Industry 4.0, Internet of Things (IoT), Industrial Internet of Things (IIoT), the smart factory, all terms that have become more and more popular throughout the years. In this blog, we tackle the novelty of Industry 4.0 and IIoT and evaluate if manufacturers should ignore the concepts completely.
- Should manufacturers ignore Industry 4.0 and IIoT?
- Learn why so many terms are overused and have become buzzwords.
- In all honesty, what’s missing is a more realistic version of Industry 4.0 – smarter analytics, not smarter machines is the solution.
- See how analytics is transforming technology and becoming the foundation of Industry 4.0
- How can the use of new technology in the manufacturing industry benefit?
- Rethink the barriers of achieving ROI in IIoT and think of continuous improvement.
- Determine the data without the hype.
Should Manufacturers Ignore IIoT?
I’m sure this sounds crazy. In fact, I know it does. Given the trajectory of the industrial market, everyone in the manufacturing world knows that a connected enterprise is likely the future of most — if not all — manufacturing businesses.
So why on earth should a manufacturer in this day and age ignore the industrial IoT? Shouldn’t they be doing the opposite? Shouldn’t they be investing resources, assigning teams, and building plans around their IIoT strategy? I don’t think so and I’ll tell you why…
At some point, you have to stop with the hype and look pragmatically at what the next era of the industrial market will require from manufacturers. It will require greater accuracy, better quality, and more precise scheduling.
But, the term IIoT is being used to present some kind of new generation of technology that will allow manufacturers to magically lasso these challenges and provide immediate solutions. Those that have been in this industry know this is not the case; nor could it ever be.
What’s Needed is a Realistic Vision of Industry 4.0 and IIoT
A lot of the Industry 4.0 hype centers around AI and Machine Learning, often with the equally buzzwordy Industrial Internet of Things (IIoT) thrown in for good measure.
Take a look at some of the platform leaders in the space, many offering business intelligence platforms that aren’t purpose-built for manufacturing. They paint a vision of a fully-automated, fully-connected plant floor, to which their enterprise platform can easily pull data and instantly provide insight.
What’s missing is a more realistic vision of Industry 4.0 that isn’t smarter machines, but smarter analytics from the machines you already have.
The Most Overused Manufacturing Buzzwords
Terms like IIoT and Industry 4.0 have come to dominate the industry press and fuel conversations in the manufacturing world. Like the blanket term “innovation”, these buzzwords are often co-opted by marketers to provide a useful shorthand around technological “must-haves”. They are powerful because once referenced, they provide a convenient umbrella under which marketers and industry insiders talk about current trends and propose future states.
But these terms also confuse many (not just the Luddites) by placing too much emphasis on the promise of the technology rather than its real-world application.
Take, for instance, business intelligence platform leader IBM’s latest take on Industry 4.0 in a recent marketing campaign. It takes you on a tour of a Model Factory.
In this fancy animation, factory workers (and robots) make shoes in two colors. The KPIs that IBM Watson is allegedly paying attention to are Throughput, On-Time, and Risk.
- In one scenario, the business intelligence provided is supply-chain related, signaling you that a storm might affect your supply chain, Watson then calculates and mitigates that risk.
- In another scenario, a bit more realistic, Watson provides you with predictive and preventative maintenance alerts helping to keep one line open so your productivity doesn’t take too much of a hit
- In another scenario, even more realistic, Watson provides you with predictive and preventative maintenance alerts helping to keep your production from going off-line.
Real-World Manufacturing is Messy and Complex
In each case in which Watson saves the day, Industry 4.0 is represented as an all-in-one solution within an already hyper-connected, fully-automated, AI-driven world, in which data appears almost magically and is conveniently abstracted into simple but ultimately meaningless KPIs.
The hard work for Watson was already done, pulling all that data and making some contextual analytics out of it. The ability to pull data and technology either requires a lot of internal work from your teams or a purpose-built software that already is one step ahead of the game, helping to provide you with valuable insights. But, how? (Don’t worry, we’ll get there.)
Ahead of the IIoT Curve and Manufacturing Industry Trends
All of that being said, it doesn’t mean that IIoT and Industry 4.0 isn’t obtainable for manufacturers, the opposite in fact. Most manufacturing businesses are actually at the forefront of the IIoT, a very useful strategy on the plant floor.
Many have had connected machines for over a decade. Manufacturers were some of the first types of businesses to incorporate connected devices into the workplace. So, why all of a sudden is the IIoT being touted as a solve-all solution for many of manufacturing’s greatest challenges?
The truth is that IIoT technology is evolving and the innovators in the industrial technology spaces are providing better software, better sensors, and more diverse options for shop floor connectivity than ever before. The problem is that many of these new solutions are expensive and complex to configure (looking at you, IBM Watson).
The Rise of the Internet of Things has Meant Smarter Machines or Smarter Analytics?
A few other platforms promise technology breakthroughs through advances in IIoT, in which machines on a production line, raise their hands to self-identify when they require maintenance or aren’t working at peak performance. Some machines even provide a detailed diagnostic to show the maintenance crew exactly what machine part might need replacing.
The concept of a fully realized “digital twin” is also problematic for most manufacturers. While this seems like a natural extension of fully-connected machines, the digital twin models are so complex as to be out of reach of all but the most advanced manufacturers, and even then, probably realistic only for their newest factories. (Check out the digital twin concept Siemens built for their IoT platform.)
These scenarios conveniently avoid the real-world of the modern manufacturing plant. In most plants, there’s a mix of machines, new and old, some connected and not connected, some finely programmable or still others largely manually operated.
We ask: “What good is a fully integrated IIoT platform if the weak link in the chain that leads to unplanned downtime wasn’t the shiny new CNC machine, but something older and less self-aware?”
Again, we’re circling back on the idea that a more realistic Industry 4. doesn’t need smarter machines to be effective, but in reality, needs smarter analytics.
Transforming Technology: The Foundation of Industry 4.0 Efforts Will Be Analytics
Many of the value propositions for these new platforms and the ideal factories of the future they promote depend erroneously on AI or Machine Learning or on hyper-connected smart IIoT devices.
The truth is that most productivity gains in the next decade won’t be through machine learning and AI, or digital twins, but are much more likely to come from data-driven manufacturing analytics, run-of-the-mill continuous improvement, applied Lean manufacturing techniques, and good old fashion automation that increases capacity and throughput.
These efforts won’t be informed by Watson, at least not in the short-term, but rather your rank-and-file employees. It will be your line operators, quality assurance, plant and operations managers, and operation specialists that will make the decisions to help your bottom-line productivity and make these efforts a success.
How Can the Use of New Technology in Industry Benefit Producers?
In 2018, we attended the Smart Factory Summit and Banner Engineering’s Matt Negaard gave a fantastic presentation on the changing paradigms of IIoT and digital transformation, pointing out that manufacturers need to stop thinking about industrial IoT as a moonshot requiring million-dollar pilots to prove its value and start thinking of it as a core capability. IIoT can be more accessible through analytics.
If you’re not already familiar with them, Banner Engineering is a leading manufacturer for sensor solutions. They range from really complex condition monitoring equipment to simple and powerful peel and stick wireless sensors. Installing one of their low-cost sensors can provide manufacturers with a lot of data around machine cycles, capacity, downtime, and quality.
His message was simple: Get connected and start collecting data as quickly and cost-effectively as you can and the ROI will prove itself out. Once this capability is in place, then start thinking about how the data can affect the bottom-line.
Rethinking the Barriers to Achieving ROI in IIoT
In a recent study from LNS Research, 34% of manufacturers complained that their top challenge for IIoT was “Building a Business Case” and 32% cite funding as a major barrier. Negaard’s approach to the IIoT puzzle is to get manufacturers to think about the business case in two ways:
1. Feasibility – Determine what the real barriers to implementing IIoT are?
“Oftentimes it’s a matter of how you frame the IIoT question and whether it’s really a question of technical feasibility rather than conceptual or cultural. Gathering data and getting connected is actually one of the easiest problems to solve, in part because the market has already solved it. Many technical challenges can be overcome with fairly low-cost solutions.”
2. Business Viability – What kind of problems do you want to solve? What are your most pressing business concerns? Machine downtime and maintenance? Capacity and throughput? Quality and yields?
Align your IIoT investment with the existing problems you want to solve.
“It’s possible now to deploy a thousand sensors on a thousand machines really quickly and affordably,” he says, dispelling the myth that IIoT is prohibitively expensive, “but manufacturers will also see the value right away at a much smaller scale.”
Instead of trying to justify IIoT formulating the business case that provides a big win, manufacturers should leverage IIoT to achieve existing business goals, focusing on a lot of small, more achievable wins, i.e. continuous improvement.
A Sober and Pragmatic Evaluation of Practices
Realistically, small-to-medium-sized manufacturers know that it will be difficult to invest in some of the infrastructure needed to connect their enterprise in the way that many on the innovative side of technology are talking about.
Complex MES systems, BI software, and integrated ERP systems are really sexy to talk about at conferences and in boardroom meetings, but those charged with executing these plans know that for their organization these initiatives will be too expensive, difficult to execute, or simply won’t help them accomplish more than they already can today.
Even the most progressive small-to-medium-sized manufacturers will have some difficulty positioning these types of IIoT projects internally to support new business objectives.
The cost and complexity of implementing new programs like this can be staggering, and most businesses this size don’t have the staff or wherewithal to execute. This is where expensive consultants come in, and to justify these costs, manufacturers need sound project plans to ensure these stay on course.
If we’re all being honest, most manufacturers this size, look at their business’ IIoT plan as something they will just talk about for the next 3-4 years.
They are most interested in improving quality, scrap rates, scheduling issues, and other things that impact their ability to stay Lean and serve their customers.
The executives will always worry about what competitors are doing to potentially get ahead in the industry, but ultimately, it is too risky to go out of pocket on an innovative IIoT project that may not offer a quick payback, unless you reference a manufacturing analytics solution.
Examples of Manufacturing Technology: What Manufacturers Will Do Instead
The smartest manufacturers in the market today are already aware of this dynamic.
They understand the market and what they need to do to enhance Lean principles and impact their bottom line. Their biggest issue is the data.
The data they need to execute on these things — in many cases — is the kind of data that can only be pulled from the costly, complex setups we discussed above. Nevertheless, they are looking at work cells one at a time and manually collecting as much as they can off of machines to make cost-savings adjustments.
These smart manufacturers know that they can ignore a lot of the IIoT hype by focusing on easy-to-implement and straightforward solutions that can deliver the information needed to improve the factory floor, and beyond.
Getting Quick Wins
The best way to avoid the IIoT hype and focus on the outcomes is by looking for quick wins inside the business by using manufacturing analytics.
The most intelligent manufacturers we’ve worked with all know what data could offer the biggest cost-payback the fastest like a better understanding of scrap rate.
Getting quick wins will allow manufacturers to save money and invest in new projects that will allow them to tackle more complex challenges.
Identifying what data you need to improve your business is not difficult. Finding ways to collect that data and finding the right people to interpret is traditionally the hard part.
ROI Should Focus on Continuous Improvement Efforts
Pattrick Fetterman from LNS Research agrees and highlights another reason why manufacturers might be struggling to find a solution that works, with people that can both understand and use the data to improve production – data has traditionally been siloed to the c-suite executives and not leveraged into the day-to-day operations where it’s impact hasn’t been obvious.
“Companies that lag peers in operations maturity tend to reserve data and decisions to top managers and the C-suite.”
The manufacturers consistently demonstrating ROI from their IIoT investments are the ones gathering from the plant floor and pushing that data back to the plant floor.
He points out that for smaller to mid-sized manufacturers, the path to ROI from IIoT can even be easier than for larger competitors since the implementation is at a smaller scale and there’s a much smaller divide between management and operations.
He also notes that for manufacturers who build out IIoT capability, continuous improvement efforts show powerful ROI.
“One of the more surprising findings in our recent research is that companies who manage their continuous improvement programs with advanced technology show a 2x to 3x improvement in outcomes, as compared to companies manually tracking these programs….[for example] SMB manufacturers that use digital technologies to track programs show a 300% increase in first-pass yield. The inference is clear – SMBs need to focus on continuous improvement programs and manage by metrics to achieve significant results; they can use advanced technologies to track the CI programs and accelerate improvements by a marked amount.”
To summarize, manufacturers at any scale should be confident that IIoT and analytics will prove value if they focus on implementing the technology with the goal of solving everyday problems and not magically transforming the business overnight.
Industry 4.0, Industrial Internet of Things (IIoT), and Smart Manufacturing
By investing in manufacturing analytics that provides context into your production lines, you can achieve a lot of the promised value of Industry 4.0, IIoT, and smart manufacturing, by providing the one thing that all the model smart factories will require to run at an ideal state: Actionable manufacturing analytics from your plant floor delivered to your employees.
We built Mingo to fill that need, providing the manufacturing analytics that can be used for daily monitoring and optimization as well as advanced and real-time modeling.
Good News! Some of the Hype is Justified
Here’s where the hype of Industry 4.0, IIoT, and smart manufacturing might just live up to itself with many benefits:
- Sensor technology and PLCs producing useful machine data are ubiquitous and came standard within most machines in the past twenty or so years.
- Retrofits, once requiring new hardware and a new PLC can provide meaningful data points through relatively inexpensive peel-and-stick solutions or non-invasive updates to a machine’s PLC.
- Cloud-based applications (like Mingo) can now provide enterprise manufacturing analytics for a fraction of the cost, substantially lowering the barrier to entry for most manufacturers.
All this means that most manufacturers can now afford to benefit from the technology that is helping to create an analytics revolution.
Collecting and Interpreting Data
Intelligent manufacturers have a good idea of what kinds of outcomes could be affecting their business, but collecting, interpreting, and acting on the data they need can be a difficult, costly, or time-consuming process.
That’s really why companies like Mingo have emerged. Mingo is a non-invasive manufacturing analytics platform designed to allow all job roles inside of a manufacturing business the ability to collect and view data in relevant dashboards that allow them to act in real-time.
By being able to collect and act on all the data from the shop floor in real-time, manufacturers can effectively improve quality, performance, and scheduling, without the implementation of a BI software system or integrating any kind of software with their ERP.
And since all the data is pre-loaded into dashboards that are designed for each job role, it is simple to evaluate, decipher, and act on.
The Data Without the Hype
Manufacturers that know what data can affect business outcomes in their organization will allow them to avoid the IIoT hype. Most small-to-medium-sized manufacturers want the data without the hassle of costly implementations or lengthy projects.
The challenge for these businesses will be finding ways to collect, decipher, and act on this data in meaningful ways.
Issues with staff and bandwidth will always make this difficult. That’s why many are turning to tools like Mingo to simplify these efforts.
Ultimately, manufacturers hate spending more money on technology without fully understanding what they will get out of it in the end and that’s reason enough they should be ignoring the IIoT until they can define what it means for their business and their customers.
If you’d like to get past the Industry 4.0 and IIoT hype and start making evidence-based decisions concerning your plant’s availability, performance, and quality measurements today, let’s talk.