Every so often I get approached by manufacturing professionals who seem genuinely interested at what do at Plataine. After all, IIoT is not just an industry in tremendous growth, it’s also a trending buzzword. But when the word ‘AI’, enters the discussion things become even more interesting. “Oh, we already use AI,” people say to me.
I’m always happy to indulge in conversations about AI use cases, so I immediately ask if they would be OK with telling me a little about their system, and how exactly it’s being used.
Then, as the discussion continues, it becomes clear that they were actually referring to BI, reports and dashboards and not AI in the deep sense that I was.
Don’t confuse AI with data analysis and dashboards : The case of Navigation Apps
Did you ever use WAZE or Google maps, the GPS navigation apps on your smartphone? Of course you have.
Imagine that all the app would do is simply collect data, analyze it and create a nice dashboard with hundreds of alerts and marks on the app’s main scree, so you would get a screen showing tiny icons for cars, roadblocks, traffic jams car accidents etc, but that’s it.
Nothing more than that dashboard.
Above is a map of Paris, imagine that you need to drive from the Eiffel tower to CDG airport, can you use the above dashboard to calculate an optimal route? and while you’re driving? I thought not.
That’s because the route is missing.
WAZE’s and other apps’ value proposition doesn’t revolve around collecting data from the road (as much as it is the app’s main enabler). It’s about solving a problem and guiding users in real-time.
The missing element:
AI is used to make predictions based on historical and present data, and then make instant calculations based on the data for an optimal route. Presenting the data on a nice report sheet or dashboard DOES NOT equal AI.
The AI continuum
“There’s smart and there’s really smart … “
In other words, the real power of what we call AI is not found simply by organizing the data or presenting it smartly, or even in alerts that are merely based on the data.
Let’s think of AI as a continuum, built on top of the data surface level. You can use AI to be responsive (let’s call it level 1): for example, as soon as the Waze system realizes that there’s a police car in your route, it can pop up an alert. Alternatively you can use AI to make smart predictions (level 5), instead of waiting for something to happen before you can. For example, if the system knows that in this road, during these hours, and only when it’s not raining, a police car is usually spotted, it alerts users of the possibility without having to wait for some user to mark it on the map first and only then react.
The AI continuum can be compared to an intelligence scale that allows us, to optimize our environment instead of making conclusions based on data.
Context is the key
Differences between AI levels are often analyzed through the lens of context, as context is a key concept when talking about AI, in any domain. Moreover, it’s impossible to discuss intelligence levels outside of context.
Even a seemingly trivial question like “what should I wear today?” is completely context-dependent, and although the answer is almost automatic, billions of times a day, all around the world, it calls for a very sophisticated process of contextualizing: What is the weather like today? Where am I going? Is it a work day or a day off? is there a dress code? What do I want other people to think of me? And most importantly: which of these questions are my top priority right now?
Pure data can only take you so far. At some point, it wont be enough and you will need to calculate the data and add a sort of algorithmic thinking, considering both your experience (historical data) and variety of parameters (the context). That’s AI.
When you think about it deeply, you see that the answer for such a “simple” question is a lot more than just list of all options that you currently have which is more or less the equivalent of data presented on a dashboard.
When it comes to manufacturing, the nature of the questions and decisions you need to deal with with is obviously quite different than your everyday life as a consumer. Still, our AI’s competence is measured by its ability to offer optimized solutions while taking all components of the relevant context into consideration. That’s exactly what systems like Plataine’s IIoT are built to do.
Let’s look at five specific use cases.
1 – Prediction of work order latency
Let’s assume that you need to meet a deadline with a specific work order. And you need the system to identify and alert you if there is a risk this deadline will not be met.
As with most other aspects of AI solutions, we set the stage by observing what (simple) MES or similar systems are capable of. Well, they do possess some capability of assessing work order latency, especially when the context is not complex. Basically, a simple “prediction” of how long it should take to get a production cycle done in some very simple situations, is enough. The problem is that some systems can be very inaccurate or identify the risk too late by not taking into account a lot of important information, such as historical data.
Using composite parts for Aerospace manufacturing as an example; in many sites, 10-30% of work orders are late on a regular basis, information on bottlenecks at various stations, tools availability , and material shelf life isn’t even a part of the calculation, and the condition of stations and machines is rarely acknowledged. Yes, there are missing parts to this context.
In this example, if you use a typical MES system that indicates the order progress and alerts if operations are running late you would not be getting any prediction on latency. When we used the Plataine system, we learned that if we kept our plan as is, we will be at risk due to the following issues:
- A couple of the workstations assigned to this project are almost always significantly late, considering historical data.
- The material we need to use is about to expire and we will, in fact, need to cut it first, thereby changing the order, or else we will need to reproduce the part again.
- A tool we were planning to use is away for service and maintenance.
The system alerted us and also provided us with specific actions to take in order to still meet the deadline: We needed to fortify a couple of workstations, cut a specific material first and assign another appropriate tool (while the one that was originally assigned is being serviced).
AI systems can improve the prediction of work order latency dramatically, and serve as a crucial enabler in maximizing on-time delivery of products and minimizing the financial losses that inaccurate predictions can cause. Context-dependent recommendations (actionable insights) allow manufacturers to make proactive & optimal decisions based on accurate predictions and prevent issues before they occur.
2 – Alerts on part misplacement’s
One of very common problems in discrete manufacturing (owning huge production sites) is the issue of misplaced parts and tools.
When a part or a tool is misplaced, it can easily trigger a chain of reactions that quickly snowballs into bigger problems, causing losses through delays and rework (needing to reproduce the same part and after a while when you do find the original it’s too late and you usually have to scrap it). Multiple systems know how to track parts when they need to without the need for AI, but AI solutions that know how to identify patterns can also identify a deviation and alert of a misplacement as it occurs in real-time, saving us the immense effort of searching for a missing part or tool later on.
So does this mean i should teach the system the different routes of all my parts, components, kits and tools? No, Definitely not,we have AI for that, and more specifically machine learning! By processing historical data using pattern recognition and other algorithms, the Plataine system “learns” the cycle per each type of object and offers an automatic alert every time it deviates from its normal course. Again, context is key, parts moving through the shop floor are not simply data to be shown on a dashboard. They’re an intricate language of behaviors that can be understood, analyzed, predicted and optimized.
3 – Quality decisions
You fry an egg. Sometimes the pan is perfect. Sometimes it’s faulty. After a while, you realize it’s the pan that sets the standard. You learn from past mistakes and go on to avoid some pans just to prevent issues from happening.
Root causes of quality issues in manufacturing are oftentimes very difficult to identify. There are so many elements in play on the shop floor, it’s almost impossible to tell them apart and pinpoint the single procedure, part, or condition that stands in the way of a better product. Intensive context analysis using AI-based algorithms, which run on production data and search for any telling patterns, is more productive than any other approach in dealing with such complex problems.
On the whole, finding differences in patterns and being able to compare them statistically is something that AI is very good at. We, as humans, are constantly running such testings, unconsciously and intuitively, but we have clear limits. When the amounts of data units are enormous, and the processes are mechanic (i.e., unrelated to our natural environment), and when there’s little time to reach a decision, our human intuition is not relevant anymore.
Moreover, were not dealing with some static one-time solution to a single specific problem but rather an ongoing quality optimization process that is based on multiple historical data. Having the ability to monitor quality decisions in real time and under dynamic conditions is a great use case of industrial AI systems.
4 – Identifying, predicting and preventing future bottlenecks
Bottlenecks are like the weather, in that famous quote by Mark Twain: everybody talks about them, but nobody does anything to solve them.
Indeed, it seems that bottlenecks are the end result of some convoluted law of physics (probably involving some quantum mechanics, too). They’re hard to predict, and yet they’re always there.
However, AI-based systems that are trained on historical production data can recognize their patterns meaning that in real time, when a certain pattern associated with a bottleneck is identified, the system can send an alert concerning the anticipated delay, so actions can be taken in order to prevent or prepare for it.
Again, context serves an important role: bottlenecks are essentially a part of the big picture. For instance, what does the usual trend look like? Can these workstations function well under pressure? What is their capacity limit? How many units can we usually cut out of this raw material? Whats the demand at this time of the year based on previous years? In what conditions does tool-maintenance cause a bottleneck? When do things actually run smoothly?
5 – Optimization in cut planning
The last example i will give here involves cut planning, a supposedly simple process: you take a piece of raw material (let’s think of a very expensive carbon-fiber), and you cut parts from it. You need it for the production. Initially, it seems like a rather simple geometrical problem. However, it’s actually one of the more interesting problems to think about, when dealing with optimization.
First, as mentioned, we need to optimize the way we cut the parts, so the smallest possible amount of material is lost. But, we must also consider our work orders – some parts might be more urgent than others, and all could refer to the same potential piece of available material.
Below is an example that shows the traditional way of cutting a specific material into different parts. Each type is cut separately.
here, the same cutting is done based on smart calculations. All parts are mixed and cut from the same sheet, resulting in almost 20% material saved:
And that’s not all…
Context is relevant here as well. If we only consider the geometric aspects of the problem, then yes, it’s still not that extremely complicated.
But processes on the shop floor are never ‘just about’ something.
There are other parameters to consider: What’s the material’s expiry date (we should cut X material before Y), what’s the fastest way to the cut parts, how can we avoid redundant machine setups, and will it be more efficient to first cut parts from group B and only then from group A (even though geometrically it might make more sense to start with A), simply because we have an available station that is ready to work on these parts now? Which part is needed ahead of others…we must now cut too many parts before it’s relevant.
And so on. So many contextual parameters, so many open optimization questions, all related to what seemed like a relatively simple process.
I hope you can now clearly see the differences between having a data system that includes fancy reports and dashboards, and a system that is based on AI algorithms. AI systems should be smarter than you in a way that allows them to help you navigate through multiple complicated factory floor processes, optimizing your decision making and giving you recommendations and actionable insights in real-time, every time.