In my experience spanning IIoT implementations in electronics and industrial manufacturing, PLM engagements across automotive, medical, and general manufacturing, and advisory and workshop engagements across multiple sectors, I have seen a framework so consistent it has become predictable.

An organisation invests in IIoT. Sensors go on machines. A platform ThingWorx, Ignition, AWS IoT, gets deployed. Dashboards appear. Management is impressed. Six months later, the dashboards are still running. Nobody is acting on them.

The technology worked. The transformation did not.

The reason is almost never the IIoT platform. It is what was missing underneath it.

IIoT gives you data. PLM gives it meaning.

IIoT tells you what is happening on the shop floor right now, machine states, cycle times, and energy consumption. This is powerful, but it’s only half the story. To derive actual value, you must answer: What should be happening? The expected cycle time for a specific product variant, the approved digital work instructions, the engineering change released last shift, and the Bill of Materials tolerances, this information doesn’t live in your IIoT platform. It lives in your PLM system.

Without a “Digital Thread” connecting PLM to the shop floor, your IIoT implementation is a window into a process whose definition is scattered across email threads and institutional memory. You aren’t measuring against a standard; you’re measuring against noise.

You are not measuring against a standard. You are measuring against nothing.

Field Evidence: The High-Mix Production Challenge

During an IIoT integration for a global electronics manufacturing leader’s High-Mix Low-Volume lines, I saw this play out. Their assembly process generated mountains of data, but decisions were still being made on retrospective reports compiled days later.  By implementing a unified dashboarding (Thingworx) environment, we integrated data from the factory floor edge to the enterprise level, connecting MES, Asset Management, and Inventory. The “moment of clarity” didn’t come from the sensors, but from the standardization. Because their product data was structured in PLM, the IIoT dashboard could finally show deviation from a known engineering standard.

This methodology is a cornerstone of my work, documented in my book, “Product Lifecycle Management (PLM): A Digital Journey Using Industrial Internet of Things (IIoT).” This work, recognized with the 2020 Taylor & Francis Award for Outstanding Professional Book, validates that the “Digital Thread” is only as strong as its weakest link. If your PLM backbone is non-existent, your IIoT journey has no destination.

When it goes wrong

This is not always how it goes. Earlier in my career, I worked with a global appliance manufacturer that had invested significantly in Windchill, a capable, enterprise-grade PLM platform. Senior leadership set a Go-Live date and drove toward it. What they did not do was map the existing processes, align the engineering stakeholders, or build the organisational readiness to use what they had deployed. The result was predictable: engineers bypassed the system, CAD data errors proliferated, and change management collapsed. The platform was running. The discipline was absent. Those two things are not the same and confusing them is the most expensive mistake a manufacturer can make.

The ownership problem that nobody names

In most organisations, IIoT is treated as an IT project. Corporate IT claims the implementation, connects the sensors, builds the dashboards, and hands it over.

I have seen this up close. In one engagement, I told the Engineering team directly: you are the runner. This process, this data, this implementation, it belongs to you. Within days, corporate IT arrived and overruled that position. They would drive the implementation.

Sensors got connected. Dashboards got built. But the people who understood what the data meant, who knew what a deviation in cycle time actually indicated about the process, which engineering change had introduced a new variable, which BOM revision applied to today’s production run, those people had been moved aside.

The result? A technically successful implementation that produces data nobody trusts enough to act on. For Industry 4.0 to move into Industry 5.0 (Human-Centricity), a concept I explore in my book Industry 5.0: The Future of the Industrial Economy, the practitioner must own the data. It is about human-machine collaboration, not human replacement by automated dashboards.

IIoT without Engineering ownership is a window nobody is looking through. PLM without Engineering ownership is a database nobody maintains. Both fail for the same reason; the wrong function is driving.

The Industry 4.0 readiness question nobody asks

Before any manufacturer invests in IIoT, sensors, or smart factory infrastructure, there is one question that should come first. I ask it in every initial engagement:

Can you give me a complete picture of your current CAD, PLM, ERP, and PLC environment?

The answer tells me everything about whether an Industry 4.0 initiative will succeed. Not the technology answer, the confidence of the answer. Whether the people in the room know immediately what systems they have, who owns the data in each, and how those systems currently talk to each other.

In most SME engagements, the honest answer is: we cannot fully describe it. The systems exist. The discipline connecting them does not.

You cannot build a smart factory on top of that. You can connect sensors to machines. You can build dashboards. But without a disciplined PLM backbone, clean product data, structured BOMs, traceable process definitions, your IIoT implementation will generate noise instead of insight.

Industry 4.0 does not begin with sensors. It begins with structured product knowledge. PLM is where that structure lives.

Three things to check before your next IIoT investment

If you are planning an IIoT initiative or wondering why your current one has not delivered, ask these three questions before spending another rupee or dollar on connectivity:

1. Does your engineering BOM reliably reach your ERP without manual intervention? If not, your product data foundation is already broken. IIoT will not fix it, it will expose it.
2. Do your process instructions on the shop floor reflect the latest engineering release, automatically, not manually? If not, your IIoT platform is measuring against outdated standards.
3. Does Engineering own your IIoT implementation, or does IT? If IT owns it, the people who understand your process are not driving the transformation. And the dashboards will stay beautiful and unactioned.

PLM is not the trendy part of Industry 4.0. Nobody puts it on a conference keynote slide next to cobots and digital twins. But it is the backbone that determines whether everything else works. Without it, your smart factory is smart infrastructure built on fragile foundations.

About the Author :

Mr. Uthayan Elangovan
Founder & CEO – Neel SMARTEC

Author of the PLM & IIoT Trilogy

Mr. Uthayan Elangovan is the Founder of Neel SMARTEC, a strategic advisory practice specializing in PLM, IIoT, and Industry 5.0 transformation. With 20+ years of hands-on experience, he provides vendor-agnostic guidance to help manufacturers bridge the gap between engineering discipline and shop-floor reality.

Mr. Uthayan Elangovan is definitive trilogy on industrial digital transformation includes:

Smart Automation to Smart Manufacturing: Industrial Internet of Things (Named one of the “Best Manufacturing Automation books of all time” by BookAuthority).

Product Lifecycle Management (PLM): A Digital Journey Using Industrial Internet of Things (IIoT) (2020 Taylor & Francis Award Winner for Outstanding Professional Book).

Industry 5.0: The Future of the Industrial Economy.

Mr. Uthayan Elangovan is currently authoring the forthcoming Handbook of Product Lifecycle Management (PLM): Fundamentals, Components, and Implementation Best Practices (CRC Press, Taylor & Francis).

Mr. Uthayan Elangovan is Bestowed with the following Licenses & Certifications:

https://www.linkedin.com/in/uthayanelangovan/details/certifications/ 

Mr. Uthayan Elangovan is Accorded with the following Honors & Awards:

https://www.linkedin.com/in/uthayanelangovan/details/honors/

Mr. Uthayan Elangovan can be contacted at :

LinkedIn |  E-mail | Instagram | Blog

About Neel SMARTEC

Neel SMARTEC is a founder-led, vendor-agnostic consulting practice helping manufacturers accelerate Industry 4.0 and Industry 5.0 adoption – through PLM strategy, IIoT integration, and ERP alignment.

Neel SMARTEC founded in 2018 by Mr. Uthayan Elangovan  – author of three  published books on PLM, IIoT, and Industry 5.0, and winner of the Taylor & Francis Award for Outstanding Professional Book – the practice is built on one principle: you get senior expertise on every engagement, not a delegated junior.

Neel SMARTEC operate as a Business-as-a-Service model — on-demand, remote-first, with no vendor partnerships, no commission incentives, and no bloated overhead. Recommendations are based purely on what fits your business, budget, and goals.

What Neel SMARTEC do:

→ PLM Strategy, Assessment & Implementation (Windchill, OpenBOM etc)
→ ERP Advisory & Implementation (Katana ERP)
→ IIoT Integration & Smart Manufacturing
→ NPD/NPI Process Governance & Automation
→ Vendor-Agnostic Platform Selection
→ Industry–Academia Collaboration & Research

Who Neel SMARTEC work with:

Manufacturers across automotive, electrical, medical, industrial, and electronics sectors — from global OEMs to growth-stage SMEs — in India and internationally.

Neel SMARTEC can be contacted at:

Support | E-mail | Partnership | E-mail (2) | Website | LinkedIn | X | Medium | Pinterest | FaceBook | Phone : +91-95380 47356

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Prof. Ajay Singh is a former CEO of an award-winning fintech firm. He is an IOD Fellow and cybersecurity expert. He has authored multiple books and serves as a mentor, professor, and advisor globally. He holds key roles in academic and industry cybersecurity committees, shaping AI, Quantum Computing and security policies.

A wave of new technologies is driving major change across industries. Technologies such as AI, IoT and Robotics have sparked a burst of innovations that are transforming  manufacturing processes by enabling greater automation, improving efficiency, and enhancing precision. These advancements are streamlining operations, reducing costs, and driving smarter decision-making through data-driven insights. As factories become more interconnected, predictive maintenance, real-time monitoring, and adaptive production systems are reshaping how goods are manufactured, leading to increased productivity and innovation across industries.

AI, IoT, and Robotics have seamlessly integrated to create the foundation for Smart Manufacturing and Industry 4.0.Together, these technologies are transforming manufacturing into a smarter, adaptive, and self-optimizing ecosystem. Industry 4.0 is not just about automation, but it is also about unlocking new levels of efficiency, innovation, and sustainability.

The world is now on the cusp of a new computing paradigm- quantum computing – that promises to revolutionize industries by solving complex problems at unprecedented speeds, enabling breakthroughs in artificial intelligence, cybersecurity, material discovery, and optimization that were previously impossible with classical computers. As quantum technology advances, it will drive more innovation across sectors, shaping a smarter, more efficient, and interconnected future.

Quantum computing works at a sub-atomic level using quantum bits (qubits) to perform complex calculations much faster than classical computers by leveraging principles like superposition and entanglement. Classical computers use bits, which are either 0 or 1, like flipping a light switch on or off. Quantum computers use qubits, which can be 0, 1, or both at the same time—this is called superposition. Imagine a spinning coin; until it lands, it is both heads and tails at the same time. Because qubits can exist in multiple states simultaneously, quantum computers can process multiple possibilities at the same time, making them much faster for certain complex calculations compared to classical computers. Qubits can be entangled, meaning that when one qubit’s state changes, the other changes instantly, no matter how far apart they are. This allows for super-fast communication and problem-solving compared to classical computing, where bits operate independently.

Thanks to superposition and entanglement, quantum computers can solve problems that would take classical computers millions of years such as optimizing complex computations, cracking encryption, designing new materials, and simulating molecules for medical research. Quantum computers do not just do one thing at a time like classical computers; they can explore many possibilities at once, making them incredibly powerful for solving tough problems. One great example is vehicle production sequencing. Ford used quantum computing to optimize the sequencing of its Ford Transit production line, a task that classical computers struggled with due to the complexity of constraints across body shops, paint shops, and assembly lines. Using D-Wave’s quantum computing technology, Ford reduced the scheduling time for 1,000 vehicles per run from 30 minutes to less than five minutes. This optimization helped Ford increase production throughput, minimize unplanned maintenance, and adapt quickly to changes in demand—something that would take classical computers significantly longer to compute. So, when it comes to  rapidly solving highly complex optimization problems, making manufacturing more efficient and adaptable, quantum computers are the next big thing in high performance computing.

Industry 4.0 focuses on automation, data exchange, AI, IoT, and smart manufacturing. Quantum computing will enhance this paradigm in several key areas:

Supercharging AI Algorithms-Quantum computing will dramatically accelerate AI training, enabling models to process vast datasets exponentially faster. This means better predictions, deeper insights, and smarter automation across industries. For example, quantum-enhanced AI can process individual user preferences to design personalized vehicle features, such as ergonomic seats or custom dashboards.

Enhancing IoT Connectivity-Quantum technology will optimize data flow between IoT devices, ensuring ultra-fast and secure real-time communication. It will also improve sensor networks, helping industries monitor systems more efficiently.

Revolutionizing Robotics-Quantum computing will enable robots to make instant complex decisions, improving precision in assembly lines, medical procedures, and autonomous navigation. It will also enhance machine learning algorithms that power adaptive robotics.

Strengthening Cybersecurity-Quantum cryptography will create unbreakable encryption, protecting AI-driven systems and IoT networks from cyber threats. This ensures secure data transmission across smart factories and connected devices. Quantum communication can enhance the cybersecurity of Industry 4.0 networks, ensuring safe machine-to-machine communication.

Solving Complex Industrial Problems-From optimizing supply chains to designing advanced materials, quantum computing will help industries make better strategic decisions in seconds—something classical computers would take years to process. For example, it can enhance vehicle routing in fleet logistics such as autonomous cars or delivery fleets. Quantum algorithms can also help optimize sourcing, inventory, and delivery routes across global supply chains.

Quantum computing is particularly suited to simulating molecular and material properties, which benefits industries like pharmaceuticals, chemicals, and materials science. Examples of this include designing new polymers or simulating new battery chemistries such as solid-state lithium or hydrogen fuel cells to meet sustainability goals. Quantum computing can also accelerate molecular simulation, cutting down discovery timelines significantly.

By bridging AI, IoT, and Robotics with quantum computing, industries will experience unprecedented efficiency, automation, and security, fuelling the next industrial revolution. At another level quantum computing has the potential to revolutionize Industry 4.0 and 5.0 by solving complex problems that classical computers struggle with.

When it comes to Industry 5.0, it represents a major shift towards environmentally responsible and sustainable production. Companies aim to reduce their carbon footprint, optimize resource usage, and integrate circular economy principles, ensuring less waste and smarter energy use. Here again quantum computing can play a key role in modelling and predicting the impact of disruptions like natural disasters or pandemics and optimizing logistics with carbon footprint minimization as a primary goal.

Quantum computing may still be in its early stages and most current benefits are theoretical or in proof-of-concept phase but given the billions of dollars committed to research and development efforts it is progressing faster than expected. In the meantime, hybrid models (quantum + classical computing) will likely be in play in the near term giving early adopters the quantum advantage.

About the Author:

Prof. Ajay Singh

Professor of Practice – Rizvi Institute of Management Studies and Research

Member of the Board of Studies ( Faculty of Science & Technology ) – ICFAI University, Jharkhand

Member of Board of Advisors – WOPLLI Technologies

Member Of The Board Of Advisors – Pace University – Seidenberg School of Computer Science and Information Systems

Prof. Ajay Singh has spent over 35 years in the IT industry in different roles and was the CEO of an award winning fintech company for over a decade. He is a certified corporate director and Fellow of the Institute of Directors. He serves as a Corporate Advisor & Mentor.

Prof. Ajay Singh has a master’s degree from Sheffield-Hallam University, UK and has further specialized in Cybersecurity, Cyber Law, and Cyber Forensics.

Prof. Ajay Singh has authored multiple books on cybersecurity. He is an Award-Winning Finalist for his book CyberStrong (SAGE 2020) at the International Book Awards, American Book Fest 2022. His book Introduction to Cybersecurity-Concepts, Principles, Technologies, and Practices (Universities Press, Orient BlackSwan 2023) is a winner of the Golden Book Award 2024.

Prof. Ajay Singh is a Member of the Academic Advisory Board of the Pace University–Seidenberg School of Computer Science and Information Systems, New York, Member of the Board of Studies, Faculty of Science & Technology, ICFAI University, Jharkhand

Member of IEE Committee on Cybersecurity for Next Generation Connectivity Systems. He is also a visiting professor for Cybersecurity and Entrepreneurship at leading Management Institutes.

Prof. Ajay Singh is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/ajay-singh-a597111/details/certifications/

Prof. Ajay Singh is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/prof-ajay-singh-a597111/details/honors/

Prof. Ajay Singh is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/prof-ajay-singh-a597111/details/organizations/

Prof. Ajay Singh can be contacted at :

LinkedIn | E-mail | QDayReady | VendorCyberRisk | LearnCyberSecurity | BeCyberStrong 

Prof. Ajay Singh is the author of the following books

Cyber Strong: A Primer on Cyber Risk Management for Business Managers (SAGE Publications, 2020): This book was recognized as an Award-Winning Finalist at the International Book Awards by the American Book Fest in 2022.

An Entrepreneur’s IOD Handbook on Cyber Security (Institute of Directors, India, 2021): A condensed guide tailored for corporate professionals and executive management.

Cybersecurity-Concepts, Principles, Technologies, and Practices (Universities Press, Orient Blackswan, 2023): This work has received the prestigious Golden Book Award in 2024.

Cyber Strong: Cybersecurity and Risk Management (KBI Publishers, 2023).

Cyber Shock: Cyber-Attacks that Shook the World (ATLANTIC,2024) : An exploration of significant cyber incidents and the lessons we can learn from them.

Navigating Supply Chain Cyber Risk, A Comprehensive Guide to Managing Third Party Cyber Risk (Taylor & Francis,2025) By Ariel Evans, Ajay Singh & Alex Golbin.

Cyber Storm: Unleashing the Power of Quantum Computing and AI, (Orient BlackSwan)-Under Publication.

Also read Prof. Ajay Singh earlier article:

The post The Quantum Advantage appeared first on Industry4o.com.

Blockchains and the Internet of Things (IoT) are emerging technologies. Blockchain notes transactions in an online ledger. A single authority does not control the block chain, unlike currency managed by the Federal authorities. Blockchain provides security, immutability, transparency, and efficiency. Industries such as decentralized finance, cryptocurrency, oil and gas, agriculture, finance, and media use blockchains.

IoT is about linking smart devices to exchange data. These are found in sensors and software used in cars, smart homes and appliances, retail, security, and domestic appliances, and machines and equipment in factories. IoT can provide a single control to increase the efficiency and knowledge of a system. This sharing leads to reductions in costs and power, better monitoring and diagnostics, and prevention of breakdowns.

When IoT and blockchain are linked, then linked devices send transaction details to secure blockchains. Some benefits are automation, smart shared contracts for, efficiency and speed is increased and higher security. Blockchains will manage a value of USD 176 billion by 2025. The IoT market is expected to reach USD 1.6 trillion by 2029.

9 Ways Blockchain IoT Can Grow Your Business

The 9 methods of how blockchain + IoT can help the business to grow:

1. Business transformation: 

The number of IoT devices by 2024 was 18.8 billion. Connected devices generate tremendous data that can be used to examine and improve services. This data remains in the IoT network. Blockchain helps to get this data out.

With IoT and blockchain, transaction data packets are stored in successive secure ledgers with peer-to-peer or P2P sharing. The data is analyzed to study patterns, behaviors, input and output, find issues, and resolve them. Services can be redesigned and transformed.

2. Securing data: 

Transactions and activities are stored in a blockchain database or platform such as Ethereum. This database is not centrally controlled. Other entities can access this data, and add to the records, but the previous data remains secure.

This data is shared with verified users. The actions they perform are recorded and tracked. They can only make changes as per their transaction. The risk of a data breach is overcome, and secure cryptocurrency can be exchanged.

3. Higher efficiency: 

Many sectors such as mining, agriculture, banking, auto, and logistics have linked their IoT devices to blockchain. The result is faster transaction processing since new entries are made in the ledger.

As an example, smart cars relay location through GPS along with car performance diagnostics. These are analyzed and posted in the ledger. The data is exchanged quickly across all network nodes. Delays and cross-checks are avoided, and cost reductions are possible.

4. Smart contracts: 

This is a software code that automates several tasks when specified parameters are met. The advantage is that smart contracts are enforced quickly reducing delays. They are not in a central database but distributed.

Parties can use the smart contracts without additional checks for trust and secrecy. Third-party verification is not needed. Results and conclusions are available immediately. The contracts are immutable and once approved, they cannot be changed.

5. Decentralization: 

With IoT and blockchain, data from devices is sent to multiple networks. Interactions are not controlled by central resources but shared and owned by players. The advantage is partners are free from corrupted data; they do not have to rely on specific controllers.

It avoids systemic failures, and the load on central servers is avoided. Only required data and transactions are shared and actions done by actors are recorded in the ledger. The network may slow, but security increases.

6. Traceability:

Actions such as order tracking, accounts, production, and payments are traced. All reported activities are recorded and available. Network members can instantly view all actions and prepare for their activities. The result is transparency and increased trust.

Traceability assures product origins, quality, quantity, costs, location, and delivery time. These are important for supply chains, and auditing is efficient. Players know exactly what they are procuring, and the lead time.

7. Flexibility: 

Flexibility helps to modify processes as per changes in external and internal factors. Supply chains and manufacturing can increase or reduce their production as per demand and supply. Optimal and acceptable schedules can be formulated that avoid congestion.

Flexibility ensures that when changes such as demand, raw material costs occur, supply chains react quickly. The bullwhip effect is reduced and a lean process is possible. Thus, supply chains become economical, and costs, and idle stocks are controlled. Lead time for productions is also considered, especially for continuous processes.

8. Operations: 

The above factors improve efficiency, reduce costs, ensure faster reaction time, and allow quick data exchange. When data transfer and actions taken are immutably recorded, feedback and operations become fast. Delay, redundancy, and waste are avoided.

When IoT sends data to the blockchain, it is processed and recorded in ledgers. Uncertainty and risks are avoided and businesses are assured that they have verified information. They can choose to proceed with their next processes.

9. Finance and costs: 

Initial costs of implementing IoT and blockchain can go up to several million. However, blockchain platforms such as Oracle Cloud, and Google Cloud offer membership at very low costs. These costs are recovered quickly when implementations are done.

As discussed above, gains are seen with faster data sharing, reduced lead and waste, and trust. Intermediaries are removed and smart contracts between only buyer and seller further reduce costs.

Conclusions

The discussions examined 9 ways in which blockchain IoT can help businesses grow. The immense number of IoT devices sends data of processes and their environment to data silos. When this silo is linked with blockchain, then business networks can use the data to improve and transform their business. Data can be studied to understand how the device performs against specifications, and the process or device can be improved.

This data is secured and shared in the blockchain network among verified members. Actions by members such as changes in production, status, movement, value addition, and location are tracked and recorded. So, the future of IoT will be shiner and brighter by integrating it with blockchain Technology. Changes on records are not possible, and updates are new ledger entries. Thus data security and transaction immutability are assured.

Smart contracts between network players further ensure trust and privacy. Since data sharing and actions are not controlled by a central authority, transactions are faster, efficient, with reduced costs, delays, and waste. Traceability allows for transparent tracking of orders and processes.

These features make the process flexible to changes in demand and other conditions. As a result, operations are faster, and costs and process time are minimized. Businesses may incur some initial costs but they are recovered quickly.

Many small and large firms such as HSBC, Wal-Mart, Ford, Unilever, DHL, AIG, Barclays, MetLife, and others are using IoT and blockchain. You should also consider investing and becoming a part of this technology.

About the Author:

Mr. Harikrishna Kundariya
CEO & Founder,
eSparkBiz Technologies Pvt. Ltd.

Mr. Harikrishna Kundariya is a Passionate Leader, Visionary Entrepreneur & Technology Evangelist.

Mr. Harikrishna Kundariya is on a mission to empower businesses and individuals through innovative technology solutions. With over a decade of experience in the digital space, Mr. Harikrishna Kundariya has the privilege of leading eSparkBiz, a dynamic technology company that specializes in creating digital experiences that matter.

As a relentless entrepreneur, Mr. Harikrishna Kundariya started his career with a dream to revolutionize the tech industry. Over the years, Mr. Harikrishna Kundariya has been fortunate to work with a talented team at eSparkBiz , where eSparkBiz has transformed countless ideas into digital realities. From startups to Fortune 500 companies, eSparkBiz has left no stone unturned in delivering cutting-edge software and digital solutions.

As the CEO of eSparkBiz , Mr. Harikrishna Kundariya focus is on driving innovation and fostering a culture of excellence.Mr. Harikrishna Kundariya is deeply passionate about helping businesses leverage technology to achieve their goals. Whether it’s mobile app development, web design, or digital marketing, his team and Mr. Harikrishna Kundariya are committed to delivering solutions that exceed expectations.

Mr. Harikrishna Kundariya Key Achievements :

Spearheading eSparkBiz to become a globally recognized technology partner

Building a team of highly skilled professionals dedicated to client success.

Pioneering projects that have disrupted industries and set new benchmarks.

A strong advocate for ethical business practices and community engagement.

About eSparkBiz :

eSparkBiz – We turn ideas into software that people love.

eSparkBiz, a CMMI level 3 and ISO 9001:2008 certified company has established itself as a leading Software and web development solution provider in the IT sector. With over 370+ trained and enthusiastic software engineers and more than 12 years of providing service, eSparkBiz have recorded 1000+ successful project deliveries all over the globe.

eSparkBiz is a one-stop solution for all your technological needs and takes care of all your requirements, right from project ideation to development, thorough testing, and service after sales.

eSparkBiz use cutting-edge technology and tools to give the best solutions to our esteemed clients, and not-to-mention, eSparkBiz also follow an agile software development methodology to develop software. eSparkBiz also provide numerous hiring models when you wish to hire our dedicated team of developers.

eSparkBiz leverage AI, Web3, and Cloud to create innovative solutions that drive digital transformation for startups and established businesses. Let’s build something amazing together.

eSparkBiz Service Offerings:

– Custom Software Development

– Web Application Development

– Mobile Application Development (Android & iOS)

– IoT App Development

– Node JS Development

– Python Application Development

– ReactJs/React Native

– SaaS Product Development

– Web App Development

– E-commerce Development

– Enterprise Solution

– Custom Solution

– MVP Development

Why eSparkBiz?

– 12 years of industry experience

– 300+ qualified senior technology architects

– CMMI level 3 company & ISO 9001:2008 certified

– Powerful Data Security: Firewall System (Palo, Alto), VPN, Anti-virus  and – Antimalware software, Access Conyrol and Data Recovery

– State-of-the-art facilities

– Agile Methodology

– 160 Working Hours/Month with Flexible Hiring Models

– 100% in-house team. No freelancers

– Long-term client retention with 90% returning and referral clients

– Strict NDA Signed for Complete Privacy

– We will be here always, understanding your ideas and carving them into
mobile or web applications with top-notched features and UI/UX.

eSparkBiz @ Social Media Platforms :

LinkedIn | X | YouTube | Facebook | Instagram | Behance

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Internet of Things (IoT) sensors can give real-time information about valuable assets and processes. Many decision-makers are interested in adding them to critical infrastructure, such as gas pipelines. Whether they do so to monitor an essential component such as a gas pressure regulator or want to use the sensors as part of a holistic inspection process, the IoT can handle those needs and many others.

Developing an IoT-Based Gas Pressure Regulator

A gas pressure regulator is an important pipeline component that detects unusual changes. If the pressure drops below a preset threshold, the sensor allows gas to start flowing through the system. However, pressure drops can increase energy consumption and result in excessive strain on key components. They can also cause some pipeline parts to fail prematurely or not operate as expected. Conversely, high-pressure conditions elevate the risk of fires and explosions.

Some people have explored how the IoT could make a gas pressure regulator work more effectively or give operators more oversight. In one case, a researcher built an IoT sensor to gather data about a liquid petroleum gas cylinder. The sensor could take readings ranging from 0 to 700 kilopascals, and then display the output on a digital screen.

It also connected to a cloud-based system that allowed people to get real-time pressure readings on their smartphones and receive statistics about potential leaks. Additionally, an audible alert sounded if the pressure dropped below 0.5 bar. Besides giving more insights into whether the gas pressure regulator was working properly, this IoT setup used a color-coded system to indicate the amount of gas remaining in the cylinder.

Although the researchers built this IoT solution for residential gas cylinders, their proof of concept shows what’s possible for other use cases, including something to monitor the gas pressure regulator on a pipeline.

Preventing Corrosion-Related Damage

Many manufacturers use IoT sensors to improve their maintenance strategies. That strategy can help prevent most breakdowns that would otherwise cause major disruptions. The same approach is well suited for pipeline problems due to corrosion. The ongoing exposure to the elements makes corrosion one of the leading causes of structural failure pipelines.

One company conducted a six-site test of its ultrasonic IoT monitoring system. The solution attaches to any metal walls, including those associated with pipelines. It then examines the structure for wall thinness potentially caused by corrosion.

In another instance, researchers created an IoT-based remote monitoring system to indicate when a pipeline’s existing corrosion protection mechanisms need maintenance. It also had a virtual reality element that provided maintenance technicians with highly realistic and immersive environments. The team believed it would aid professionals who could not easily schedule physical visits. The details in the virtual reality environment also assisted those with little or no experience analyzing corrosion protection on pipelines.

Data analysis aspects also helped technicians gauge the likelihood of corrosion causing spills and other unwanted events. Another feature showed when the pipeline was currently well-protected but had a high risk of disbandment of the protective coating soon.

Tightening Cybersecurity Measures

When researchers profiled pipeline accidents across nine years, they found more than 5,500 incidents had happened, along with approximately 600 injuries and more than 125 deaths. Fortunately, IoT sensors can collect statistics that warn people of possible problems before any catastrophes occur.

Getting alerts about physical issues is undoubtedly important. However, people can and should also use IoT sensors to maintain high cybersecurity over pipelines. The 2021 Colonial Pipeline incident was a wake-up call about how a ransomware attack could cause costly disruptions for millions of barrels of oil.

Cybersecurity has not always been a top-of-mind concern for those developing IoT hardware and software. Instead, the goal is often to beat competitors, bringing products to the market more quickly than they can. Security is an afterthought in such cases, or not considered at all.

An engineering team from the University of Arizona wants to make cybersecurity for the IoT more straightforward. They developed a two-part system geared toward engineers without cybersecurity knowledge. It prevents sophisticated attacks with advanced protocols. Their vision is that IoT technology can be securely deployed anywhere, whether in a pipeline, a power grid or an industrial control system in a manufacturing plant.

In June 2023, the United States announced a cybersecurity and labeling program for popular IoT devices. A public comment period is underway, but this initiative should be in place by 2024. Many of the targeted products are consumer-oriented. However, once the plans are well-established, the requirements may also affect industrial devices. That’s particularly likely if manufacturers view the labels as competitive advantages.

Pipeline Sensors Increase Visibility

IoT sensors are essential additions to the world’s pipelines because they raise people’s awareness. When the responsible parties have reliable data showing everything’s functioning as expected or that there are potential problems to investigate, they can feel more confident about making decisions during challenging or time-sensitive situations.

However, if you want to use IoT sensors wisely, think carefully before proceeding. Choosing which statistics to monitor, how many sensors to install per pipeline and which baseline parameters to set will aid planning and help companies stick to manageable budgets.

About the Author

Emily Newton
Editor in Chief
Revolutionized

Emily Jade Newton is the Editor-in-Chief of Revolutionized a popular science publication that dives into the latest innovations in science, technology and industry.

Emily Newton is an industrial writer who enjoys showcasing innovation in her stories.

Emily Newton specialize in writing in-depth articles and blog posts for the industrial and sci/tech sectors.

Emily Newton enjoys researching the latest trends and optimizing articles to perform better in search engines.

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Over a period of many years manufacturing companies tried to standardize their OT levels with a particular brand from Automation world. However, in their pursuit of exploring industry 4.0, they will need to contemplate becoming automation platform agnostic to achieve flexibility, agility, and cost competitiveness in the future.

IIoT (Industrial Internet of Things) software being platform independent offers several advantages and is desirable for multiple reasons:

1. Interoperability:

Platform-independent IIoT software facilitates seamless integration with diverse hardware, devices, and systems, irrespective of their underlying platforms or protocols. It encourages interoperability within an industrial ecosystem, enabling data exchange and communication across heterogeneous systems. This flexibility empowers companies to connect and utilize a broad array of devices and technologies, regardless of the vendors or platforms involved.

2. Scalability and Flexibility:

Platform-independent IIoT software allows for easy scalability and adaptability. It can accommodate the addition or removal of devices, sensors, or systems without requiring extensive modifications or reconfigurations. This flexibility is crucial in dynamic industrial environments where the technology landscape is constantly evolving. It enables companies to scale their IIoT deployments, integrate new technologies, and adjust their systems as needed without being limited by platform-specific constraints.

3. Vendor Neutrality:

Platform-independent IIoT software avoids vendor lock-in and promotes vendor neutrality. It allows organizations to choose hardware, devices, and systems from different manufacturers or suppliers based on their specific needs and preferences. This independence empowers companies to select the best-of-breed solutions for each aspect of their IIoT infrastructure, avoiding the risk of being tied to a single vendor and enabling them to leverage competitive pricing, innovation, and support options from multiple sources.

4. Future-Proofing:

By adopting platform-independent IIoT software, companies can future-proof their investments. Industrial systems often have long lifecycles, and technology advancements may require updates or replacements of certain components over time. Platform independence ensures that the IIoT software can continue to function and integrate with new technologies, protocols, or devices that may emerge in the future, reducing the risk of obsolescence and protecting the longevity of the investment.

5. Collaboration and Ecosystem Integration:

Platform-independent IIoT software facilitates collaboration and integration within an industrial ecosystem. It enables companies to seamlessly connect and share data with partners, suppliers, or customers who might be using different platforms or technologies. This openness promotes data sharing, collaboration, and the creation of value-added services or applications that span across multiple organizations or industries.

In summary, platform-independent IIoT software enhances interoperability, scalability, flexibility, vendor neutrality, future-proofing, and collaboration within industrial ecosystems. It allows organizations to leverage diverse technologies, integrate with different platforms, and adapt to evolving requirements, ensuring optimal utilization of IIoT capabilities and maximizing the potential benefits of digital transformation in industrial settings.

“While brand loyalty can be beneficial in certain cases, it is important for manufacturing companies to periodically assess their options, explore different automation brands, and make informed decisions based on their specific needs, technological advancements, competitive landscape, and market conditions. This approach ensures that the chosen automation solutions align with their requirements, drive efficiency, and contribute to long-term success”
-Paresh Pansare
CEO, MCPM Software India Pvt.Ltd.

About the Author :

Mr. Paresh Pansare
Chief Executive Officer,
MCPM Software India Pvt. Ltd.

Mr. Paresh Pansare can be contacted at :

E-mail | LinkedIn

About MCPM Software India Pvt. Ltd.

MCPM stands for “Measurement & Control for Process & Manufacturing” and was formed to provide State of the Art Solutions to a wide range of industrial clients. It represents esteemed organizations like Auvesy MDT, Ocean Data Systems, Atvise, Bachmann, Intelecy, Parsec etc.

With offices in Norway, Thailand, India, and the United Arab Emirates MCPM is engaged in offering state of the Art Solutions by world leading principals to the System Integrators and End users. MCPM is a company primarily focused on delivering solutions in software and building a closely knit Ecosystem of partners and System Integrators.

MCPM can be contacted at:

Website | E-mail

The post Industrial Internet of Things appeared first on Industry4o.com.

In recent years, both drones and IoT (Internet of Things) have gained significant attention in the technology industry. As IoT continues to evolve and become more widely adopted, it’s clear that the technology has the potential to significantly impact the capabilities of drones. This article will explore the ways in which IoT can impact drones, including improving their accuracy and control, enhancing their communication capabilities, and increasing their overall efficiency.

One of the main benefits of IoT for drones is the ability to improve their accuracy and control. By using sensors and other IoT devices, drones can collect data in real-time and use it to make more informed decisions about their movements and actions. For example, IoT sensors can help drones detect obstacles and adjust their flight paths accordingly, making them safer and more efficient.

Secondly,  IoT can impact drones is by enhancing their communication capabilities. With the help of IoT devices, drones can communicate with other drones, as well as with ground-based operators, in real-time. This can be particularly useful in situations where multiple drones are being used for a specific task, such as search and rescue missions or surveillance operations As both IoT and drone technology continue to evolve, it will be interesting to see how they continue to intersect and impact each other. The Internet of Things (IoT) has the potential to revolutionize the way drones are used and operated, bringing a range of new capabilities and applications to the field.

Here are some ways in which IoT can impact drones:

Enhanced Connectivity:

IoT can enable drones to be connected to a variety of devices and systems, including sensors, cameras, GPS, and other drones. This enhanced connectivity can enable drones to work together more effectively, share data and communicate in real-time.

Real-time Data Analytics:

IoT can provide real-time data analytics capabilities to drones, enabling them to quickly analyze data from sensors and cameras, and make more informed decisions. This can lead to better performance, increased safety, and improved efficiency.

Autonomous Operations:

IoT can enable drones to operate autonomously, without the need for human intervention. This can be particularly useful in scenarios where it is difficult or dangerous for humans to operate, such as in disaster response or search and rescue operations.

Precision Agriculture:

IoT can enable drones to gather data on crop health and growth, and make more precise and informed decisions about where and how to apply fertilizers, pesticides, and other treatments. This can lead to increased crop yields and improved sustainability.

Remote Monitoring and Inspection:

IoT can enable drones to perform remote monitoring and inspection of infrastructure, such as pipelines, power lines, and bridges. This can help to identify potential issues before they become major problems, and improve safety .

IoT can hence  increase the overall efficiency of drones by enabling them to collect and analyze data more quickly and accurately. By using IoT devices to collect data on weather conditions, wind speeds, and other environmental factors, drones can adjust their flight paths and operations to optimize their performance. This can help drones operate more efficiently and effectively, ultimately improving their overall performance. 5G will play a big part that can  enable drone technologies across various industries including last mile delivery,  supply chain & logistics, retail, agriculture, and Healthcare . Beyond Enterprise and consumer use cases and applications, drones are being leveraged in many other ways by Government Departments of different countries  and Emergency Services.

The Global drone market is poised to become a US$ 54 billion market by 2025 as per some reports.  The Drone manufacturing potential in India could be worth US$ 4.2 Billion by 2025, growing to US$ 23 billion by 2030 and thus will play a major enabler in ensuring India hits its target of a $ 1 trillion Digital Economy by 2026. The Government’s ‘Aatmanirbhar Bharat’  is going all out to support domestic enterprises by fine-tuning policies and regulations so that more and more products are manufactured within India . As the Government  shows its clear intent to make India a global hub for the manufacturing of drones by 2030 by building a conducive regulatory and legal framework  , the growth of the Drone  sector is immensely strong.

The Drone Shakti initiative was rolled out during Budget 2022, with the objective  to promote and facilitate drones as a service through start-ups. This initiative, along with the government’s Production-Linked Incentive (PLI) scheme for drones, is  expected to increase domestic manufacturing and create employment. Given its potential, the support of the government for manufacturing of drones has comee at the right time, in sync with a comprehensive push at regulation.

The Drone Rules (September 2021), which superseded the Unmanned Aircraft System (UAS) rules of March 2021, were specifically  meant to simplify and ease the process of the Drones adoption. In line with the intent, the Drone Rules dispensed with the requirements of geo-fencing, no permission no take-off, and real-time tracking. These requirements might be incorporated in the future, once drone manufacturing in India stabilizes. Last December the Union minister for Youth Affairs and Sports, and Information and Broadcasting, Anurag Singh Thakur, inaugurated India’s first Drone Skilling and Training Virtual E-Learning Platform. One lakh drone pilots are likely to be required by 2023. In the year  2022, many private players including many startups, encouraged by the government’s focus on  the drone industry, offered a vast array of services, including food delivery, logistics, medicine and diagnostic delivery, and more. Garuda Aerospace launched  ‘Drone Yatra 2.0’, aiming to cover all 775 districts in 75 days.

In conclusion, both the Enterprise segment  and the Government are realizing the economic and social opportunities and sustainability that UAVs  like Drone Technology (unmanned aerial vehicles) can offer. The faster adoption of UAVs is therefore critical to realise the potential of this technology and the use of IOT powered by high speed 5G networks is bound to give a significant fillip to the Industry creating massive Business and Societal impact.

About the Author :

Mr. Sunil David
Digital Technology Consultant
Ex – Regional Director – IOT , AT&T India.

Mr. Sunil David, a well known IOT Industry Thought leader in India has 28 years of experience in the IT and Telecom industry of which close to 20 years was with AT&T, one of the top Communication Service Providers of the World and one of the top 20 companies in the Fortune 500 list 2021. Until recently, Sunil was the Regional Director ( IOT ) India and ASEAN for AT&T India where he was responsible for building the IOT strategy, Sales, Business Development and also worked on building a robust IOT partner ecosystem. He was also actively involved in a number of Marketing initiatives to help enhance the AT&T brand in the IOT space. In his new phase of life, Sunil will be Advising and Consulting AI and IOT Startups that are aspiring for the next level of growth.

Mr. Sunil David has been a recipient of a number of Awards and Recognitions including 6 awards in 2021 and 3 this year from various Industry bodies and media conglomerates in recognition for his work in Digital Technology advocacy, Digital Skilling initiatives, contributing his inputs towards IoT policy creation for India and for his contribution to National Inst of Electronics and Info Tech, an Autonomous Scientific Society of MeITY, Ministry of Electronics and IT, Govt of India for contributing his inputs on the syllabus and specific courses in the Emerging tech space ( IOT, Cloud, AI ) that needs to be incorporated into the Curriculum of State and Central Govt Universities. NASSCOM Foundation and IBM India have also planted tree saplings in Sunil’s name for his contribution to the Tech Industry.

Mr. Sunil David is affiliated to several Industry Bodies : – Empaneled as IoT Tech Guru for NASSCOM Women Wizards Rule Tech Program, Telecom Working group Council Member of IET Future Tech Panel, Chair of the Working group of the AI – Networking and Community Enablement Working group constituted by CII, Elite Member and Mentor for CII Institute of Logistics, Member of the National IOT Committee constituted by Internet and Mobile Association of India. ( IAMAI ) etc.

Mr. Sunil David has spoken on a wide and diverse range of topics – Industry 4.0, IOT, AI, Supply Chain Digitalization , Metaverse, Cyber Security, Digital Marketing, Digital Transformation, Future of
Work etc., at more than 150 Industry forums in India and abroad as well as done workshops on IOT for a number of India’s leading Educational institutions including IIM Shillong, IIM Sirmaur, IIM Jammu, IIM Kharagpur, IIT Delhi, ISB Hyderabad, IIT Madras, SITM Pune, Amity University Delhi etc.

Mr. Sunil David has also written a number of articles in the past for various B2B publications, IEEE and Online Media Content providers on topics related to Use of IOT in Shipping industry, Supply Chain Digitalization Industry 4.0, Digital Transformation, Disruptive Technologies, 5G, IOT Platforms and Security etc.

Mr. Sunil David is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/sunil-david-8165971/details/certifications/

He can be contacted at:

Email | LinkedIn | Twitter

Also read Mr. Sunil David earlier article :

The post IoT impact on Drones appeared first on Industry4o.com.

IoT has arrived, changing the way field service companies do business.

With the recent explosion in IoT devices, it’s now easier to turn a service into a data center — and some are already doing just that.

This is excellent news for consumers because it means more convenience and competition with providers and other firms within your industry.

Read on as we explain why IoT isn’t a fad by putting forth practical ways how this technology is revolutionizing the field service industry.

1.  The Ability to Remotely Monitor Field Service Vehicles and Workers

The growth of IoT has led to the development of remote monitoring solutions that allow field service companies to keep tabs on their vehicles and workers.

These systems can be deployed across several industries, but they are beneficial for those who operate in remote locations or have multiple sites.

For example, you might want to know if there is an emergency at one of your facilities or if one of your employees is having a problem with their vehicle. With these systems, you can monitor everything from maintenance issues to vehicle breakdowns and accidents.

2. The Use of IoT In Field Service Management Software

Field Service Software (FSS) is a software application that field service companies use to manage their customer relationships and operations. FSSs have replaced paper-based processes such as manuals, checklists, and job logs.

The emergence of the Internet of Things (IoT) has changed how we operate daily. The use of IoT to automate operations has become a norm, from smart homes to smart cities. The same applies to field service companies looking to improve their efficiency through automation.

With the help of IoT technology, field service companies can automate order processing, asset tracking, fleet management, and customer record management. These tasks can be done remotely via your mobile phone or laptop computer.

3. The Ability to Alert Technicians When a Job Is Completed or Needs Attention

One of the more exciting ways IoT can impact field service is by improving how technicians are notified about jobs.

This is especially indispensable for industries such as construction, where there are often many jobs at once, and not all workers have the same level of access to technology.

Using intelligent sensors on machinery will help technicians avoid injuries and accidents because it’s easier to know when something isn’t working correctly than someone telling them about it later.

Smart sensors also allow for better reporting of what equipment is used in each job, which can help contractors track down any problems that might arise later on.

4. Smart Sensors on Machinery Will Help Technicians Avoid Injuries and Accidents

The field service industry is rated amongst the most dangerous industries. For example, a recent report revealed that construction workers claimed $2.5 billion as compensation for work-related injuries in 2021.

The cost of equipment accidents is also crippling for SMEs, and it’s no wonder that many companies are evaluating ways to reduce field service risk factors.

One way they’re doing so is by installing intelligent sensors on machinery that can detect when there is an issue, such as a malfunction or injury. This will make it easier for technicians to identify the problems before they become serious injuries or worse.

For example, suppose a technician encounters a problem with one of the machines in your warehouse and realizes it needs to be taken apart. In that case, they could switch off the machine instead of taking it apart manually. This would prevent injury and save time as well as money.

5. Zero-Stock Inventory

In the field service industry, inventory management is one heck of a challenge. However, things are changing thanks to IoT technology, empowering businesses to maintain a zero-stock inventory with minimal effort. The device and applications connected as an IoT network automate inventory management without requiring human intervention. So, with dedicated IoT systems, your business won’t have to face the hassle of running out of stock or surplus inventory, leading to monetary losses.

6. Improved Work Efficiency

The IoT has the potential to improve work efficiency by allowing field service engineers to monitor and control equipment remotely. This is especially true for small businesses, those having limited resources. If a technician is on-site with their equipment and needs assistance, they can contact their supervisor or other technicians for help. If something goes wrong, the technician can also alert their IT department so that it can be fixed immediately.

The Bottom Line

IoT integration will streamline field service management and its customer satisfaction ratings. Also, companies that don’t adopt innovative measures to reduce costs and increase customer service will face lower profits and potentially lose customers. Of course, some companies can challenge this, but they can change the playing field with the right systems.

About the Company:

Bursys is a software technology consulting and development company based in Houston, TX with branch offices in Germany and India. Bursys partners  with customers to deploy high quality technology solutions through the lens of trust, social responsibility, innovation and expertise. Its highly skilled global workforce provides strategy consulting, combined with unmatched software development and support services, to help businesses transform digitally.

Bursys is proud to be engaged with clients throughout North America, Europe, and Asia, leading them through the full cycle of digitization. This includes workflow automation through the use of Mobile Apps, Cloud based software applications, equipment/hardware IoT integration, and Machine Learning/AI.  Bursys operates in multiple industries, with primary focus in Oil and Gas; Postal, eCommerce, Package & Logistics; and Finance/Payments.

Bursys has been engaged actively with projects involving migration of legacy applications to Cloud (AWS, Azure, Google), as well as designing and developing new Cloud based, dynamically scaling, application platforms. Our skills in DevOps on Amazon/Azure cloud, along with Docker/Kubernetes/Swarm, is unmatched.  We continue to assist our customers with Cloud integrations, providing highly efficient and scalable designs, allowing our customers to save millions of dollars per year!

Bursys Specialises in the following fields :

Software Product Development, iOS Android, Cloud Development, Java / .NET, Microsoft, Big Data, Augmented Realty, Field Service Mobile, HSE Mobile App, Internet of Things IoT, Microsoft Silver Partner, Cloud Migration, DevOps, AWS, Azure, Machine Learning, Artificial Intelligence, Digital Transformation, Digitization, Analytics, Kafka, Data Engineering, Docker, Kubernetes, Software Consulting, Software Architecture, Enpterprise software, Angular, Ionic, React, Barcode Scanner, RFID Scanner, Node.js, Node Red, MQTT, Oil Field Service, Oil and Gas, Production Chemicals, Well Completion Chemicals, Postal, eCommerce, Package Distribution logistics, and Logistics.

Web : https://www.bursys.com/

LinkedIn : https://www.linkedin.com/company/bursys/

https://www.linkedin.com/products/bursys-fieldequip/

Twitter : https://twitter.com/Bursys_

FaceBook : https://www.facebook.com/BURSYS/

YouTube : https://www.youtube.com/bursys

FieldEquip a Product from Bursys :

FieldEquip provides a seamless digital workflow experience between customers, field technicians, service providers, and equipment using advanced cloud technology. FieldEquip increases operational efficiency and addresses workforce skill gaps by placing all necessary field service information at the field technician’s fingertips, working online and offline to provide remote diagnostics and analysis capabilities.

Using the EquipConnect IoT edge gateway, FieldEquip uses real-time equipment diagnostics and performance data analytics to identify and initiate proactive and predictive maintenance measures. Machine Learning (ML) enabled data analytics provides artificial intelligence (AI) for field service operations.

FieldEquip empowers field service providers to build new revenue streams and business models for the future and keep track of service KPI’s. Asset owners will have real-time visibility into productivity, efficiency, inventory, and work order activity.

Web : https://www.fieldequip.com/

FaceBook : https://www.facebook.com/fieldequip/

Twitter : https://twitter.com/fieldequip

LinkedIn : https://www.linkedin.com/company/fieldequip

YouTube : https://www.youtube.com/channel/UC1u9YvUmQ3mqvfVTKg4BApQ

Response Team :

Mr. Ankit Goyal

Email : ankit.goyal@bursys.com

LinkedIn : https://www.linkedin.com/in/ankit-goyal-digital-marketing/

The post 6 Ways IoT Is Transforming The Field Service Industry appeared first on Industry4o.com.

Self driving cars are a good example of what artificial intelligence can accomplish today. Such tech-centered advances are built on the back of highly trained Machine Learning (ML) algorithms.

These algorithms require exhaustive data that help the AI effectively model all possible scenarios. Accessing and pooling all that data needs complex and very expensive real-world hardware setups, aside from the extensive time it takes to gather the necessary training-data in the cloud (to train ML). Additionally during the learning process, hardware is prone to accidents, wear and tear which raises the cost and lead time in training the AI.

Technology leaders like GE have side stepped this hurdle by simulating real world scenarios using controlled-environment setups. This controlled environment simulations coined the term ‘Digital Twins’. A digital twin can model a physical object, process, or person with abstractions that might be necessary to bring the real world behaviour in digital simulation. This is useful to data scientists who model and generate the needed data to train complex AI in a cost effective way.

An example is GE’s Asset Digital Twin – the software representation of a physical asset (like turbines). Infact, GE was able to demonstrate a savings of $1.6 billion by using digital twins for remote monitoring.

Research predicts the digital twin market will hit $26 billion by 2025, and major consumers of this technology being the automotive and manufacturing sector.

Tools at hand for digital twins

Through experimentation, Extended Reality (XR) and tools used to create XR have been found best suited to leverage digital twins with ease. Also, one of the most popular tools used to create digital twins is [Unity3D], also mentioned in ThoughtWorks Technology Radar.

Unity3D ensures an easier production of AI for prototyping in robotics, autonomous vehicles and other industrial applications.

Use case: autonomous vehicle parking

Self driving cars have a wide domain of variables to consider — lane detection, lane following, signal detection, obstacle detection etc. In our experiment at ThoughtWorks, we attempted to solve one of the problems; train a typical sedan-like-car to autonomously park itself in a parking lot through XR simulation.

The outcome of this experiment only produced signals such as the extent of throttle, brake and steering needed to successfully park the car in an empty lot. Other parameters like the clutch and gear shift were not calculated as they were not needed in the simulation. The outcome was applied in the simulation environment by a car controller script.

This simulation was made up of two parts, one was the car or the agent and the second was the environment within which we were training the car. In our project, the car was prefabricated (CAD model of car) and attached with a Rigidbody physics element (that simulates mass and gravity during execution).

The simulated car was given the following values:

A total body mass was about 1500kg
A car controller with front wheel drive
Brake on all 4 wheels
Motor Torque of 400
Brake Torque of 200
Max. steering angle of 30 degrees
Ray cast sensors (rays projecting out from the car body for obstacle detection) in all four directions of the car. In the real world, ray cast sensors would be the LIDAR-like vision sensors.

The picture below, displays the ray cast sensors attached to the car:

Raycast sensors in all sides of the car to detect obstacles

Ray cast sensors in action during simulation

The car’s digital twin was set up in the learning environment. A neural network configuration of four hidden layers and 512 neurons per hidden layer was configured. The reinforcement training was configured to run 10 million cycles.

The Reinforcement Learning (RL) was set up to run in episodes that would end or re-start based on the following parameters:

• The agent has successfully parked in the parking lot
• The agent has met with an accident (hitting a wall, tree or another car)
• The episode length has exceeded the given number of steps

For our use case, an episode length of 5000 was chosen.

The rewarding scheme for RL was selected as follows:

• The agent receives a penalty if it meets with any accident and the episode re-starts
• The agent receives a reward if it parks itself in an empty slot
• The ‘amount of reward’ for successful parking was based on:
  • How aligned the car is to the demarcations in the parking lot
  • If parked facing the wall, the agent would receive a marginal reward
  • If parked facing the road (so that leaving the lot is easier), the agent would receive a large reward

A training farm was set up with 16 agents to speed up the learning process. A simulation manager randomized the training environment with parking lots having varying lot occupancy and the agent was placed at random spots in the parking area between episodes – to ensure the learning covered a wide array of scenarios.

Reinforcement learning with multiple agents parallelly

The tensor graph after 5 million cycles looked like this:

Not only had the agent increased its reward, it had also optimized the number of steps to reach the optimal solution (right hand side of graph). On an average it took 40 signals (like accelerate, steer, brake) to take the agent from the entrance to the empty parking lot.

Here are some interesting captures taken during training: ​

Agent has learnt to park both side of the parking lot

Agent learnt to take elegant maneuvers by reversing

Agent learnt to park from random locations of the lot

Way forward

We’ve just looked at how digital twins can effectively fast track production of artificial intelligence for use in autonomous vehicles at a prototyping stage. This applies to the industrial robotics space as well.

In robotics, specifically, the digital twins approach can help train better path-planning and optimization for moving robots. If we model the rewarding points around the Markov decision process, we can save energy, reduce load and improve cycle time for repetitive jobs – because the deep learning mechanisms maximize rewards in a lesser number of steps where possible.

In conclusion, XR simulation using digital twins to train ML models can be used to fast track prototyping AI for industrial applications.

About the Author :

Mr. Kuldeep Singh
Head of XR Practice
ThoughtWorks

Kuldeep has built his career empowering businesses with the Tech@Core approach. He has incubated IoT and AR/VR Centres of Excellence with a strong focus on building development practices such as CICD, TDD, automation testing and XP around new technologies.

Kuldeep has developed innovative solutions that impact effectiveness and efficiency across domains, right from manufacturing to aviation, commodity trading and more. Kuldeep also invests time into evangelizing concepts like connected worker, installation assistant, remote expert, indoor positioning and digital twin, using smart glasses, IoT, blockchain and ARVR technologies within the CXO circles.

He has led several complex data projects in estimations, forecasting and optimization and has also designed highly scalable, cloud-native and microservices based architectures.

He is currently associated with ThoughtWorks, as a Principal Consultant, Engineering and Head of XR Practice, India. He has worked with Nagarro as a Director of Technology.

Kuldeep holds a B.Tech (Hons) in Computer Science and Engineering from National Institute of Technology, Kurukshetra. He also spends his time as a speaker, mentor, juror and guest lecturer at various technology events, and x-member of The VRAR Association. He is also mentor at social communities such as Dream Mentor, tealfeed.com and PeriFerry

Mr. Kuldeep is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/kuldeep-reck/details/honors/

Mr. Kuldeep is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/kuldeep-reck/details/certifications/

Mr. Kuldeep is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/kuldeep-reck/details/volunteering-experiences/

He can be Contacted at:

E-Mail : kuldeeps@thoughtworks.com / kuldeep_reck@yahoo.com

Linkedin : https://www.linkedin.com/in/kuldeep-reck/

Twitter : https://twitter.com/thinkuldeep

Facebook : https://www.facebook.com/kuldeep.reck

Instagram : https://www.instagram.com/accounts/login/?next=/thinkuldeep/

Blog : https://medium.com/xrpractices

Personal Website : https://thinkuldeep.com/

Company Website : https://www.thoughtworks.com/profiles/kuldeep-singh

About the Author:

Mr. Raju Kandaswamy
Lead Consultant
ThoughtWorks

Also read their earlier articles :

The post Training autonomous vehicles with XR simulation appeared first on Industry4o.com.

A discussion of how IoT and AI can be used around the world to protect animal species as well as protect humans from unintended, but dangerous, animal interactions.

Wildlife Issues

Despite significant work to protect wildlife and manage national parks and forests, many incidents continue to occur every year, either causing loss to human beings or to wildlife.

While we are using Artificial Intelligence to solve complex problems such as predicting failures of complex equipment, performing natural language processing, making driver-less cars, and so on, applying technology to protect wildlife needs more work. Presented here is a non-evasive method and framework which effectively uses IoT enabled cognitive systems to make a drastic improvement in this domain.

Framework for the Monitoring and Management of Wildlife

The proposed framework is based on some of the proven behavioral attributes of different animals which can be further customized as we learn more about these animals. The framework consists of four stages which occur simultaneously to effectively monitor and manage wildlife movement:

1. Detection: Detect movement patterns with thermal imaging.
2. Identification: Identification of key entry and exit points of protected wildlife regions.
3. Prevention: Preventing wildlife from exiting protected regions by the deployment of IoT-enabled devices.
4. Control: Driving wildlife into safer regions such as a forest/protected area with the deployment of IoT-enabled devices.

Detection : Detect Movement Patterns With Thermal Imaging

The area under surveillance will be divided into four quadrants – with smart cameras and IoT devices (motion and proximity sensing devices) installed in a way that they cover at least a quadrant. These will be used to detect movement across the monitored perimeter and techniques such as thermal imaging will detect animal movements.

A solar or battery powered LORA (long range) WAN network will be used to orchestrate and control the IoT devices and relay back the detection message or patterns.

Illustrative example of detection using four quadrants and IoT devices

Identification: Identification of Key Entry and Exit Points of Protected Wildlife Regions 

The proposed cognitive method will identify and establish a logical smart zone radius with key entry and exit points of protected wildlife regions using a combination of:

1. Geo spatial data (terrain, contour vegetation, etc.).
2. Weather data.
3. Movement patterns detected using IoT devices and techniques such as thermal imaging.

The smart safe zone’s radius will iteratively self-adjust based on data insights from above mentioned parameters.

Prevention and Control: Identification of Key Entry and Exit Points of Protected Wildlife Regions

The proposed IoT-based cognitive framework will use a multi-layered learning model to:

1. Dynamically adapt situation-aware tactics based on real-time factors (ex. animal conditions, aggression levels, etc.) and history, to trigger suitable action to guide the animal to safety.
2. Combine spatial and temporal learning by co-relating features extracted from contextual sources (terrain, trajectory of movement, etc.), to assess time/frequency and direction of action.
3. Recursively predict further reaction based on proximity and location trajectory within a bounded area by learning from previous actions, historical movement patterns, and surrounding factors, such as weather/mating season, presence of other animals, and so on.
4. Iteratively adjust/re-adjust tactics by inferring temporal co-relations with parameters retrieved from sensors, cameras, etc., to dynamically classify/re-classify the situation when an animal moves within threshold proximities.
5. Dynamically learn and enforce policies based on the nature of the animal and its history of interactions, by identifying physical/virtual zones using components like cameras, sensors/beacons, and entry/exit access data, in combination with historical access patterns and entity relationships (ex. leopard spotted in vicinity of sheep, avoids town center, etc.).

Example of a stacked deep learning model, such that coarse-grained features extracted from previous layers can be fed to subsequent layers to identify fine-grained patterns based on expert training and continuous feedback

Illustrative Case 

Below is an example of how this framework will work and help prevent and control animal attacks.

Let us consider a Masai village in remote Kenya which is surrounded by predators and other types of animals and has no access to modern energy sources (e.g., electricity/gas).

A group of animals is entering the square boundary or the monitored perimeter in middle of the night and gets detected as an alien object by a pre-trained system, a system which is a combination of smart cameras and IoT sensors that are constantly scanning the perimeter.

1. If the pre-trained system detects a friendly object (e.g., livestock, villagers), then it does not initiate any action but still records the information via a control center. The information is used to enrich the analytics which are used to predict attacks.
2. But if the system detects unfriendly objects (e.g., a lion, or a pride of lions), it will initiate three actions:
   1. It immediately alerts the control center using the solar or battery powered LORA network. The control center is a centralized mechanism which is established to protect a cluster of villages from animal attacks.
   2. It alerts the villagers through a pre-established solar or battery powered signaling mechanism which can produce either sounds familiar to villagers as danger signals or visual signals.
   3. Triggers the cognitive scare tactic assistant which uses a combination of supervised and unsupervised machine learning techniques to:

α. assess the severity of the attack using data from object detection systems, sensors, etc. installed in the village and/or clusters and in conjunction with the control center.

β. iteratively deploy scare tactics using a framework comprising of:

1. 1. 1. 1. 1. pre-trained images that scare animals projected using techniques such as holographic imaging. In this particular case, a holographic image of a Masai group, fire, or another pride can be projected.
            2. pre-trained associated sounds that scare animals emitted alongside heat effects. In this particular case, the sounds of a Masai dance, a drum beat, and the crackling sound of wood along with fire, or a lion roar from another pride can be emitted along with heat effect to cause severe discomfort to predators.
            3. using a network of drones to induce external elements such as spray deterrent fluids or fire tranquilizers to temporarily immobilize animals. In this case, if the above two tactics fail to scare animals away from the safe radius zone, drones will be used to fire tranquilizers to temporarily immobilize lions. If the above two tactics succeed to scare way a lion pride, drones can be used to move them in a particular direction and track their further movement.  

γ. self-learning from the event – iteratively improving the level of scare tactics based on an effectiveness quotient and improving the attack predictability analytics. Overall, the cognitive scare tactic assistant helps us to iteratively deploy appropriate levels of tactics which are used to (a) prevent the predator from crossing the safe zone radius (b) make the predator move in a favorable direction and to learn from those events. 

4. The entire event is captured using a flexible data framework and is used to iteratively improve the effectiveness of the deployment of scare tactics and lessen human-animal conflict.

5. In a worst case scenario, if all deployed measures fail, the framework will signal for explicit human intervention via the control center.

The below diagram provides a visual representation of the above scenario:

Key Takeaways

Considering how quickly endangered species are disappearing from the face of earth and how humans continue to encroach on their habitats, there has never been a better time to exploit state of the art technology to accelerate wildlife conservation and to prevent loss of human lives because of unintended movements of animals. Our proposed non-evasive method and framework leverages the power of:

• inter-connected (IoT) devices to enable real-time wildlife movement detection across heterogenous networks and improve associated decision making.
• artificial intelligence to iteratively establish smart safe zones along with entry and exit points based on animal movements.

This tech-for-good method and the framework of leveraging IoT and cognitive technologies has the potential to dramatically improve the way people and animals can co-exist without causing harm or undesired fatalities.

About the Authors:

Mr. Rajeev Mittal,
Associate Partner with IBM Consulting

Rajeev Mittal has 22+ years of extensive experience with services,  software development, development processes, client relationships, and  project & people management.Led Transformation programs for client in Digital, Analytics, Cloud, Automation, Dev Ops, Agile development. He is also a Certified Professional Google Cloud Architect.

He is bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/mrajeev74/details/certifications/

He can be Contacted at :

LinkedIn : mrajeev74

Email : mrajeev74@gmail.com

Mobile : +91 9811 542076

Twitter : mrajeev74

Mr. Pinaki Bhattacharya
Delivery Project Executive at IBM

Pinaki Bhattacharya has 17 years of extensive experience in digital  transformations, industry consulting, managing P&L, and people  management. He has multiple technical disclosures published. He is  passionate about education at grass root level and volunteers himself as an Educator at e-Vidyaloka.

He is a Strategic Advisor to Charity IT Association – CITA. He is also a Fund Raiser & Social Investor at Rang De

He is bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/pinaki1/details/certifications/

He can be Contacted at :

LinkedIn : pinaki1

Twitter : GlobetrotterPB

Mr. Harish Bharti
Distinguished Engineer and Master Inventor at IBM Consulting

Harish Bharati has wide experience in designing IT Solutions to Client Business Problems, using wide spectrum of Software Technologies. He is a key inventor for 34 File rated Patents on emerging technologies including Cloud, Cognitive Computing, API Modeling, Mobility Digital Integration
and Internet of Things.

Check his Google Scholar profile :
https://scholar.google.co.in/citations?hl=en&user=_z0CoMIAAAAJ

He is bestowed with the following Licenses & Certifications :
https://www.linkedin.com/in/harishbharti/details/certifications/
https://www.credly.com/users/harish-bharti.019bb41b/badges

He can be Contacted at :

LinkedIn : harishbharti

Mr. Dinesh Wadekar
Senior Software Developer at IBM

Dinesh Wadekar has over Fifteen years of IT industry experience with wide range of skill sets, roles in various accounts in IBM. He has 4 Intellectual Capital/ Patents are issued by US Patent Office (USPTO) & 19 research  disclosures Published on IP.com. He has got an honor of IBM Services Senior Inventor for the ability to capture business and technical innovation through patenting.He has in depth understanding of the principles and best practices of software configuration management (SCM) in Agile, Scrum and Waterfall methodologies.

He is working as a Tableau Developer and Analyst in Enterprise Environments with Tableau 9, 10, 2019, 2020. His expertise is in designing and developing dashboards using Tableau Desktop and Tableau Server as well as designing, developing, and publishing visually rich and intuitively interactive Tableau workbooks. He has knowledge on Devops Tools – Git, Maven, Jenkins, Ansible, Sonarqube, Docker.

He is bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/dinesh-wadekar-64bb8789/details/certifications/

He has received following Honors & Awards :

https://www.linkedin.com/in/dinesh-wadekar-64bb8789/details/honors/

He can be Contacted at :

LinkedIn : dinesh-wadekar

The post Cognitive Framework for Wildlife Monitoring and Management appeared first on Industry4o.com.

Various analyst reports predict that the IIOT market size would be anywhere between $200 – $400 B by end of 2021. This growth is predicted to be driven by use cases spanning different industry verticals across various geographies.

However, a significant risk to this projected growth is the ability of organizations to drive the adoption of these technologies. In many cases, organizations have put in a lot of effort to start pilot deployments only to see these initiatives fizzle out due to multiple reasons.

In our experience of working with several tier 1 and tier 2 manufacturers across verticals we have seen some organizations successfully embrace IIOT & digitization technologies to reap the benefits of digital transformation while some organizations have struggled to go beyond small pilot projects despite having the right intentions.

Some of the key factors that are key to the successful adoption of digital technologies in manufacturing organizations are given in subsequent sections.

Stakeholder Buy-in

• Critical to get management backing for all digitization initiatives.
• This ensures an organization wide perspective is taken into account while defining the scope of the pilot and avoids projects getting developed in departmental silos.
• Without top management support, successful localized pilots cannot scale out across the organization due to lack of visibility, limited or no budgetary support or due to not being in sync with overall organizational objectives.

Breaking Organizational Silos

• The success of any digitization initiatives requires participation from multiple departments. These include personnel from Manufacturing, Finance, HR and Marketing in addition to the executive leadership. In some cases, we have also seen the Sales team getting involved as a successful IIOT solution can be leveraged as a competitive advantage in the market.
• Broad participation ensures that implementation risks are identified early in the cycle and in the development of effective mitigation plans for these risks.
• A multi-functional team with personnel from all key departments should be created to drive digitization efforts across the organization. It is advisable to have representation from the senior management in this team as well.
• It is recommended that this team be staffed with personnel who are open to experimenting with new ideas and who have the ability to think out of the box for leveraging new technologies.

Use Case Selection

• Select a use case that is relevant across all business units and has the potential to deliver significant benefits to the organization.
• Choosing use cases that are an incremental in nature will provide lesser benefits and may put the larger transformative projects at risk.
• Use cases considered should tie into overall business strategy and should have demonstrable success criteria.

Start Small & Scale Fast

• Choose a pilot implementation area that is representative of the processes to be digitized.
• This ensures that all implementation related issues can be surfaced during the pilot, mitigation strategies can be devised and enables quick organization wide roll-out post successful completion of the pilot with minimal risk.
• It is recommended that he pilot line / cell be chosen in a plant in which the plant head has bought in to the concept and is ready to provide full support for the implementation.

Resistance from SMEs

• SMEs lay an important role in the success or failure of digitization initiatives. The critical operational knowledge and skills possessed by SMEs needs to be codified and incorporated into digitization initiatives to significantly increase benefits that can be derived.
• SMEs have to be taken into confidence to alleviate any fears that they may have about loss of their importance in the organization if their knowledge is institutionalized through digitization.
• Some form of reward schemes can also be put in place to incentivize knowledge sharing by these SMEs.

Heterogenous Machinery

• Most production lines consist of machinery acquired from different OEMs. These machines would have different controllers / SCADA / DCS systems with different communication protocols and connectivity interfaces.
• Devising a data acquisition strategy for acquiring the relevant data points from these machines is the key for successful factory digitization projects.
• This requires collaboration with the machine OEMs to understand the recommended connectivity mechanisms and also to get information about tag maps required for retrieving the right data points.

Lack of Infrastructure

• The OT infrastructure would need to be updated in order to ensure the right form of connectivity on the shop floor. This will include laying of cables, setting up VNETs for production lines / cells, Static IP allocation for machines etc.
• It is also critical to ensure clear separation between the OT and IT networks while allowing for authorized data exchange between OT and IT systems in a controlled manner.
• Redundancy considerations also have to be addressed in order to ensure that data acquisition strategies do not fail due to failure of gateways, IPCs etc.

Lack of Internal Competency

• A successful IIOT implementation requires various skill sets including instrumentation, automation, Cloud technologies, machine learning etc. Traditionally manufacturing organizations, especially those in the SMB segment would have not have all these competencies in-house.
• Organizations should look to complement their inhouse skill sets with external consultants or partners who can bring the required expertise.
• Choosing the right partner / consultants itself is tricky since the field itself is new and validating past implementation successes is difficult.
• Having multiple partners increases the risks associated with lack of ownership and communication complexity leading to increased bandwidth requirements from the project sponsors.
• Various engagement models can be used with partners to ensure that they have a a stake in the success of IIOT initiatives.
• The long terms aim would be to build the required competency in-house but having the right partner eco-system can significantly improve the chances of success by reducing implementation risks.

Change Management

• This is the most important factor that determines the success of shop floor digitization projects.
• People on the shop floor need to be taken into confidence and educated about the fact that automated data acquisition is not meant to provide another level of oversight but is required
  to accurately identify operational bottlenecks that can be addressed to improve efficiencies.
• In most cases, existing efficiency baselines and metrics that are derived using information collected from manual logbooks will reduce once automated data collection mechanisms are put in place. This should be consciously be discussed prior to the implementation in order to avoid shop floor personnel from becoming defensive when the new efficiency figures are calculated.
• In addition, shop floor personnel have to sensitized about the benefits that can be derived from digitization efforts.

Solution Adoption

• Mechanisms to measure solution adoption have to be thought through in the solution design phase.
• Cultural factors as well as user characteristics have to be considered during UI/UX design of solutions to minimize user resistance for adoption of digital solutions.
• In order to drive adoption of solutions, it is imperative to create a team of ‘Digital Champions’ within each department.
• These champions would be responsible for user training, propagation of digital solutions and for addressing user concerns locally in order to drive acceptance and increase usage.
• These champions act as a bridge between users and the digital team by soliciting user feedback and ensuring that user concerns are heard and addressed in the solution roadmap.
• These champions would also be tasked with identifying potential use cases within their departments that can be taken up under future digitization initiatives.
• A sense of competition should be fostered among departments with incentives for adoption digitization initiatives to drive adoption.

IIOT initiatives rarely succeed unless they are tied to tangible business objectives. We have seen quite a few examples where organizations start implementing IIOT solutions as research projects that do not have a clear business objective. These projects then meander along without and executive sponsor and are ultimately scrapped or are forgotten.

Our recommendation would be to start a pilot implementation with clearly defined, measurable goals. Once the pilot has been commissioned, monitor it for a few weeks and then start the process of scaling out to the rest of the organization. This approach reduces implementation risk and ensures that a robust solution is rolled out.

About the Author

Mr. Ashish Nene is the CEO of Websym Solutions Pvt. Ltd.

He can be contacted at :
Email : ashish@websym.com
LinkedIn : https://www.linkedin.com/in/ashish-nene-4620a71/
Skype : ashishnene

Websym Solution Pvt. Ltd. ( https://www.websym.com/ ) offers IIoT and Analytics solutions to the manufacturing industry and industrial & consumer OEMs . Our solutions help organizations utilize the power of IOT, big data and analytics to increase operational margins and improve efficiencies through real-time generation of actionable insights driven by real-time equipment performance and health data integrated with enterprise systems.
Linkedin : https://www.linkedin.com/company/websym/

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The process of Helping machines get resourceful & smart

One major driving force behind Industrial IoT (IIoT) investments, is the ability to predict and correct machine failures before they occur. IoT Analytics research says the market for predictive maintenance applications will expand from $2.2 billion in 2017 to $10.9 billion by 2022. Predictive maintenance strategies, which aim to predict machine failures before they occur, are based on the combination of traditional condition monitoring enhanced with analytics algorithms. The IoT and ever-more sophisticated analytics are driving widespread market adoption with users reporting as much as 25 to 30% efficiency gains.

For example, a U.S. automobile company recently implemented an IoT platform. The vehicle organization’s objective was to quantify the proficiency of its artistic creation process for auto parts on the transport lines and right any slacks or blunders found in the framework. The adaptable IoT stage associated the business’ items to the cloud consistently by means of little sensors for altered checking and control. After each part was painted, the sensors estimated the part’s development to decide whether the paint was being destroyed by over the top movement. This arrangement enabled the organization to effectively and rapidly distinguish and adjust issue regions before they caused expensive oversights.

This is one example of what IoT-enabled sensors can measure to minimize plant shutdowns and increase productivity and profits. IoT platforms are also capable of providing diagnostic data such as vibration, temperature, movement, and humidity that decision-makers can use to analyze and predict the health and performance of machines. IoT-powered analytics also can be used for comparative analysis between machines or to determine if any steps in the process are causing costly slowdowns.

Platform Operations – IoT

Each IoT platform operates a little differently, but typically, small sensors are applied to motors and machinery. A self-contained electronic circuit board embedded with a code connects the object with the sensor and collects data. Each circuit board has its own power source as well as communication capabilities with other circuit boards and the software program or app where the data is accessed.

The company’s software development kit (SDK) allows developers to build customizable mobile applications to interact with the circuit boards and the cloud and integrate with whatever third-party hardware kit makes the most sense for the business and the application. Numerous IoT platforms offer additional features and customization. For instance, a platform can offer cloud services and a plug for connecting additional enhancement modules.

IoT platforms can also vary in what data gets reported. Manufacturers most likely want an IoT platform that uses edge computing. This creates a profile for “normal” behavior of the object and then only reports the exception occurrences, which means instead of sifting through endless amounts of data, company operators are alerted in real-time of unusual movements or conditions that could signal a problem.

Big Data, Easy Integration

Firstly IoT doesn’t require hiring a team of tech experts or building a system from ground zero. IoT promises a lot of benefits for manufacturers, but the benefits luckily aren’t matched by the price or integration time.  Numerous IoT platforms are easily scalable and offer end-to-end integration solutions that encompass on-premise, legacy and cloud systems and platforms. Many also operate under a Platform as a Service model, which makes integration cost-effective and quickly puts fully customized data into the hands of the customer.

Capital equipment can last years, so it may be impractical to purchase new equipment embedded with smart technology. Integrating with an IoT platform using small sensors fills the gap, enabling manufacturers to get the benefits of today’s technology without investing in entirely new machinery.

Before integrating with an IoT platform, manufacturers should identify potential problem spots to determine what data would be most useful and ask questions such as:

What could potentially be automated that currently requires manual monitoring?

What components or steps in the process cause the most trouble?

Questions like these are a good place to start when deciding how to start with IoT. Manufacturers should also think through what capabilities are needed in an IoT platform. Communication protocols vary, as does pricing, customizability and flexibility. The technology landscape is evolving and manufacturers should look for an IoT provider that can keep up.

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Industry 4.0 or the fourth Industrial Revolution became a reality today owing to the latest entrant on the manufacturing scene, Internet of Things.

A ‘smart factory’ is connected to supply chain networks; the data flow from the machines and the factory to the cloud. This enables synchronized and united control in all aspects of manufacturing, offering an easier, smarter, more productive and more prosperous manufacturing processes lifecycle, with real-time information and real-time management.

However, the increasing usage of the internet also has its vices, particularly in the form of online security. These smart manufacturing units, now face threats and vulnerabilities of a data breach, corruption, data stealth along with Trojans, water holes etc in the supply chain infrastructure.

How IoT security saves the day?

Stopping or reducing usage of the IoT framework is a non-option. Following methods are the panacea that manufacturers of today have for these threats:

• Security by Design

When we add security measures into application programs, it is known as security by design. It strengthens embedded systems and single applications and enables them to resist attempts at reverse engineering, malware insertion and/or tampering. Eg. prplSecurity.

• Threat Detection using Machine Learning

If all production processes which use IoT could detect threats on their own, it would make the system immensely stronger and that is exactly what machine learning does. However, the addition of these algorithms does add a performance overhead to the system as a whole.

A secure IoT structure is the foundation of a strong smart manufacturing system and the recent developments are on the right course if not already there.

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