- Correlate various independent factors such as raw processing time (RPT), equipment counts, maintenance intervals, equipment usage, and corrective maintenance schedules to produce an accurate Spares CPW calculation.
- Create an algorithm that leverages historical forecasts and actual expenditures to derive a coefficient value that highlights discrepancies and automatically adjusts the Spares CPW data based on the calculated results.
- Establish a process for updating the coefficient values using the most current data available, ensuring that the cost model remains applicable in dynamic scenarios.
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Intern - Intelligent Automation Solutions - Singapur, Singapore - Micron
Description
Our mission is to revolutionize the way information is utilized to enhance life for everyone.
We are seeking individuals who are enthusiastic about leveraging their skills in the continuous pursuit of innovation for our clients and partners. The technologies we develop enable everything from immersive virtual experiences to significant advancements in neural network applications. We are committed to integrity, sustainability, and contributing to our communities, as these principles drive the very innovation we aim to achieve.
The focus of this internship is on the optimization of Spares Cost per Wafer (CPW), a critical metric in our financial planning for equipment maintenance costs. The existing methodology is manual and lacks alignment with the Bill of Material (BOM) data in Micron's databases.
This automation initiative aims to:
The project primarily requires proficiency in Python and SQL for formulation and automation. Familiarity with tools like Power Automate is beneficial for enhancing business processes.
Internship Duration:
4 - 6 months
