Forbes: ‘Machines As A Service’: Industry 4.0 Powers OEM Aftermarket Revenue Growth

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Original equipment manufacturers (OEMs) are no strangers to boom or bust sales cycles. Traditionally, they’re either ramping up production to meet demand or seeking ways to slash costs when sales are down.

But Industry 4.0, or the Industrial Internet of Things (IIoT), is enabling new sales models that generate more consistent revenue streams for OEMs. There are considerable benefits for forward-thinking manufacturers that transition from selling a product to offering, “machines as a service.” Rather than relying on a one-time sale, they’re charging customers based on machine use and service.

Machines as a service can revolutionize the way OEMs design, sell and service products. It will be a win-win for OEMs and their customers, as both partners benefit from increased predictability.

Read more on Forbes.com

Oden Talks Industry 4.0 in Wire Journal International

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I recently had the pleasure of speaking with Wire Journal International on the increased adoption of Industry 4.0 and how more manufacturers are embracing digital transformation and analytics.

Below is an excerpt from the feature.


 

How has your activity in Industry 4.0 most changed since the 2016 report?

In 2016 it felt like we had to do a lot of education and evangelization about Industry 4.0, but now we don’t have to introduce the topic anymore. Our company, Oden Technologies, and client base has grown exponentially, and we’re seeing bigger audiences at our speaking engagements and webinars. However, the one thing I’m very excited by is that we’re moving deeper and deeper in the Industry 4.0 technology to deliver more value. We’ve hired a VP of Data Science, Deepak Turaga, who led IBM’s AI and Machine Learning group so we can move further and truly deliver on the promise of Industry 4.0 with predictive quality and predictive maintenance.

Other industries and media, outside of manufacturing, are catching on to the potential of Industry 4.0. I’ve been asked to be a contributor to Forbes.com on the future of manufacturing and Industry 4.0. Additionally, we just closed a new round of funding with Atomico, and throughout the process we saw just how much venture capitalists and large financial institutions are seeing the tremendous value data analytics can provide manufacturers.

Read the Full Feature

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.

Forbes: ‘What’s At Stake In The Race To Industry 4.0?’

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What’s At Stake In The Race To Industry 4.0?

 

To put it simply, the answer is that the future of your business is at stake. But most executives will see that answer as too simple or abstract, if not too glib.

To truly understand the risks and rewards of being among your industry’s leaders during this transformation, bring the debate down to earth. Consider one of the fundamental issues you deal with every day: the cost of poor quality and the cost of downtime.

Read more on Forbes.com

Forbes: ‘Industry 4.0: The Journey Towards Perfect Production’

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I was thrilled when Forbes invited me to contribute to their website on the future of manufacturing. Given all the time I spend speaking to people in manufacturing about Industry 4.0, its competitive advantages, and how they can leverage their factory data to improve production, I’m excited to now share my perspective with the readers on Forbes.

Below is an excerpt from my first article in the series. Join me every other week for my take on how manufacturers should approach Industry 4.0 in addition to other challenges facing the industry.


“Manufacturers are under constant pressure. They need to decrease waste while increasing uptime, throughput and quality to continue to compete effectively. Manufacturers are no stranger to disruption either, and in recent years lean manufacturing practices and automation have applied further pressure on them, even forcing some out of the game altogether.

The next disruptive phase in manufacturing is already well underway. “Industry 4.0” builds on the previous three phases of industrialization – mechanization, mass production and controls. It’s an intelligent production environment enabled by an integrated platform of enterprise data systems, the Internet of things (IoT) and cloud computing.”

Read more on Forbes.com

Oden Co-Founders Make Forbes 30 Under Europe Industry List

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The Oden Team is proud to announce that our co-foundersWillem Sundblad and Peter Brand, have made this year’s Forbes 30 Under 30 Europe Industry List. The Industry List recognizes those individuals whose companies are making a significant impact in transforming the manufacturing industry.

Oden Technologies, which was incorporated in London in 2014, is not so quietly disrupting the industry by empowering manufacturers to improve quality, eliminate waste, and achieve perfect production with our Industrial IoT technology. We are thrilled to have our innovate leaders recognized on this list, as well as continuing to work with forward-thinking manufacturers to usher in the Fourth Industrial Revolution.

For more information on the Forbes 30 Under 30 Industry List, read the article in full.

Learn more about our Industry 4.0 technology.

Using Big Data in Manufacturing

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During the 90s and early 2000s, the big trend in many industries was transferring company operations onto digital mediums, such as computers and wireless networks. This provided managers with a better understanding of their organization and the ability to stay informed. Information was distributed more effectively in an electronic format, and basic information like current sales, general production speed, etc. was more readily available.

With these tools, managers had the necessary information to make informed and sound decisions. The ways in which big data is currently being leveraged is very similar. What makes big data (and data analytics) different, however, is that it is much more powerful. If used properly, big data can help solve real-time problems, swiftly detect bottlenecks, make extremely accurate projections, and much more.

Big data can be likened to the “next wave” of innovation, and it is clearly changing the way that people are making decisions in the manufacturing industry, among others. Although this sounds exciting, it’s definitely not the whole story. There’s plenty of confusion around the way in which big data can be applied to a factory’s operations. Our goal with this article is to clear the air a bit and talk about the details. By the end of this article, you’ll have a better understanding of exactly what big data enables you to do in your factory.

Leveraging Big Data for Dynamic Bottleneck Detection

Data gathered on the actual performance of a machine (speed, length, unit variation, etc.) is not just limited to calculating defect rates. In fact, this data can be leveraged to determine bottlenecks in the entire factory. In some cases, a large majority of defects may come from a single machine, recipe, or small group of machines that are underperforming. By leveraging big data properly, these underperforming machines or processes can be found quickly and accurately, which saves money in the short and long-run.

Prior to the adoption of big data and analytics, it was difficult for manufacturers to detect bottlenecks and get accurate data to confirm where they were occurring. Manufacturers had to manually check the performance of each machine and spot check any issues. This process would take hours or even days. Big data and analytics platforms can now shorten this process to mere minutes. Manufacturers can react faster and have more ability to prevent future problems from occurring.

Unit Consistency Using Big Data

Consistency is an absolute necessity in any manufacturing plant. Over time, it’s inevitable that machines will warp and produce units with decreasingly accurate dimensions. In fact, this is a common problem that manufacturers deal with. Big data helps combat this problem via IoT devices that gather comprehensive data points on the machine’s status, productivity, and more. By having access to all of this information, manufacturers can make predictive decisions to avoid product defects rather than waiting until they become an actual problem.

Having a solution like this will also help prevent other complicated problems from occurring, such as customer dissatisfaction, faulty units, and machine downtime. Big data is invaluable even for quality issues that cannot be avoided preemptively. Having large amounts of machine data allows managers and engineers to quickly detect and make decisions based on productivity trends. For example, if a unit’s dimension variation falls out of spec, the manufacturer can be prompted to re-calibrate the necessary machine. This can be done by operators and engineers with ease.

Big Data to Avoid Organizational Siloing

Using the right data analytics tool gives plant managers a better understanding of the overall productivity of a given factory. For example, Oden’s innovative analytics platform gives managers, engineers, and operators the ability to limit or distribute analytics information to their team members accordingly. Everyone from the assistant manager to the CEO can have access to information pertinent to their job and can make decisions based on that information. Rather than having to rely on reports performed by fellow employees, an organization can now distribute that information in real-time, anytime, and to anybody who needs it. This reduced siloing makes for a more dynamic factory that can react to and prevent problems quickly.

Big data is a powerful tool that manufacturers can use to make decisions. The insights that come from data lead to decisions that maximize an organization’s productivity and minimize its inadequacies. As data analytics tools become finer tuned, and IoT devices are integrated into more factories, big data will subsequently become even more powerful.

manufacturing plant

A Manufacturer’s Glossary to Industry 4.0 Technology

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What Does Industry 4.0 Mean?

Digitization. IoT. Big Data. There are plenty of technical Industry 4.0 buzzwords being thrown around in the world of manufacturing, and it can easily be overwhelming for manufacturers who might not have a background in technology. Innovation happens at lightning speed, and keeping up with the times is difficult. We’re here to clear the waters with this brief glossary for non-technical manufacturers.

manufacturing plant

What’s the Buzz Around Industry 4.0?

Industry 4.0 can be thought of as the 4th revolution in the manufacturing industry. First, there was the invention of the steam engine — which garnered in the industrial revolution. Just under 100 years later, the assembly line and the adoption of electric-powered manufacturing tools increased manufacturing speed exponentially.

Another 70 years after that, computers found their place in the industry, giving manufacturing robots a new level of efficiency. Just under 50 years later, Industry 4.0 is bringing to the table a completely new level of analysis and efficiency to manufacturing. By improving data collection and aggregation to maximize efficiency while also creating a seemingly autonomous factory, Industry 4.0 is completely changing the way manufacturing is done. Almost everybody wants a piece of this technology, but even with all the technological advances, the goals still remain the same (reduce waste, increase output), technology just makes it easier to achieve those goals and remain competitive.

Advanced Manufacturing — The process of leveraging the most advanced technology available at the current time in order to maximize the output and/or product quality of a manufacturing facility.

Artificial Intelligence (AI) — A technology that gives computers the ability to learn based on data, previous experiences, and their environment in order to make decisions in order maximize results.

Big Data — Large compilations of data that can be analyzed in order to reveal patterns, trends, and associations. Big data is especially used in order to detect bottlenecks in productivity, predict outcomes, and find patterns that otherwise wouldn’t be noticeable through informal analysis.

Cloud Computing — A secure data center managed outside of your company, much like the bank you use, with the resources to scale and store billions of data points.

factory cloud

Cyber-Physical Production Systems (CPPSs)— An unnecessarily complicated term for the concept of when machines are connected in a process or line and one machine’s actions can influence another.

Digital Native —A person who was born into the world of digital technology. They typically have a high level of intuition and understanding of using technology and the Internet.

Digitization —The process of moving information onto a format that can be understood by a computer in order for that data to be used in computational calculations.

Human-Machine Interface (HMI) —A user-interface consisting of hardware and software that lets a person send request/commands to a machine. Typically HMI’s are meant to make it as easy as possible for a person to control a machine with little difficulty. A great example here would be a smartphone. With a smartphone, a user would perform various actions in order to navigate to the phone-call application and place a call.

Industry 4.0 —The current trend in the manufacturing industry that uses a combination of IoT, big data, and cloud computing in order to develop factories that can make decisions based on large amounts of data. A couple benefits that Industry 4.0 offers is the ability to detect bottlenecks and deficiencies using big data, high-level customization, and automation of production.

Internet of Things (IoT) —The concept of connecting otherwise separate machines or data sources so that people can take better decisions and actions faster. This large number of data-gathering devices is the backbone of Industry 4.0 that allows people to make decisions in alignment with varying productivity goals.

Interoperability/Machine 2 Machine (M2M) —The ability of machines to communicate together and make decisions using information without the need of human intervention.

SaaS (Software as a Service) —The process in which software is centrally hosted by a vendor and licensed to users on a subscription basis.

Smart Factory —A smart factory is a learning factory, where people leverage data and technology constantly. Essentially, it’s the implementation of Industry 4.0 technology.

This Is All Helpful, But Where Do I Start?

It may help to provide a practical example for using some of these terms. I’ll use my company as an example, Oden Technologies. We provide a cloud platform that can help people develop a smart factory. We provide IoT devices for all sorts of machines for data collection while providing real-time data visualization and actionable insights for manufacturers to make things better.

Our service also comes with unlimited user accesses that provide their customers witha n option to share access to the platform data with anyone they want to. Overall, Oden requires minimal work and provides a straightforward and simple integration solution for manufacturers being pressured to digitize their entire factory!

What is the biggest production constraint in your operations? Where can you leverage data and Industry 4.0 technology to fix these issues? If you look at many manufacturing companies thriving today being built today, you may find plenty. Can you make data one of your competitive advantages?