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If you clicked on this article to read it, then I assume that you are either curious about MES or familiar enough with it to have an opinion. I have some viewpoints too and one of them is that manufacturing is underserved.
However, that is changing with IIoT, AI/ML, and no-code. Over the past few decades, manufacturing has relied on traditional Manufacturing Execution Systems (MES). In recent years, new technology has been developed and Next-Generation MES solutions have joined the landscape. Evaluating MES solutions the same way may cause you to miss out on key criteria and valuable results — agility, speed, better user experience, more accessible data, faster time-to-value.
In this article, I will cover industry trends that should cause you to reconsider what you are looking for in an MES platform.
And maybe, after all this time, what your operations needed was beyond the capabilities of what a traditional MES solution has to offer.
1. Digital transformation is at its core a human initiative.
Still, in the 2020s, over 70% of mistakes in manufacturing environments are human-related. Automating away humans is expensive and futile while augmenting human workflows so that they are more efficient and result in fewer mistakes — that makes sense.
In other words, human error isn’t actually a human problem. It’s a problem with work systems, along with the lack of flexibility to augment worker processes.
Investment in automation, sensors, connected devices, etc., and then calling it a day is not digital innovation. True change only happens when you put technology in the hands of those who understand manufacturing best and empower frontline workers.
What this looks like is allowing engineers and sometimes frontline workers to reinvent their work by working within a flexible, dynamic user experience. Some examples of these augmentative technologies include digital work instruction, IoT-connected inline quality checks, and no-code development platforms that improve working systems and decrease errors to close to zero. Any solution that shows you how you can replicate analog processes on a digital screen as its core feature is fundamentally flawed.
2. New technology is blurring the existing manufacturing stack.
If you search for “MES is Dead”, you will find a whole bunch of articles declaring it really dead versus others that simply resurrected it back in another form inserting IIoT.
Clearly, there is a lot of underlying frustration about MES solutions in the market. Just ask IT teams that are tasked with supporting MES! You’ll be listening for a while...
Designed in the pre-IoT, pre-AI/ML era, traditional MES was never designed to process data from an array of sensors, let alone, aggregate it and deliver real-time insights. Instead, what you get is another data silo - like a plant historian.
Because MES was architected in the pre-cloud era, it often constrains operations to a single plant and is not configurable to automation and ERP due to intricate customizations.
Blissfully unaware that the ISA-95 model is just a good abstract model whose lines between the levels are blurring quickly, MES buyers are seeing greater gaps in what they expect and what they are getting as a product.
Here’s an example of what that gap is like:
A buyer may expect to connect a torque gun to collect data, or a printer to print out a label. And they may anticipate a max of 30 minutes. However, what actually ends up happening is that the buyer will need an additional expert at each level to build out this process.
What does that configuration look like in a next-gen MES like Tulip? Unlike the traditional solution, buyers can immediately plug their device into an edge device that runs node-red, set up a trigger in the no-code app, and have the data show up instantly on a dashboard or a tablet, all in real-time.
3. Platforms for Manufacturing are coming.
Microsoft just announced their Cloud for Manufacturing to support end-to-end manufacturing solutions using IoT, cloud-based computing, AI, and mixed reality services. They are specifically targeting what they call industry priority scenarios, like agile factory of the future and workforce transformation, that connect frontline workers, machines, workflow, and business processes.
AWS is doing something similar as well. They provide a host of services that support manufacturing use cases. Their main solutions are AWS IoT Greengrass and the 5 industrial ML services including Vision and Equipment Monitoring.
If these initiatives by two tech powerhouses teach us anything, it’s that the shift towards cloud-based solutions has already begun.
With enterprises continuing to lift and shift applications to the cloud and getting more comfortable with cloud adoption, vendors that can natively run on these platforms and utilize these services will have huge advantages.
What should I consider when evaluating MES platforms?
Recently, I presented at a webinar “5 Crucial Criteria When Evaluating Next-Gen MES Solutions” with Jason Dietrich of Tulip where we covered what to consider when evaluating next-gen MES solutions and what pitfalls to avoid.
|Questions to Ask||Criteria to Consider|
|How can we meet everyone’s expectations and speed up time-to-value?||Solution Implementation|
|How do we avoid rigid and difficult-to-update data structures?||Data Architecture|
|Will frontline workers actually adopt it and want to use it? Can we use it to improve productivity?||User Experience|
|How do we avoid costly maintenance and IT/vendor bottlenecks that limit improvements?||IT Updates|
|Can we improve the return on investment? How do we avoid unnecessary costs?||Cost of Ownership|
Tulip is a Frontline Operations Platform that is no-code, IoT-native, and integrates with other systems such as ERPs, productivity tools, and more.
Companies of all sizes, across industries including pharma, consumer electronics, aerospace and defense, contract manufacturing, automotive, apparel, and medical devices have implemented Tulip to augment workers, error-proof processes, collect data, and improve operations.
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