Machine Learning in Manufacturing
By: Jen Mckenzie
Tags: Machine Learning, Manufacturing

Machine Learning in Manufacturing

Machine learning is the talk of the technology sector, but it’s such a broad and poorly understood concept in the popular consciousness that it can often be interpreted as something akin to magic.

source: pixabay.com

People often understand what machine learning actually means, but the truth is that its application across various disciplines actually is as sweeping as many predict.

One place where machine learning can have a major impact is in the manufacturing sector.

Let’s look at that more closely.

Building on Existing Principles

When Henry Ford introduced the assembly line, it was a revolution that changed the world of manufacturing altogether.

It could reasonably be seen as the first step in the automation of the labor process, and it’s still in use today.

And while Ford’s principles are at work in practically every manufacturing process alive today, it hasn’t remained static.

Other companies have honed and perfected the technique to keep themselves competitive.

That was the case with Toyota who, in the 1970s, found themselves falling behind General Motors in terms of efficiency. They invented what became known as the Toyota Manufacturing Technique.

While its DNA was squarely rooted in the assembly line, they took the notion of lean manufacturing a few steps further by identifying the seven most common wastes that arise in the manufacturing process and using that as a legend to streamline their process.

It became such an effective model that years later Toyota would teach the principles to GM in an exchange where General Motors would help them acclimate to the American market.

While competition drives the market, there can often be identified as the best way to accomplish tasks, and the best companies will learn from each other to develop their own processes.

Success in manufacturing is evolutionary in the purest sense, predicated on the notion that the company that creates the most efficient processes for development will prosper while those that fall behind will die.

Such appears to be the case with machine learning.

Revolutionizing Quality Control

The assembly line is built on the premise that a larger group of employees each performing repetitive tasks can achieve greater efficiency than a smaller group of employees who are multidisciplinary.

Some tasks are inherently more complicated than others. Defects were identified by Toyota as one of the critical wastes in the car manufacturing process.

Machine learning could help with this.

By understanding the underlying problems that cause defects and identifying the potential risk factor for such defects, they can dramatically reduce waste and accelerate the timelines for production.

A case study in the steel production sector further bolstered such notions.

It’s not that machine learning algorithms will replace humans, more that the roles that humans will need to fill in the process are becoming different.

By creating a tight nucleus consisting of data engineers, domain experts, and plant managers, this study demonstrated the dramatic effects that machine learning could have manufacturing safer products with fewer defects and less risk to the consumer.

Forbes discovered that machine learning could actually improve defect detection rates by a whopping 90%.

Machines have long been used to identify risks that can’t be detected by eye, like those predicated on weight or shape.

Humans are typically far better at identifying colors, cracks, shine, and other issues that could indicate a quality control issue. But the ability for machine learning to identify these visual cues has begun to exceed what humans can accomplish.

This study is perhaps the most important discovery regarding machine learning in manufacturing and one that could change the industry to a level matching the introduction of the Toyota Manufacturing Technique.

Predictively Identifying Issues

Minimizing the presence of defects can have a significant impact on minimizing the need for maintenance further down the line (or to prevent putting customers at risk), but even the best-made products are going to break down eventually.

This isn’t just the case with the products rolling off the assembly line but with the machinery that creates them in the first place.

In the past, maintaining equipment has been a time-intensive process. Equipment had to be taken off the line and carefully assessed by workers or machines to identify problems and tighten them up.

Maintenance is a necessary evil that’s worth the time because an equipment breakdown on the assembly line can cost far more.

Machine learning can reduce waste by better determining when equipment should be taken out of production for maintenance.

● If you perform maintenance on equipment too early, you’re wasting valuable resources that don’t need to be wasted.

● If you perform it too late, you could potentially see a full breakdown of the assembly line process.

Machine learning can determine the ideal time to maintain equipment, creating a safer and more efficient environment.

Machine Learning Beyond the Factory

The assembly line process and the Toyota Manufacturing Technique are all about improving efficiency in the factor or the plant, but that’s not the only part of the pipeline where efficiency can be beneficial.

The process of storing and then delivering products creates its own inefficiencies that can have every bit as much of an effect on the bottom line as problems on the assembly line can.

Fortunately, machine learning algorithms can benefit the dual needs of inventory optimization and supply chain optimization.

Storing inventory is one of the highest costs for a company that deals in manufacturing, eating up about 20–30% of the total cost involved.

Inventory is all about finding a balance between how much you need to produce: having enough that all of your customers can get their hands on what they need while reducing the need to spend costly sums storing overstocked goods.

But the amount of data that determines demand is far too sweeping for human analysts to work on.

AI can parse that information more accurately and thanks to machine learning, it can take into account more complex patterns to find the perfect balance between supply and demand.

Increasing Efficiency Throughout the Whole Supply Chain

Machine learning help manufacturers by:

● Predicting how much and what type of product they need

● Determine when they need it

● Knowing the most efficient shipping route to get products to its destination

● More accurately predicting possible complications that could slow down the supply chain

Efficiency applies not just to production but to the process of getting the products you need and getting the products you make to the consumer in the shortest amount of time.

With machine learning, the whole supply chain improves.

[1] P.Chojecki, How Artificial Intelligence Is Changing the World (2019), Towards Data Science

[2] R.Jindal, The Ultimate Guide to Car Production Lines (2018), Bunty LLC

[3] J.Sutter How Toyota Trained Gm (2019), The Innovation Enterprise Ltd

[4] Unknown, Product Quality Prediction and Optimization in Steel Manufacturing, Rapidminer

[5] L.Columbus, 10 Ways Machine Learning Is Revolutionizing Manufacturing In 2018 (2018), Forbes

[6] P. Trujillo, The Real Cost Of Carrying Inventory (2015), Wasp Barcode Technologies

[7] L. Ampil, Basics Of Data Science Product Management: The Ml Workflow (2019), Towards Data Science