Industrial AI – Top use cases and challenges


After decades of research and development, Artificial Intelligence (AI) is finally moving into a real manufacturing world. It can now outperform humans on a range of tasks, constantly transforming all facets of manufacturing, including efficiency, supply chain optimization, reducing downtime, improving product design, and transporting finished goods.

Manufacturers worldwide have embarked on an AI transformation, led by big data analytics, miniaturization of sensors enabling the Internet of Things (IoT), and machine learning (ML), that opens up potential new revenue streams and reduces costs time in existing processes.

Unlike the older days, manufacturers now have a much granular view of the quality of their products. In addition, as manufacturers take on digital transformation initiatives, building better quality products is top-of-mind for the business.

Custom ML models can help manufacturers better align their products to changing customer preferences by understanding customer behavior patterns, product recall data, product seasonality, a preferred mix of ingredients (for food producers), and more.

Use cases of Industrial AI

1. Predictive maintenance

This is one of the most widely sought-after use-cases for manufacturers. However, accurately predicting machine failure using all available sensor data from well-instrumental equipment can be a monumental task. Deploying the suitable ML model can help manufacturers prepare for potential equipment downtime and schedule a technician visit at the right time.

2. Supply chain optimization

All global manufacturers must manage a complicated network of vendors and suppliers. It can be difficult to source raw materials from the most cost-effective supplier while maintaining high product quality and a low procurement cost. It is critical to ensure that essential goods arrive at manufacturing plants in the shortest time possible and that finished goods are delivered as quickly as possible. Manufacturers have found that machine learning has helped them manage this complexity with far fewer resources and greater accuracy.

3. Yield prediction

Maintaining a high product yield has a direct impact on a company’s bottom line. As a result, predicting any changes in yield is more important for maintaining production capacity. Artificial intelligence techniques can predict changes in factory output caused by changes in raw materials, temperature variations, and equipment tuning.

4. Transportation optimization

Manufacturers must ensure that their products arrive in good condition depending on the type of goods they produce. As a result, quality control in transit is critical, and transportation optimization is a top priority for manufacturers. Manufacturers can predict the quality of their products under specific transit conditions, allowing them to improve refrigeration (for food) or optimize routes.


In general, there are four significant challenges in realizing industrial AI.

1. Data

Engineering systems generate a large amount of data these days, and modern industry is a big data environment. Industrial data, on the other hand, is usually structured but of low quality. As a result, the quality of the data may be flawed. Furthermore, unlike other consumer-faced applications, data from industrial systems usually have precise physical meanings, making it harder to compensate for the quality with volume. Moreover, data collected for training machine learning models typically lack a comprehensive set of working conditions and health status/fault modes, which may cause false positives and false negatives in the online implementation of AI systems. Finally, industrial data patterns can be highly transient, and interpreting them requires domain expertise, which can hardly be harnessed by merely mining numeric data.

2. Speed

Because the manufacturing process is fast and the equipment and workpieces can be costly, AI applications must be used in real-time to detect anomalies and prevent waste and other negative consequences. Cloud-based solutions can be powerful and quick, but they won’t meet all of your computation efficiency needs. In this case, edge computing may be a better option.

3. High fidelity requirement

In contrast to consumer-facing AI recommendation systems, which have a high tolerance for false positives and negatives, even a very low rate of false positives or negatives can jeopardize AI systems’ true credibility. Typically, industrial AI applications deal with critical issues such as safety, reliability, and operations. Any failure to predict the future could have a negative economic and/or security impact on users, discouraging them from using AI systems.

4. Interpretability

Besides prediction accuracy and performance fidelity, industrial AI systems must also go beyond prediction results and give root cause analysis for anomalies. This necessitates that data scientists collaborate with domain experts during development, incorporate domain knowledge into the modeling process, and have the model learn and accumulate such insights as knowledge on an adaptive basis.