• Written by Muhammed Abdulla NC, CEO

A maintenance log that nobody checks is a common sight in many organisations, with entries piling up and no clear insight into what is working and what is not. This is often the starting point for implementing AI in maintenance, with the goal of gaining meaningful insights and making data-driven decisions.

Initial Setup

The first fortnight of implementing AI in maintenance is often spent on connecting systems and data sources, rather than on analysing data or making predictions. This involves linking up maintenance records, equipment sensors, and other relevant data sources to the AI platform, such as AsscherAi. During this time, the focus is on ensuring that the data is accurate, complete, and in a format that the AI system can understand.

This initial setup phase is crucial, as it lays the foundation for the entire AI project. If the data is not properly connected or formatted, the AI system will not be able to provide accurate or reliable insights. As such, it is essential to take the time to get this phase right, even if it means delaying the start of analysis and prediction.

First Insights

The first question worth answering with AI in maintenance is often related to equipment performance or failure rates. For example, a shift supervisor might use AsscherAi to ask which machines are most likely to fail in the next month, based on their maintenance history and sensor data. This type of question can help maintenance teams to prioritise their work and focus on the most critical equipment.

Answering this type of question requires the AI system to have access to a significant amount of data, including maintenance records, equipment specifications, and sensor readings. The AI system must then be able to analyse this data and identify patterns and trends that can inform predictions about equipment performance.

Measurable Change

A leading measure of success in AI-powered maintenance is often a reduction in equipment downtime or an increase in overall equipment effectiveness. This type of measure should start to move within a few months of implementing AI, as maintenance teams begin to use data-driven insights to inform their decisions. For example, a maintenance team might use AsscherAi to identify the most common causes of equipment failure and then develop targeted maintenance strategies to address these issues.

As the AI system provides more accurate and reliable insights, maintenance teams should start to see improvements in equipment performance and a reduction in downtime. This, in turn, can lead to cost savings and increased productivity, as equipment is available for use when it is needed.

Common Delays

One of the main reasons why AI projects in maintenance can take twice as long as expected is that they often require significant changes to business processes and workflows. For example, maintenance teams may need to develop new procedures for using data-driven insights to inform their decisions, or they may need to invest in new technologies, such as sensors or mobile devices, to support the AI system. To get started with implementing AI in maintenance, organisations can visit our production page to learn more about how AsscherAi can support their maintenance operations.

Additionally, AI projects in maintenance can be delayed by a lack of clear goals or objectives. If the project is not properly defined, it can be difficult to measure success or identify areas for improvement. As such, it is essential to establish clear goals and objectives from the outset and to regularly review progress against these goals.

Limitations of AI in Maintenance

While AI can be a powerful tool in maintenance, it is not a solution for every problem. In some cases, the cost and complexity of implementing AI may outweigh the potential benefits, particularly for small or simple maintenance operations. In these cases, more traditional approaches to maintenance may be more effective. For organisations that are considering implementing AI in maintenance, our contact us page provides a convenient way to get in touch with our team and discuss their specific needs and requirements.

Ultimately, the decision to implement AI in maintenance should be based on a careful consideration of the potential benefits and drawbacks, as well as the specific needs and requirements of the organisation. By taking a thoughtful and informed approach, organisations can ensure that they get the most out of their AI investment and achieve meaningful improvements in maintenance operations.