I hate when I receive press releases with lots of vague benefits and buzz words.)
1. You mention AI, What specifically is meant by AI and how is it used?
Sensai uses many different aspects of Artificial Intelligence. We are specifically focused on machine learning (ML), natural language processing (NLP), deep learning, data science, and predictive analytics. When used together correctly, these tools serve a specific use case allowing us to generate knowledge from the resulting data. We use NLP to enable human and computer interaction helping us derive meaning from human input. We use ML and deep learning to learn from data and create predictive and statistical models. Finally, we use data science and predictive analytics to extract insights from the unstructured data deriving from multiple sources. All of these tools and techniques allow us to cultivate an environment of meaningful data that is coming from people, sensors, programmable logistics controllers (PLCs) and business systems.
2. “Learn processes through operators”—How do you get the input, how do you log it, how does it feed it back?
Our primary sources of data (inputs) are people, sensors, PLCs, and business systems. In the case of people on the shop floor or operators, we created a very intuitive and easy to use interface that they can use on their cellphones or in the Human Machine Interfaces (HMIs) that are installed in their machines, so they can give us feedback about the root causes of failures and machine stoppages. We acquire this data in real-time and utilize complex machine learning algorithms to generate knowledge that people can use in their day-to-day operations. Currently, we offer web and mobile interfaces so that users can quickly consume this knowledge to make decisions. We then store their decisions in our system and correlate it with the existing data allowing us to optimize their decision-making process through time. The more a set of decisions and conditions repeats, the easier for our system is to determine the expected outcome of a given set of data.
3. Pattern? What patterns? How is it derived? Where did the data come from? How is it displayed to managers/engineers?
We create “digital fingerprints” (patterns) with ALL the data we are collecting. These “patterns” allow us to see how indicators look before a failure occurs, enabling us to then predict when another failure will happen. Data comes from the machine operators, the machines or equipment, our sensors, and other systems that have been integrated to Sensai’s IIOT hub.
We trigger alerts to let managers and engineers know that a specific situation is happening. They are then able to review it in their cellphones as a push notification that takes them to a detailed description of the condition in their web browser where they can review more information in depth.
4. What specifically are you looking for from the pilots?
We are not a cumbersome solution, for us is all about staying true about agility and value creation. We look for pilots that can give us four main outcomes:
– Learn more about our customer needs and how to better serve them
– A clear business case that can deliver ROI in less than 6 months after implementation and can begin demonstrating value in less than 3 months.
– A pilot that is easy to scale up and replicate across the organization so we can take the findings from the pilot and capitalize them in a short period of time.
– A pilot that can help Sensai and its customers create a state of suspended disbelief that technology can truly deliver the value that is intended and that can be quickly deployed across the entire organization.
by Gary Mintchell | Apr 5, 2018 | Data Management, News
Innovation springs from small and new companies, so holds Andrew Johnson, CEO of ShelfAware, a software startup in the what could be called the MRO commodity business. His family business, Oringsales.com, is a master distributor of those crucial but often overlooked components called O-rings. Suppliers to maintenance shops and OEMs have been tackling vendor managed inventory and other inventory tracking processes for many years. But how do you economically track something as small as an O-ring?
The brothers running the distributorship business figured out that RFID tags were becoming inexpensive enough to warrant use in small bags of these small components. They wrote an application, embedded RFID tags on the bags, and established a workflow. Originally for their customers of O-rings, customers soon demanded the system for other small components, as well.
The key proposition—remotely monitor consumption of small parts. He calls it get “the dudes in trucks” off the streets to better utilize their time rather than driving around counting parts.
Johnson is entrepreneurial and evangelistic. He told me, ”I’m reaching out to you because I have a message I would like to send to our USA manufacturing friends that I think they would find very interesting. The time is now to innovate our manufacturing infrastructure if we intend on bringing manufacturing back to the USA in a big way. Its easier than ever before to take the tech leap with many Internet of Things systems popping up every month that don’t require integration into your ERP systems to achieve a big ROI. It’s literally plug and play technology for manufactures.
This is his innovation story.
I am a young entrepreneur (32) that has grown up in the industrial manufacturing industry as a member of a family run industrial parts distributor. I spent many summers of my childhood inspecting o-rings, gaskets, and other seals… very exciting summer job. Now I am working with my 3 brothers in our family business as we try to innovate the industrial landscape. We recently invented & patented an intelligent inventory supply chain that is powered by passive RFID technology. We deployed our Internet of Things supply chain system at 3 Midwestern manufactures last year (Eskridge Mfg, Energy Mfg, Oilgear Mfg) and the system is performing better than we could have ever imagined.
Very simply put, our system, ShelfAware, monitors the consumption of commodity inventory in real time using RFID chips that are embedded into the product packaging. This consumption data, Big Data, is then analyzed and fed to the manufacturer’s supply chain partners to guarantee no stock out, lean inventory, and lean inventory pipe-lines which all means the right parts, at the right time, cheaper. Our three key questions we practically chant while working with our system are: Is this Accurate?, Efficient?, Effective?
Yes, RFID inventory systems have been around for years, but never really applied to the consumable commodity products, aka “small parts”. The main tech advancements that have made a system such as ShelfAware viable now are:
- RFID tags are getting really really cheap, sometimes less than $0.05 each
- The internet has allowed the software driving the system to be flexible and easily accessible
- RFID hardware is much less expensive and now highly reliable
The business plan, a bit audacious, announces “The Opportunity to Disrupt a Marketplace with a Collaborative Platform”.
The traditional large industrial supply incumbents who offer vendor managed inventories (VMI) have expanded their product offering horizontally leaving them spread too thin. They are good at some product groups, but great at very few product groups. This has created vulnerabilities related to product expertise like sourcing, engineering, and general product support. ShelfAware intends on exploiting these vulnerabilities by giving many niche product vendors the ability to collaborate on ShelfAware’s IoT Inventory Platform thus creating a more efficient crowd sourced inventory supply chain.
Objective of ShelfAware as a company states “To create an IoT Intelligent Inventory Platform that can support multiple independent product vendors who collectively support large supply chains demanded by Large or complex OEM’s. ShelfAware will create value in the industrial supply market by revolutionizing supply chain theory through the use of a collaborative IoT, RFID Intelligent Inventory Platform.
The Platform must fulfil two primary roles for this supply chain model to be successful with emphasis placed on the IoT vendor collaboration software.
- Deploy an intelligent inventory management system inside manufacturing facilities.
- Deploy a Vendor Side supply chain cooperative system.
I am intrigued by the whole concept. And it seems to be working now, even in its infancy.
