Enough Things Already! Why Industrialize the Internet of Things? (IoT vs IIoT)

If, like us, you have googled the famous Internet of Things (IoT) and found example after example of IoT devices (Smart watches! Thermostats! Traffic lights!) and still aren’t able to describe WHEN and HOW those things can make up the IIoT (Industrial Internet of Things), then this article is for you.

We explain below how the Internet of Things is a crucial technology for optimizing manufacturing. The Industrial Internet of Things is a modern necessity that organizes and interprets IoT data in order to meet customer demand in our volatile, post-pandemic global market.

IoT & IIoT: What's the Difference?

A lot of IoT and IIoT explanations focus on contrasting the two, but we don’t think that’s an accurate separation to draw.

IoT and IIoT technology can be inextricably linked in factories that have invested in making their systems compatible and lean for optimized output.

When people talk about the Industrial Internet of Things, they usually reference the current age of Industry 4.0. They see the Industrial Internet of Things as an extension of the modernizing world moving on from Industry 3.0.

However, that incorrectly frames the IIoT as an inevitable evolution and replacement of the IoT.

This is NOT the case.

IoT and IIoT technologies are meant to address different industry needs through seamless integration. Moreover, the Industrial Internet of Things has become increasingly necessary to manage the sheer spread of IoT devices throughout an entire production process.

We prefer to think of the Internet of Things and the Industrial Internet of Things as overlapping webworks of expanding influence over technology-based manufacturing.

Imagine a Venn diagram with many circles of overlapping association, and you’re starting to get a better idea of how different technologies can draw from other technological fields in order to solve specific problems that persist throughout all areas of production.

Keeping Up With Industry 4.0

So if the Industrial Internet of Things is so complicated, what’s stopping businesses from choosing a few key IoT devices to speed up a couple processes and then ending there? Why is the aggregated data from IIoT applications even worth considering?

The 2022 Advanced Manufacturing Outlook Report by PLANT and Canadian Manufacturing magazines polled 208 manufacturers across Canada about key aspects of our current era, Industry 4.0, and the results are clear:

IIoT solutions are necessary in order to keep up with the rapid pace of Industry 4.0

A similar paper, Salesforce’s Trends in Manufacturing in 2020, draws from over 750 responses by manufacturing decision-makers all over the world. It reports that 81% say “they need both new approaches and new tools for accurate forecasting” (6). That same 81% says that “moving their planning process to the cloud is a critical or high priority” (11).

IIoT technology is even more important in a post-pandemic landscape.

“One interesting finding is that when asked how prepared their company was for the pandemic, those already applying IIoT solutions made up 83% of the respondents who stated their organization was prepared and only 17% of this same group reported that they were not really prepared for the pandemic” (8).

It is clear from these reports that in order to keep up with competitors AND the shifting supply chain due to the pandemic, IIoT forecasting tools are absolutely necessary.

IIoT Integration and Implementation

One of the best ways to see how to start implementing IIoT technology is to take a look at how other, larger companies are coping. Most manufacturers agree that some proven benefits of adopting IIoT technology are “increased throughput (37%), increased quality of product (33%), reducing downtime (27%) and reducing time to market (23%) (21).

The Industrial Internet of Things has become so integral to modern manufacturing that the cost of implementing IIoT solutions is no longer a truly limiting factor. With all of these benefits brought by IIoT solutions, the biggest cost to manufacturers is delaying their implementation!

The Industrial Internet of Things can help you analyze your business as a functioning ecosystem, but it also helps your business remain connected to the larger biome of manufacturing trends. “To remain connected to the supply chain, manufacturers and distributors have recognized that digital transformation will be instrumental in engineering a bounce-back” disrupted by the pandemic (4). Avoiding the Industrial Internet of Things isolates and starves your business instead of allowing it to grow and interact with a healthy, global market.

IIoT Architecture in the Smart Factory

The architecture of the Industrial Internet of Things is fundamentally different from Internet of Things examples because it aims to connect all the different IoT devices that separately work perfectly but don’t fit in perfectly with a combined, large dataset for overall performance. Here are the biggest areas of the Industrial Internet architecture developing in Industry 4.0 right now:

• Cyber-physical systems (CPS)
• Cloud computing
• Edge computing
• Big data
• AI & Machine Learning

The Cloud is the most popular accessible solution for storing data, and can facilitate other practices like machine learning techniques for optimized output. If entire management software solutions feel too intimidating, an SaaS model can give you many of the IIoT benefits without all of the complications or extensive setup.

The Smart Factory at Work

Fully optimized Smart Factories really only work smoothly if there are systems in place to ensure the accurate compilation of production data. Depending on the industry, there are many techniques to compiling and analyzing complete datasets.

Here are a few examples of types of analytical IIoT software solutions:

• CAD (computer-aided design)
• CAE (computer-aided engineering)
• CAM (computer-aided manufacturing)
• CAPP (computer-aided process planning)
• CAQ (computer-aided quality assurance)
• PPC (production planning and control)
• ERP (enterprise resource planning)
• APM (asset performance management)
• MES (manufacturing execution systems)
• PDM (predictive maintenance)

It is totally normal to have overlapping spheres of technology working together towards a common goal. In fact, it is actually more common than not to have a dedicated application specifically for synthesizing all your data for targeted reasons within your niche industry. This is why all the IIoT options possible may feel overwhelming in list form.

Enough jargon—let’s interrupt with a quick example of scalable Industrial Internet of Things integration with a tale of two kitchens!

EXAMPLE: A Tale of Two Kitchens

Kitchen #1 is serving family restaurant food at a popular chain on a busy Saturday night. A party approaches and the hostess checks the electronic booking system to verify their table reservation. After being seated, the party orders their selections from the waiter. The waiter enters the items into the restaurant’s electronic order system. The waiter sees a note on the software that they are out of fish and must make an alteration to an order. The waiter finishes inputting the order, and a ticket is automatically printed in the kitchen with a detailed list of the order, including the time it was sent and the time it must be ready.

When the food is ready to be served, the waiter gets an electronic notification alerting them the food is ready at the expediting station. When the party finishes their dinner, they request separate checks, which are easily divisible in the electronic receipt system. The tax and the tip suggestion are also automatically included. The group pays with several credit cards on a handheld, wireless payment device. When the waiter finishes cleaning off the table, they mark it in the electronic system as empty, which notifies the hostess to begin seating more people.

Kitchen #2 is cooking freshly-made, pre-packaged dinner portions for distribution across North America. The factory cooking equipment is much larger than the equipment at the chain restaurant because it needs to process a high volume of food items per hour. A worker starts up the assembly line and the ingredients are processed and packaged automatically. When enough items have been produced, a sensor shuts off the equipment, switching the production line to shipping and distribution.

Executives analyze sales data and see that they are rapidly selling out of turkey dinners for the upcoming holiday season, so they alter production schedules to increase the time spent producing turkey dinners. This altered schedule is automatically sent to the distribution team so that truck drivers will arrive just in time for the new order to be finished and shipped out. A predictive maintenance system has scheduled an automatic sterilization of all equipment during the evening so that the factory will be ready to run again first thing next morning.

Key Takeaways:

In Kitchen #1, the devices that cross-communicate to ensure proper service are small and specifically placed, like the printer in the kitchen automatically receiving a food order from the seating area. The printer, credit card reader, and hostess seating chart are all IoT devices that are integrated for seamless communication. The restaurant saves time and money and eliminates the frustration that would come from workers verbally giving confusing instructions to each other that may be misinterpreted or misheard.

In Kitchen #2, there are still IoT devices built into the industrial cooking equipment, like the sensors and processors that count items as they pass through the manufacturing process. However, there is a tertiary level of automation regarding data about the most popular meals during the holidays. Execs are able to isolate this statistic and look at sales projections so they can better meet the needs of a changing market. Therefore, they can change their approach immediately to satisfy changing demand.

The difference is, in Kitchen #1, an executive manager would need to pore over the restaurant’s months, maybe years, of receipts in order to see what menu items were best-sellers and which ones didn’t pay off the cost of ingredients.

Both kitchens provide a complete product to slightly different types of customers. The Industrial Internet of Things is more visibly necessary in Kitchen #2 because of the scale and size of the customer base. This kitchen also uses IIoT technology to forecast predictive maintenance on equipment.

Kitchen #1 has little need for complex IIoT architecture, but it may benefit from an SaaS management system if they want to track different chain restaurant locations’ sales in the future. In the meantime, Kitchen #1 has an electronic management system that keeps track of the restaurant’s inventory so the waiter can make substitutions for out-of-stock items; this shows a smaller application of IIoT data.

Hopefully this clarifies the difference between the Internet of Things and the Industrial Internet of Things in practice: Kitchen #1 relies more on IoT devices to function, whereas Kitchen #2 has a larger, more integral application of IIoT solutions to manage their IoT devices and extrapolate about the current market's supply chain.

Both kitchens, however, make use of both IoT and IIoT technologies in order to satisfy their respective client demands.

Help! I Still Don't Know How to Differentiate IoT And IIoT!

Don’t panic!

As we said above, sometimes it is impossible to tell the difference, especially with the adoption of many Things connected to the internet and personalized software solutions to group output data. If you think you need a customized solution due to your many moving parts, experts can help create a system specifically for your needs.

Otherwise, here is a short list of 5 basic questions that will help you hone in on what areas of the Industrial Internet of Things you’re specifically interested:

1. What do your data collection and information pathways look like?

Do you have so many IoT devices that their data needs to be processed further via edge computing before the data can be understood?

2. What scale are you working towards, and what metrics define success?

How many products need to be manufactured in a certain timeframe? Is quality assurance built into the production line or is that a separate, manual check?

3. How much human interference with technology is necessary for it to run?

Can you control your automated systems with a few buttons, or does a human worker have to be constantly monitoring and feeding input?

4. What are your forecasting projections like for the future?

In the current market, will you need to adjust production to fit future supply and demand chains because you anticipate growth? Expected growth may need to be calculated through IIoT data before goals are set.

5. What does your interoperability of Things look like?

Are all your IoT devices the same brand or part of the same system? You may need a worker or IIoT system to compatibilize your different datasets.

Because the development of computerized automation can be virtually endless in its applications, it is hard to pinpoint exactly where the dividing line rests between the Internet of Things and the Industrial Internet of Things.

Nevertheless, by interrogating your own company’s manufacturing context within the rise of Industry 4.0, you can identify and implement IIoT techniques for optimizing your IoT devices and your eventual, fully optimized Smart Factory.