Welcome to the MIT SCM Research Symposium 2024 and thank you for joining us! We hope you find the day exciting and informative. To enhance your experience, we are utilizing this platform in order to help identify projects of top interest to you. Below you will find not only the schedule, but also the abstracts and attached executive summaries of each project. Additionally, you can better acquaint yourself with the student presenters and advisors through their profiles. Thank you again and we look forward to hosting you.
In the oil and gas industry, the fluctuations of semiconductor component delivery time significantly impact Printed Circuit Board Assembly (PCBA) production planning. The discrepancies between the supplier quoted lead time and actual delivery lead time present a substantial challenge, as the sponsor company`s MRP system lacks a robust safety stock model to mitigate component shortage. To enhance the predictability of the delivery lead time and the standard deviation of lead time, this project introduces a machine learning-based framework. To mitigate semiconductor components shortage, this project also introduces a dynamic safety stock model, which incorporates the predicted delivery lead time and standard deviation of lead time. Through a comparative analysis of model performance, the tree model emerged as the most effective in predicating delivery lead time. The dynamic safety stock model also demonstrated improvements in inventory management and production planning. These improvements significantly reduce the risk associated with semiconductor supply chain variability and strengthen the company`s operation resilience.
