INDUSTRIAL AI 101 SERIES
What Are the Use Cases AI Can Address to Improve Industrial Production?
Elena Samuylova
Chief Product Officer, Mechanica AI
This blog is a part of the "Industrial 101" series. You can read the previous publications here:
- What is the difference between AI, machine learning and data science?
- How does AI bring value to industrial companies?
What Are the Use Cases for Industrial AI?
No business needs technology just for the sake of it. While some researchers may be focused on inventing new algorithms or elegant solution concepts, and the public attention may be geared towards some flashy examples of AI-created music or witty chatbots, the business journey in AI is, as always, a very practical one.

The challenge is to move quickly from the discussion of possible innovations to specific applications where AI creates measurable business value. As we have explored in our previous post, using AI to improve the operational efficiency of already-existing processes can be a good way to start: such applications do not require capital investments or major process changes and thus yield returns fairly fast.

How should the specific areas to work on be chosen? It makes sense to review the known pain points – the same old major cost centers and sources of inefficiency or excessive waste – which can now be approached with a new toolset. The list below may help structure your thoughts.
Quality Control and Assurance
If scrap rates and frequent fluctuations in product quality are a major concern, AI can identify the likelihood of defect occurrence in production early on or predict the expected values of major quality parameters so that corrective action can be taken in time. It can also recommend the best processing parameters or operational decisions to decrease and prevent related losses. Such improvements can be achieved by leveraging existing data sources – such as highly granular sensor data collected in real time – which are now often left underused.
A machine learning application would be able to detect minor deviations that are imperceptible to the human eye but that give AI the ability to make the right judgement.
Productivity and Yield
Often, throughput levels fluctuate from batch to batch – or operator to operator – leaving the potential for optimization. Process variability results from a large number of factors, such as raw material quality, temperature control, residence times and aging equipment.
Similarly to the quality improvement use case, AI can operate on top of existing process control systems to optimize for throughput and productivity.
For example, it can suggest the best set-up parameters for a given batch to increase the speed of production without affecting the quality, or it can choose optimal processing modes in real time to increase the yield of a higher-margin product. Such complex decisions, often reliant on human experts, can now be supported by AI recommendations, leading to an extra efficiency boost.
Energy and Raw Material Use
If production costs are a major concern, AI can also be tasked with exercising predictive process control to optimize for such metrics as the consumption of energy, water, expensive additives and so on. Given the usual cost structure of industrial production, such solutions can be of particular use for many high-volume processes. In this case, an AI system would be trained to suggest optimal parameters in real time, with the goal of keeping the use of energy or raw materials to a minimum while complying with necessary process and quality requirements.
Asset Utilization
If equipment downtime and breakdowns are a regular source of losses, AI can be applied to learn to predict such events beforehand. However, despite predictive maintenance being the darling of AI use cases, its applicability is limited when there are only a few known breakdowns in history to learn from. It often makes sense to start with alternative problem settings, such as detecting anomalies and deviations for human experts to look at, or reformulating the task so that enough training data is available – for example, to predict product quality or yield that is dependent on equipment performance and wear.
To Wrap Things Up
AI should be a tool that solves real operational problems – not just becomes an exercise in innovation done for its own sake. Having a clear alignment with business priorities and KPIs is essential to succeed.
To anyone thinking to employ AI on the production floor, we suggest first looking at the usual suspects and known priorities – be it the need for zero-defect production or improved energy efficiency.
The outcome is not going to be that flashy, but it is likely to impress the CFO: doing the same thing, but better.
Curious how AI can be applied at your plant? Talk to us!
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