Forbes: Data Is The Foundation For Artificial Intelligence And Machine Learning

By | Blog, Featured Post

Artificial intelligence (AI) and machine learning (ML) are going to have a huge impact on manufacturing. With these technologies, manufacturers will gain the computational power needed to solve problems that humans can’t possibly solve. They will ultimately be able to provide prescriptive answers to production issues manufacturers have been asking for centuries. Namely, how do we make our product as efficiently as possible, with zero waste and the least amount of downtime.

As with most reports about groundbreaking technology, this discussion of the ‘holy-grail’ is way ahead of industry practices. The vision serves a useful purpose in suggesting what’s possible. But with many manufacturers lacking the data infrastructure necessary to obtain real AI and ML capabilities, the journey towards perfect production can also be so abstract that it confuses the very people looking to achieve it. I’m often asked by corporate leadership, “Where and how do we adopt AI technology?”

Begin with data.


Understanding the State of Digital Transformation Technology

By | Blog

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.

Oden Featured In Atomico’s Take on Industry 4.0, Data, AI & Robots

By | Blog

Our investors at Atomico published an insightful report on the future of manufacturing. I am thrilled to see venture capitalists not only understand the value of Industry 4.0 but are investing in this technology. The future of manufacturing is bright, but the true digital transformation requires investments from many different industries to scale quickly. Reports like this prove that a broader group of people are seeing the exciting opportunities that lie in manufacturing.

In a cable manufacturing plant near Chicago’s O’Hare International airport, a few small, sleek black boxes sit discreetly, plugged into decades-old plastic extrusion machinery, silently gathering data.

Distilled down locally on each of the boxes into a smaller set of meaningful variables, the data gathered is then wirelessly streamed to a cloud-based analytics platform, so that factory staff can monitor the production process in real time and from any device.

Oden Technologies — the company behind the platform (in which Atomico has just led a $10m Series A investment) — combines industrial hardware, wireless connectivity and a sophisticated data pipeline to produce an unprecedented (in this industry) view of the factory floor and its production processes.

At this deployment, issues are caught up to 95 percent faster (i.e. in minutes or hours, versus up to weeks), cutting waste by hundreds of thousands of dollars per year per plant, while increasing output by 10–15 percent through increased steady-state line speed.

Read the Full Report

Using Big Data in Manufacturing

By | Blog

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.

Big Data

Big Data — What Is It Really?

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Big Data

Ahhh… Big Data. It’s one of the most commonly used buzzwords in the world of tech. With the manufacturing world moving in the direction of smart factories and Industrial IoT, it is vital that manufacturers understand the terminology associated with those advances, and the technology behind it, or risk getting left in the dust of innovators.

A couple weeks ago, we touched on some Industry 4.0 terminology for manufacturers, but we didn’t really talk about big data, in-depth. With big data serving as one of the basic pillars of Industry 4.0, it shouldn’t be ignored, but this is also true for just about every term we listed on our article. In this new blog series, we’ll explore various aspects of Industry 4.0 in detail, in an understandable and easy-to-digest format created solely for manufacturers. Pressured to digitize? We’ll help you get your feet wet.

What is Big Data?

The term big data is associated with a database of extremely large amounts of data (billions or trillions of records). However, that’s only one of the two main components of big data, with data processing, or analysis, strategies being the other. Let’s talk about both aspects.

datasets web

Large Datasets
This aspect of big data is arguably the most simple one. One way to think about big data is to imagine it as a bunch of information housed in a series of databases… but how is it gathered? The most common and efficient method that’s currently used is a WSN (Wireless Sensor Network) coupled with thousands of sensors. WSNs serves as the web that pulls together information and communication across an entire collection of sensors.

For manufacturers, sensors are attached or built into machines all over a factory. these sensors are constantly gathering countless data points by detecting environmental changes, machine productivity, temperature, etc. Information coming from each sensor travels throughout the WSN, and is stored in a series of databases.

This concept is nothing new. What is new is that the sheer number of devices that are connected to these sensors, and the Internet, to gather data in real-time. This number has grown rapidly each year, and by 2019, it’s estimated that there will be 30+ billion devices connected to the Internet across the world. This collection of devices is also known as the IoT (Internet of Things).

Data Processing & Analytics
There are thousands of sensors all over a factory, connected to devices that gather countless bytes of data. Great, right? It depends on if you have a way to read it. Regardless of its size, information is only useful if it’s actionable and understandable. I Since big data is used to make decisions, the only thing that could potentially be worse than not having data is making the wrong decisions by misreading data.

Cue analytics. At scale, the speed at which one can process and understand the characteristics of the data they’re presented with becomes extremely difficult. This is why proper analytics, and the technology with the power to process large amounts of data, is key and is one reason why we started Oden Technologies.

Why is Big Data Important? What Is It Used For?

Big data is important because it helps manufacturers make data-driven and logical decisions. In any fast-moving industry, making decisions both quickly and accurately is imperative to success and remaining competitive. Especially with large factories, keeping track of important metrics is difficult. It is essential for manufacturers to find a tool that makes this possible.

machine data sensors

With devices that continuously gather machine data, manufacturers can use that data to track real-time information and make informed production decisions based on current activity. Additionally, these numerous data points can be stored for future use, which enables manufacturers to keep track of what occurred in the past, to determine trends and make decisions for the future. At the end of the day, technological advancements are meant to either increase capacity and output or decrease costs. Depending on how new technology is implemented, big data and analytics bring these benefits to the table.

To tie it all together, big data is the combination of large amounts of data and the analytics that break down those databases into actionable, digestible insights. Both are equally important and must function in harmony to be useful. Otherwise, it may result in data fatigue. Big data is important because it helps manufacturers track future trends and maintain optimum efficiency. Are you using big data in your business?

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?

How Big Data Improves Manufacturing

By | Blog

In the present fast-changing world, innovation takes place and technology changes seemingly every day. This often makes people feel as if they’re “out of the loop” on technology changes in various industries. In the realm of manufacturing, there have been three primary periods of massive innovation: first was the industrial revolution, then the electrical revolution, and lastly the computer revolution.

In the last half-decade, the manufacturing industry began stepping into the early stages of the what’s called “The Fourth Period of Massive Innovation — Industry 4.0”. This is the revolution that brings together digital, physical and biological systems in harmony to hyper-boost productivity. It will inevitably impact human life by implementing what is called “smart factories”.

Smart factories are already transforming the way manufacturers approach production processes, allowing them to be adaptive, profitable and sustainable. Big data and analytics are playing a pivotal role in helping manufacturers to improve efficiency and driving down costs… but what are big data and analytics?

Big Data In Manufacturing

What is Big Data and Why Does it Matter?
Big Data is a collection of information (data) from traditional and digital sources inside and outside companies that represent a source for ongoing discovery and analysis.

At the heart of the big data, there’s the Internet of Things (IoT) — a collection of various data-gathering sensors embedded in everyday objects. The vast number of devices with sensors (estimated to grow to 50 billion by 2020) allow communication with other devices/people through the Internet. Today data is collected everywhere, via IoT and the cloud.


This data is what allows computers to perform tasks efficiently based on current information that it receives through data gathering tools. Big data is powerful, but building computers that can effectively interpret massive amounts of data is challenging.

  • Turning the raw data collected from the manufacturers into actionable insight requires multiple levels of processing:
  • Collecting the data from machines, production lines and factories
  • Conditioning the data
  • Combining disparate data streams into manufacturing models that enable meaningful analysis
  • Conducting the analysis; and
  • Interpreting the results to enable understanding and action

This is where analytics comes in.

Analytics In Manufacturing

There’s a clear distinction between being able to collect data and compile it, but being able to use that data effectively is a completely different game. Using data effectively is the sole purpose of analytics. Given the sheer number and complexity of manufacturing activities that influence production, manufacturers need a more granular approach to diagnosing and correcting process flaws, saving both time and money.


Improving Operational Performance
Improving operational performance yields multiple benefits, so it should be every manufacturer’s goal. That’s why leveraging big data is critical. In particular, big data leads manufacturers to be more efficient with how they use and manage resources.

The ability to analyze data being collected is where potential weaknesses that exist in a factory supply chain are revealed. Using the harmonious combination of IoT, big data, and analytics enables manufacturers to make better decisions. Beyond manufacturing speed, “Industry 4.0” technology also allows machines to detect product failures/defects. Some other additional benefits are:

  • Increasing the accuracy of maintenance and repair schedules
  • Predict workloads based on market trends
  • Product quality management
  • Process controls
  • Operations management
  • Process design and improvements
  • Predictive maintenance/asset management
  • Supply chain management/sourcing
  • Safety and facility management
  • Targeted capital spending


Preparing Manufacturers For The Future

Although Industry 4.0 enables factories to produce more, waste less, and innovate faster, there are many challenges associated with building smart factories. One challenge is sourcing the sensors required to implement those smart factories for industry specific needs. This is where we come in.

Using a combination of IoT technology, wireless connectivity, and big data, Oden Technologies is helping manufacturers optimize production efficiency. At Oden Technologies, we provide a variety of sensor devices that plug into almost any kind of machine and are capable of sending real-time data wirelessly and securely to the cloud. We help compile important productivity metrics into one clear interface, with no complicated integrations.

Regardless of if manufacturers are preparing for Industry 4.0, it’s coming and it’s in their best interest to prepare for it and see what the available technologies are available to optimize for it. For those who don’t take this seriously, they may end up just like Sears did.