Smart manufacturing, Industry 4.0, big data, the Industrial Internet of Things (IIoT) — however you refer to the advent of cloud computing, affordable sensors, and connected machines and devices, they are changing manufacturing in profound ways.
Depending on who you talk to only 3% – 8% of the world’s manufacturing equipment is connected to a network. Over the last 10 years, most companies have offered some kind of connectivity solution but few customers purchase or use it. Want some IoT advice?
Now is the time to change that. Even if you are not planning any automated data collection or manufacturing analytics projects, rest assured you will want it in the next few years.
This blog will:
- Outline some of the top use cases of IoT on the plant floor.
- Examine how IoT devices and software will be leveraged for productivity in the future
- Explain the practical solutions of manufacturing software that don’t require a ridiculous amount of money, time, or effort
What is IoT?
We hear a lot about IoT, but what does it mean? How is it related to Industrial IIoT? Before we dive deeper into the usefulness of IoT, it’s best to tackle the definitions of IoT and IIoT, first.
Internet of Things (IoT): (n) a network of everyday devices, appliances, and other objects equipped with computer chips and sensors that can collect and transmit data through the Internet
Industrial Internet of Things (IIoT): (n) interconnected sensors, instruments, and other devices networked together with computers’ industrial applications, including manufacturing and energy management
The two definitions, are in fact, very similar, but IIoT is specifically related to the world of manufacturing. In fact, that’s what we reference most in our writings just because it relates to our customers.
It is important to note that IoT and IIoT have become very ‘buzzwordy’ because the goal is to create a completely automated, smart factory, but for many manufacturers, that isn’t in reach because of high costs in both dollars and time, at least for the next few years.
So, yes, there is a bit of hype surrounding Industry 4.0 and IIoT, but it’s still important to understand the concepts and the fact that there are solutions out there (ahem, Mingo), that make this obtainable for manufacturers.
Anyway, let’s move on to discuss the top use cases of Industrial IoT in manufacturing.
What are the Top Use Cases in Industrial IoT?
IoT is transforming many industries, and manufacturing is no exception.
Even in the world of high-end strategic technology consulting, analysts have jumped on board and are recommending Industrial IoT solutions to manufacturers to help them grow their businesses.
Gartner, Forrester, LNS Research, ARC Advisors, Accenture, Deloitte– all smart people who know their business for sure; but in the world of manufacturing more often than not, the solution to complex problems is often a simple answer.
Take for example the dizzying array of IIoT solutions and platforms that have hit the market in the past few years either as startups or from the established manufacturing giants.
These products offer a powerful value proposition for those pursuing digital transformation:
- Bi-directional data flow
- A fully-realized digital twin
- Centralized automation
- Enterprise system integration
- Closed-loop engineering and production
- Predictive maintenance
- Artificial intelligence and machine learning
The main driver is the need for visibility into the factory floor, but often there are limitations and myths surrounding the use cases for IoT, even those analysts claim to be a magic bullet.
For the rest of this blog, we’ll tackle practical IoT solutions in manufacturing and how manufacturers can get started.
The 3 top uses cases of IoT manufacturing are:
1. Remote Monitoring and Operations: Is the machine running or not?
2. Predictive Maintenance and Smart Asset Management: If the machine is running, is running correctly? Are there any problems that could arise?
3. Autonomous Manufacturing: Can the machine run on its own without human monitoring?
How to use IoT Sensors & Machine Data for Remote Operations
IoT has opened the door for a flood of big data with sensors collecting everything from part counts and downtime to motor vibration and temperature.
For manufacturers to take full advantage of this, they will need to invest in data visualization software. Thankfully, cloud-based solutions like Mingo make this investment accessible and easy to implement.
What once required thousands of dollars, months of implementation time, and costly consultants can now be done in a few days with minimal technical skills. It’s that simple.
IoT shouldn’t be confused with big data all the time, although many think of them as synonymous.
A few data points can provide a lot of visibility on the plant floor, so it’s not strictly necessary to have complex IoT platforms and edge computing or even to manage huge data sets.
Most IoT analytics needs can be met with business analytics software like Tableau or Power BI these days, but historically ERP and MES have been leveraged to transform the data into something meaningful.
Nowadays, purpose-built analytics software like Mingo significantly reduces the investment cost of this foundational IoT pillar.
How to use IoT for Smart Maintenance & Asset Management
The standard method for machine maintenance for years has been “run to failure”, with the idea that a certain amount of downtime when a machine was being fixed was acceptable and indeed unavoidable. With IoT however, remote monitoring of machines and conditional reporting means the machine can provide fault warnings and provide your maintenance team with advanced warnings of issues.
Better maintenance using IoT can be viewed in this order:
- Conditional Monitoring
- Predictive Maintenance
- Predictive AI-Driven Maintenance
How to use Remote Monitoring and IoT for Autonomous Production or “Lights Out” Manufacturing
Another futuristic application of IoT and industry 4.0 promises to provide fantastic productivity gains through remote management of the plant. The benefits of this IoT marks a breakthrough but requires a huge investment in Manufacturing Execution Systems (MES) fed by data and manned by tech-savvy operators.
Another version of IIoT, the digital twin, can also be costly and out of reach for many manufacturers. Check out the digital twin concept Siemens developed for its IoT platform to learn more.
If discrete manufacturers can reliably produce products 24/7, similar to the way some process manufacturers work, they could conceivably produce more goods with fewer people.
For most manufacturers, lights out manufacturing will require significant investment in things like predictive maintenance, condition-based monitoring as well as robots and co-bots run by software or as a part of a highly automated cell.
With this set-up, so-called “tombstones” or pedestal-based manufacturing allow robots to handle complex part manufacturing and assembly without the loading, unloading, and re-tooling typically requiring operator intervention.
To reduce the need for human workers, a highly automated factory requires IoT sensors, smart asset management, programming skills, and new equipment, but the productivity gains and lowered costs will be significant.
To see an example of Lights Out in action, check out the Lego Factory. Even there though, workers are required to manage and maintain robots, write software, and conduct some quality checks. Still, what they were able to do in ’08 can give you an idea of what can be done in 2018.
Why Machine Connectivity Matters
We are predicting a large increase in demand for smart manufacturing software, including automated data collection and reporting software, over the next 5 years. The expansion of IoT in manufacturing will come as no surprise – it will be the only way small to medium-sized companies can compete with globalization.
Customers will demand price decreases; industries will be disrupted by the manufacturers that can leverage their data. The companies that don’t use their data will be quickly left behind.
The biggest barrier manufacturers face today with data collection projects is connectivity. Adding the connectivity options to existing later can be expensive, require downtime and software upgrades.
The Practical Solutions of Manufacturing Software vs. The Platform Pipe Dream
The dream of a completely connected factory is amazing, but the path to that dream is being so obscured by marketing and sales gimmicks, it’s hard to see through the hype anymore. Much of what makes up the dream is driven by futurists and isn’t what our customers say they need or want out of digital transformation, at least not in the short term. Plus, let’s not forget that often, traditional solutions are very expensive.
The more we talk to analysts, read their research reports, attend their conferences and listen to the speakers, the more we see the unfortunate consequences of the dream peddlers: A huge swath of the manufacturing industry is being scared away by the exorbitant price tags and huge risks they have to shoulder for the kind of digital transformation the consultants, the analysts and the platform solution providers promote.
“I don’t need an IoT platform,” an executive from an automotive manufacturing supplier recently told us. “We’re manufacturers, not developers.”
In the words of one of our solution partners from Banner Engineering, a leader in industrial sensors, “If you go to some of the IIoT conferences they all sell the dream. But for most manufacturers, like a lot of automotive parts suppliers, for example, they don’t need a quarter of a million-dollar ERP/MES solution necessarily or a million-dollar budget for a pie-in-the-sky proof of concept. They need to start simple with a solution that provides connectivity and gives them visibility onto the plant floor.”
Listening to him was like listening to ourselves talk about Mingo.
We are both solving customer real-world problems with plant floor visibility affordably and a proven ROI.
While Banner is revolutionizing data harvesting by offering a peel-and-stick wireless solution that works for any machine and communicates via a protected network, Mingo is aggregating and making sense of all the data that comes from your machines and your operators in a protected cloud environment.
“There really is no excuse now,” says a former analyst from one of the leading research firms to whom we vented a bit, “the barrier to entry on IIoT and manufacturing analytics has never been lower.”
“Why then?”, we asked, “Are all the analysts pushing the dream and not the solutions?”
IoT is incredibly useful in manufacturing, but the traditional solutions don’t have to be, well, so complex. There are better, easier, and cheaper solutions that offer even more benefits. It’s simply a matter of getting those in the hands of manufacturers.