How Do Cloud-Based Analytics Work?

By | Blog

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

By | Blog

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.

By | Blog

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 McDonalds 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.

Examples of IIoT In The Workplace

By | Blog

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?

By | Blog

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 inoperational 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.

What is IIoT?

By | Blog

The Industrial Internet of Things (IIoT) refers to the industrial applications of a network of internet connected devices. The devices that are used in such a system measure, track, and share information about various components in the manufacturing process. All of the data that the hardware collects is sent to a central, cloud-based service, where it can be properly organized and analyzed.

The application of the IoT for the manufacturing industry is sometimes referred to as “Industry 4.0,” and is already being offered by some companies. The IIoT will allow manufacturers to acquire and access a far greater amount of data at quicker speeds. This ultimately leads to more insight and efficiency for a factory. The IIoT is continuing to evolve and is providing immense value for manufacturers, so let’s take a look at the benefits the IIoT has to offer.

How Does it Work?

IIoT can be seen as the “back-end” of the various IoT categories. Although it is largely hidden from the average consumer’s view, many of the everyday objects that people use are heavily driven by IIoT technology. Almost every modern manufacturer makes use of some sort of IIoT ecosystem to optimize and streamline their operations.

IIoT incorporates machine learning and big data to generate powerful insights on how a factory is functioning. To do this, IoT devices are installed to collect data from sensors that are added to or built within the factory equipment. This can include almost any device or measure any piece of information that manufacturers think may influence their operations. The data is aggregated onto a central platform where it can be easily digested by a plant manager or other factory employee.

Benefits of IIoT

A powerful network of IIoT devices can significantly improve operational efficiency, scalability, monetary savings, time optimization, and communication across an entire organization. The various IIoT devices that are installed in the machines of a factory will continuously gather machine performance information (which consists of potentially millions of data points). This information is stored via the cloud, processed through a data analytics platform, and then used by plant managers, engineers, operators, and executives in a variety of ways.

When a factory integrates internet connected devices into their operations, it gives the factory a sort of sensory system. This new ability for the factory to collect information about itself will translate into manufacturers making much more informed decisions. In fact, with enough information, manufacturers are able to perform predictive maintenance on their machines so that they can avoid the cost of downtime.

This all leads to the optimization of machine performance, which will save manufacturers time and money. IIoT can also enable manufacturers to maximize their factory’s overall output. Whether it is utilizing big data to make predictions or to detect underperforming machines, IIoT is poised to drive this industry.



Why Should Manufacturers Invest in IIoT?

IIoT is already changing the way that manufacturers operate in the age of the internet. With access to significantly more information, manufacturers are optimizing and breaking informational silos with their access to shared analytics platforms.

Data is one of the most valuable commodities for technology integrated organizations. Unlike most investments, an investment in data is guaranteed to appreciate in value over time. The more data that is gathered, the better it can be used to make previously uninformed decisions.

Decision making is a huge bottleneck in manufacturing, mainly due to its risk-management factor. It is crucial to bring together as much information as possible to assess the risks of a given decision. The introduction of the IIoT allows manufacturers to monitor and store detailed factory data for quick access to production metrics and fast historical querying capabilities.

It is also crucial for manufacturers to gather as much data as possible when thinking about the future application of artificial intelligence (AI) in their factory. Without getting into too much detail, AI is as powerful as the quality and quantity of data that it has access to. For AI to make or suggest these decisions, it would need a large pool of information at its disposal. The longer that you have been collecting data, the more data will be available to feed into the AI. This is why it is increasingly important to implement the IIoT into a factory.

A Solution for the Future

IIoT solutions in the manufacturing space can be accessed by anyone. If you want to enable the utmost level of performance at your factory, this is absolutely the way to go. The benefits of a properly integrated IIoT system will even carry over to the sales and marketing department where factory productivity information can be used to make financial projections, targeting decisions, and much more.

IIoT is clearly driving the future of manufacturing. Innovation has been taking place at an increasingly faster pace, and it won’t be slowing down anytime soon. One way to get ahead, however, is by taking advantage of IIoT technology and the insights that it provides.

Big Data

Big Data — What Is It Really?

By | Blog

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 are 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 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?