5 mistakes manufacturers can’t afford to make today

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This post is brought by Plataine, the leading provider of Industrial IoT and AI-based manufacturing optimization software. Learn more or get the latest guide on How Smart your Factory Really Is.

Complex-manufacturing challenges were always relatively tough, but in recent years have become nearly impossible to overcome.

The pressure, competition, decreasing margins and increasing market demand create a landscape that even the biggest players struggle to adapt to fast enough.

A recent survey revealed that talent, competition, and profitability are among the main barriers that manufacturers are dealing with.

With margins remaining relatively low and the risk of making mistakes getting increasingly high, even when it comes to the most experienced and recognized manufacturing brands, the need to avoid common and painful pitfalls increases.

Following over a decade of close collaborations with top discrete manufacturing players, we’ve come to know and learn to spot some of the most widespread mistakes.

The following issues exist “across the board”, meaning in every region and type of complex manufacturing industry. Keep reading to learn what they are and how to avoid them.

Mistake #1: Unoptimized OEE 

The importance of optimizing the Overall Equipment Effectiveness (OEE) is well known to manufacturers. In fact, if you ask anyone that has been in the business long enough, they’ll probably tell you that they are focused on optimizing it and have successfully done so thus far.

Unfortunately, chances are that they’re wrong about that, and what they consider an optimized OEE can reach far better results.

OEE greatly affects the manufacturing business bottom line and can make or break a manufacturer’s profitability.

Failing to measure it properly may create the false impression of a maximized machinery availability level, for example. The first step involves calculating a correct link between OEE and profitability and an accurate measuring system. That’s not easy to do, I am quite aware, since measuring OEE and its influence on ROI in real-time is a challenge, but it’s important, nonetheless.

Some manufacturers have learned to create a clear and visible OEE metric that is based on the quality of work, the level of performance and the availability rate of their equipment. Factors such as the cost of owning redundant tools or taking too many safety margins, for instance, must play a part in this calculation as well. When everything is taken into account, the picture becomes vividly clear and the areas that require optimization are easier to detect.

Industrial IoT technologies and optimization solutions can help you calculate our real OEE and significantly optimize it.


Mistake #2: Poorly maintained tools 

Do you know exactly where each tool your production floor relies on is currently placed and why? Can you tell how many high-temperature hours this tool has already absorbed since it’s last maintenance cycle? Do you keep track of tools that are about to wear out, and require critical maintenance?

If you’re anything like many known manufacturers out there, chances are that the answer to both questions – as well as other tool-related ones – is not always, not 100%.

And that is a problem. Lost, outdated, unmaintained tools result in delays and reduced quality products that lead to rework and cost manufacturers a fortune.

Though the industry is well aware of the need to reduce scrap and rework costs, and indeed numbers are decreasing (much due to the adoption of Industry 4.0 technologies), studies still report an averagerework and scrap costs of approx. 8% of sales.  

We’ve long discussed the importance of machine and tool predictive maintenance and the message remains just as simple: real optimization isn’t just solving maintenance issues as they arise, it’s knowing which tool is about to demand some attention and handling it before it creates workflow issues. If you’re not one step ahead, you are lagging behind.

Mistake #3: Using insufficient or outdated technological solutions 

It may be strange to hear in the year 2020 (and it sure is strange to write), but at the age of Industry 4.0 solutions, complex manufacturers are still using manual procedures, manual repetitive activities, and manual decision making. 

It is easy to understand the challenge around catching up with technology, as indeed, things are changing rapidly… 

Not long ago, manufacturers moved to digitalize their factories and simple ERP software solutions took over. 

But, today, things have changed so dramatically, and AI-based IIoT optimization solutions offer manufacturers an unfair advantage over their competition.  

No simple ERP or any other data analysis system can beat AI-based actionable insights, in real-time. 

Imagine this: Manufacturer X enjoys automated real-time alerts such as: “This tool was misplaced”, “that station can’t be used for this task as it will provoke a missing deadline incident”, “This tool needs to go to maintenance now or there’s a rework risk”  or “please use this material with this serial number. It’s located here. as it faces the higher risk of being expired, better use this first” 

While all along manufacturer Y uses outdated software solutions and hence runs manual calculations and reaches insights alone. At a certain production scale, there is no way manufacturer Y can keep being competitive. 

Some manufacturers are intimidated by adding more software to the blend, while others are more traditional at heart or don’t fully understand the ROI of industry 4.0 technology solutions

Either way, the result is that this mistake may play into the hands of the competition.

“A real-time alert of a tool misplacement, based on previous historical patterns – plataine’s system:

A “regular” material cutting plan:

An optimized cutting plan:

Mistake #4: Missing out on today’s automation 

A more specific mistake involves choosing manual labor over automated procedures. 

Manufacturers that opt for manual data feeding are losing 4 times: once, when they have to work that much harder to manually feed their systems with data; twice, when it takes them far longer to complete the task; a third time, when they reach the wrong result due to human errors and the fourth, when the data isn’t changing processes, in real-time, as it should. 

“humans are the least reliable components in the manufacturing system” 

Well, no surprise here. Us humans may not like to hear this, but a study conducted by the U.S. military and aerospace programs finds that we are the least reliable component in the complex manufacturing system. If even highly-trained and disciplined US Military personnel make 20% more errors, one can only imagine what the gap is when it comes to the average worker. 

It’s hard to blame humans, as doing this repetitive work for hours on end is bound to result in less motivated employees that use most of their time manually feeding data into a system. 

Mistake #5: Confusing old school methods with AI-based optimization

Like mentioned before, artificial Intelligence rose to fame so fast that many of us haven’t quite figured out what it practically means for manufacturers (if you think that understanding AI is hard, wait till you read about Quantum technology). 

This has led to some confusion that is leveraged by some industry players. I’ve seen multiple simple manufacturing management solutions vendors add the buzz term ‘AI’ to their title, despite the fact that they lack the former’s unparalleled capabilities and competitive edge. 


While humans will always be making mistakes, some mistakes are heavier than others. Out of many mistakes that I keep being exposed to, I’ve shared 5 that are both very common and very risky, in terms of manufacturing profitability. 

Since all of these mistakes can be solved, as shown in this article, and since some sophisticated industry players have already worked these challenges out, and provided us all with best practices, I truly believe that being aware of these heavy mistakes is a good start. 

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