More accessible than ever, ML will be leveraged to reshape essential processes within the industry.

By Ingo Mierswa, PhD, Founder & CTO at RapidMiner

Since the Industrial Revolution, manufacturing has undergone several transformations to harness new technologies as they become available. Over the past decade, advancements such as complex robotic systems, IoT technology, and artificial intelligence (AI) have pushed us into yet another round of technological adoption, the Industry 4.0 Revolution. As manufacturers embrace cutting-edge technologies, old methods are being augmented to achieve better, safer, and more profitable operations.

Recently, machine learning (ML)—a sub-domain of AI that provides systems with the ability to automatically learn from experience without being explicitly programmed—has become increasingly pervasive in nearly every industry, but it’s only just beginning to live up to its full potential, especially in manufacturing. Because ML models need lots of training data, the ongoing digitization of manufacturing (particularly through the Industrial Internet of Things (IIoT)) has created the perfect conditions for ML to have a huge impact.

As manufacturers continue to adopt ML into their everyday operations, here are four ways I expect this technology to transform the industry in the year ahead:

Revamping Quality Control

Quality control is often done by humans because it usually requires a visual inspection. Because human senses (and attention spans) have their limits, though, errors can still happen.

ML reduces the possibility of error while improving manufacturers’ quality control efforts. Image recognition and other ML programs can be trained to analyze images of products and detect anomalies, significantly reducing flaws and ensuring products are correctly packaged and labeled.

Factories are also able to efficiently identify possible causes of these defects. A case study involving steel manufacturing uncovered the waste reduction impact that ML has when defects are identified earlier in the process, reducing waste and saving money.

Improving Inventory Management

Inventory management is a key financial component of supply chain management because the cost of storing inventory is massive. Holding unsold or undelivered products means paying for storage space, and while this may not sound like a major problem, its effect on cash flow can be sizable—typically around 20-30% of the cost of a product, according to Wasp Barcode. Even a modest reduction of 10% in holding costs can reduce your per-unit costs by 2-3%.

In this segment of operations, the role of ML is to calculate at what point it makes economic sense to retain or sell inventory, as well as increase or decrease production. This is done by monitoring the above supply chain elements, as well as market prices, holding costs, and production capacity.

Modern demand forecasting is volatile, but AI can help manage the risk.
Modern demand forecasting is volatile, but AI can help manage the risk.

Forecasting Consumer Demands

In the midst of the current pandemic, forecasting consumer demands can be an increasingly difficult task, and one that isn’t always successful.

While demand forecasting isn’t a new practice, by leveraging AI programs, manufacturers can now forecast demand with an unmatched level of accuracy. Drawing new and historical data from sources like resource planning systems, point-of-sale (POS) systems, and social media marketing programs, ML models can highlight which factors drive demand, as well as how businesses can adapt to variables on the spot. This data can be combined with other relevant variables, including raw material prices, supplier issues, and weather disruptions, to help you understand how hot your product is right now, and how you can make sure you’re getting your product into buyers’ hands.

ML allows enterprises to identify the relationship between these variables and the specific factors driving demand. Most importantly, unlike traditional forecasting solutions, models are constantly updated with new data, enabling more agility for manufacturers to adjust when the world changes.

Unlocking the power of unstructured data

Manufacturers can leverage machine learning to better utilize their core production data, from inventory to shipping, in countless ways. In fact, any manufacturing data that fits in a spreadsheet, known as structured data, can probably be input into ML software to create AI models that deliver positive impact.

However, the most unappreciated application of ML is in analyzing unstructured data – the kind of raw data that doesn’t fit cleanly within a spreadsheet but can be considerably valuable when unlocked.

Written maintenance reports, for example, can be analyzed for recurring causes of equipment failure, or images from the shop floor can be mined to look for irregularities and other problems. With ML and tools like natural language processing (NLP), this wealth of information can be treated like structured data, and thus unlock even more possibilities for identifying problems and developing solutions.

Machine Learning Makes Every Stage of Manufacturing More Efficient

Simply put, ML can offer substantial cost savings in every phase of the manufacturing cycle – from buying raw materials to maintaining equipment. Because the technology is more user-friendly than ever, you don’t need a team of data scientists working around the clock to implement it, which is why so many manufacturers around the globe will increasingly leverage ML for big gains in 2021.

ingo mierswa rapidminer
Ingo Mierswa

About Ingo Mierswa, PhD. (Founder & CTO, RapidMiner)
Ingo Mierswa is an industry-veteran data scientist since starting to develop RapidMiner at the Artificial Intelligence Division of the TU Dortmund University in Germany. Mierswa, the scientist, has authored numerous award-winning publications about predictive analytics and big data. Mierswa, the entrepreneur, is the founder of RapidMiner. He is responsible for strategic innovation and deals with all big picture questions around RapidMiner’s technologies. Under his leadership RapidMiner has grown up to 300% per year over the first seven years. In 2012, he spearheaded the go-international strategy with the opening of offices in the US as well as the UK and Hungary. After two rounds of fundraising, the acquisition of Radoop, and supporting the positioning of RapidMiner with leading analyst firms like Gartner and Forrester, Ingo takes a lot of pride in bringing the world’s best team to RapidMiner. www.rapidminer.com