It’s natural – you implement a strategy, experience its results and respond accordingly. Every action you execute, with the intention of further honing your ability and efficiency in achieving the desired outcome. Like anything else in this world, the industrial sector has gone through a steady evolution, continually augmenting control over and reliability in the collective tasks at hand. However, until now, industrial operators have been forced to function in the blind - offering up improvements on an as-needed or reactive basis.
Fortunately, it was only a matter of time before the full extent of problem-solvers’ expertise would be exploited and empowered.
That time has arrived with the advent of the full-suite, data-curated Predictive Operations Platform. The narrative that brought us this revolution in industrial efficiency offers us interesting insight into how the modern-day manufacturing mindset came to be.
Here are 6 transformational developments that have driven meaningful change in how your operation comprehensively maximizes its potential today. The pain points found throughout this resource, that have prompted these changes, have nearly all been eradicated by the Predictive Operations Platform.
Just-In-Time Manufacturing
Just-in-time manufacturing has transformed most manufacturers and their respective supply chains - generally, in a very positive manner. However, of the manufacturing developments mentioned in this resource, just-in-time manufacturing may be the one that presents the most significant challenges, from an operational efficiency standpoint. As a reactionary process methodology that focuses squarely on production needs instead of production optimization, organizations that subject themselves to this approach will be forced into a perpetual state of catch-up or hurry-up and wait.
What’s more, since all of an operator’s attention is allotted to output needs, there is little to no consideration being invested into process optimization and increased efficiency. For this reason, it takes a substantially lengthier period of time to identify impactful advancements, and even longer to dial in the process - if that ever occurs. All of this adds up to a culture of instability, uncertainty and variability. Without a continued augmentation of control over your processes, not only will you suffer from an inability to improve, but you’ll see sporadic performance when it comes to efficiency.
You’ll have no method for (accurately) gauging what the quality, uptime or throughput of a particular production cycle will be. It goes without saying - that’s a nightmare. Fortunately, this pain hasn’t fallen on deaf ears and no operation needs to endure this struggle of uncertainty any longer.
Through the real-time insights that predictive data analysis affords, organizations are emboldened to clearly uncover where their assets’ sweet spots are and how best to exploit them to an advantage. Imagine having a solution that informs you of the realities of your operations, on a given day and product run, as they are happening. With machine learning providing data-driven insight, your operators can realize their problem-solving potential in an expeditious fashion, allowing them to continuously implement impactful tweaks and changes to process that will optimize all operational efforts.
Evolution of the Operator
Next up - a little bit of history. Through the 1960s and 70s, PLC and automation technology was introduced and integrated into the manufacturing sector. At first, this evolution did not prompt the complete removal of the human element from production. It wasn’t uncommon to have one operator physically stationed per asset. This operator, held responsible for the proper function of their assigned asset, could easily oversee the full scope of operation as the asset automated the production process. By way of example, when a box jammed in a machine, the operator was there to unjam it - problem solved.
However, as industry began to open itself up to the idea of technological infusion and as trust grew from experience, this provoked a steady scaling back of the human element - with regard to proximity. More and more, operators were physically relocated further and further away from the assets they claimed dominion over. However, this progress created somewhat of a “one step forward, two steps back” scenario. When operators moved from the factory floor to a control room, they gained more expansive control and greater safety, but lost direct situational awareness resulting from spatial disconnect. Then, when they were relocated again, operators were made capable of managing an entire facility’s assets instead of just a line and equipped with far more sophisticated control systems that fed them seemingly endless amounts of information.
However, the price of admission for these advancements was, again, further disconnection from real-time asset operation and factory conditions, as well as the introduction of information overload. It seems counter-intuitive but, without anything tying together the wealth of data points, the technology was introducing risk by blindfolding operators in a way they had never been before.
Yes, the machine automation worked and yes, the operators were now able to control assets from a removed position. However, with the lack of situational awareness introduced by this evolved approach and the overbearing delivery of information that could not be meaningfully interpreted, it was a bit like they were riding a runaway train. Good news is, no one needs to accept the potential (and probable) chaos this scenario presents. With the aligning technology of today, operators and engineers can accurately and immediately interpret performance across their entire operational spread, and even contrast assets, one against the other, to identify opportunities to optimize process and functionality.
At TwinThread, we believe predictive data centers will, in some ways, give operators back an element of the situational awareness that they’ve lost. By being enabled to “listen” to the process through constant data analysis, they will once again be made aware of what’s going on with their assets - at a far deeper level. This reconstitution of awareness through accurately curated data will empower problem-solvers to make critical decisions with the best information their assets have to offer.
Information Overload
Through the better part of a century, the industrial sector has shifted from operating with no data whatsoever, to attempting to manually manage and interpret large swaths of data (to no avail), to fully curated data sets that allow problem-solvers to exploit the operational information that matters most when targeting greater efficiency. Without that last evolutionary step, operators have no choice but to drown in a sea of information that is too diverse and sweeping to be effectively interpreted and operationalized.
The majority of factories today have no issue collecting information. In fact, they are most likely inundated with data pertaining to their assets - via their Process Historian. However, just collecting data is only half the battle and only one step up from having no data at all. The real value lies in collating the data in a manner where operators and engineers can easily and instantaneously identify the insights that would otherwise be hidden in the vast data spectrum. There’s almost certainly a wealth of gold nuggets buried in any given manufacturer’s data - but where to look?
No one could be reasonably held to fault for suffering from analysis paralysis when faced with the accosting prospect of having to delve into a mountainous data set that offers no clear pathway to the information needed to action meaningful process change. An overload of information without curation simply becomes unintelligible. The reality is, too much data (with no way to meaningfully interpret it) can be just as much a barrier to insight as no data at all.
There can be no more inefficient method in striving toward improved efficiency than haphazardly stumbling through a collection of data that does not point you in any particular direction. Fortunately, we no longer live in an era that requires problem-solvers to allot hours of effort just to locate a needle in a haystack. Applying techniques like Digital Twins to structure data and machine learning to surface insights held within - a Predictive Operations Platform can uncover the insights that empower action.
The Modernity of Emerging Markets
It’s no secret that emerging markets have shown incredible prowess when it comes to the continual modernization of their infrastructure. It also shouldn’t surprise anyone to read that western market organizations have made the conscious decision to find a way to make do with the aging technology and assets they have been drawing on for years or, in many cases, decades.
Without a total restructure or substantial upgrades, it may seem like your older equipment doesn’t have any more to offer in the way of increased efficiency. This is a disconcerting thought given that it will be difficult (or next to impossible) to meaningfully compete against your industry counterparts, who are doing everything they can, each day, to further modernize their operational capabilities. Frankly, every day that goes by where you execute the manufacturing status quo, your ability to acquire and retain your target market diminishes.
However, as luck would have it, there is now a way in which those organizations that lean on well-used assets can breathe new life into their tried and tested production lines.
The super-brain of a comprehensive Predictive Operations Platform informs your engineers to the highest regard and arms them with the insight necessary to exploit every ounce an asset has to offer. Harnessing the informative power of predictive data insights, you’ll know your assets like the back of your hand and how, to greatest effect, you can leverage their operation to serve your success.
Evolution of Maintenance Regime
The greatest threat to success in production is unplanned downtime or, in a worst-case scenario, unforeseen catastrophic failure of an asset. It is for this reason there has been a very evident and aggressive evolution toward the prevention of such circumstances. Asset maintenance is, of course, the preventive process being referred to. The accepted methodology for a comprehensive maintenance program has shifted as time has gone by. The first iteration of maintenance approach was, you guessed it, one of a reactive nature. Something failed so the maintenance cycle was focused on simply fixing, patching or replacing the stripped or blown-out component.
It didn’t take very long to determine that this approach is incredibly inefficient, as well as costly. Since this methodology was eventually determined to be unsustainable, operators started with a new approach - scheduled or routine maintenance. Certainly, a step in the right direction when comparing it to the last methodology, but still very far from optimized. Even where schedules for maintenance are not arbitrarily defined, they’re made arbitrary by the lack of real-time data and insight regarding the current state of an asset. Under this methodology, the operator still has little understanding of when maintenance should be optimally scheduled and runs the highly probable risk they will improperly target a timeframe for maintenance, that’s either too late or too early - each with their own consequences. From this approach, industry began to favor conditional maintenance.
This particular methodology is interesting because it represents a shift toward consideration for the condition of the asset itself. Maintenance would take place after a standardized parameter. For example, every 10,000 rotations of a cylinder or every 20,000 cuts of a saw blade. The right mindset is now there, but without the technology necessary to gauge real-time conditions of the asset, the parameters for maintenance are once again rendered arbitrary. It’s not until the operator has predictive data capabilities at their disposal that they can enact a truly holistic and next level approach to conditional-based maintenance.
This “predictive maintenance” allows engineers to learn from current processes and what the equipment is telling them to confidently build a strong understanding of asset reliability - informing them on when the optimal time for maintenance is.
Without a predictive solution that cuts through the informational overload, operators may not be able to identify the key signals that inform their decisions on maintenance. A Predictive Operations Platform is not a full-suite maintenance solution. However, it can meaningfully support your efforts in optimizing your maintenance program.
Machine Learning
The final transformational development we’d like to offer up in this resource is the evolution that most facilitates an organization’s ability to drive decisions with data - machine learning. This has been stated throughout, but it bears repeating - a lack of information is generally not the problem encountered by those operators motivated to improve efficiency. Frankly, they’re most likely overwhelmed with the quantity of data they have at their disposal. However, just having the information doesn’t empower the problem-solver to action it.
This is the differentiator that properly applied machine learning, delivered through a fully curated Predictive Operations Platform, offers. Yet, even with the advent of machine learning, there are challenges. How can you deliver the predictive insights in a scalable and production ready form? A predictive data center should absolutely expedite the user’s ability to manifest a pathway to operational optimization. So, instead of monotonously searching for the proverbial needle in the haystack, engineers are immediately informed on the answers they are seeking - the why behind the what.
Whether they are determined to improve production quality, optimize asset uptime, strengthen energy efficiency, augment throughput, better understand asset reliability and asset life, or all of the above - operators and problem-solvers alike need a solution that empowers them to accomplish a high level of intimacy with the data their assets are pumping out.
There is no solution that achieves this better than a comprehensive Predictive Operations Platform. Wherever efficiency is continually exploited and pushed to greater levels of optimization, there is a full-suite, easy to interpret Predictive Operations Platform.
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This white paper has been an exploration into how the modern-day manufacturing mindset came to be. The content provided within has only scratched the surface of how we’ve collectively arrived at the Predictive Operations Platform.
Our team at TwinThread is passionate about all topics relating to machine learning, artificial intelligence, data-driven efficiency, predictive analytics and the like. Should you wish to discuss any of the above topics further, or how our industry-tested Predictive Operations Platform can empower your operators to optimize efficiency in your organization, please don’t hesitate to contact us.
