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

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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

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.

How Do Cloud-Based Analytics Work?

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Cloud-based analytics is at the heart of every major technology company. Data analysis itself is a common activity in any business, but when it takes place in the “Cloud,” it can truly change the way that a company operates.

In the technology industry, “The Cloud” refers to a network or group of networks that give connected devices access to shared pools of resources like servers, applications, and services. The internet is an example of this type of network, and therefore, any internet connected device is a part of the cloud.

Cloud-based analytics provides a handful of tools that enable companies to extract insight from large data sets, present those insights in an intuitive and understandable way, and make it all available via a web browser.

 

Data Centers

Most cloud-based analytics systems run on a network of secure data centers, which are owned by some of the biggest tech corporations in the world including Google and Amazon. These data centers are regularly upgraded with most efficient computing hardware, and this is also what makes cloud services much more reliable.

The servers, in addition to other hardware at a data center, is what physically supports a cloud-based analytics platform. It makes data accessible at very fast speeds and ensures that if a company’s internal systems malfunction, all of the data will still be safe at the data center.

 

Cloud-Based Analytics

First and foremost, a cloud-based analytics system must be hosted on some sort of platform or website over the internet. Usually, this is a software system that takes data inputs, processes the information, and provides feedback. This is obviously dependent on the platform itself, but there is always something in common — 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.

In regards to manufacturing, a cloud-based analytics system usually connects with various hardware devices that all incorporate sensors and communication modules. These devices constantly collect information on the performance of the factory’s operations. The data that gets collected from a machine is uploaded to the cloud and managed on an analytics platform.

A business can choose to build their own, in-house platform, or they can decide to use a third-party service. Usually, a service is cheaper, and these services already have built out functionality that can benefit a company immediately.

The functionality that is provided via a cloud-based analytics platform is made possible through proprietary algorithms that can organize, process, and make meaning out of data. This data goes through various mathematical equations and produces values that are represented in intuitive ways to the users.

A cloud-based analytics platform can provide tremendous value for a company versus an offline version. By being connected to the internet, users will also have the ability to integrate offline data into their datasets with the click of a button, for far greater insight.

 

Cloud-Based Analytics and AI

Cloud-based analytics also paves the way for any future integration of artificial intelligence in a company’s operations. AI is only as powerful as the data that it is utilizing to make its decisions, so by having a digital data infrastructure, you are building the foundation for future tools that will be made available by service providers.

For example, in a manufacturing setting, it is important to track information about the performance of a machine. There are already tools that are available for manufacturers to track and upload this information to a cloud-based analytics platform. Once this data is uploaded, it is processed by the platform’s algorithms and learns about the machine.

In the field of AI, machine learning is a subset that refers to a computer’s ability to learn by itself. As more powerful machine learning algorithms are developed and made available, cloud-based analytics platforms will gain the ability to learn by on their own. One major benefit of this is that the platform is then able to more accurately predict future outcomes based on all of its previously collected data. This will translate into more efficiency, and allow you to avoid things like unplanned downtime. This is why having a cloud-based analytics system is so important.

 

Improved Functionality

The true value of cloud-based analytics comes from the improved functionality for the user. With data being uploaded to services that function over the internet, users gain more accessibility and value. Data is managed more efficiently and represented in more intuitive ways. Cloud-based analytics presents a big opportunity for any business, so if you have not made the transition to the cloud, this might be the best time for it.

Tools To Aid With Predictive Maintenance

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In the manufacturing industry, predictive maintenance refers to the use of big data and analytics to predict when a machine or system will need maintenance. This is made possible through the integration of smart sensor devices that can communicate with external programs to gather and process the necessary information.

Predictive maintenance is important in a smart factory because it allows a business to mitigate unplanned downtime. By having accurate predictions of when a machine may lose efficiency or malfunction, a company can plan ahead to ensure that the lost production from that machine will not influence their operations.

The tools that aid with predictive maintenance include everything from hardware plugins to areas of artificial intelligence (AI). These tools allow businesses and individuals to implement predictive maintenance in their factories.

Connected Machinery

First and foremost, to create a smart factory you will need to enable your machinery with the ability to connect to a central network. This can be through a wired medium, but most services make use of wireless communication. For example, Oden Technologies offers a simple data collection device that can be plugged into any machine or PLC to gather and wirelessly communicate data to their online platform.

A good way to view the machinery of a connected, smart factory is by likening it to the nervous system of a human. Let’s pretend that your factory is an organism, and for it to function, it needs the ability to detect information about itself and its environment. This IoT device and the machines that it is connected to makes that possible.

Cloud-Analytics Platform

 

The next tool that will aid with predictive maintenance is a cloud-based, analytics platform. In more simple terms, this is just an online service where you can connect you machinery to so that the true value of your data can be extracted.

The platform that a company decides to use for their connected factory is where all of the gathered data will be aggregated and processed. Depending on the algorithms that are in place, various insights will be provided to the user about the current and future state of the factory. If we go back to our human body analogy, the platform would be the mind while the machinery is the body.

Machine Learning

Machine learning is a subset of AI that enables a computer with the ability to learn by itself. It is also an important tool for manufacturers to use in predictive maintenance. When machine learning algorithms are applied to a smart factory, the system will be able to learn how different inputs correlate to outputs. This is necessary for predictive maintenance because it will allow a computer system to learn how the conditions of their operations are affecting the outcomes of those operations.

Over time, the AI will see that, for example, if a factory is operating at an average of 70% capacity for 2 weeks, there will be a 92% chance that machine X will underperform and need maintenance. Therefore, if the company wants to avoid this downtime occurrence, they can figure out what they need to do to make the malfunction less likely to occur.

Maintenance Scheduling

The main value that comes from predictive maintenance is that it allows you to figure out the optimum time frames for when maintenance should occur so that unplanned downtime can be avoided. The best platforms for predictive maintenance will automate as many processes as possible to a degree where the employee is empowered to make smarter and better decisions. The first step in this process is to determine when a system or machine may lose productivity or malfunction. Once this is detected, the next step is for the employees to fit the maintenance work into the proper time slot.

Scheduling to Save Money

Predictive maintenance, at its core, is a method for saving money. When a machine or system malfunctions, it can drastically affect the bottom line of a company. This is why it is important to have systems in place to be able to predict and plan for when these machines will need maintenance.

The best way to take advantage of the value that predictive maintenance provides is to make use of connected hardware and a powerful, software platform. It is important to make sure that whatever service and technology you are using has the features that makes this all possible.

Will Smart Factories Replace Humans? No.

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It is a common belief that the artificial intelligence (AI) revolution will be detrimental to the job market and lead to significant losses in jobs. In fact, some even believe that AI will completely outpace humans in all areas of productivity, which would result in a fully automated workforce.

It is true that people will lose jobs as automation increases. We have seen companies like McDonald’s integrate digital kiosks to replace cashiers, and can imagine how self-driving vehicles will disrupt their industry, but the question for us is if smart factories will replace humans. The answer is no for 3 reasons: 1) Technological limitations, 2) Worker augmentation, and 3) Type of labor affected.

Narrow vs. General AI

The context that an AI system operates in is significantly more narrow than what people realize. The form of AI that is implemented in the products and services that we use today can be referred to as “narrow AI.” Narrow AI focuses on single functions and domains and is completely non-sentient.

When the average individual thinks of AI, they sometimes inflate their view based on the movies, tv shows, and articles they engage with. The type of AI that we see in movies is called general AI — a self-aware, AI system that can make general decisions about anything. This form of AI is only theoretical, at this moment, and if it is possible, we are still decades away from that reality.

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.

Worker Augmentation

It is clear, at least for the next couple of decades, that robots and AI systems will not fully replace humans, but it still remains unclear how humans and machines can work together. In manufacturing, and specifically in a smart factory, there will always be the need for humans to work along with the autonomous systems in place.

 

There is even evidence that when a factory fully automates their operations, the number of employees that they had stayed the same or went up. This was evident for Siemens’ Amberg Electronics Plant in Germany where their productivity went up over 1000% by integrating a smart factory, all while maintaining 1200 employees throughout the process. This is also the trend with countries who are leading the way in smart factories. There is no evidence that with more automation in factories comes a trend of less employment in the workforce.

A Push For Higher Skilled Workers

There is one more reason why we will always have humans in the smart factory, and this has to do with the type of work that is performed in a factory. At this point, we have demonstrated why it is more realistic that humans will work alongside with robots and AI, but we know that it is still possible for people to be replaced through automation. So then, why can you not replace employees in a smart factory? Well, the answer is that you can, but not those who are highly trained and skilled.

Automation will impact workers who do repetitive tasks that could easily be replicated by a machine. Things like customer service, driving, and food service, among others, will be the main areas where jobs may be lost. In fields where more complex decisions must be made, there will always be a need for humans to make the final resolve, and this is the case with the smart factory.

A Combination Of Factors

There are many factors that go into whether or not a specific business will replace jobs with AI and automation. With the smart factory, however, it is clear that this will not be the case. Even when there are opportunities to replace an employee with AI, factories have shown that it makes more sense to re-apply those same employees to other tasks so that they remain productive, but this also can depend on the decisions of that company themselves.

At Oden, we are clear in our stance that we will always create technology for humans no matter how advanced automation technology becomes. We also look to work with companies that value this perspective, as well. The fact that AI is narrow, will augment workers, and would mainly replace repetitive tasks, leads to the conclusion that factory workers can look forward to the rise of the smart factory.