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manufacturing Archives - Oden Technologies

Oden Co-Founders Make Forbes 30 Under Europe Industry List

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The Oden Team is proud to announce that our co-founders, Willem 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. Through 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 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 an 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?