Why AI and Machine Learning are the next big thing to close the Productivity Gap

The UK's productivity is relatively low compared to other developed countries. According to the Organisation for Economic Co-operation and Development (OECD), the UK's productivity per hour worked was 15.1% below the average for G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) in 2019. In fact, the UK's productivity gap has widened in recent years, and it has consistently been lower than countries such as Germany, France, and the United States.

In terms of manufacturing productivity specifically, the UK's performance has been mixed. Some sectors, especially in the high-tech industry such as pharmaceuticals, have higher productivity levels than other countries. However, the UK's overall manufacturing productivity remains lower than countries such as Germany and the United States.

To meet this challenge of bridging the productivity gap, many companies are now exploring how they can better use the data that is being captured from their plant assets. There is also a shift away from utilising dated rules-based approaches, as many look towards adopting solutions underpinned by AI and Machine Learning.

One form of machine learning which is growing in popularity is Anomaly Detection, where a model is trained to classify data as either ‘normal’ or ‘abnormal’ based on what has been previously observed. Anomaly Detection has many applications which can lead to improved productivity in manufacturing, including:

• 1. Early Detection of Issues

  Anomaly Detection can help detect unusual behaviour in machines, equipment, or products at an early stage, before they become a major problem. This helps to address issues quickly, minimising the impact on production.

• 2. Predictive Maintenance

  Anomaly Detection can help predict potential equipment failure by identifying patterns that are indicative of equipment failure. This can help manufacturers perform preventive maintenance and schedule repairs before a failure occurs, reducing unplanned downtime and increasing productivity.

• 3. Quality Control

  Anomaly Detection can help identify defects and abnormalities in products during production, enabling manufacturers to improve product quality and reduce waste.

• 4. Resource Optimisation

  Anomaly Detection can help optimise the use of resources (e.g., raw materials, energy or labour) by identifying areas where resources are being wasted or underutilised.

• 5. Continuous Improvement

  Anomaly Detection provides manufacturers with data and insights that can be used to improve production processes, leading to increased efficiency and productivity.

The use of Anomaly Detection in manufacturing helps improve overall productivity by reducing downtime, increasing product quality, and optimising the use of resources.

To hear more about how the Anomalyse platform can help you close the productivity gap using Anomaly Detection at your site, contact our team today.