The widespread implementation of the Internet of Things in industry, along with concurrent advances in machine learning and cloud storage, is thought to represent a fourth Industrial Revolution.
Manufacturers who can integrate digital, analog, physical, and human components into their production systems will generate unparalleled efficiency and value, while manufacturers who lag behind on implementation will struggle to compete. In Accelerate Your Data-Drive Transformation, a commissioned study conducted by Forrester Consulting on behalf of RapidMiner, 56% of manufacturers surveyed said that investment in artificial intelligence, machine learning, and advanced analytics will be the single most important investment area in the next two to three years.
Here’s what you should understand about the industrial Internet of Things and how it’s already being leveraged to create smarter, safer, and more productive plants.
What is the Industrial IoT?
The Industrial Internet of Things, often abbreviated as industrial IoT or IIoT, refers to the network of machinery, instruments, and other physical devices that have been embedded with digital sensors for the purpose of monitoring, collecting, and sharing data over private internet connections.
The industrial IoT effectively takes the same wireless technology that drives the consumer Internet of Things (think fitness trackers, home security systems, and smart thermostats) and applies it towards industrial purposes, including core manufacturing operations.
Some aspects of the industrial IoT may not seem particularly new or novel. Manufacturers have, after all, collected and analyzed machinery data for decades.
What’s changed, though, is the emergence of small, low-cost sensors along with expansive, high-bandwidth wireless networks. By combining connected sensors with machine learning software that can analyze the data they’re collecting in real-time, it becomes possible to quickly and autonomously address inefficiencies while optimizing and synchronizing any number of processes.
Top 5 Industrial IoT Implementations in Manufacturing
Industrial IoT technology is already transforming manufacturing operations across the globe through several common implementations. Let’s look at some examples.
1. Asset Monitoring
It’s 3 a.m. Do you know if your next big shipment from Shanghai is on time? If your equipment in Canton has been serviced recently? If power outages across Central Europe will impact your operations in Hungary?
IoT-enabled sensors are helping manufacturers, especially remote manufacturers, track production processes in real-time—across locations—and keep key personnel updated on status changes. This means production problems at remote sites can be solved from a centralized location based on the exact same data that site operators are seeing.
Industrial IoT provides not only increased connectivity with specific devices and facilities, but more comprehensive intelligence about entire production systems. This data can be combined with other sources of information, including weather conditions and historical enterprise figures, to help augment logistics, sustain inventory, and avoid quality control problems.
For example, if you have shipments coming from multiple factories in China that are running late, and you know this in advance, you can draw up new shipping plans to save you money.
2. Predictive Maintenance
Across the globe, countless millions of dollars are spent each year on machine operational and maintenance costs.
Factories have historically either adopted a reactive (meaning run-to-failure) or preventive (meaning periodic examinations) model for keeping their equipment up-and-running. No matter the approach, the objective is always the same: avoid downtimes and expensive pauses on production.
Today, though, IoT sensors are driving a transition to a predictive maintenance model. That’s because the data these sensors are continuously collecting can be fed into machine learning models, which will compare new numbers with older data to actually predict when failure is likely to occur. What’s more, combining IoT with cloud computing lets you leverage information from multiple machines, making your predictions even more reliable.
This predictive approach translates to fewer instances of mechanical error, increased machine lifetimes, and huge cost savings. In fact, a recent McKinsey study notes that “predictive maintenance typically reduces machine downtime by 30-50% and increases machine life by 20-40%”.
3. Optimizing Legacy Machines
The high-tech benefits described above might seem unattainable for older manufacturing plants. After all, legacy equipment represents the backboard of manufacturing in the United States, as most facilities continue to depend on decades-old production equipment. Replacing these older machines can cost anywhere from hundreds of thousands to millions of dollars.
Thankfully, industrial IoT upgrades are not prohibitively expensive.
Smaller IoT-enabled sensors that detect factors like vibration or temperature can be attached to legacy machinery to provide connected feedback and data at a fraction of the cost of replacement. And even simple sensors like these can provide great benefit by identifying normal operating parameters and then sending out warnings when data indicates the machine is beginning to malfunction.
This is a key consideration for older pieces of equipment, which typically have higher maintenance costs and greater risk of failure. Upgrading existing machines with sensors can reduce such costs while saving time and labor.
4. Operational Intelligence
Operational intelligence is something of an umbrella term that reflects asset monitoring, predictive maintenance, and other industrial IoT benefits.
But it’s more than that, too.
Operational intelligence can be thought of as a real-time analysis of operational visibility—i.e., the monitoring of your system’s operations, performance, and readiness. Industrial IoT facilitates end-to-end operational visibility, including data from remote assets and systems. By collecting and analyzing operational visibility data and comparing it with historical information, organizations can generate actionable insights.
What’s more, the sort of data-driven, prescriptive advice generated by OI not only informs decision-making, it also reduces the time necessary to operationalize those decisions by automating key actions (especially time-sensitive ones).
The benefits to this approach are clear. Imagine, for instance, how operational intelligence could help you not only detect potential machine failures at overseas facilities before they occur, but also automatically order replacement parts and take other steps necessary to reduce disruptions via prescriptive analytics.
Operational intelligence for manufacturing is the most common industrial IoT application according to PTC, and it’s easy to understand why.
5. Safer Workplaces
Maintaining a safe work environment is critical for numerous reasons, not least of all protecting workers and avoiding production interruptions. Industrial IoT can help improve safety by providing facility managers with real-time understanding of worker activity, safety shutdown causes, machinery compliance, and other relevant trends.
The potential safety applications are quite numerous. For example, wearable smart devices can track workers’ biometrics (including body temperatures) and alert supervisors when there are health concerns. And the increased visibility provided by sensors can provide insights into safety-system performance and even help identify the root causes of shutdowns. Safety-system diagnostics can also be used to identify leading indicators and address machine issues before they lead to machine accidents.
In short, connected systems present greater opportunities to monitor performance, assess risk, and proactively improve safety.
Why it matters
Manufacturing has been transformed over the past decade by the convergence of several related technological advances. When working together, these complementary technologies can help you create automated plants that produce large volumes at maximum cost efficiency, or even smart plants that produce highly customized products.
But coordination is the key, as harnessing the most value from Industrial IoT-enabled sensors, cameras, and other devices requires sophisticated machine learning software to analyze the data.
As a leading data science platform, RapidMiner provides a blueprint to make your operation successful. We help manufacturers across the globe deliver business impact with machine learning and AI.
If you’d like to see how we can help your business, sign up for a free, no-obligation AI Assessment, and see what kind of impact machine learning can have on your organization.
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