• Written by Sonali Subhadarsini, Product Manager

A maintenance team in a chemicals plant often finds itself waiting on a report to answer seemingly straightforward questions, such as which equipment is due for maintenance, or whether a particular spare part is in stock, before they can plan their next move, and this waiting game can have a significant impact on the team's ability to keep the plant running smoothly and efficiently.

The maintenance questions that currently wait on a report

The maintenance team's daily routine is filled with questions that require data from various sources, including work orders, asset history, and spare parts inventory, and while they may have access to this data, it is often scattered across different systems and departments, making it difficult to get a clear picture of the plant's maintenance needs, and as a result, they often have to rely on manual reports and updates from other teams, which can be time-consuming and prone to errors.

For instance, a shift supervisor may need to check the maintenance log to see which equipment is due for maintenance, while a maintenance planner may need to review the asset history to identify potential issues before they become major problems, and in both cases, they are relying on data that may not be up-to-date or accurate, which can lead to delays and inefficiencies in the maintenance process.

The data behind them: work orders, asset history, spare parts inventory, condition data where it exists

The data that the maintenance team needs to answer their questions is typically stored in a variety of systems, including computerized maintenance management systems (CMMS), enterprise asset management (EAM) systems, and enterprise resource planning (ERP) systems, and while these systems can provide a wealth of information, they often require specialized knowledge and training to use effectively, which can be a barrier for maintenance teams who need to access this data quickly and easily.

In addition to the data stored in these systems, the maintenance team may also have access to condition data from sensors and other monitoring equipment, which can provide real-time information on the status of equipment and assets, and this data can be particularly useful for identifying potential issues before they become major problems, allowing the maintenance team to take proactive steps to prevent downtime and reduce maintenance costs.

Sector constraints: yield drift and feedstock variability

The chemicals industry is subject to a number of unique constraints that can impact the maintenance process, including yield drift and feedstock variability, which can affect the quality and quantity of the products being produced, and as a result, the maintenance team needs to be able to adapt quickly to changes in the production process, which can be challenging, especially when they are relying on manual reports and updates from other teams.

For example, a change in feedstock quality can require adjustments to the production process, which can in turn affect the maintenance needs of the equipment, and in this situation, the maintenance team needs to be able to respond quickly to these changes, which can be difficult if they are waiting on reports or updates from other teams, and this is where production planning can play a critical role in helping the maintenance team to anticipate and prepare for these changes.

What improvement looks like: planned versus reactive ratio, mean time between failures, spares availability

Improving the maintenance process in a chemicals plant can have a significant impact on the plant's overall efficiency and productivity, and there are a number of key metrics that can be used to measure this improvement, including the planned versus reactive ratio, mean time between failures, and spares availability, and by tracking these metrics, the maintenance team can get a clear picture of how well they are performing and where they need to improve.

For instance, a maintenance team that is able to increase the planned versus reactive ratio is able to reduce the amount of unplanned downtime and improve the overall efficiency of the maintenance process, while a team that is able to increase the mean time between failures is able to reduce the frequency of maintenance activities and improve the overall reliability of the equipment, and in both cases, the team is able to improve the plant's overall productivity and efficiency, which can have a significant impact on the bottom line.

In order to achieve these improvements, the maintenance team needs to be able to access the data they need quickly and easily, which is where AI for maintenance in chemicals can play a critical role, by providing the team with the insights and information they need to make informed decisions and improve the maintenance process, and for more information on how to get started with AI for maintenance, please contact us.

What this does not do, or when it is the wrong choice

While AI for maintenance in chemicals can be a powerful tool for improving the maintenance process, it is not a silver bullet, and there are certain situations where it may not be the best choice, such as when the maintenance team is dealing with a highly complex or unique piece of equipment, or when the plant is undergoing a major overhaul or upgrade, and in these situations, the team may need to rely on more traditional maintenance methods, such as manual inspections and repairs.

In addition, AI for maintenance in chemicals is not a replacement for human judgment and expertise, and the maintenance team needs to be able to interpret and understand the insights and information provided by the AI system, which can require specialized knowledge and training, and as a result, the team needs to be careful not to rely too heavily on the AI system, and to always use their own judgment and expertise when making maintenance decisions.