The world is within an AI revolution. Where this revolution is acting as a real catalyst for change is within the manufacturing industry. AI applications allow decision makers and leaders with the precise information they need to make informed decisions. Within decision-making, information is power. AI can provide unparalleled information. It's not just an AI revolution, it’s a decision-making revolution.
In a recent review of the academic literature, Silby Plathottam at the Argonne National Laboratory provided an in-depth overview of how AI is affecting the manufacturing industry. This blog will provide a direct-to-reader distillation of the key points discussing recent advancements within Plathottam’s academic literature review.
Multiple Application Possibilities
Artificial Intelligence (AI) and Machine Learning (ML) assist manufacturers in predicting machine failures. Predictive maintenance uses sensor data to provide an estimate of when equipment might break and thus require maintenance. For example, an automotive production line can lose $20,000 each minute that it is not producing. AI models trained on past breakdowns detect warning signs. Factory workers and specialists can provide necessary maintenance before failures occur. In essence, the AI is acting like an expert consultant to workers in the factory.
AI supports quality checks by spotting flaws that could be invisible to the naked eye. In semiconductor plants, electron microscope images contain patterns that indicate likely product defects. AI models, such as convolutional neural networks (CNNs), scan these images to detect issues. One study showed that AI found defects 6% more accurately than traditional methods.
AI aids energy consumption forecasting by analyzing temperature, humidity, and power usage. In steel manufacturing, energy costs are significant. A steel mill in China used AI to predict electricity demand, cutting expenses by 5% without reducing output.
AI enhances supply chain management by forecasting demand and monitoring stock levels. In Amazon's warehouses, robots guided by computer vision locate and move parcels, saving time in retrieval and shipment. AI also reviews market data using natural language processing (NLP) to inform purchase decisions. These methods streamline production and reduce costs.
The key advancements can be put into two buckets. In the first, are those AI methods that identify or classify. The primary way that this has been leveraged in manufacturing thus far has been in quality assurance. In the second bucket, are those AI methods that optimize. This optimization translates directly into savings. This concludes the first of a three-part series where a deep dive into an academic literature review is taken and then distilled for a wider audience.
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