Know the background
Begin with researching the necessary background information about the current situation. This might include understanding the various business units that will be stakeholders in predictive maintenance, for example, the asset management, engineering, operations, and maintenance teams. Identifying the the key individuals in these teams is worthwhile. An internal sponsor that can provide financial backing and the necessary domain expertise is always useful.
It is then necessary to understand the problem area and be able to describe it for a general audience. Set out the reasons for why you are under taking predictive maintenance. Understand whether your organization has used AI or predictive analytics in the past and were there any lessons learned. Preparing informational materials on predictive maintenance to share within the organization will help people better understand what you are trying to achieve.
Take a look at what the current solutions are to the problem and be able to articulate the pros and cons of those solutions.
Determine the business objectives
After the research phase, you will need to work with the stakeholders in this project to clearly define the predictive maintenance objectives that you want to achieve, as specifically as possible. So, it might be something like: Reduce the failure rates of Acme Industry large centrifugal pumps by 10%
In order to get to defined business objectives, you will also want to clearly define the problem you are seeking to solve, clearly address any business questions , and outlining any requirements from the business (for example, not increasing associated maintenance costs for the specified assets).
Defining the success criteria
Once the objectives are defined, youâ€™ll need to think about the measures by which the success can be measured. For each objective, the success criteria should be documented as precisely as possible and have agreement from the necessary stakeholders, especially if the measurements are subjective and not necessarily quantifiable.
The next step in the process is data understanding, where we will be getting our hands dirty with some code for data analysis. Weâ€™ll take a closer look at that in our next recipe so stay tuned!