M2M vs. IoT - Why You Need to Listen to What Your Machines Are Telling Each Other
IoT represents an evolution from M2M that allows businesses to harness machine data to make better decisions.
Machine-to-Machine and the Internet of Things are systems based on connected devices that can collect, store, exchange data with one another without human input or interaction.
Both technologies have found a place of pride within capital-intensive enterprises that want to improve efficiencies and performance without adding headcount. Yet, even though both M2M and IoT have existed for decades, there's some confusion about what they actually mean and how they can be best applied for enterprise use cases.
In this article, we'll answer some key questions people have regarding these technologies, why thinking in terms of “M2M vs. IoT” isn’t the correct frame to think about them, and how IoT delivers business value in a way M2M doesn’t.
What is M2M?
M2M, also known as machine-to-machine, describes a communication method in which two or more machines interact via wireless or wired connections without human intervention.
M2M technology enables devices to connect and interact with each other without an Internet connection by sending data through cellular networks.
M2M use cases have expanded widely since the technology's inception. M2M technology now includes security, tracking and tracing, automation, manufacturing, facility management, and other critical business processes.
A typical example of M2M technology would be ATMs. If you make a transaction order on an ATM, the internal computer will constantly send the information to a host processor. Then, the processor will work to route the transaction to the appropriate banks and accounts. Finally, they will follow up with the approval codes and go through the host processor again.
What is IoT?
The Internet of Things refers to a broad connection between different physical devices. IoT devices, such as sensors and actuators, are affixed to machines or capital assets, and connected to the Internet via a WiFi connection or through cellular networks. Then, they use cloud platforms to send and receive data that can be used to make informed decisions about the assets, users, or business as a whole.
IoT may be most popularly known for its consumer applications. Smart home devices such as smart thermostats and doorbells come to mind. But according to a recent report from McKinsey, the most significant opportunity for IoT to create economic value comes from its applications in the enterprise.
Specifically, IoT applications have the potential to drive significant value in areas such as
- Operations optimization
- Human productivity
- Condition-based preventative maintenance
- Energy management
- Safety and security
How M2M and IoT Are Different
Conceptually, IoT is both an evolution from and a subset of M2M, according to Raniz Bordoloi, Product Marketing Manager at Particle.
While some overlap exists between the two technologies, some essential distinctions around networks, scalability, interoperability, and human input make them different.
Based on M2M's concept of connectivity, IoT technology expands from simple machine-to-machine communication to a broader cloud-based network with an adaptation of various devices. From a user experience perspective, IoT technology can provide more flexible and fast networks.
"IoT is much more of an evolved concept, and the idea is that you are connecting assets to the Internet and centralizing all of the information to one system, which is the cloud. That data is much more secure," Raniz said. "M2M is generally more vulnerable to security hacks because you're putting the data inside the data warehouse."
Another core difference is scalability for IoT. Since most enterprise IoT platforms are integrated, they have the flexibility of adding new devices to the existing network with minimal hassle.
However, though M2M platforms are deployed on machines that interact without human intervention, setting up or maintaining a machine can be labor-intensive because you can only manually set up the point-to-point connection.
Compared to M2M, IoT offers a greater degree of interoperability because it allows connections between different kinds of devices. This interoperability makes IoT more practical for a wide array of use cases.
For example, if you need to combine trash compactors and pumps with generators and other machines, an IoT solution can provide the variety of devices required to track different data types and send them to decision-makers.
"Every machine has its own programming language. With M2M systems, you need to find devices that are programmed to talk to those machines," Raniz said. "With IoT, you can easily connect different sensors and assets via an IoT gateway. You can easily connect a generator pump and a trash compactor in one factory floor."
IoT generally offers a far more data-rich experience that improves human productivity than M2M does. IoT deployments usually have applications that make it easy for users to see relevant data and take action.
For example, M2M-enabled machines in a factory setting wouldn't allow users to check fuel levels remotely. They would still have to physically travel to the machine and manually check the fuel level.
Advancements in industrial IoT, however, make it possible for users to get alerts via SMS, email, or through a central dashboard.
Transitioning from M2M to IoT - How IoT Drives Business Value Beyond The Machines
Today, adopting M2M is usually not a conscious decision organizations make. Unless you’re relying on legacy equipment and machinery, most modern machines have some M2M features and functionality built into them by the manufacturer.
Thus, if you’re researching or comparing M2M vs IoT, the real decision to be made isn’t in selecting one or the other. It’s deciding if you need to go from having your machines communicate with each other without readily providing you business intelligence to being able to extract insights from your machines to drive better outcomes.
“The question you should ask yourself is, ‘Do my machines generate data that could be used to solve a key business or customer problem?’, Dan Kouba, Sr. Solutions Architect at Particle, said. “Most of the stakeholders we talk to want to transform their business in some way, and they want to know if IoT can unlock the data they need to do it.”
By starting with the business problem you want to solve, you can work backward to understand how an enterprise IoT solution can be a value driver. Being able to monitor industrial equipment can provide considerable value in key business areas.
Implementing Condition-Based Preventative Maintenance
Preventative maintenance can mean a few different things. For example, a yearly tune-up for a machine, regardless of its condition, is time-based preventative maintenance. Usage-based preventative maintenance is another common model in which machines are serviced after a set number of hours used or distance traveled.
Scheduled preventative maintenance is valuable, but it comes with hidden costs. Schedule-based maintenance opens you up to the risk that your maintenance is either unnecessary or insufficient. A machine might be in perfectly good condition after a full year’s use, or it might be well past the point of needing maintenance and on the verge of a breakdown.
That’s why condition-based preventative maintenance—enabled by IoT—can be a true difference maker in terms of cost savings for maintenance programs. IoT allows for condition-based maintenance by providing you real-time condition data on your machines, as well as their components and subassemblies. Automated alerts warn you when a machine needs maintenance, so you’re not guessing or simply following a time-based schedule.
This is where M2M alone isn’t enough. You need relevant condition and usage data available for human users who can direct maintenance resources to the areas of critical need. IoT can provide that data to ensure asset reliability, improve decision making, and create a more efficient maintenance program. Research from McKinsey confirms this, finding that companies that digitize their maintenance programs can see cost savings of up to 20-30 percent.
“To do preventive maintenance, you need years’ worth of machine condition data so you can truly detect meaningful patterns and make accurate decisions,” Dan said. “The longer you wait to start collecting data, the longer it takes to build a robust condition-based preventative maintenance program.”
Most industrial machinery uses fuel, coolants, lubricants, and other consumables. If those run out, you or your customers may be dealing with unplanned downtime as parts seize up or otherwise fail. Sensors that track consumables’ levels can make it easy to track them across an entire fleet and prioritize refills where they’re most needed.
Consumable tracking is also vital for manufacturers and distributors who lease or rent equipment. The ability to remotely track consumable usage via sensor data makes it easy to bill customers based on usage.
Understanding User Behavior
IoT gives you granular insights on machines whose condition is directly influenced by user behavior. This is useful both for manufacturers who lease equipment to customers and businesses that want to ensure safe usage of machines.
For example, if users are keeping machines running longer than they should, or are pushing them harder than they were intended, the manufacturer or machine owner can see that and take corrective action before the machine breaks down.
“Say you rent diesel engines. They might have a light that flashes when someone needs to regenerate the particulate filter on the exhaust system. Users may ignore the warnings as the operation causes downtime, meaning it's then on the owner of the engine to take the engine out of use to perform the regeneration step and properly maintain it. IoT can help you track that sort of usage, and reduce unnecessary costs and downtime,” Dan said.
Additionally, the ability to track usage opens up the opportunity for usage-based pricing. This can be a major value add for manufacturers or distributors who want to build new business models beyond flat-rate pricing. The idea of “selling uptime” comes into play here. By showing customers that they only pay for uptime and usage, you can demonstrate that you have a vested interest in ensuring the machine is working properly. Competitors with unconnected machines don’t have that value proposition.
Remotely Controlling Machines
Monitoring condition and usage data is only part of what makes IoT valuable for equipment monitoring. Gaining remote access and control of your machines is also crucial for improving operational efficiency.
If a specific component or subassembly is operating at a dangerous level - say, with excess temperature, vibration, or power usage - an IoT platform will send an alert that will prompt a user to shut the asset down. This can be done within minutes, rather than the time it takes to physically send a technician out to the machine, which can be hours or even days.
Additionally, reliable over-the-air updates can accelerate your time to repair, particularly if it involves pushing security patches or software updates live quickly. If you’re managing assets over a geographically wide area, an enterprise IoT platform with global connectivity makes OTA updates much more reliable.
Remote control over machines can also make it easier for manufacturers or distributors that rent equipment to businesses to collect payment. Late or nonpayment by customers forces machine owners to spend considerable resources to collect their payments and get the equipment back. By enabling remote shutdown capabilities, an IoT platform makes it easy to turn equipment off remotely until delinquent customers pay their bills.
The correct way to compare these two technologies isn’t “M2M vs IoT,” but rather it’s understanding the use cases of each one and determining if machine data can help you achieve a defined business goal. Collecting and harnessing machine data can facilitate improvements to operational efficiency, such as preventative maintenance or consumable tracking, and also unlock new business models.