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5G networks Connected Devices IIoT Industrial industry 4.0 manufacturing trends Tech

Cellular Connectivity Will Revolutionize Industry 4.0

cellular connectivity

Few manufacturing trends in recent years are as buzzworthy or as promising as Industry 4.0. This data-driven industrial revolution promises to make factories a safer and more efficient place, but today’s technology can’t see it through. While currently connected factories are a marked improvement, manufacturing needs better cellular connectivity to experience Industry 4.0 in full.

With more than 50 billion IoT devices in the world, today’s connections will soon be insufficient. Manufacturers can already integrate many IoT technologies into their facilities, but modern connections may not support bigger busier networks. That’s where 5G IoT comes in.

5G will take the IoT to the next level. This upgrade is particularly beneficial for manufacturers. Here’s a closer look at how these new networks will revolutionize Industry 4.0.

Shortcomings of Hard-Wired Connections

Some people may push back against the onset of 5G networks. After all, the U.S. needs eight times the infrastructure to support these new connections, which may seem too substantial an inconvenience. Why switch to cellular networks for Industry 4.0 when hard-wired connections already provide such speed and reliability?

While fixed connections do present some advantages of wireless ones, they come with their fair share of shortcomings. In a factory, where people and machines are continually moving, physical wires present a problem. Someone could easily unplug an ethernet cable, jeopardizing any mission-critical operation relying on it.

Hard-wired connectivity also limits flexibility, which is a problem many facilities already have in excess. If a factory needed to reorganize or adjust its operations, it would take time to cost money. Since many new technologies only support wireless connections, sticking to a hard-wired system could restrict facilities to legacy tools.

Physical connections, although reliable, aren’t suitable for manufacturers. Wireless connectivity is a necessity, and 5G provides the kind of wireless network the industrial IoT needs.

How 5G Improves Cellular Connectivity

The advantages of wireless over ethernet connections are evident, but why is 5G necessary? The fifth generation of cellular networks benefits IIoT in three primary ways: speed, latency, and bandwidth. Each of these improves with 5G, and each is essential for the IIoT to work.

Experts expect 5G to be at least 10 times faster than today’s 4G LTE connections. Some have even predicted it will be as much as 100 times faster. Such a tremendous increase in speed would make it possible to run virtually any operation online.

With near-zero latency, these connections would also be far more reliable for handling mission-critical workloads. Many companies may be hesitant to move some functions onto the cloud in fear of disruption on current networks. They wouldn’t have to worry about that anymore with 5G.

Finally, an abundance of IoT devices requires a considerable amount of bandwidth. That’s one of the most significant barriers to IIoT adoption today, but it wouldn’t be an issue with a 5G-powered IoT. 

The Internet of Everything

That bandwidth upgrade is one of the immediately noticeable advantages of 5G in manufacturing. Since it can support more devices in the same area, manufacturers can implement IoT devices on a massive scale. The industry could move beyond the IoT into the internet of everything (IoE).

In the IoE, everything — including processes and sometimes people — is online instead of a few physical devices. Imagine a factory where every machine, product, utility, and function can communicate on a single network. This level of connectivity would be impossible without the bandwidth improvements of 5G.

If the IoT makes manufacturing more efficient, then the IoE will revolutionize it. In a sense, everything in a factory is already connected since a mistake at one point can disrupt the entire process. The IoE would give facilities the ability to see and react to these mistakes before disruptions happen.

Predictive Maintenance

One of the most promising benefits of the IIoT is being able to perform predictive maintenance. Instead of repairing machinery as it breaks, sensors communicate when it might need attention. This practice is possible with today’s networks, but 5G can enable it on virtually every machine in a facility.

Even a regular maintenance schedule isn’t always optimal for machines’ health. Too many factors can affect a system’s condition, and maintenance needs, even if frequent, rarely occur on a schedule. Constant monitoring and analysis is the best solution, but running these sensors on several pieces of equipment takes a lot of bandwidth.

On a 5G network, bandwidth wouldn’t be an issue so that manufacturers could use widespread predictive maintenance without worry. Since this gives machines 10 to 15 more days of availability a year, this would lead to a considerable boost in productivity. The savings from this application alone would make up for the cost of 5G infrastructure.

Remote Monitoring and Service

The sensors within a machine aren’t the only part of monitoring and maintenance that would improve with 5G. On a cellular network, workers could look at monitoring data no matter where they are. This accessibility isn’t only convenient but would also save time workers would otherwise spend walking to each machine to check on it.

Remote monitoring doesn’t just apply to machine maintenance, either. Data analysis is a cornerstone for many business practices today, and being able to do so remotely makes data-driven processes far more flexible. Companies could show real-time data to investors, share information with analysts while out of the building, and more.

Not only would workers be able to look at data remotely, but they could also act on it. 5G IoT devices could run troubleshooting and even basic repairs without workers needing to be physically present. With these advantages, manufacturers could make service a far more efficient process, reducing downtime and saving money.

Automated Guided Vehicles

5G networks in cities could finally make self-driving cars a reality, thanks to its speed, bandwidth, and low latency. Manufacturers can take advantage of this benefit before municipalities, enabling more automated guided vehicles (AGVs) in their facilities. Some factories already use AGVs, but Wi-Fi can’t support too many of them, limiting their usefulness.

With 5G in manufacturing facilities, it would be possible to run an entire fleet of AGVs. Numbers aside, the lower latency these vehicles have, the better since any network disruptions could hinder their navigation. If these are to work safely alongside people, they need a reliable network.

Despite their efficiency and safety benefits, AGVs haven’t seen high adoption rates in manufacturing. Nonmanufacturing environments have deployed more than 12 times as many AGVs as manufacturers as of 2018. The onset of 5G networks could make these technologies viable for more facilities.

Operational Flexibility

Flexibility is becoming increasingly critical for manufacturers, but the industry is historically inflexible. Today’s market expects on-demand, personalized service, and products, which requires manufacturers to adapt quickly to changes. Since cellular connectivity enables further automation, it leads to greater flexibility, thanks to higher efficiency.

Automation predates 5G by decades, but 5G makes it more reliable and efficient. Its benefits in maintenance, communication, and accessibility enable manufacturers to use more robots and efficiently. As a result, facilities can move toward a more on-demand model, cutting down on in-house inventory, enabling flexibility.

Without sitting inventory, facilities could adjust their operations without much disruption, which is crucial in today’s digital world. Since 5G would also allow manufacturers to run all machinery on a wireless network, they could issue updates far faster. Today, automated machinery is notoriously inflexible, but the connectivity benefits of 5G could change that.

New Cellular Networks Enable and Improve Industry 4.0

The shift toward Industry 4.0 is already taking place, despite the lack of 5G networks. Without these new cellular connections, though, manufacturers won’t be able to push Industry 4.0 to its fullest potential. Today’s systems are too slow, unreliable, and limited to handle the scale of IoT devices that manufacturers need.

5G in manufacturing will help the industry move past the IoT and into the IoE. When everything in a facility can run on a single network and do so reliably, manufacturers will become safer, more efficient, and more profitable. The 5G IoT will help the industry become what it needs to be to meet the modern world’s demands.

Widespread 5G networks are still several years off from becoming a reality. When they do become available, they could revolutionize the manufacturing industry.

Image Credit: panumas nikhomk; pexels

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fleet management IIoT Industrial Industrial Internet of Things Industrial IoT Internet of Things IoT process automation Tech

All You Need to Know About Industrial IoT

industrial IoT

The term IIot, or Industrial Internet of Things is used to refer to the industrial applications of the Internet of Things. We are talking about using the technology in anything from the machines in a factory to engines inside a car – these are all filled with advanced sensors equipped with wireless technology. These can collect and share data enabling extensive use of digital intelligence across various industries. Here is all you need to know about industrial IoT.

Industrial IoT Use Cases

Applications of Industrial Internet of Things

Process Automation

One of the best use cases of the industrial Internet of Things has been the process of automation across many industries.

With the help of smart sensor networks that can connect with each other through cloud computing systems, industries have been able to automate a number of their crucial processes and achieved a higher level of productivity and efficiency.

It has provided better control of the process and has significantly decreased the number of people required to get the job done.

Restaurants have been using process automation to get rid of food wastage. With the incessant developments in IoT technology, the evolution of traditional industries will become inevitable.

Predictive Maintenance

To be able to run effective predictive maintenance, you will require the processing of large amounts of data and will have to run sophisticated algorithms on it. This cannot be implemented within SCADA.

Therefore, an IoT-based solution, that can store terabytes of data and can still run the required machine learning algorithms, was introduced on several computers to keep a tab on the progress and have prior knowledge of industrial equipment failing.

A robust IoT-based predictive maintenance ecosystem has become essential for modern industries. The architecture consists of field gateways, cloud gateway, streaming data processor, a data lake, and machine learning algorithms.

Asset Tracking

Asset management and tracking have become much easier and efficient as an IoT-based digital asset tracking can now connect different components of the business chain and create an integrated strategic system.

We are talking about connecting multiple stakeholders, processes, workforce, and assets to a single digital IoT-driven system that provides a unified view of a process now backed by effective data analytics.

Industrial IoT can be added to your traditional solutions to make them more intelligent and get automated workflows, real-time alerts, dynamic edge control of assets, cross-domain analytics, real-time visibility and more.

Fleet Management

IoT-enabled solutions have revolutionized fleet management by making the process more environment-friendly. An IoT solution can help monitor the carbon emissions and monitor the service condition of the fleet.

Industries can build sensors-equipped fleets that can send automated signals and warning alerts like system failure, low battery, engine temperature or maintenance, and more. IoT-based solutions can also regulate driver behavior which can result in improved fuel efficiency.

The fleet manager can keep a tab on all such data and get actionable insights. IoT solutions allow managers to implement changes and make data-backed decisions.

Technologies in Industrial IoT

Kubernetes, k8s

1.   Front-End Edge Devices

The sensor data is what industries need to get important insights into their processes. This makes the hardware containing the sensors a crucial component of the IIoT system.

Many front-end devices and control devices are installed to capture critical process-related data and analyze it in real-time. Therefore, the devices must be reliable and of very high-quality so that the stream of data captured is consistent and accurate as well.

Some of the traditional industries already have devices installed that collect data for them. It will be easier for them to develop their process and make it IIoT-enabled.

However, if your data collection process isn’t there in the first place, you will have an opportunity to install a modern set of tools for your industry. It’s quite a win-win situation for you because your process has to evolve some or the other day. So, why not now?

2.   Connectivity Technology

Industrial IoT solutions rely heavily on wireless technologies to transmit and receive commands from the cloud. You have got Wi-Fi, Bluetooth, mesh networks, cellular networks, LPWAN technology, and what not to choose from.

Before going forward with technology, you should pen down your requirements. Different connectivity technologies have different range and capacity. It’s not just about wireless connectivity. Many of the industries have established IoT devices and connected them through wired systems as well.

If your setup allows for a wired connection, you should go for it as it will save a lot of money and provide even better reliability.

3.   Industrial IoT Platforms for Data Analytics

Once your setup is complete, you can focus on the data analytics part for which you will software that can analyze the collected and transmitted data. The software can be trained or programmed to make decisions for the processes.

The software is called the industrial IoT platform and it helps connect the hardware, access points, and data networks and also the end-user applications. All the data and command management happens with the help of real-time task management and data visualization.

The IoT platforms act as the middleman between the data and the processes or applications. One of the most common issues you will face is that you won’t get an off-the-shelf software solution for your process.

You might have to build the solution for yourself or buy the whole end-to-end software solution and the hardware and align your industry setup accordingly.

Wrapping Up

There you have it. We have discussed what you need to know to understand IIoT in depth. Let us know if this piece of content provided you with great value in the comment section. We’d love to hear from you. Also, since you are here, don’t forget to follow us on social media, we bring all the latest news and updates from the world of technology, startups and more.

Further Reading

What can we expect for IoT in 2020?

Is it time to implement IoT in the warehouse?

Top 5 areas where companies want IoT solutions?

Will companies embrace digital transformation?

Demystifying the 8 core myths that surround the Internet of Things

The post All You Need to Know About Industrial IoT appeared first on ReadWrite.

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employee recruiting Entrepreneurs Industrial recruiting Recruiting Software Recruitment remote recruitment Tech

Remote Recruitment – Tools, Best Practices, and Latest Trends

remote recruitment

With an increase in companies and organizations working remotely, there has been a significant rise in remote recruitment. Naturally, this has created a change in the organizational structure and the means of communication between the recruitment team and hiring managers.

HR professionals and recruiters are trying to adjust to remote recruitment while ensuring that the efficiency of information flow among the team is maintained. In such a scenario, it is imperative to choose the right set of tools and follow the best practices that can aid in remote recruitment.

Unless you do so, you risk losing the best talents due to the sheer lack of adaptability from your end.

How to choose the right set of HR tools to use in 2020?

remote recruitment tools
Remote Recruitment: Tools to use

There are many recruitment tools available, and choosing one depends on the goal that you want to achieve using the tool.

The remote recruitment process involves sourcing the candidates, scheduling interviews, screening amp; interviewing candidates and hiring them.

Recruitment tools assist in all these areas and help in streamlining workflow.

Recruitment tools help in sourcing candidates, screening amp; assessing candidates, scheduling interviews, maintaining a database of candidates, engaging with the candidates, interviewing them across different platforms, sharing the candidate profiles with the hiring managers, and getting feedback from the candidates.

Use tools that help you with either all or some of the steps involved in the hiring process.

Here are some of the tools that recruiters use to streamline their hiring process.

Applicant Tracking System (ATS)

According to research by Capterra, almost 75% of recruiters and talent managers use recruitment software or ATS. Of those, 94% agree that using those software has improved their hiring process by reducing cost and fastening the entire hiring process.

  • ATS is one of the most widely used recruitment tools and helps recruiters simplify their hiring process. Most ATS has these following features.

    -Advanced job sourcing and job board integration.
    -Saved candidate profiles and candidate databases for easy search.
    -Resume parsing and screening.
    -Interview scheduling, customized interview questions, and automated email replies.
    -Hiring metrics and insights.
    -Easy team collaboration.

    Some well-known ATS are- Taleo, Jobvite, Greenhouse Software, Homegrown, etc.

  • Candidate Sourcing Software

    Candidate Sourcing Software assists recruiters in sourcing both active and passive candidates through the web. Recruiters can source high-value candidates without frantically searching across different platforms.

    Job aggregators, candidate sourcing platform, and AI sourcing tools are some common forms of Candidate Sourcing Software.

  • Candidate Relationship Management (CRM) Software

    CRM Software is used by recruiters to improve candidate engagement and experience. According to a Glassdoor study, about 47 % of job applicants cited untimely responses and lack of information about job responsibilities as the primary causes of frustration.

    The importance of candidate engagement and experience has increased now, more than ever. Through CRM software, HR professionals can engage with candidates in a timely and personalized manner and build a good relationship with them.

  • Some well known CRM software are – Smashfly, Avature, etc.
  • Recruitment Marketing Software

    The significance of the employer brand has never been so important. A study by TalentNow highlights that, for 84% of job seekers, the employer’s reputation is an important factor, with 50% of candidates stating that they wouldn’t work for a company with a bad reputation even if the pay increases.

    Recruitment Marketing Software helps recruiters use marketing techniques to create brand awareness, generate interest towards your company, and attract top talent for open positions.

    Recruitment Marketing helps in employer branding, candidate engagement, CRM, candidate experience, and uses analytics for data-driven decisions.

  • Video Interviewing Software

    With remote recruitment gaining momentum, Video Interviewing Software has also become an essential tool for the recruitment process.
    Video interviewing software enables recruiters to connect with candidates anywhere globally and enjoy a seamless interview experience powered with analytics.

    Well known video interviewing software are ConverIQ, Hire Vue, and Spark Hire.

  • AI and automation Software

    In recent years, AI in recruitment has been tremendous growth and potential. AI for recruitment uses Artificial Intelligence to automate repetitive tasks and helps HR professionals streamline their entire workflow.

    AI software not only assists in reducing the bulk load of recruiters and hiring faster, but it also reduces human bias in recruitment, diversity hiring, facial expression analysis, personalized candidate engagement, and automated interview process.

    Some of the emerging AI tools for recruitment:

    -Arya- Automated sourcing of candidates.
    -X or Chatbot recruiting assistant that communicates with candidates across various platforms.
    -FreJunHR– Streamlines the entire interview process from scheduling to sending.
    -WhatsApp messages; auto-dialing to generating candidate analytics using AI.

How to communicate with recruiters working remotely?

When working remotely, communication is one of the most critical factors for ensuring accountability and information sharing among team members. This explicitly holds for recruiters who need to ensure that information flow between hiring managers (frejun) are maintained, and the hiring process is working smoothly.

Here are the tips for communicating effectively with recruiters working remotely.

  1. Plan a remote communication policy with your team.
  2. Choose the tools to use for communication.
  3. Minimize emails and focus on automation.

Best practices for remote recruitment

remote recruitment best practices
Remote recruitment: Best practices

Whether you are new to remote recruitment or have been recruiting remotely for a while, there are some best practices that you should follow to up your recruitment process. Here are some of the things remote recruiters should do daily for an efficient remote hiring process.

  1. Maintain a standard recruitment process

    Since remote recruiters work from different locations, a centralized process is vital for proper information flow between recruiters, teams, and managers.

    Recruiters should follow a standard recruitment process through a well-documented recruitment policy. Among other things, the policy should include your mode of communication, meeting schedules, and tools amp; platform to be used for different levels of the recruitment process.

  2. Use technology to assist you in the hiring process

    Hiring can be a daunting task when you are continually juggling from one step to another. But with assistance from digital tools and technology in every step of the hiring process from sourcing to remotely screening to interviewing to onboarding, you can significantly improve the speed and efficiency of your hiring process.

  3. Maintain regular communication with teams

    Remote recruitment thrives in communication. To ensure that recruiters are sourcing the right candidates, recruiters should not only be clear about the job description but should also be clear about the company’s culture and the type of candidate that fills the culture.

    Hence, it is essential for recruiters to maintain regular communication with their teams and hiring managers.

  4. Engage with candidates

    Engaged candidates are more likely to show up for the interview and have a good candidate experience. Remote recruiters should understand the popular digital channels their target profiles use to engage with them accordingly.

    Some of the ways to engage with candidates are through social media channels, emails, text messages, and phone calls.

  5. Practice video interviewing often

    For remote recruitment, video interviewing is a core skill that will help recruiters and hiring managers to understand and evaluate the candidates better.

    Practice video interviewing every day to get comfortable with the process. Also, always double-check your network connection, software, and hardware before starting the interview.

    Always keep a plan B handy, in case there are some unexpected errors or glitches from either end.

  6. Focus on building the employer brand

    According to a survey by Careerbuilder, 64% of candidates said they research a company online after finding a job offer, and 37% said they would move on to another job offer if they can’t find information on the company.

    Along with an updated company website, an active social media presence that highlights the company’s values and cultures significantly improves employer branding.

    Ways to build employer branding:-Use your company’s career page to highlight your company values and culture.
    -Use social media platforms like LinkedIn and Facebook to engage with your prospects.
    -Hold interactive sessions for students through podcasts, webinars, job-related quizzes, hold virtual career fairs, etc.

  7. Use AI tools for smart hiring and streamlining

    A LinkedIn study states that 76% of recruiters believe that AI’s impact on recruiting will be significant. Using AI to assist in high volume repetitive tasks helps recruiters to focus on the more important steps of the recruitment process.

    Areas where you can use AI in recruitment:

    Automate candidate sourcing
    -Automate scheduling calls
    -Rediscovering the required candidate from the database
    -Getting candidate insights through natural language processing and facial expression analysis.

Recruitment industry changes

Over the years, there have been significant changes in the recruitment industry, mostly owing to technological advancements.

  1. The growing importance of company culture

    According to a Glassdoor survey, three in four adults consider a company’s culture before applying for a job and more than half of employees prioritize a company’s culture over salary.

    The trend by Glassdoor is an interesting trend that emphasizes the need for better employee engagement, building a clear mission statement, and focusing on a strong employer brand.

  2. Job search through mobile

    The rise of smartphones for job search among different age groups has been phenomenal. A Glassdoor survey reveals that 58% of Glassdoor users search for jobs using their mobile phones.

    Hence, employers should focus on mobile-friendly applications to attract more talent and not frustrate them when applying for the job.

  3. The increasing use of AI in recruitment

    AI in recruitment has been the talk of the town for quite some time now. And the trend is likely going to increase in 2020. And no, it is not here to replace humans. It is here to assist them.

    As John Jersin, vice president of LinkedIn Talent Solution says, “I certainly would not trust any AI system today to make a hiring decision on its own. The technology is just not ready yet.�

  4. Read: Tapping into the human side of AI
  5. Gaining prominence of soft skills

    The importance of soft skills in recruitment has been increasing with HR professionals selecting candidates based on the culture-fit. Candidates now are evaluated not just on their hard skills but also on their soft skills.

  6. Diversity inclusive hiring

    Bias in recruiting in terms of culture and gender is likely to decrease as the focus on company culture and employer branding increases. According to a study by McKinsey, companies were likely to have 33% higher financial returns than their industry counterparts.

    Focusing on inclusive diversity hiring will not only shed a positive light on the company culture but also enhance the creativity and productivity of the organization.

  7. Growing prominence of candidate experience amp; employer branding

    Research by Talent Adore states that, for 78% of job seekers, the candidate experience that they receive indicates how much the company values its employees.

    This is a very powerful message for companies to increase their focus on candidate experience and employer branding.

  8. Use of Analytics in HR

    Data and analytics are driving HR decisions at every level. The use of various tools to streamline each stage in the recruitment process and subsequent use of metrics and data improves the decision-making process.

    With the rise of big data and analytics, HR managers are more likely to make informed decisions backed with data and analytics.

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cloud computing Connected Devices connectivity Future Tech Industrial Internet of Things IoT Tech

The Future of IoT Devices: What it Means for Connectivity

iot devices connectivity

A shift from the cloud to the edge might signal a real autonomous revolution in IoT connectivity. While previously, we witnessed how cloud computing allowed for centralization and collaboration — edge devices are all about abilities to work offline, autonomously, without sending data to the cloud for processing and storage. Here is the future of IoT devices and what it means for connectivity.

Does edge connectivity mean we’ll outgrow Cloud-based connectivity and that we are heading towards the era where edge computing takes central place? Good question.

When we say IoT, what do we mean?

When the Internet of Things term was first introduced around 20 years ago, it alluded to the Internet, which was a big thing back then.

The concept of miniature sensors sending and receiving the data from the cloud over WiFi was huge and breathtaking. When talking about the Internet of Things today, we mean a remotely controllable ecosystem of devices connected to the cloud and to each other with some kind of connectivity.

Most importantly, these devices must be able to perform some actions.

In terms of smart homes, we talk about smart speakers/voice assistants like Alexa or Google Echo, that can issue commands to switch on the lights, tune the conditioner or order a pizza at the nearest Domino or Pizza Hut.

The connected-concept can be rigged up to smart systems controlling commercial real estate across a variety of scenarios. When talking about the Industry 5.0 factories and other industrial installations like wind farms, the IoT means an ecosystem of devices capable of communicating with each other and able to perform some actions based on the commands received.

However, as the technology evolves, the meaning of the terms like IoT and connectivity broadens, and we must take into account this updated image of what connectivity is today — and what it will become in the future.

Why IoT is not enough anymore

The concept of IoT as an independent development entity centered at the gathering, sending, and receiving data has overstayed its welcome. In short, the IoT, in its original meaning, is long dead.

Such systems must provide much more business value to be feasible nowadays. They must enable the users to analyze the data gathered and perform meaningful actions based on the results of this analysis.

The focus of the IoT and connectivity has shifted from the brilliance of myriads of sensors to the value of data they gather. The data, not the sensors, is king. There are surely more sophisticated sensors to come, but their main value is the data they can gather — and the actions we can perform based on this data.

Of course, we only need a smart kettle to be simply switched on when we are close to home so that we can get a cup of tea or coffee faster.

But an autonomous car must be able to react to the changes in the road situation around it, and a smart factory must be able to adjust complex working scenarios should something go awry.

Therefore, the IoT alone as a concept of Digitally Connected Assets, or DCAs, is not viable. It cannot exist in a vacuum, as such systems must be able to process the data quickly and make use of it either through analytics or through issuing some commands.

Performing the task in the cloud means too large latency — so we need something faster. “Faster” is where the edge computing concept comes into play.

Edge computing — the next stage of the IoT evolution

The edge computing term refers to the concept of local computational nodes that form the hearts of the sensor networks in some locations. These sensor networks can be a server node on a factory or in an agricultural complex, an aforementioned Google or Amazon smart home system.

The system can also be the smart utility control system for commercial real estate like malls or office buildings.

In short, edge computing provides a Local Area Network connection for sensors, enabling lightning-fast data transmission. It is also connected to the cloud to enable centralized data gathering and analysis, storage of historical data, and training of AI/ML models on this data.

But most importantly, edge computing nodes provide sufficient computing capacity to host Artificial Intelligence / Machine Learning algorithms locally, which allows these models to issue the needed commands based on the data received from the sensors.

Let’s imagine the fully-automated Industry 5.0 factory equipped by various sensors (movement, temperature, humidity, etc.), a fleet of robots, and multiple actuators.

The robots perform the production operations while the sensors monitor the situation — and one sensor signals the drastic overheating in one of the conveyor belt engines.

The local edge computing node receives the signal, and the AI/ML algorithm running it enacts one of the response scenarios. The scenario can shut down the engine, apply the coolant if possible, disconnect the engine from the conveyor belt (if there are backup engines – start them).

To minimize the production disruption — or reroute the flow of production to other conveyors. All of the functions are done within milliseconds, preventing fire and saving the manufacturer millions in potential damage.

To make operations possible, the edge computing nodes must have three key abilities:

  • To control the processes in the physical world. Edge computing nodes must be able to gather the data, process it, and enact some response actions.
  • To work offline. Deep underground mines or sea installations far from the shore can have issues communicating to the cloud, so their systems must be able to operate autonomously.
  • Zero-second response time. With automated production or utility operations, a delay in several seconds can results in huge financial losses, so the response scenarios must be enacted and executed immediately.

The future of IoT: cyber-physical, contextual and autonomous objects

As we can see, the meaning and the value of the IoT have shifted from the ecosystem of interconnected devices for gathering data to the ecosystem of devices able to gather the data, process it, and act based on this data. Therefore, we can define three main categories of existing and future IoT devices:

  • Cyber-physical objects.

    The sensors that collect physical signals and transform them into digital data. Think of smart wearables that track our vitals, digital printers, many machine-to-machine and telematic equipment, various smart home systems like thermostats, etc.

    All the consumer devices that can perform only a single function like switching the light on/off or rolling the blinds up/down also belong to this group.

  • Contextual objects.

    Simple cyber-physical DCAs just provide the data or execute single commands, but more complex systems allow understanding the context in which these sensors and actuators operate and make better decisions.

As an example, let’s imagine an agricultural complex, where DCAs control the irrigation systems or the location and operations of a fleet of automated machines.

By supplementing this with an edge computing node, the farmer can consolidate this data to a single dashboard and augment it with weather forecasts and other crucial information, which will help get much more value of the data and control all the systems effortlessly.

  • Autonomous objects: the highest level of the “gather-process-reactâ€� chain, these systems combine the sensor networks, edge computing nodes, and the AI/ML algorithms to form autonomous objects that take the responsibility from humans to machines. An example is the factory incident we mentioned earlier.

Summing up: call it as you wish — connectivity will not die

We must operate in the real world and use the tools available to us. Basic gateway devices provide ample capacities for data gathering, storing, and processing within an edge computing node.

These nodes enable the ML model in it to take action. Nevertheless, they cannot provide sufficient computing resources for training a model like this, as it requires processing mounds of historical data over hundreds of computational cycles, which can be done only in cloud data centers.

Connectivity is still crucial for connecting edge computing nodes to the cloud, gathering statistical data, training new AI algorithms, and updating the existing ones. It is an integrated ecosystem, where every component plays its role.

What are we going to call this new and exciting ecosystem?

IoT 2.0? Cyber-physical edge computing-enabled objects? The terms itself matters little, while we understand what stands behind it. These objects will have the ability to connect the physical and digital worlds, gather the data with sensors, process it in context with other input, and take actions based on this analysis.

While this ecosystem works and is feasible, it matters little what we call it.

Most importantly, connectivity is still crucial for connecting edge computing nodes to the cloud, so connectivity will never die.

What do you think of the future of IoT and the importance of connectivity? Please let us know in the comments below.

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Industrial Productivity Project Management Project Manager Software Tech

Effective Tips to Ensure Project Management Success

project management

The job of a project manager is a challenge itself, whether you are involved in construction projects or software development projects. There are often hindrances that threaten to derail projects. From managing resources to ensuring adherence to budget and keeping up with the timelines, there is a lot that needs the project manager to complete a project successfully. Here are effective tips to ensure project management success.

Project manager

Apart from the big picture, you will also have to understand the nitty-gritty, plan effectively, and evaluate the project performance at all stages. Embracing a project management software solution is a great idea.

You will have to combine it with the best practices and the right approach to get remarkable results. Although there is no single proven way to ensure success, here are a few tips you can rely on.

Know your Project Inside-Out

A strong foundation is certainly the mainstay of a successful project. The foundation is something that every project manager should consider as the top priority. To create one, you need to know the project inside out, right to the smallest detail.

Start by identifying the clients and stakeholders and understanding their expectations regarding the project. Developing a robust project plan is equally essential so that you have targets and timelines that you can follow.

As a part of the planning phase, you will also need to define the roles and the job responsibilities that you would want to have for the project clearly.

After all, you will have to onboard the resources accordingly. Finally, defining the goals and objectives is vital to get better clarity and understanding.

Planning is just half the work done. You will also have to establish measurable success criteria for ensuring that the project stays on track.

Identify Project Requirements

Once you have a clear understanding of the project and are through with the planning phase, identification of the project requirements should be the next thing on your checklist.

Only when you know what you need will it be possible to get the right resources on board. Build a team that is fully capable of implementing the project plan effectively.

Once you have the people on board, you will have to define specific roles and allocate apt tasks to them while keeping in mind their strengths, skills and expertise.

If you require a professional who specializes in a specific skill, consider hiring them permanently because they can serve as a long term asset to the business. For instance, you can have an architect on the team for a current construction process but use them for the future ones as well.

Knowing your team at an individual level is beneficial because these are the people who will take it ahead and ensure successful completion as well.

Know Your Team

The skills and strength of the team you pick have a significant impact on the success of a project. A good manager goes the extra mile to get to know about the inherent strengths and weaknesses of their team members.

The precise knowledge of the individuals on their team lets them allocate the right work to the right person. The single most important benefit of tapping into the strengths of the team members is that it ensures higher productivity.

At the same time, you can expect faster task completion, along with better time management as well.

With all the team members putting their best foot forward, the project has good chances of being a success. While capitalizing on the strengths of people is invaluable, you cannot deny that they will have some weaknesses as well.

Make an extra effort to help people overcome their weaknesses. But be tolerant and flexible enough to deal with errors and delays in the right spirit.

Strengthen Communications

Consistent and effective communication between the stakeholders and the client is vital to run a project smoothly. It is important to connect with them through the project.

Communicating new changes to the team members ensure that there are no nasty surprises. As a project manager, it is your responsibility to streamline communication between the team members. At the same time, you should be approachable all the time.

Make sure that any member can connect with you anytime and without any second thoughts. Lack of effective communication is one of the key reasons for the failure of projects.

You need to make sure that everyone is on the same page and has the information they need for making decisions and proceeding with the project. The best way to facilitate communication is by leveraging project status reports. These reports should offer the team members information about the latest developments in a project.

Well-Defined Project Milestones

Identifying the critical and defining moments throughout the journey is helpful for making your project a success. You can do it best by creating a project life cycle that clearly defines the main phases. The phases include initiation, planning, implementation and closure. Further, you need to evaluate the project performance and progress after every phase is complete.

At the conclusion of a project, it becomes important to consider the granular details of each milestone. With this detail, you will also be able to exceed the expectations of the clients.

Additionally, these milestones are reliable indicators of the team’s performance and the way they contribute to the project’s success.

Manage Potential Risks

When it comes to successful project management, you cannot overlook the role of risk handling. Every project involves evident and unexpected risks that can creep out of nowhere and jeopardize its progress.

It is wise to invest your efforts in identifying the potential risks beforehand. This will help you plan the effective measures to implement in the need of the hour.

If you have the right expertise and experience with similar projects, you will be in a better position to foresee the imminent risks and take proactive measures to prevent them.

At the same time, you should also be able to take corrective measures well in time. Organizations now understand the value of risk management and are taking a proactive approach towards it.

Ramp up your Project Management Skills

Project management goes much beyond managing your team and tracking the progress of the project phases; it is also about ramping up your skills so that you can do all these tasks effectively and efficiently.

Resting on your laurels is the last thing you should do; instead, you should always aspire to upgrade your skills and get better with time. Giving your best is equally important to make the project a success. While technical skills matter, the potential to deal with people also counts.

Only someone capable of bringing together people with different personalities and ensuring that they contribute in their own ways can drive them to deliver their best.

While you should go the extra mile to entrust the team with the decisions they can take, gaining the trust of the clients is equally significant. You should also be able to act as a bridge that communicates the expectations of the client to the team so that they can work together to achieve a shared goal.

Invest in a Project Management App

You may have all the traits of a successful project manager. But it is still a challenge to handle the entire lifecycle of a project alone.

Consider the volume of work and the number of tasks that are part of a typical construction process. However, you can rely on technology to assist you in enhancing the quality and ensuring a successful project outcome.

A project management software application (zepth dot com) acts as a one-stop platform for all the relevant information that relates to the project. There isn’t an end to the capabilities of a good piece of software.

The correct software tool can house all the files to documents and enable file-sharing for getting feedback from clients and stakeholders. Further, it can streamline communication and collaboration and do a lot more.

There are several apps available today, so you have the option to choose one with the right features. Make sure that it matches your needs and fits into your budget.

Test Deliverables

By now, you will have a proper framework and best practices in place for the success of your projects. But it is important to go the extra mile before the actual delivery. You can do this by thoroughly testing the deliverables at every critical milestone.

By doing so, you get a fair idea of whether the project is going where it is meant to.

You can also decide if you are close enough to the target outcome. Testing deliverables enable you to determine whether they are meeting or exceeding client expectations. Conversely, if you see fallacies, you can take immediate corrective measures to bring them back on track.

Project Evaluation

The final tip to ensure success for all your projects at present and in the future is to evaluate them.

Consider every project to be a learning tool. A good manager reviews the project holistically. The project manager will analyze each project on a granular level by reviewing various project components in detail.

A comprehensive review enables you to understand the strengths and weaknesses of the team and the process. You can see what led to success and what bottlenecks led to problems. You can even understand what you can do to prevent problems or issues in your future projects.

Conclusion

Whether you are a newbie or a seasoned project manager, managing a small, mid-sized, or large project — these are the tips you can rely on to drive success for your team and the project as a whole.

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AI autonomous robots Industrial ReadWrite robot autonomy robotics robots Startups Tech Venture capital

Time to Build Robots for Humans, Not to Replace

robots

Thinking about the future of robots and autonomy is exciting; driverless cars, lights-out factories, urban air mobility, robotic surgeons available anywhere in the world. We’ve seen the building blocks come together in warehouses, retail stores, farms, and on the roads. It is now time to build robots for humans, not to replace them.

We still have a long way to go. Why? Because building robots that intend to work fully autonomously in a physical world is hard.

Humans are incredibly good at adapting to dynamic situations to achieve a goal. Robotic and autonomous systems are incredibly powerful at highly precise, responsive, multivariate operations. A new generation of companies is turning their attention to bringing the two together, building robots to work for humans, not replace them, and reinventing several industries in the process.

Innovation through limitation

New methods of ML, such as reinforcement learning and adversarial networks, have transformed both the speed and capability of robot systems.

These methods work extremely well when:

  1. Designed for well-known tasks.
  2. Within constrained environments and limited variable change.
  3. Where most end states are known.

Where the probability of unforeseen situations and ‘rules’ are low, robots can work miraculously better than any human can.

An Amazon robot-powered warehouse is an excellent illustration of well-characterized tasks (goods movement), in constrained environments (warehouse), with limited diversity (structured paths), and all end states are known (limited task variability).

Robots in a complex world

What about in a less structured environment, where there are greater complexity and variability? The probability of errors and unforeseen situations is proportional to the complexity of the process.

In the physical world, what is a robot to do when it encounters a situation it has never seen before? That question conflicts with the robots’ understanding of the expected environment and has unknown end states.

The conflicted robot is precisely the challenge companies are facing when introducing robots into the physical world.

Audi claimed they would hit level 3 autonomy by 2019 (update: they recently gave up). Waymo has driven 20 million miles yet operationally and geographically constrained.

Tesla reverted from a fully robotic factory approach back to a human-machine mix, the company stating, “Automation simply can’t deal with the complexity, inconsistencies, variation and ‘things gone wrong’ that humans can.�

Yes — this complex issue will be figured out — but the situation is not solved yet.

To solve these problems in the physical world, we’ve implemented humans as technology guardrails.

Applications such as driverless cars, last-mile delivery robots, warehouse robots, robots making pizza, cleaning floors, and more, can operate in the real world thanks to ‘humans in the loop’ monitoring their operations.

Humans are acting as either remote operators, AI data trainers, and exception managers.

Human-in-the-Loop robotics

The ‘human in the loop’ has accelerated the pace of technology and opened up capabilities we didn’t think we would see in our lifetime, as the examples mentioned earlier.

At the same time, it has bounded the use cases to which we build. When we design robotic systems around commodity skill sets, the range of tasks is limited to those just those skills.

Training and operating a driverless car, delivery robot, or warehouse robot all require the same generally held skill sets.

As a result, what robots are capable of today primarily cluster around the ability to navigate and identify people/objects.

As these companies bring their solutions to market, they quickly realize two realities:

(1) Commodity tasks make it easier for others to also attempt a similar solution (as seen with the number of AV and warehouse robot companies emerging over the past few years).

(2) High labor liquidity depresses wages, thus requiring these solutions to fully replace the human, not augment, in high volumes to generate any meaningful economics. E.g., Waymo/Uber/Zoox needs to remove the driver and operate at high volumes to turn a profit eventually.

The result of the commodity approach to robotics has forced these technology developers to completely replace the human from the loop to become viable businesses.

Changing the intersection of robotics and humans

The open question is: is this the right intersection between machine and human? Is this the best we can do to leverage the precision of a robot with the creativity of a human?

Expert-in-the-Loop robotics

To accelerate what robots are capable of doing, we need to shift focus from trying to replace humans, to building solutions that put the robot and human hand-in-hand. For robots to find their way into critical workflows of our industries, we needed them to augment experts and trained technicians.

Industries such as general aviation, construction, manufacturing, retail, farming, and healthcare could be made safer, more efficient, and more profitable. Changing the human’s role of operator and technician to manager and strategist.

Helicopter pilots could free themselves from the fatiguing balance of flight and control management. Construction machine operators could focus on strategies and exceptions rather than repetitive motions.

Manufacturing facilities could free up workers to focus on throughput, workflow, and quality, rather than tiring manual labor. Retail operators could focus on customer experiences rather than trying to keep up with stocking inventory.

These industries all suffer from limited labor pools, highly variable environments, with little technology, and high cost of errors. Pairing robotic or autonomous systems that work hand in hand with the experts could invert from the set of dynamics compared to commodity use cases.

Companies could build solutions that need only to augment the operator, not replace him or her, to meaningfully change the economics of the operation.

Building for an expert-robot generation

The current generation of technology innovation is starting, with a new generation of companies using robotics and autonomy to change the operating experience across industries.

  • Innovative companies such as Skyryse* with complex aircraft flight controls.
  • Built Robotics in the construction.
  • Path Robotics in manufacturing.
  • Caterpillar in mining.
  • Blue River in agriculture.
  • Saildrone in ocean exploration.
  • Simbe Robotics* in retail.
  • Intuitive Surgical in healthcare.

Robot solutions that share many key dimensions:

  • Introduce advanced levels of automation or autonomy that can pair with its human operator.
  • Deliver at least two of the three value dimensions: safer operation, improved cost of operation, high total utilization of assets.
  • Shift the operators’ time to higher-value tasks; eventually to manage across multiple functions in parallel.
  • Primarily software-defined across both control and perception systems.
  • Easily retrofit into customers’ assets base at price points less than 20% of the cost of the underlying asset.
  • Can go to market ‘as a service’ with recurring revenue and healthy margins.

Technology has empowered humankind to be capable of the impossible.

The impossible means we can make more complex decisions at orders of magnitude more precision and speed. Yet so many industries still rely on human labor and operations over human ingenuity and authority.

As the world adapts to social distancing and remote work, it’s more important than ever to leverage technology as our proverbial exoskeletons to maximize what humans are great at, and let technology do the rest.

*Venrock is an investor in Skyryse and Simbe Robotics

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AI Artifical intelligence Computer Vision Industrial Machine Learning manufacturing ML Software Tech

The Value of AI-Based Visual Inspection in 2020

ai visual inspection

For over a decade, manufacturers have turned to automated solutions to improve their bottom line. Automation and machine vision are now being augmented and even replaced by AI. Here is the value of AI-based visual inspection in 2020.

Value of AI-Based visual inspection

Being replaced by AI is especially true when it comes to visual inspection. The use of AI-based visual inspection technology is transforming manufacturing’s ability to improve business operations.

AI-based visual inspection relies on two of AI’s main strengths: computer vision and deep learning. Every AI system is built with the core capacity to perceive its environment (computer vision) and act on those perceptions (deep-learning).

As a result of deep-learning, AI adapts to a range of environments, making it useful across a multitude of industries. It has unlimited potential and can be developed rapidly to meet a manufacturer’s needs.

Concept of AI-based visual inspection

Well-trained human eyes can detect defects. A well-trained AI-based vision system can do the same — but with greater efficiency. Like a human eye, AI-based vision systems capture an image and send it to a central “brainâ€� for processing.

Like a human brain, an AI “brain� makes detailed meaning from the image by contrasting it against its existing knowledge.

AI-based vision systems are made of two integrated components. A sensing device acts as an “eye,� while a deep learning algorithm acts as a “brain.� The integrated system successfully mimics the human eye-brain ability to interpret images.

AI-based vision systems are more efficient than human eyes because the AI “brain� stores greater amounts of information.

Robust computational power can parse through available data at rapid speeds. The system can classify objects in both photos and videos and perform complex visual perception tasks.

AI-based vision systems can search images and captions, detect objects, and classify multi-media.

Thanks to deep learning-based visual processing, AI-based visual inspection systems can perceive cosmetic flaws and detect defects across general or conceptual surfaces (mobidev dot biz).

Benefits of AI-based visual inspection

1. Fast Implementation

Decades-old automated systems depend on defect libraries, lists of exceptions and complicated filters. The time it takes to accrue this information, clean it for accuracy, and re-implement it decreases its efficacy. It also wastes labor.

AI and deep learning do not require prolonged programming or tediously lengthy algorithms. AI-based visual inspection systems might be constructed by several quality engineers and a dataset of training images. The system learns rapidly and is integrated over several weeks.

2. Improved Analytics and Quality Control

Manufacturers can use AI to document inspection results and to assess product quality. Some overall process improvement initiative metrics that can be successfully tracked and correlated with concrete vision data include:

  • process recipes
  • equipment differences
  • component suppliers
  • factory location

In addition, inspection images and results can also be tracked and documented. These initiatives prevent future failure, which saves time and additional production costs. Applying deep learning-based machine vision across all initiatives and inspections helps manufacturers recognize and address defects early.

3. Labor Costs Reduction

AI solutions have higher rates of consistency than most expert human inspectors. Human inspectors must be trained and are only able to maintain a high degree of focus for 15-20 minutes at a time. Labor costs are incurred yearly and staff turn-over is an issue. For these reasons, AI-based vision inspections are more cost-effective than manual labor.

Use Cases

AI is increasing the competitiveness of manufacturers across every industry. Here are recent use cases from the aviation industry, semi-conductor manufacturing sector, and bio-science.

Alibaba has risen to meet healthcare challenges created by the coronavirus. Alibaba’s deep-learning-based visual recognition system is capable of detecting the coronavirus in chest CT scans at a 96% accuracy rate. The system accessed 5,000 COVID-19 cases and can provide a diagnosis within 20 seconds. Moreover, the system can differentiate between images of viral pneumonia and images of coronavirus.

Fujitsu Laboratories implemented an Image Recognition System at Fujitsu’s Oyama factory. The system ensures that parts are produced at optimal quality levels by supervising the assembly process. The system was so successful that Fujitsu implemented it across the entirety of the company’s production sites.

Airbus introduced an automated, drone-based aircraft inspection system in 2018. The system has improved the quality of inspections and reduced aircraft downtime.

GlobalFoundries is a leader in semiconductor manufacturing. The company designed a visual inspection system that detects defects in a scanning electron microscope (SEM) images. The system detects defects in a wafer map which then helps to determine the semiconductor device’s performance.

The use cases listed above reveal the extent to which AI is capable of automating many aspects of our lives. Although AI vision will never replicate human vision, the technology continues to classify information and advance in ways human eyes and brains cannot. And only humans might consider how to use this technology to get advantages.

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Connected Devices connected transport Industrial Internet of Things IoT ReadWrite trucking

5 Technologies a Trucking Business Must Be Aware Of

trucking business

In long-ago-days, the trucking businesses and fleets may have refrained from adopting new technologies because of the inherent resistance to change. However, that attitude has changed drastically — especially recently. Today, more and more truckers are opening up to the use of technology as an integral part of their work. Here are five technologies a trucking business must be aware of.

Are you among the truckers who are using the best technologies?

Here are the top 5 technologies for truckers to be aware of. Know more and apply these to your advantage.

Electronic Logging Devices (ELD)

With the deadline for the ELD mandate 2019, 16th December, fast approaching, all truckers are now quite aware of this technology and are working hard to adapt to it. The ELD consists of an electronic logbook to track a driver’s ROD (Record of Duty) status, among other functionalities.

The advanced technology of the ELDs ensures less chance of tampering besides reducing the costs of fleet management and ensuring 100% compliance. At this point, it’s one of the most important technologies for trucking businesses to be aware of.

However, before you invest in an affordable ELD for owners and operators, make sure that it is in compliance with the FMCSA regulations.

Trailer Tracking Systems

Many trucking companies are increasingly adopting this technology to manage their assets and avert losses, especially in high-theft areas. Besides installing a GPS transponder in your vehicles, you can also take advantage of wireless technology to feed location and other data directly into the fleet management system.

Be it Blackberry’s radar tracking system or the Asset Manager by MiX Telematics, the trailer tracking technology is here to make asset management hassle-free for the trucking businesses.

Some ELD solutions also include elements of Fleet Management that could work like a charm for smaller fleets too.

Collision Avoidance Technology

No matter whose fault it is, the consequences of accidents are always severe, causing loss of lives and goods. Hence, trucking businesses should invest in effective collision-avoidance systems to ensure safety on the road.

This technology aims to inform the driver about any obstacle on the road ahead, as well as prevent possible crashes by taking emergency actions.

You can opt for a range of efficient collision-avoidance systems like the OnGuard system by Meritor WABCO, Wingman Fusion by Bendix Commercial Vehicle System, and Drivewyze.

Platooning Technology

Truck Platooning is recently gaining a lot of attention in the fleet industry. The process involves installing advanced driving support systems in the trucks and using the same to communicate with each other and move in the form of a platoon.

This initiative to group vehicles, being driven by smart technologies, not only improves communication and streamlines the transport process but also reduces the chance of accidents with the help of advanced braking and accelerating systems.

Truck Platooning is also looking to adopt automated driving technologies, and according to the biggies in the transport sector, this will eventually help the industry realize its long-time dream of self-driving vehicles. Thus, it could also resolve the issues caused by the shortage of truckers.

Temperature Tracking Software

One of the most interesting trucking technologies that have gained prominence in recent years is temperature tracking and record-keeping for food and beverage transport.

The Food Safety Modernization Act has mandated that all carriers and suppliers should follow a set of important regulations and proper sanitary requisites while transporting food and related products.

It has become necessary to maintain appropriate temperature and moisture conditions even inside the trucks to ensure safe delivery and maximum compliance to the regulations. The temperature tracking technology thus helps the fleet management businesses to track and record temperature conditions inside the vehicles and take corrective actions whenever deemed necessary.

Most of the temperature tracking software in the market aspires not only to fulfill proper temperature requirements in the refrigeration units but also to send the information to the fleet management system tied to its GPS location.

The supply chain and trucking industry are going through a massive change, most of which is caused by their changed attitude towards adopting improved trucking technologies to better the system.  Be it installing ELDs in their vehicles to comply with the ELD mandate 2019 or taking the help of advanced collision-avoidance technology, both business owners, and truckers are leaving no stone unturned to grow and prosper.

As always, some regions inspire others to follow suit. For instance, owners and owner-operators of mid-sized and small fleets are at the forefront of investing only in the best ELD for trucks in California that are in compliance with the FMCSA standards.

If you have a trucking business, or even if you are a trucker yourself, staying updated with these technologies will help you drive into a better future. That’s a safe bet.

Image Credit: Quintin Gellar; Pexels

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IIoT Industrial Industrial Internet of Things IoT

IIoT Trends and Challenges to Watch

IIot trends

The Industrial Internet of Things (IIoT) is the application of IoT in an industrial setting. IIoT is sometimes referred to as Industry 4.0, though the latter primarily focuses on the manufacturing sector, using upgraded technologies to reduce waste and increase value in this field. Here are the IIoT trends and challenges to watch.

IIoT encompasses all areas in which industrial equipment is used.

Like Industry 4.0, IIoT will revolutionize processes through connected machines that can optimize productivity and revenue. IIoT can be seen in a variety of industries, from transportation to public safety and from energy to, of course, manufacturing.

There are new trends in this space and we need to see why challenges leaders are trying to manage these trends.

Solving for the Influx of data from disparate systems

More and more data is coming in for anyone using IoT, but this is especially true in the world of IIoT. Operators have become overwhelmed by the massive amount of data, making it difficult to harness its power for decision-making. One reason why it’s so difficult to make sense of the data is that it comes from so many disparate systems.

Angie Sticher, COO/CPO of UrsaLeo, the only company to offer photorealistic 3D digital twins combined with live sensor data, asset data, maintenance data, notes, “the varying types of data streams from different systems that don’t connect to one another and can’t give a realistic view of what’s happening in a given environment.

To help manage the deluge of data, technology is being deployed and creating a workflow that moves between these systems giving employees and managers the tools to triage issues quickly and get to problem-solving.

Ultimately this also helps in getting to the resolution phase of an incident more quickly.”

Manufacturing Success in IIoT

Though current trends do not indicate an uptick in US manufacturing, some in the IIoT industry think this may change. Joy Weiss, President, and CEO of Tempo Automation, a smart factory startup for printed circuit board assembly (PCBA), has seen this trend come to light. “We have seen a growing trend among companies preferring to switch to US-based manufacturing partners.

Using these partners instead of contracting overseas for a number of reasons, including the recent global health crisis due to Coronavirus,” she said. “Some of these advantages include geographic proximity, added IP and security certifications and standards, as well as the use of US-sourced, authentic components, and parts.”

Christine Kyle-Remmert, CEO and Founder of LoneStarTracking, a company that provides telematics solutions that include the latest Cat-M1 cellular technology and cellular-free LoRaWan deployments across North America, explains that power consumption, transmission distance, and price are three factors that play a role in the successful deployment of this technology.

“As technology progresses, sensors are getting smaller, more lightweight, and more affordable. However, no one has time to run around and replace batteries. Just a couple years ago, IoT sensors would only last 1-2 years; however, today, we are deploying sensors that can last 10+ years on a single coin cell battery,” Kyle-Remmert explains.

“Using technology like LoRaWAN, IoT sensors can now communicate 10+km and even further, with very little power. If you can develop a sensor that is a low cost, then there is nothing restricting you from deploying more sensors to get denser coverage.”

Skills Gap in the World of IIoT

Like many new technologies, a skills gap permeates through this industry. Ekaterina Lyapina, Solutions Architect and AI and IIoT Consultant at Zyfra, a company that develops industrial digitalization technologies for machinery, metallurgy, mining, and oil and gas notes, “The qualifications needed to install new smart robots in production lines are often not available in most companies.

Facilities and factories lack free time and robot technicians to update their ongoing production. This leads them to a fall behind AI and IIoT trends, as they are not capable of using the latest robotics technology. They are missing skills in integration, implementation, and debugging artificial intelligence enhanced systems.

So, the hindering factor in AI automation is workers’ qualifications at the foremost front. Especially the training and customization of neural networks require deep specialists’ knowledge to dig the treasures of AI.”

Sticher offers a potential solution to this skills gap, noting, “virtualization is driving cost reduction for training in a number of sectors. Digital Twins and 3D Models make it easier to train staff because they mirror real-world environments and shorten the learning curve.

Coupled with combining and scaling data from many systems, digital twins also offer a realistic and readily accessible information hub to an environment’s current status.”

Conclusion

Especially with the new order of the world, due to new restrictions and regulations brought on by Covid-19, it will be interesting to see where IIoT stands at the end of 2020.

While innovation in this industry continues, companies are grappling with the changes and safety precautions that need more immediate attention.

Image Credit: Pexels

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