The evolution of modern equipment manufacturing – what’s next?

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The modern manufacturing industry has experienced several incremental operational improvements over the last 50 years. We were introduced to W. Eduards Deming’s statistical quality management and TQM, followed by lean initiatives and Value Stream Assessment. We’ve experienced the rise of robotics and pervasive computer technology.

But none of these technologies have been as transformative as the digital revolution of the recent decade. We’ve entered the era of Machine Learning and Artificial Intelligence being applied on the factory floor, the era of Industry 4.0.

The term ‘Industry 4.0’ is typically interpreted liberally and despite being around for some time still causes quite a bit of confusion among manufacturers.

Originally an initiative by the German government with the objective of enhancing competitiveness of German industry and machinery manufacturers specifically, it’s been broadly adopted by machinery manufactures. It provided a standardized control interface and communication protocol, mostly OPC UA and specifically in the plastics industry – Euromap standards. This development fashioned different equipment with the ability to communicate and exchange data.

While the ability to collect and review production data is transformational, it’s just the beginning of this manufacturing revolution. The next step? It’s developing Machine Learning and AI capabilities that will provide meaningful and actionable insights into making products at the highest possible throughput, with the highest possible quality and yields, and the most economical material usage.

Oden is already ahead of this curve, and offers manufacturers the ability to predict the dimensions, quality, throughput, material usage on an extrusion line 5 minutes into the future. The Oden platform can alert manufacturers about undesirable, suboptimal process conditions and tell the user on how to adjust processing parameters. Besides the obvious operational cost benefits, this also aids the experienced workforce shortage problem, by providing less experienced shop floor personnel the ability to perform at the highest level.

Oden’s analytics infrastructure also has tremendous benefits for machinery manufacturers. Predictive analytics can be applied in monitoring critical components and sub systems of production equipment, leading to truly predictive maintenance alerts and the ability to predict when components will fail. This will enable machinery manufacturers to effectively eliminate unplanned downtime for their customers and offer post-sales real-time equipment monitoring service

Finally, Oden’s analytics platform can integrate and support existing manufacturing production data that is tied to inventory logistics through ERP and MES solution.

Oden launches partner program to help machinery manufacturers eliminate unplanned downtime

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In current competitive market, machinery manufacturers struggle to build ongoing relationships with customers post-sale. Once equipment is deployed by a customer, they lose visibility into machines’ performance, heavily relying on field service teams in case of failure. At Oden, we believe there’s a better way.

That’s why today we’re announcing our partner program to enable machinery manufacturers to leverage Oden’s IoT technology infrastructure and our Industry 4.0 expertise, and immediately begin adding value for their customers. Our goal is to help machine makers re-imagine customer aftermarket service.

Oden’s Vice President of Business Development Steve Braig will oversee the partner program and lead the development of machinery OEM relationships and strategic partnerships with system integrators and software providers. Steve is the former CEO of Engel Machinery and Trexel who served on the U.S. Manufacturing Council, advising Congress and the administration on manufacturing conducive policies.

The machinery manufacturers who are providing remote monitoring and maintenance services to their customers are already realizing considerable gains. More than 80 percent benefited both from increased customer satisfaction and improved uptime and machine availability. One third have also decreased the number of on-site service calls and lowered the cost of problem resolution. The Oden real-time, data-driven technology approach has the potential to increase these gains exponentially.

Using Oden’s platform, machine makers can provide their customers with real-time alerts and fix equipment before it fails, effectively eliminating unplanned downtime. Their customers will benefit from the option to outsource the maintenance and repair of their assets to the equipment maker, reducing their MRO costs and increasing machine availability.

Oden’s platform, which spans both hardware and software, offers machinery manufacturers easy to install wireless IoT devices, and production-ready, intelligent industrial automation platform. Its predictive analytics can be applied to monitoring critical machine components and sub-systems of production, delivering real-time insights into the inner workings of their equipment, and predictive maintenance post-sale.

If you would like to learn more about Oden’s partner program for machine manufacturers, please get in touch.

Why IIoT is Essential for Every Factory

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Enterprise-grade connected technology associated with the ever-expanding internet of things has continued to expand its reach. The latest iteration of IoT has hit manufacturing in the form of the Industrial Internet of Things, or IIoT, allowing workers to harness the power of the internet to streamline and strengthen production processes and more effectively meet consumer demands.

On paper, IIoT seems like the technology poised to drive innovation in manufacturing – and industrial organizations are following this thread in their investments. Enterprise IoT spending is expected to surpass $772 billion in 2018 and reach $1 trillion by 2021.

That said, for IIoT to be a sustainable solution, upgrading manufacturing technology needs to be more than just “keeping up with the Joneses.” The question then becomes: Is IIoT an operational necessity or just a flashy add-on?  Manufacturers and other industrial firms need to look clearly at their existing technology and determine where IIoT can translate to substantial – critical – improvements.

 

Human and automation symbiosis

Businesses in the manufacturing space are among the most enthusiastic adopters of IIoT technology, accounting for $189 billion in investments related to these cutting-edge assets in 2018. An estimated 38 percent of factories are already leveraging IIoT processes. As a result, you see some of the most mature IIoT workflows in this space – warehouses and production facilities where man, machine and advanced data analytics are working in harmony to achieve incredible results.

Perhaps one of the most prominent examples of IIoT at work is taking place at one of the world’s most successful companies: Amazon. In what MIT dubbed a “human-robot symbiosis,” Amazon has transformed their warehousing and fulfillment centers through the deployment of automation, allowing for the company to cut operating costs by 20 percent.

What makes the Amazon deployment so novel is that IIoT is being viewed as an enhancement of industrial processes rather than a replacement for human labor.

“It’s a natural outgrowth of efforts to harness cheap computing power to make robots more collaborative,” Wily Shih, a professor at Harvard Business School who studies manufacturing, said at the time.

Smaller companies have similarly employed IIoT to improve operations in their facilities. Robotics maker Fanuc, for instance, sought to address the issue of downtime by employing a cloud-based analytics software that would predict imminent component failures and flag for maintenance. The Zero Downtime system Fanuc pioneered ultimately resulted in the company being awarded GM’s prestigious Supplier of the Year Innovation Award in 2016.

 

250% increase in productivity with automation

Smart factories represent the pinnacle of IIoT technology and early facilities, such as those discussed above, have revealed that connected industrial-devices, deployed at scale can have an immense impact on the shop floor. This is why an estimated 76 percent of manufacturers worldwide are developing these advanced sites. In fact, almost 60 percent of the industry have $100 million or more invested in these efforts.

Embarking on the IIoT journey: Where to start

As both of these use cases show, IIoT is more than just a flashy add-on to existing workflows. It’s hard to argue with the ability to seamlessly turn digital designs into reality via automated production lines or a whopping 250 percent increase in productivity.

Manufacturers that have yet to roll out concrete IIoT development plans should certainly consider doing so quickly, as it seems that this technology may soon drive the industry. Still, this can be an intimidating undertaking. Even smaller scale IIoT deployments have lots of moving parts – literally and figuratively – that even the most advanced internal IT teams might struggle to juggle without external assistance and guidance. Even worse, some some manufacturers may not even know where to start with their needed upgrades.  

This is why we built Oden Technologies: to assist manufacturers that want to embrace IIoT technology, but aren’t sure where and how to begin. Our hardware and software solutions allow manufacturers to collect actionable shop floor data and use it to improve and streamline their production flows, thereby building the data-backed foundation needed to move into more advanced IIoT deployments.

Connect with us today to learn more about how our technology can future-proof your manufacturing operation.

Understanding the State of Digital Transformation Technology

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Digital transformation, particularly within manufacturing, assumed its place as the driving force behind industry innovation. An estimated 42 percent of CEOs worldwide have greenlit such programs, according to Gartner. Manufacturers are, of course, have enthusiastically taken part in the gold rush and seen their efforts rewarded in critical bottom-line metrics: Decreased downtime, scaled up production, reduced costs and improved ROI.

As Industry 4.0 matures, moving forward with digital transformation becomes less about “if” and more about “when.” Firms on the outside looking in on these developments need to learn from the lessons of early adopters that have found sustainable success.

 

Innovation Takes Root

The first digital transformation technologies entered the mainstream in the early 2000s. Now-standard fixtures such as cloud computing services started largely as experimental early-stage digitization efforts which were eyed by many but adopted relatively slowly among manufacturers. After all, in 2004, it was hard for many industry leaders to picture how the ability to store and access data remotely would meaningfully improve manufacturing practices when much of the “innovation” seemed focused on social media and sharing media.

This changed with the growth of formalized cloud offerings and the rise of the internet of things. Suddenly, the digital transformation wasn’t just about connecting people and exchanging ideas, but creating automated networks of technology.

Today, manufacturers are among the biggest supporters of these technologies. Connected sensors and robust back-end systems have given firms the power to streamline their operations in the age of lean manufacturing, enabling them to navigate turbulent markets and more effectively meet customer demands. One-third of modern manufacturing companies attest to managing highly digitized workflows. By 2020, that figure is expected to surpass 70 percent.

What about the businesses that haven’t yet embraced digital transformation? While seemingly inevitable, going all-in on full-scale adoption is daunting. Luckily, early adopters have essentially paved the way for deployment and their trajectory can serve as an effective roadmap.

 

Understanding the Transformation Roadmap: Getting It to Work for You

The most common digitization roadmap centers around technology that generates data analytics and insights.. Manufacturers pursuing this use case install connected sensors and platforms that allow them to collect and analyze shop floor insights of all kinds, from data on machine performance and wear to information on the supply chain.

Harley-Davidson was among the first enterprises to take this approach. Back in 2010, the company outfitted the 10 year-old production assets in its York, Pennsylvania plant with sensors capable of collecting key mechanical insights, including machine temperature and rate of vibration. These data points allow Harley-Davidson engineers and maintenance specialists to proactively address problematic equipment and therefore reduce downtime, while simultaneously ensuring that production lines ran at capacity.

Digitization strategies centered on supply chain integration are also common. Manufacturing firms employing this approach leverage cutting-edge online communication and data-sharing tools to craft collaborative processes that enable product design, and production teams to connect with third-party partners that provide mission-critical services. Smart warehousing technologies, advanced procurement modules, and prescriptive analytics engines drive these streamlined workflows.

 

Taking the First Steps

Manufacturers can learn a lot from these deployments and embark on digital transformation with a relatively clear picture of the end product. However, there are numerous pitfalls implementation teams must manage. For example, firms with aging shop-floor workers must focus on change management. Manufacturers with legacy systems will also encounter problems as modern industrial IoT assets normally do not mesh with older technologies.

That said, manufacturing firms intending to modernize cannot let these roadblocks dissuade them, for those that continue to put off digital transformation will soon find themselves unable to compete with more advanced, forward-thinking industrial players. But how can manufacturers looking to stay competitive avoid implementation pitfalls?

Industry 4.0 platforms that streamline digital transformation are the ideal solution. We created Oden to provide hardware and software for manufacturers of all sizes to launch their digitization efforts through an easy-to-install system, machine agnostic sensors, and robust in-house configuration services.

Connect with the Oden team to learn more about our software and services that have already given manufacturers the actionable insights they need to improve production.

How IIoT Brings You Closer to Customers

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The manufacturers gaining the competitive edge on product quality and customer satisfaction appear to be those that adopt smart-manufacturing technologies, including the Industrial Internet of Things (IIoT). Why is IIoT such a key differentiator in today’s economy? It lies in the ability to monitor, correct and optimize operational issues in real time.

A headline in Chief Executive Magazine recently posed the question: “Who’s Pushing U.S. Manufacturing Forward?”

“Accelerating the rebound in U.S. factories depends on what they make and how they make it, not just where,” wrote Chief Executive contributor Dale Buss.

It’s a statement that rings especially true today. Talk of trade wars, changing consumer demands and continuing workforce shortages mean U.S. manufacturers must place a greater emphasis on quality, innovation, and efficiency to remain competitive.

Dependence on low-cost sourcing won’t win the game anymore.

Many manufacturers have relied on outsourcing to remain competitive. But this model isn’t quite as effective as it once was. For one, wages are increasing in many low-cost countries, such as China. Also, consumer demands for faster delivery and more customized products mean manufacturers can’t afford long lead times, supply chain disruptions or quality issues.

The winners appear to be manufacturers that adopt smart-manufacturing technologies, including the Industrial Internet of Things (IIoT). Why is IIoT such a key differentiator in today’s economy? It lies in the ability to monitor, correct and optimize operational issues in real time. If you make things efficiently at home there’s no reason to source materials halfway around the world. And that means you can serve customers faster and be agile enough to meet their demands for more customized products or just-in-time deliveries.

The key is finding IIoT systems that provide the in-depth analytics you need to make immediate decisions to correct variations in quality or increase throughput. Sensor-based data systems are nothing new. IIoT has become a catchphrase for any manufacturer that deploys a sensor to its equipment or production line to receive critical performance data over the Internet.

But truly effective IIoT includes dashboards, alerts and statistical analysis to correct problems as they happen and adjust production to meet changing customer requirements. Many IIoT systems still deliver information to traditional spreadsheets, such as Excel, which requires manual manipulation to begin root-cause analysis.

In fact, according to Deloitte’s “Global Cost Survey Report,” digital solutions, such as data analytics, are the most effective ways to drive cost savings. Analytics and automation empower “companies to analyze mountains of data and identify key costs savings opportunities. These technologies will help increase efficiency and effectiveness — evolving new platforms and driving cost improvement across the entire enterprise,” according to Deloitte.

Chief Executive Magazine recognized rapid prototyping manufacturer Protolabs as a company that’s “pushing U.S. manufacturing forward.”   The St. Paul, Minnesota, company helps manufacturers respond to customer demands faster with services such as 3-D printing. Company President and CEO Victoria Holt noted that about half of companies’ annual revenues are from products they launched within the last three years. In other words: product lifecycles are getting shorter.

“To be able to address that, we’ve got to be taking advantage of manufacturing technologies and Manufacturing 4.0, or we won’t be able to compete,” Holt told Chief Executive.

Is IIoT Secure? 5 Tips to Protect Your Factory

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Disruptive innovations come with risks. Think about the recent tragedy involving a self-driving car in Arizona. While this is an extreme example of what can happen when technology fails, it’s a reminder that safety must always be a priority.

The Industrial Internet of Things (IIoT) is no exception. Operational technologies (OT), such as plant-floor equipment and machinery, have traditionally lacked exposure to the type of attacks that IT systems face. But now more manufacturers are tying their IT and OT systems together as they adopt IIoT technologies.

We understand that companies have concerns about security when connecting their devices to the Internet. Fortunately, there are strategies manufacturers can implement to secure their IIoT investments.

1. IT and Operations Must Collaborate

The operational side typically doesn’t consider security requirements, so it may lack an organized security practice, notes Gartner contributor Susan Moore. However, a primary focus for IT is protecting information. This is why IT and operations need to collaborate early in the IIoT implementation process.

“IT and OT cultures are not incompatible, but they require executive guidance to realize initial alignment,” Moore writes.

2. Know What You Have

A simple, yet often overlooked safeguard is knowing exactly what IIoT devices you have in place and where they’re located.

“You can’t secure what you don’t know you have, so an effective IoT security strategy must begin with a comprehensive inventory of all networked assets,” according to an IndustryWeek article by contributors from Crowe Horwath LLP. “In addition to known and authorized devices, the inventory also must capture unauthorized or previously unmanaged devices, such as security cameras, monitors, machine sensors and other devices that have been plugged into the company’s network by employees or vendors without the IT department’s knowledge or participation.”

 

3. Opt for Multi-Layered Security

Make sure your IIoT network doesn’t rely on a single technology for security. For example, Google Cloud offers security at the device and network levels as well as security for apps running on the network, data storage, and hardware. With Oden’s IIoT solution, all data sent over the network is encrypted and transmitted over a secure VPN connection.

 

4. Stay Up-to-Date

Don’t ignore updates or patches.

“If a device is running out-of-date software, it may contain unpatched security vulnerabilities. Such vulnerabilities may allow exploitation of the device and its data by attackers,” notes the IoT Security Foundation.

This includes updated an IoT device’s firmware to the latest version, “which incorporates all current bug fixes, vulnerability fixes, and mitigating or compensating controls,” according to Crowe Horwath.”

5. Assess and Test

Continuously assess your security. This includes risk-based analyses with business impact statements, Crowe Horwath suggests. This will allow you “to prioritize projects and make the most of limited resources.”

Security issues related to IIoT are rare. Taking a few precautions can ensure IIoT technologies remain safe. Don’t hesitate to consult with IIoT experts to determine how you can safeguard your system while reaping the benefits of a smart, connected enterprise.  

Preparing For Artificial Intelligence In Manufacturing

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In today’s technology space, it is almost impossible to go a day without reading or hearing about artificial intelligence (AI). AI has applications in seemingly every major industry, but what is really going on?

AI, as a subject, can sometimes be confusing to understand. There are many subsets of AI, and each one serves a distinct purpose. Our goal in this post is to dive into the ways that AI is being implemented in manufacturing and to give you ideas for how your manufacturing business can prepare for artificial intelligence now and in the coming future.

 

Applications In Manufacturing

Machine learning is the main area of AI that is currently being applied in manufacturing. The purpose of machine learning is to give a computer the ability to learn new information without being explicitly programmed to know that information. This is made possible through machine learning algorithms, which dictate the way that an AI system should break down and process information.

As an AI system gathers more and more data and learns from it over time, it can gain the ability to make accurate predictions, automate decision-making, detect real-time malfunctions, and much more. For a manufacturer, this will translate into greater efficiency in production, waste management, cost-savings, and will also introduce new features like:

  • Improvement in the accuracy of maintenance and repair schedules
  • Predict workloads based on market trends
  • Product quality management
  • Process controls
  • Management of operations
  • Predictive maintenance/asset management
  • Supply chain management/sourcing
  • Safety and facility management

All of these bring tremendous value to a manufacturer, and that is why AI is such an important area to focus on.

 

It Is All About Data

Data is the lifeblood of AI. For an AI system to function effectively, it needs a substantial amount of data at its disposal. The better the data, the better the AI. This is why it is absolutely necessary for a manufacturer to move their operations onto a digital medium.

The first step in preparing for the integration of AI in a factory is to enable the machinery with data tracking and communication ability. At Oden Technologies, we offer a simple data collection device that can be plugged into any machine or PLC to allow for data to be wirelessly transferred to our cloud-platform.

Once the hardware is in place, the next step is to connect the machinery to a central platform where the data will be recorded and processed by different algorithms. Every platform offers some differences in features, but the main objective is to ensure that you are collecting valuable data about your manufacturing operations. Having a large quantity of quality data is necessary to train an AI to be able to more accurately do all of the things that it is capable of. With less data comes less accuracy.

 

Third-Party Services

In most cases, it makes the most sense to use a 3rd-party service because of the significant amount of capital needed to develop such a platform. There are various platforms available, and the best part about competition is that it will drive the products and services to deliver more value to the customer.

When you use a platform to manage and operate your smart factory, you are limited to the features that this service provides. The good part, however, is that these companies are already providing features based on AI and will continue to develop and make use of new forms of that technology. By working with a provider, you can trust that they will focus on these features.

 

Straightforward Preparation

Preparing for the integration of AI in manufacturing is pretty straightforward. You either spend hundreds of thousands, if not millions, to hire AI engineers to develop a system for you, or you use a 3rd-party service that is already building out these features. Once you find the right company to work with, it is as easy as adding a plugin to your machines and connecting them to the platform online.

If this is the route that you choose to go, the best part is that you do not need to worry much about any other preparation. As long as the service that the customer is using is implementing effective machine learning algorithms and other aspects of AI, the benefits of this technology will be easily attainable.

3 Ways IIoT Helps Attract and Retain Workers

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“The challenge for manufacturers in the U.S. isn’t foreign manufacturing; it’s the high school guidance counselor,” Brian Fortney, global business manager for Rockwell Automation, told Design News. They don’t understand that manufacturing is high tech. The plants are not dark and dangerous.”

Yes, the workforce skills gap still plagues manufacturing. In fact, 71 percent of manufacturers responding to The Future of the Manufacturing Workforcesurvey by Manpower say “insufficient manufacturing skills is increasing in severity now and will continue to get worse over the next several years.”

For manufacturers, the skills challenge often comes down to an image problem. On the one hand, many people still view manufacturing as “dark and dangerous.” Another common misperception is that automation or the Industrial Internet of Things (IIoT) are replacing workers. In reality, highly automated, smart operations help manufacturers empower their current workforce and attract top talent.

Let’s take a closer look at some of the key ways IIoT empowers the workforce, while helping manufacturers close the skills gap.

Developing the Next Generation of ‘Problem Solvers’

In a previous post, we discussed the benefits of real-time production performance tracking. Sensor-connected devices provide workers with more information at their fingertips. The modern workforce is becoming tech savvy and expects to have instant access to critical information, whether it’s displayed on a workstation monitor or via mobile devices. They also want more fulfilling jobs that provide opportunities to create value.

IIoT frees your workers to focus on problem-solving activities rather than repetitive, sometimes dangerous tasks. These connected workers have “easy access to smart operating procedures, and both generic and asset-specific instructions and checklists. Carrying hundreds of pages of unwieldy manuals prove a thing of the past,” according to an Accenture report. 

 

Breaking Down Productivity Barriers

Of course, all manufacturers want their workers to be more productive. Unfortunately, data often exists in silos, which means your workers don’t have access to critical information they need to increase productivity. It also means workers are expending more energy on mundane, physically demanding tasks.

Frustration mounts when workers must stop the line or their machine to troubleshoot a maintenance or quality issue. IIoT allows for true predictive maintenance. In an IIoT environment, workers often receive real-time condition-monitoring alerts, such as vibration data, temperature fluctuations and energy consumption. This results in less downtime and improved employee morale. They also may receive real-time analytics that show variations in product quality or yields.

A Single Source of Truth

When data exists in silos, workers in separate departments may view or interpret data differently. This creates frustration, disagreements about the data integrity and oftentimes low employee morale. Consistency across your enterprise is essential to ensure everyone is working in concert to achieve a common goal. A cloud-based analytics platform can help that’s accessible anywhere, from any device helps break down data silos.  The system takes data inputs, processes the information and provides feedback. The data that is collected gets uploaded to the cloud and is safely stored so that it can be accessed by any Internet-connected device.

 

Other Things to Consider …

The workforce shortage isn’t going to remedy itself. IIoT is becoming a critical component to addressing current and future workforce challenges. Consider IIoT solutions that are accessible to an unlimited number of users. This reduces information siloing and helps you build a more empowered, collaborative team of problem solvers. Also, if you’re an early adopter, look for out-of-the-box solutions that don’t require lengthy, complex commissioning times, which will only complicate your workforce challenges.

Examples of IIoT In The Workplace

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The Industrial Internet of Things (IIoT) is influencing the manufacturing industry in a big way. In short, the IIoT makes use of networks, like the internet, to aggregate data from connected devices. The data is centralized on a platform where it can be processed and translated into meaningful insights. There are many examples of IIoT being used in the workplace. The platforms and services available to manufacturers can help with everything from machine performance monitoring to predictive maintenance. Many of these systems also incorporate machine learning and other subsets of artificial intelligence to optimize performance and more accurately make predictions.

In this post, we will look at some of the best examples of how IIoT improves the way a factory operates.

Real-Time Performance

One of the best uses for IIoT in a manufacturing plant is for the real-time performance tracking capabilities. When a factory connects their machines to an IIoT platform, the machines will start reporting data for how they are operating. The platform will provide a view that shows every input and corresponding output for all of the machines that are in operation.

This functionality offers huge benefits for a company. With real-time performance tracking, a plant manager or employee can monitor the entire factory and get immediate notifications when a machine or group of machines are not performing at their expected or optimal level. This will ultimately lead to quicker fixes, increased efficiency, and higher quality products.

Factory Integration

Another one of the examples of IIoT in the workplace revolves around the way in which the factory’s machines are enabled with the tracking and communication features. In most cases, the machines already have ports where external devices can be plugged in.

Oden Technologies offers simple data collection device that can be plugged into any machine or PLC and can communicate wirelessly to their platform. This simple integration is what makes some services stand out from others. Factory integration is big part of IIoT but it should be a short process and shouldn’t require factories to purchase new machines and equipment.

Predictive Maintenance

The last example of IIoT in manufacturing is in a system’s ability to make predictions on when maintenance should take place for a machine. This is part of the IIoT incorporates machine learning algorithms. These algorithms look through large amounts of data to recognize patterns, which help the AI form models that represent the correlation between inputs and outputs. Over time, the machine learning algorithms will have enough ‘training’ to be able to make predictions about what may happen based on given data.

In the example of predictive maintenance, the machine learning algorithms will be able to take the performance data from the machines and figure out the likelihood that this machine will need maintenance and by what time. The more data that a factory has at its disposal, the more accurate these predictions will be. This is an approach that leads to cost savings over time and higher productivity.

The Smart Factory

All of these examples of IIoT in the manufacturing industry come together to form what is referred to as a “Smart Factory.” A smart factory has a sort of sensory system that allows it to monitor itself and communicate this information through a unified network. When paired with AI and machine learning, a smart factory can start to make predictions that lead to smarter business decisions for a manufacturer.

The IIoT brings tremendous value and is very easy to take advantage of. It is as simple as getting in contact with the right IIoT platform and integrating this technology into your business. It is important to start collecting this data today so that you can benefit from now and in the future.

What Is Machine-to-Machine (M2M) Communication?

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Machine-to-machine communication, or M2M, is at the core of what makes a smart factory smart. The smart factory model relies on a machine’s ability to track and report on data that is relevant to its operation and productivity. Without this capacity, the machinery is useless and cannot contribute to the insights that may be generated through a cloud-based platform.

In this post, we will look at how M2M communication influences the smart factory and the ways it can be applied in manufacturing. This will help give you an understanding of the smart factory from the ground up.

 

How It Works

M2M communication is just what it sounds like; it is the act of two or more machines or systems sharing information with each other. This transfer of information can occur through a wired medium, but it is more commonly done wirelessly. M2M is also not just limited for physical machinery, but can be between individual chips, sensors, or components within a machine.

In manufacturing, many companies offer plugins that can enable traditional machinery with the ability to connect wirelessly to the internet and with online, cloud-based platforms. For instance, Oden Technologies offers a simple device that can be plugged into any machine or PLC to communicate wirelessly to their cloud-analytics platform. Once the machinery is enabled with wired or wireless communication, it now has the physical means to communicate with each other and with any system that is connected to the same network.

 

Applications In Manufacturing

M2M communication influences the way that a machine reports on and shares data in a manufacturing environment. It is incredibly important for plant managers to understand the current state of their factory, and one way to give them this information is to allow machines to communicate it directly to the manager through a software interface. Having accurate and relevant data will allow employees to gain meaningful insights on ways to improve their business.

Downtime in manufacturing occurs when a system or machine is not operating at its expected efficiency. This results in significant costs for manufacturers, and because of this, it is vital that manufacturers have systems in place to accurately report on the state of their factory at any given time. With M2M communication, each individual sensor on a machine reports its data to a central platform to solve this problem.

fourth industrial revolution factory

When a machine is operating at a lower efficiency or malfunctions, it will talk to the other machines and to the platform itself. This is for two reasons: 1) So that the factory employees can be notified and make the necessary manual adjustments or fixes, and 2) So that the other machines can make automated adjustments, like changing their rate of production or operating times.

 

M2M Automation

A major theme in M2M communication is automation. Automation can be good and bad depending on how it is implemented, but its purpose is always to improve efficiency, lower costs, and optimize production. Every manufacturing company is looking to reach these goals, and to do this, the proper technology must be deployed in their factory.

M2M communication is crucial to automation because it allows developers to dictate how a machine should operate based on certain conditions. For example, if a machine malfunctions, the system would be able to detect what part of this machine is inoperable and could even place an order for a replacement component.

 

The Value of M2M Communication

M2M communication creates the foundation for a smart factory that can be powered by artificial intelligence and machine learning. This form of a smart factory can bring tremendous value for the manufacturer such as increased productivity, reduced material waste, predictive maintenance and more. A smart factory is powered by big data, and any AI system is only as useful as the data that it has access to and it’s ease of use by factory employees.

A core component to machine learning is giving a computer system the ability to learn without explicit programming of that knowledge. The only way to do this is for a system to have access to information about itself and its environment, and that is only possible through M2M communication.

 

M2M For Optimization

On a more general level, the true value the M2M communication brings is the capacity for optimization. Whether it is through manual maintenance on machinery from real-time notifications, or it is an automated chain of actions that developers design to complete tasks, the ability for machines to exchange information is at its core.