• Written by Muhammed Abdulla NC, CEO

The maintenance log for a large manufacturing facility is a critical tool, used by shift supervisors to track and schedule repairs, and by management to plan and budget for equipment upkeep. However, the decision to automate the decision of when to perform maintenance, or to support it with data and analytics, is not a straightforward one. In fact, it is a choice that requires careful consideration of the potential benefits and drawbacks of each approach.

Commitments of each option

Automating the decision to perform maintenance commits you to a rigid schedule, with little room for flexibility or adjustment. This can be problematic if the automated system is not able to account for unexpected changes or variables, such as changes in production levels or equipment failures. On the other hand, supporting the decision with data and analytics commits you to ongoing data collection and analysis, which can be time-consuming and resource-intensive. However, this approach also allows for more flexibility and adaptability, as the data and analytics can be used to inform and adjust the maintenance schedule as needed.

The choice between automating the decision or supporting it with data and analytics is not just a technical one, but also a strategic one. It requires careful consideration of the organization's goals and objectives, as well as its resources and capabilities. For example, a company with a large and complex equipment fleet may find it more beneficial to automate the decision, while a smaller company with more straightforward equipment needs may be better off supporting the decision with data and analytics.

The case for the other option

There are certainly cases where automating the decision is the right choice. For example, in a high-volume manufacturing environment where equipment failure can have significant consequences, automating the decision to perform maintenance can help to minimize downtime and ensure consistent production levels. In such cases, the benefits of automation, including increased efficiency and reduced labor costs, may outweigh the potential drawbacks. However, it is also important to recognize that automation is not always the best solution, and that supporting the decision with data and analytics can be a more effective approach in certain situations.

A shift supervisor at a manufacturing facility, for instance, may use data and analytics to inform their decision of when to perform maintenance, taking into account factors such as equipment usage, production schedules, and maintenance history. This approach allows the supervisor to make a more informed decision, and to adjust the maintenance schedule as needed to ensure optimal equipment performance and minimal downtime.

The hidden cost

One of the potential drawbacks of automating the decision is the cost that only shows up later. For example, if the automated system is not properly configured or maintained, it can lead to incorrect or incomplete data, which can in turn lead to poor decision-making. Additionally, automating the decision can also lead to a loss of institutional knowledge and expertise, as the people who previously made the decisions are no longer involved in the process. This can make it more difficult to troubleshoot problems or adjust the maintenance schedule as needed.

This is where our platform can help, by providing a flexible and adaptable solution that supports the decision with data and analytics, rather than automating it. Our platform allows users to collect and analyze data from a variety of sources, and to use that data to inform their decisions. This approach can help to minimize the risks associated with automation, while still providing the benefits of data-driven decision-making.

Deciding without a six month evaluation

Rather than conducting a lengthy evaluation, organizations can use a more iterative approach to decide between automating the decision or supporting it with data and analytics. This can involve starting with a small pilot project, and then scaling up to a larger implementation based on the results. This approach allows organizations to test and refine their approach, and to make adjustments as needed, without having to commit to a lengthy and expensive evaluation process.

For example, a category manager at a retail company may use our platform to compare the maintenance schedules of different suppliers, and to identify areas for improvement. This can help the manager to make a more informed decision, and to adjust the maintenance schedule as needed to ensure optimal equipment performance and minimal downtime. If you would like to learn more about how our platform can help, please contact us.

What this does not do

It is also important to recognize what this approach does not do. It does not provide a one-size-fits-all solution, and it does not guarantee a specific outcome. Rather, it provides a flexible and adaptable framework for making decisions, and for using data and analytics to inform those decisions. Additionally, this approach is not suitable for all organizations or situations, and it is important to carefully consider the potential benefits and drawbacks before implementing it.

In some cases, automating the decision may be the better choice, and in others, supporting it with data and analytics may be more effective. The key is to carefully consider the organization's goals and objectives, as well as its resources and capabilities, and to choose the approach that best aligns with those factors. By taking a thoughtful and intentional approach, organizations can make more informed decisions, and can use data and analytics to drive business success.