The Factory of the Future is a connected world of everything.
We will explore the benefits of IoT in manufacturing, including asset monitoring and utilization, quality control, predictive analytics, automation, safety and compliance, OEE visibility and productivity, and more.
With IoT solutions, companies can achieve their Industry 4.0 goals and increase their bottom line.
What is the Internet of Things (IoT)?
The Internet of Things (IoT) is the network of physical objects, people and other assets that are connected to the internet. IoT allows you to collect data from devices and analyze it in real time.
The goal is to make processes more efficient by optimizing performance and reducing costs.
IoT can be used to monitor machines in factories, track inventory at warehouses or even monitor traffic conditions on roads. It’s also used for home automation tasks like turning off lights when you leave a room or adjusting your thermostat based on your location inside (or outside) the house.
An IoT ecosystem refers to the network of devices, sensors, software, and services that are interconnected and work together to collect, analyze, and act on data.
They are often utilized to gather data on connected assets, and tools in order to gain real-time insights into their condition for predictive maintenance purposes.
The goal of predictive maintenance is to detect and address problems before they cause equipment failure, downtime, or safety issues.
By predicting maintenance needs in advance, organizations can schedule maintenance during planned downtime, minimize the impact of maintenance on operations, and extend the lifespan of equipment.
A smart, connected product (also known as a smart object or SCoT) is a product, asset, or other object embedded with processors, sensors, software, and connectivity that allows data to be exchanged between the product and its environment, manufacturer, operator/user, and other products and systems.
Smart, connected products enable the comprehensive monitoring of a product’s condition, operation, and external environment through sensors and external data sources.
This technology stack provides a gateway for data exchange between the product and the user and integrates with other systems to enable a new level of customer experience.
Similar to connected products, a smart, connected factory is a digitized manufacturing facility that uses connected devices, machinery, and production systems to continuously collect and share data.
This data is then used to inform and improve processes and proactively address any issues that may arise.
It is an interconnected network of machines, communication mechanisms, and computing power, which uses advanced technologies to analyze data, drive automated processes and learn as it goes. It uses the sensors and software of the connected products to bring together the factory floor.
A connected factory is implemented to track the overall equipment efficiency or effectiveness (OEE) of the factory.
A smart connected factory is the telltale sign of a manufacturing floor that is functioning at its most optimal ability.
Having access to data insights regarding production health helps companies optimize earnings and minimize production downtime.
Integrating augmented reality (AR) with IoT involves using sensors and other IoT devices to collect data from the physical world, which is then used to augment the user’s experience in the digital world.
For example, AR can be used to display real-time data from IoT sensors, such as temperature or humidity, on a user’s mobile device. When a cell phone or tablet uses Augmented Reality (AR) to display data, it overlays virtual information on top of the real-world view seen through the device’s camera.
This can be particularly useful in industrial settings, where workers can use AR to monitor the performance of machines, detect any issues immediately, and take corrective action. This kind of data can be turned into a Digital Twin.
With a Digital Twin, you create an exact replica of a working product, process, or service as a simulated model in a virtual space that performs under real-world conditions.
A Digital Twin in manufacturing helps companies find performance issues, schedule predictive maintenance, reduce downtime, and minimize warranty expenses.
This allows anyone to take a digital version of your factory with you anywhere in the world and know the exact, real-time data of how it is functioning. This is extremely helpful for decision-makers who often travel and need to know how their factories are doing.
Additionally, a digital twin of your factory helps maintenance teams find precisely where an issue may have occurred by giving them visual prompts of where the problem is originating from.
The Benefits of IoT in Manufacturing
IoT has a plethora of benefits for manufacturers that overall saves companies time and money, and solves frustrations with outdated processes and siloed data.
Bellow are different ways the IoT solutions could benefit your production process:
Asset Monitoring & Utilization (AMU) & Real-Time Production Performance Monitoring
Gain real-time insights from connected assets and legacy systems such as your PLM and ERP systems to align your IT and OT systems
Make more informed decisions faster
Increasing flexibility and agility
Monitoring the status of inventory in real-time
Monitoring Distributed and Outsourced Processes
Spare parts management
IoT sensors allow organizations to gauge the specific use and deploy practices for more effective usage of resources.
Machine Learning & Predictive Analytics
By integrating machine-learning capabilities there is a whole new level of predictive intelligence brought to the factory floor – identified problems and resolved issues with minimal impact on operational performance. Other benefits include:
Detect problems before they occur
This helps to maximize factory production utilize data analytics to perform predictive maintenance
Optimizing Maintenance Schedules
Automation & Connected Work Cells (CWC)
In manufacturing, many processes are streamlined with IoT technology. For example, one IoT strategy is to use Automation. With IoT, using sensors allows you to automate certain tasks such as temperature monitoring or product tracking.
This means less time spent on manual labor and paper-based processes while increasing productivity simultaneously!
Safety & Compliance
Making sure that machines are running at a safe capacity and within the compliance standards creates a well maintained work area that brings employee satisfaction and confidence in a safe workspace.
All these things can be tracked through IoT technology, along with others:
Increasing energy efficiency of machines
Reduce human errors
Ensuring Products Comply with Set Standards
Increased employee and customer satisfaction
OEE Visibility & Productivity
A factory enhanced by IoT solutions offer complete visibility into all of your factory operations. You can see all of your work orders, lines, and all critical KPIs through dashboards that pull together
IoT sensors allow organizations to gauge the specific use and deploy practices for more effective usage of resources.
Increase bottom line
Decreased Machine Downtime
Optimizing Factory Production
Reduced lead time
Improvement of Operational Performance
These are just some of the many ways companies can reach their goals with Industry 4.0. With out-of-the-box solutions or even custom IoT apps, the possibilities are endless.
The first step in implementing IoT in manufacturing is to identify your goals. Are you looking to improve efficiency, reduce waste or increase profitability?
Do you want to improve customer satisfaction by delivering products on time, or do you want to create new revenue streams with data collected from connected devices?
Once you’ve determined what kind of impact IoT can have on your business, it’s time to evaluate the current technology that’s not only available but that easily integrates with your current systems and shop floor.
Finally, develop an action plan for implementing these strategies broken down into digestible phases. It’s critical to understand what solutions fit best and most align with your unique business and prioritized initiatives.
We hear from companies all the time regarding what challenges they feel stops them from implementing IoT in manufacturing.
The first is cost. However, with any good investment, the benefits of using IoT solutions offset the cost. While IoT ROI doesn’t happen overnight, the full impact IoT has on manufacturing organizations is tenfold.
NORMA Group met with EAC experts to understand what sort of impact an IoT initiative would have on their business growth and determined the challenges were well worth the wait – something we find other companies can relate to. Another early adopter, JR Automation was able to save $1.4 million by investing in IoT.
The second common challenge that comes with implementing IoT is security. This includes both physical and data security issues that need to be evaluated and addressed before implementing any type of data management change.
Finding a solution with integrated automated tools and detailed monitoring is key to preventing attacks. Check out this article that talks further about data security and how PTC’s Kepware supports cybersecurity.
Another challenge we see is considering how well your organization will be able to manage the new information coming from all over your facilities. It’s key you create a plan to integrate the new data flow into your existing systems.
With user-friendly IoT-connected solutions like PTC Thingworx, data is captured, consolidated into a dashboard, and presented in a consumable visual format for real-time insights.
Another consideration is requirements: What sort of hardware do you have to support that software change? Replacing or updating existing systems and hardware to increase efficiency may be necessary to keep up with the fast pace environment of shop floors.
There is no doubt that these are important things to consider when making enterprise-wide changes. While implementing IoT can feel difficult and intimidating, it does not have to be.
EAC has a number of business assessments that can help you evaluate your current state and create a highly strategic roadmap to successfully scale your digital transformation initiatives.
Ultimately, Digital Transformation is a game changer for manufacturing companies who are unsatisfied with the “status quo” – and IoT will open up major opportunities for long-term success and sustainable growth that would not have been possible without making changes.
Empower your organization with enhanced connectivity to your products, systems, and customers.
IoT can provide significant advantages for manufacturers across the enterprise, but it is important to properly evaluate, plan and implement the right technology and the right solution at the right time in order to maximize the potential benefits.
Our IoT consulting and connect services provide comprehensive support from extracting valuable insights, and developing strategic plans to executing and implementing efficient IoT solutions that accelerate your digital transformation.
Chat with one of our experts on how we can help you identify the best IoT solution for your needs and how we can help your company implement it the right way.
They say a picture is worth a thousand words, so here’s a hypothetical situation to paint the story ‘how real-time information and predictive analytics unlock value.’
To start, imagine a fully functioning assembly line with a robot, pneumatic system, a series of conveyors, and a vision system.
Lets pretend the supply station in the back is bringing in our raw materials. The robot is assembling those materials with precision. The resulting assemblies are than passed on to the quality station, and the vision system inspects each of those assemblies to insure proper alignment of the parts.
This is a pretty generic operation, but it can show how unified real-time information and predictive analytics unlock value.
Now imagine yourself as a maintenance engineer, who wants to check the status of your asset pool.
Using a software, such as ThingWorx Navigate by PTC for example, you launch a role-based maintenance application. All of a sudden you see a complete list of your assets with real-time performance stats and relevant alerts or notifications. You also have a complete list of all your outstanding maintenance work orders.
From here, you have the ability to drill into any of your assets, but you start with the quality station. You immediately see the key characteristics of the station. You see that speed vibration and temperature are all operating within their specified range. You could also see notifications of any warnings, malfunctions, or potential future problems.
Next, you use your device to take a look at the pneumatic system. The pneumatic system also looks fine. Both pressure and flow are operating within the specified range, and there are no outstanding maintenance tickets or work order notifications on your screen.
Now, let’s consider a situation where there was a leak in the pneumatic system. Let’s say a loose fitting was releasing pressure, a fairly common problem in pneumatic systems. Now, rather than looking fine, your device displays flow readings outside of the designated operating range. Furthermore, an alert has automatically been sent to notify you of a system has an error. The overall status indicator on your screen has now switched from green to orange – operational, but not optimal.
Your software solution’s machine learning is now predicting that this air leak, if not repaired, will result in a pneumatic gate failure in approximately 10 day’s time. The good news for you is the system has already issued you a maintenance work order address the problem before asset failure and unplanned downtime.
This scenario is made possible by a system equipped with primary and secondary sensors, and a complete Industrial Internet of Things (IIoT) solution that can turn raw machine data into valuable information.
For example, your pneumatic system has an air flow sensor, as well as a pressure sensor. The conveyor systems are equipped with motor temperature sensors and vibration sensors.
In addition to the sensors, the rest of the assets on your line are controlled by typical PLCs, which are connected to a software such as ThingWorx and Kepware.
You have also used your software to integrate manufacturing floor systems with a real-time IT applications, asset maintenance tools, and ERP systems. This provides you with a real-time alignment of your IT and OT systems.
Now, all of your systems are throwing data out at a staggering 800 data points per second.
Your software’s machine learning then uses that real-time streaming data to establish a baseline of normal operating conditions. This way it can immediately connect and broadcast any anomalies that occur. It uses these anomalies, in conjunction with its prediction capabilities to notify you of future problems, just as in the case of the pneumatic failure.
Now that you have an understanding of what is happening under the hood, let’s take a look at how all this comes together to enable real-time operational intelligence.
Pretend you are a production manager. Using software like ThingWorx Navigate and Kepware you have complete visibility into all of your factory operations. You can see all of your work orders, lines, and all of their critical KPI’s.
On your device you notice an orange status indicator on line one (that was created from the air leak earlier). Once that air leak has been repaired, everything returns back to normal, just as you would expect.
Let’s explore one more hypothetical situation. Consider yourself to be an operator. In this case, you have just been assigned a new order for a thousand units that need to be delivered and expedited for an end of day delivery.
You’re notified of the order and in this smart connected scenario you, as an operator have a single portal from which you can see and execute all of your work. Through a single pane of glass you now have access to your business systems information and your operational data including the KPIs from your line.
On your device you also have up to the minute visibility of the OEE (Overall Equipment Effectiveness). You see real-time data measurements of your manufacturing operation’s availability, quality, and performance.
Let’s see how some of these metrics might change if we go ahead and speed up the line to accelerate the current order, in order to make room for that expedited order.
To do that you switch the line speed from level one to level two. What you see in seconds on your device is that line speed has increased, and your assemblies are still passing the quality check.
Within a couple minutes and a few additional cycles, on your device you see both your performance and OEE trending upwards.
As an operator you now are assured that you are going to meet your end of the day deadline.
Using these hypothetical situations, together we have painted a picture demonstrating how you can connect disparate assets from different vendors, to provide real-time information.
You’ve also seen how you can leverage role-based applications that combine business systems information and operational data to empower your workforce with real-time actionable intelligence.
By integrating machine-learning capabilities you brought a whole new level of predictive intelligence to your factory floor, identified problems, and resolved issues with minimal impact on operational performance.
This is exactly how real-time information and predictive analytics can unlock value for your organization.
I have a twin! Well, I have a digital twin. You probably do too. If you’re unfamiliar with the concept of a digital twin, don’t fret—you’re not alone. In fact, this technology is relatively new and still developing.
The idea of creating virtual models to simulate real-life situations isn’t new. NASA uses digital twins to run simulations and test flights on airplanes before they’re actually flown by pilots in person or sent into space with astronauts aboard them (pretty cool right?). However, until now there hasn’t been much focus on how we could apply these same concepts outside the aerospace industry — until now that is…
The idea of a digital twin is simple to understand. A digital twin is a virtual model of a process, product, or service that can be used to:
- Improve performance: Understand how a process works, and improve it.
- Explore new ideas: Imagine what could happen in the future, and create it now.
- Make better decisions: See what’s happening on the ground in real time, so you can make confident decisions for your business.
- Reduce risk: Identify potential problems before they occur and fix them before they cause issues for customers or colleagues.
- Improve efficiency: Maximize resources to get more out of them than would be possible otherwise – whether that’s staff time, materials or energy consumption – by turning data into insights for everyone involved in a system (including those who aren’t currently involved).
Digital twins are used to run simulations using predictive analytics and data from sensors that are attached to airplanes and engines. These “test flights” for engines and airplanes allow for safe experimentation and troubleshooting without risking human life or harming the equipment. More recently however, the potential use cases for digital twins have expanded beyond industry.
NASA’s journey with the digital twin
NASA’s Advanced Turbine Systems Project (ATSP) has created a digital twin of their Pratt & Whitney PW1000G geared turbofan engine used in aviation systems like Boeing’s 737 MAX series aircrafts. This makes it possible for engineers at NASA’s Glenn Research Center in Cleveland, Ohio to monitor real world conditions on an airplane remotely via computer software without having any physical connection between themselves and the airplane itself – all from their office desktops!
Digital twins aren’t limited just to planes though – they can be applied anywhere where there is an application that would benefit from being able to predict future outcomes based off current data gathered through sensors placed around said device/application/process etc…
Today, digital twins are being used in healthcare to help monitor a patient’s health in real time. Augmented Reality (AR), simulated environments, and virtual reality (VR) can all be used with the data provided by digital twins to improve patient outcomes. For instance, AR could be used by surgeons during an operation or VR can be used by physicians to practice risky procedures in a simulated environment before they operate on an actual patient.
The list of potential uses for a digital twin is seemingly endless, but one thing they all have in common is their ability to collect data. For example, an AR system could be used by surgeons to visualize a patient’s anatomy in real time and allow for better planning of surgical procedures.
Virtual reality (VR) can be used by physicians to practice risky procedures in a simulated environment before they operate on an actual patient. The benefits of this approach include the reduction or elimination of unnecessary risks during surgery as well as the reduction or elimination of costs associated with conducting unnecessary surgeries that did not need to take place because the physicians were not sufficiently trained prior to operating on real patients (which can lead to malpractice lawsuits).
The idea behind digital twins goes beyond the practical uses of this technology—it is rooted in the desire to create a more connected world where people’s decisions can be made with better information than what has been available in the past. When we’re able to see how our choices impact different systems—for example, seeing how changing one variable will affect overall energy consumption—we gain better insight into how we can create a more sustainable future.
As you may have heard, a digital twin is an avatar that represents your physical system. It’s kind of like an actor who plays the role of “you” in the virtual world and learns how to be more efficient, safer, and easier to use over time. This concept can be applied across systems ranging from trains to buildings to entire cities. Since all systems are made up of parts that must work together in order for a system as a whole to function properly (think about how many things need to go right just so you can take a shower), it makes sense that we’d want an accurate representation of those parts—and their interactions—in order for us humans running them not to make mistakes or waste energy unnecessarily.
As we’ve seen in this post, digital twins can be used for many different purposes. The technology has already been applied to industrial processes, healthcare, and the energy sector. In the future, we’ll likely see more uses for digital twins in retail and other industries as well. What will your digital twin look like?