I work as an integrity engineer in floating structure asset integrity management for an oil and gas company as a service provider. I got inclined towards learning data science and business analytics for dealing with data efficiently. My role involves studying degradations of structural components and learning how data is controlling operations, maintenance, cost management, etc. in an organisation. As I am working in an engineering service-based company the scope towards the same was too narrow.
I worked on tasks to automate time-consuming data gathering, data cleaning and Data preparation activities, where I was able to reduce the execution time of these repetitive tasks from 9-15 hours to 5-10 mins using simple python codes with pandas and openpyxl libraries. This further improved the process by avoiding human error when manually executed and quality consistency of the data preparation activities. I’ve also deployed Tableau for generating reports and dashboards for Marine Terminal’s day-to-day maintenance activities. The problem was affecting the organisation by keeping the resources busy with the repetitive task of no creativity with more probability of human errors. I used Python pandas to manipulate and clean the data and openpyxl to move the data prepared to the required templates to generate reports. Furthermore, Tableau for generating clear and presentable reports. The key factor considered for the tool was the simplicity of the current requirement though there is advanced technology like RPA in order to automate processes. I chose python to be the most optimal solution for automating a specific portion of a process.Â
I identified and learned the usage of new libraries like openpyxl. I faced problems in working independently and got stuck. This is when I learned to think about the different ways to solve the problem. We as a team were able to track the Mean Repair duration of machinery to understand the interval between maintenance and estimate the requirements for repair. I have proposed reshaping the industry through digitalization in the industry with data analytics. The two ideas proposed were to optimise the process by identifying the scope for automation and using data science and analytics to build an efficient platform to stay ahead of customer evolution.
The process efficiency and efficient utilization of the resources was improved drastically. The efficiency of time utilization is increased by ~400% (9 hours to 5mins) and this exercise helped me in recalling the contribution by which I feel like an artist in the day-to-day routine.
