Artificial Intelligence and Machine Learning in the Manufacturing Industry

Jun 24, 2021 | Insights


Artificial Intelligence and Machine Learning are rapidly becoming more prominent and critical factors in the manufacturing industry. AI refers to the overall intelligence exhibited by machines that are designed to mimic human intelligence. ML is a branch of AI which focuses on utilising computer algorithms and harvesting the power of data to create self-learning applications that improve over time without being directly programmed to do so. In ML, we train an algorithm to detect specific patterns and features in mass amounts of data and then apply these algorithms to new data in an attempt to make decisions about current data as well as predicting the future. 


When it comes to ML technologies, the majority of focus is around creating new algorithms or combining and utilising current ones in innovative ways to achieve the desired results. ML falls into a few main categories:

  • Supervised Learning – All training data is labelled, for every input we know the expected output. The algorithm then uses this knowledge to obtain outputs based on incoming inputs. This type of learning is effective in any sort of quality control, where we can clearly identify products as working or faulty.
  • Semi-supervised Learning – Small amount of labelled data to go along with larger amount of unlabelled data. Particularly useful for solutions utilising data where labelling workload would be substantial.
  • Unsupervised Learning –  No data is labelled, we have the inputs but we don’t know the corresponding outputs. This method is a good option for anomaly detection applications.
  • Reinforcement Learning – this is when learning is done based on consequences of previous actions taken by the algorithm combined with exploration of new options (essentially trial and error methodology). This can be used in situations where collecting all the required data for a different type of training is too difficult and it’s easier to provide a basic environment for the machine to interact with and learn from. A good application of this would be teaching a robot how to put together a component by providing it with a correct working version and the parts needed to put it together.

How do Machine Learning technologies work?


According to Forbes, 76% of companies are prioritizing AI and ML in their IT budgets for 2021. This means that if your company is not doing so then you are automatically putting yourself at a disadvantage compared to your competition. There is good reason for these figures too as the benefits of utilising AI can really be substantial. For example, Danone Group recently reported that by using ML technologies for better forecasting across all branches of the company they obtained a 20% reduction in forecast error, a 30% reduction in lost sales, a 30% reduction in product obsolescence and a 50% reduction in demand planners’ workload.

Yet it is important to remember that the transition towards full incorporation of AI in your company is a journey, and not something that happens over night. But as reported by the Boston Consulting Group, there is a clear 4 step process towards the implementation of AI in your company that, when followed fully, drastically increases your chance of successful implementation of the technology with each step.

Working toward real ROI


According to Capgemini, the most common use case of AI in manufacturing is for machine maintenance with 29% of all AI applications in manufacturing revolving around this but there are several key applications of ML and AI within the manufacturing and industrial sectors:

  • Predictive maintenance – This aims to foresee when machine maintenance should be performed in order to prevent machine breakdown before it occurs. Through this, manufacturers can directly increase their Overall Equipment Effectiveness parameters (OEE) leading to better production line performance.

  • Defect Detection – most commonly done via image recognition based algorithms, defect detection uses ML and AI technologies to automatically spot errors with a product without requiring them to be checked by a human user. Not only does this make the process more efficient but also more accurate.

  • Digital Twins – a digital copy of the physical production system. This enables a variety of useful features such as real-time monitoring, product performance analysis or process analysis. We can then use this information to help optimize the physical system.

  • Intelligent supply chain – through ML and AI we can optimize delivery routes, warehouse stock control or predict product demand.

Applications of AI and ML within the manufacturing and industrial sectors