Debunking the myths of Industrial IoT

RtTech - Busted

I’ve got a half a pound of explosives. I’m about to put on my black beret and spectacles. Going to bust some IIoT myths, ahem, ‘beat’ some myths. (Lest we break a copyright law, Discovery channel)

While automation and tech giants are embracing Industrial Internet of Things (IIoT) value props, discussions with leaders at small and medium manufacturers reveal that IIoT innovators have failed to give tangible examples of how IIoT solutions can be applied on the shop floor today. Lack of information leads to, well, a bad intro referencing a beret and my thoughts on the top myths of IIoT.

 

Myth #1: “IIoT innovations are too expensive. They lack clear ROI (Return on Investment).”

How do you quantify productivity improvements and return on investment from new technology? Good question. A recent report cited that of the companies that haven’t yet incorporated IoT technology, 23 per cent cited “unclear and unproven ROI” as the primary reason.

Here’s the confusion: Many IIoT solutions offer the same benefits as traditional on-premise solutions (like Asset Performance Management, for example). So while IIoT technology is new, the benefits of asset management aren’t. On-premise and SCADA solutions have long served industrial manufacturers, but the high upfront costs meant benefits were relegated to those with capital flexibility. IIoT apps shift the up-front investment to an approachable subscription-based operating expense.

In this example, we don’t need to guess at the return. The benefits are proven from years of on-premise installations and are the same whether you store your data on-site or on the cloud, so there’s no myth to ROI.

Let’s do a quick ROI breakdown with the same asset management example where downtime can be a major source of profit loss. A scan of IIoT apps for asset management show manufacturing efficiency improvements of 3-5%. In taking a pessimistic approach, let’s assume efficiency is only improved by a mere 1%. For a company with 24/7 production operations, that results in a 7.2-hour increase in productivity per month (30 days). Another example: A Monday to Friday, 9-5 production operation, at a 5% efficiency increase would achieve an 8-hour productivity increase per month (30 days). Providing there’s a market for the additional material produced, an added day’s production is likely at least as valuable as the modest monthly subscription fee.

 

 Myth #2: “With the cloud, my data won’t be secure.”

In a meeting just yesterday, our CTO commented that ‘it doesn’t matter how fast or well we present the data, clients are first concerned with cloud security’. We fear what we don’t know, and this myth is fueled in large part due to a lack of understanding of the security practices involved in data sharing platforms. So rather than the blanket ‘your data is safe’ – lets take a closer look at data security.

There are three essential stages that comprise a data sharing network: On-site, in-going and out-going communication, and data hubs. For each stage, there are best practices to maximize security:

On-site

Warning: On-site best practices are obvious, but this is the most vulnerable area of data security.

  1. Do not share usernames and passwords – Sharing usernames and passwords with co-workers or outsiders lowers the security level within a data sharing network as it allows for increased users with access to the network which may compromise the integrity of the network.
  2. Do not leave your computer unattended – If you are leaving your desk, simply “lock” your computer screen by pressing “Windows logo key+L”. Locking your screen when you are away from your desk ensures that only authorized individuals (those with usernames and passwords) can access the network.
  3. Install anti-virus software – Having an anti-virus software package will help protect your computer and network from external threats like untrusted web pages or unrecognized email attachments which can compromise the integrity of your secure network.

In-going and out-going communication

Tip: When choosing a cloud-based software program, always do background research on not only the software but, perhaps more importantly, the company providing the service.

  1. AMQP (Advanced Message Queuing Protocol) – AMQP is a standardized protocol used by reputable software providers when communicating data from a client’s devices (within a secure network) to a data hub or host cloud. The data is sent with the same technique as used in online banking or secure shopping websites. The encryption means that even if someone were able to intercept the data while it is being transferred from the client to the cloud, the data would be protected by an encrypted code.
  2. HTTPS (Hypertext Transfer Protocol Secure) – HTTPS is another protocol used by reputable software providers when exporting data from the cloud hub to the client’s devices. Similar to AMQP, the data that is exported from the cloud hub to a client’s devices is securely encrypted.
  3. OPC – The OPC is a series of specification standards developed in collaboration by software vendors and developers. OPC defines the security of an interface between a client’s devices and a server, as well as from one server to another. Software that has OPC certification has been tested for its secure practices to ensure ultimate data privacy.

Data Hubs

The third component of a secure network is the actual cloud (is that an oxymoron?) or data hub. When selecting a cloud provider, look deeper into the provider’s organizational ethics, practices and reputation.

In a recent Microsoft study, clients responded that after the adoption of a cloud-based network, 94% of experienced additional security benefits that were not available with on-premise solutions, while 62% of customers felt that data privacy had increased since moving to the cloud.

 

Myth #3: “IIoT solutions provide too much data with not enough context.”

I’m tired of the ‘Big-Data is like teenage sex’ joke (let’s give our youth a little more credit, RtTech - Big datacringe). And yes, fewer than 1% of North American companies have achieved the highest level of Big Data usage, once again, according to IDC. However, advancements in IIoT technology mean the data no longer has to be overwhelming for those willing to dig into it with the right tools.

Available sensors, cloud-based networks, API integration, advanced analytic software and even data historians enable the data to be easily collected, stored, and classified, allowing for deep analysis of the data. The trick is to select the right data points and analytics tools so that your ‘medium’ data can offer big value.

One thing to remember with IIoT solutions is that they are ultimately a tool. The software and solutions we are seeing emerge in today’s manufacturing industries are meant to empower the workforce and improve operating process but they’re not a magic wand. Which leads me to the last myth.

 

Myth #4: “IIoT solutions aren’t proven to work yet. They have no track record.”

Going to play with semantics for a sec. No innovation has a track record. If it had a track record, it wouldn’t be “new” or “innovative”.

With that being said, early adoptions of IIoT solutions are starting to build strong business cases and if we circle back to my answer for Myth #1, at least some IIoT applications offer benefits that have been proven via on-premise installations, it’s simply that they’re now affordable for a much larger market.

About your guest blogger:  Teri Maltais has been driving marketing efforts within the manufacturing industry for over 15 years and is now Marketing Director at RtTech Software (www.rttechsoftware.com) which develops and markets industrial analytic apps. RtTech’s solutions include RtDuet designed to improve asset availability and utilization in industrial facilities like manufacturing, mining and power generation, and RtEMIS, an energy management information system to help industrial facilities reduce energy consumption.

Comments

  1. Teri, it’s a good list, but I think this list misses the biggest myth: IoT has something to do with the internet. If a customer has sensors, connectivity (cell, wifi, zigbee), and analytics (ability to extract insights from collected data) then they have the makings of an IoT solution that delivers value. After all the shows, all the industry conferences, all the customer case studies, it is those three attributes that are the common story behind “IoT” value. And ONLY those three. Not IP addresses at the edge, not the internet in between (on prem or a cell connection works too), not a browser interface. IoT advantage comes from leveraging sensors, connectivity, and analytics, not the “internet.”

    • Michael, the best explanation of IoT that I’ve encountered is the Three A’s: Aware (sensor), Autonomous (connected) and Actionable (analytics) – so we’re on the same page as to what constitutes IoT.

      In the blog, I wanted to address some misconceptions I’ve heard first-hand from small-to medium-sized manufacturers that were skeptical of the tech and value; but the fact that we’re discussing the basic requirements of IoT as a myth reinforces your point that the list could be even longer!

  2. Good comment Michael and a good post Teri.

    Also need to distinguish between IOT and IIOT.

    While there can be a component of Internet in IOT there is very little Internet in IIOT.

    In fact, the typical ‘Industrial’ world is Internet phobic, having been Stuxnetted once. However, secure and ‘Read Only’ transactions have been happening thru intermediaries like nearline systems.

    • Love that we’ve hit on a hot topic!

      I agree Krishna, ‘internet-phobic’ and I could add ‘somewhat skeptical’ – hence this blog post.

      On the topic of IIoT vs. IoT: I’d like to ask if you think IIoT is a subset of IoT, or clearly NOT IoT? And does it matter?

      Here’s a great blog post ( http://us.profinet.com/iot-vs-iiot/ ) by Michael Bowne that highlights the key differences of IIoT vs. IoT but less than a week later, the point is ‘corrected’ in the same blog forum by Carl Henning which addresses IIoT as a subset. (http://us.profinet.com/iot-and-iiot/).

  3. Teri, while conceptually same, IIOT has to be ‘enterprise class’ in its sensors, connectivity and analytics, borrowing from Michael’s simplified definition. Especially security is a much more significant component in IIOT. Some classify IOT into Industrial IOT and Human IOT or Consumer IOT. Dont want to conclude superset or subset- it doesn’t matter to someone who wants to just do it as long as he / she understands enterprise class.

  4. I tend to agree with your point Krishna, ‘it doesn’t matter to someone who wants to just do it as long as he / she understands enterprise class.’

    At the end of the day, the finer points of the definition of IIoT matter a whole lot less than a structured IIoT solution that can begin to deliver value to your process and then be built upon.

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