The manufacturing industry is poised on the edge of a huge wave of change. Machine learning is arriving to transform this sector entirely. According to a report by IDC, in 2018, manufacturers invested $634 billion in the implementation of IoT technologies to collect data at their factories and warehouses. However, analytical data-based insights into past performance aren’t enough to be transformative. 

In order to achieve a big impact on product yields, increase profit margins, and reduce testing time, manufacturers need a way to harvest influential predictions about the future from this data – and machine learning is the key.’s automated machine learning platform enables manufacturing and industrial companies to build effective predictive models to reduce overstocking and understocking, cut machinery downtime, and reduce testing time, without the need for an advanced data science department. Here are three of the most important use cases for AutoML (automated machine learning) in the manufacturing sector.

Demand Forecasting 

Overstocking results in lost income due to sales that go unmade, and failing to meet demand means that companies miss out on potential revenue. Accurate demand forecasting is therefore the key to optimizing production, improving cash-to-cash cycle length, and reducing stockouts. 

According to McKinsey, using AI to enhance supply chain management could reduce forecasting errors by between 20% and 50%. This raises accuracy and increases granularity in demand forecasting, and improves stock replenishment cycles. With the input of AI, lost sales due to lack of stock could decrease by up to 65%, and inventory could be reduced by up to 50%. 

One of the biggest challenges that supply chain managers face in forecasting demand is allowing for fluctuations due to seasonality and periodicity. When you use, you can easily and quickly build a time-sensitive model that takes these issues into account. 

Predictive Maintenance

To keep machinery in its best working condition, manufacturers need to carry out maintenance at regular intervals, but downtime inevitably affects the bottom line. Using machine learning to cut downtime by a meaningful percentage can have a significant impact on your margins, without compromising on tool health. 

Predictive maintenance based on ML allows companies to identify potential supply chain failure as early as possible, so they can take proactive steps to keep machinery fully functional. McKinsey reports that AI-based predictive maintenance could increase asset productivity by up to 20%, and reduce total maintenance costs by up to 10%. 

Manufacturers who use can automatically apply advanced anomaly detection algorithms to supply chain data such as fault history, average usage, and operating conditions, in order to build accurate predictive models with a short time-to-solution. 

Testing Optimization

Manufacturers who apply machine learning to historical test data can predict test outcomes before the test even begins, speeding up testing and calibration significantly. By reducing testing and calibration periods, they can then significantly accelerate production time and increase profit margins.

Using allows manufacturers to achieve higher accuracy when predicting testing outcomes by creating several models, a specific one for each machine, process, or location — all in a short span of time. brings new possibilities for the manufacturing industry

AutoML is the key to unlock huge savings for the manufacturing industry. Incorporating machine learning enables industrial companies to boost their profit margins and accelerate production time by reducing overproduction or understocking, decreasing machinery downtime, and significantly speeding up testing and calibration periods. With’s simple, automated platform, manufacturing and industrial companies can access the power of machine learning without the need for an advanced data science department.