The Industrial Internet of Things (IIoT) is a major segment of the much broader Internet of Things (IoT). Sometimes referenced as M2M or Industry 4.0, IIoT relates much more to the data and automation of factory machines than wearables. Estimates are that the IIoT will boost Gross World Production by $11 trillion (by 2025) according to McKinsey, and to $14 trillion (by 2030) per Miramar Global. Manufacturers and supply chain managers have good reasons to engage IIoT for the future of their businesses. For any IIoT investment, a due diligence evaluation is essential, and we can help you start that process by addressing the essentials.
Understanding the Differences – IoT, IoE and IIoT
What is the difference between IoT and IIoT? How does IoE fit in? It’s easy to overthink definitions, maybe you remember the infamous remark, “It depends on what the definition of is, is.”
Technical definitions in the world of the Internet of Things can be tricky because they frequently overlap.
- Internet of Things (IoT)- Comprised of electronic devices capable of receiving and transmitting data over the Internet via a unique identifier (UID) or address.
- Internet of Everything (IoE)- Encompassed by the Internet of Things, but distinguished as devices with intelligent connections involving software or algorithms capable of comparing and sorting multiple data points to produce a decision or action.
- Industrial Internet of Things (IIoT) – A segment of IoT for use of electronic devices in an industrial capacity, using sensors, data, and robotics in production processes.
It’s worth noting that the IIoT is sometimes referenced as Machine to Machine (M2M) – Machines able to communicate with other machines like in a factory, even if they are not connected to the Internet. M2M existed long before IoT – and is largely what these technologies evolved from.
There are at least ten major segments of IoT, where some like Smart City and Connected Cars or Smart Grid applications can and do overlap. With the Industrial Internet of Things we are talking about IoT devices, sensors, software and data being applied to factories, facilities, fleets of vehicles and equipment – usually for an operational benefit.
Potential Benefits of IIoT
Most businesses adopt IIoT to improve their operations aiming at one or more of the following:
- Improve operational efficiency
- Increase productivity or “just in time” production
- Reduce downtime and enhance worker safety
- Optimize the use of machines and other assets
- Reduce asset lifecycle costs and improved preventative maintenance
- Faster and improved decision making
- Sell products as a service
- Enhance the product innovation process
- Obtain a better understanding of customer demand
Amazon’s use of CMC CartonWrap 1000 is an IIoT solution that provides an exceptional example of increased productivity. It packs customer shipments 4 to 5 times faster than a human: 600-700 boxes per hour compared against 120-150. Each machine costs roughly $1 million and is expected to pay for itself within one year.
According to IoT World, there are ten major reasons why some executives are hesitant to implement IIoT in a productive capacity. I believe these can be narrowed down to three primary points. In either case, IIoT adoption has accelerated over the past 18 months as more and more data pours in substantiating its benefits – albeit, from mostly large businesses.
Cost of Implementation is a potential issue for IIoT device price tags, the infrastructure needed for 99.95% or better uptime, and custom solutions for interoperability with legacy systems. The counterpoint is you need a long-term investment strategy.
Some also point to a lack of standards and that many IIoT solutions lack maturity because they are often new to the market. We can tie these points to insufficient knowledge of available solutions. Many companies are already benefiting from IIoT. The market rarely decides standards and merely provides options. If otherwise, we’d only have Windows-based operating systems. The solution is to be found through research – the due-diligence effort to determine what is best for your business.
Technical expertise encompasses a bevy of issues from security to data privacy. It also includes inadequately defined workflows and uncertainty or lack of confidence in achieving IIoT’s benefits. These impact the cost of implementation, as well. Another issue is a shortage of Data Scientists. McKinsey estimates there’ll be 250,000 more data scientist jobs than candidates to fill them by 2020, and 1.5 million managers needing more data analytics skills.
Defining Work Processes
The #1 Prerequisite to Getting into IIoT
One reason why I believe Amazon does so well (in everything) owes to their taking the time to write out and create a schematic for every job function and process in its Operations. Each process has a clearly defined desired outcome. Each is also associated with a performance metric. Moreover, Amazon constantly works to improve its processes and metrics. If you are going to get into IIoT – if you want your business to survive the next decade, you need to start doing the same. Everything starts with mapping out your processes and knowing your performance metrics so you can compare it with any changes. As you do this, take care to note the data you need to collect from each process. Perfect the Process and you’ll continuously get great results.
Any change to a process needs to be validated before fully implemented. Will it generate an improvement over present results? How much of an improvement? Will it impact other work processes — upstream or downstream? Does it even fit to your business? Using the CMC Carton Wrap 1000 from above, your ROI changes if your pickers are producing only enough to keep it going half the time. Leastwise, to properly evaluate jumping into IIoT means knowing how long each step in a job takes, costs and total throughput per unit of time.
Developing a Long-Term IIoT Plan
One of the major reasons why some companies getting into IIoT don’t fare well is that they try to do too much at once. For one, that’s hugely disruptive. Seeing the big picture is important, but the odds are you will want to start small. Minimize disruption while allowing the benefits to feed further development. Developing a long-term plan can start with these five steps.
- Define your business goals – and the results you would like to achieve. It is useful to at least start defining your budget at this stage, too. Remember, this is a long-term plan that should ultimately proceed in stages. Each stage should help you finance the next one.
- Define your end-to-end data requirements, the specific data you need to collect, how you will collect it (wired or wireless), and how you intend to use it. Tip: Avoid collecting unnecessary data, as filtering it or parsing it adds to cost.
- Research and compare the IIoT equipment needed to make each business goal happen (package boxes faster), whether you need an end-to-end solution or can implement solutions in modules. Evaluate each according to your data collection and sharing requirements.
- Initiate a program for your employees to encourage their continued education in data science. Many IIoT projects can replace a lot of jobs. They may create a need for other, newer and more technical jobs. Given the difficulty and cost of finding data scientists, work with your existing employees. Help them pick up the skills they will need for your future jobs.
- Talk with an expert. Here’s how to create your own short list of IIoT specialists.
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