Sunderesh Heragu, Lijian Chen, Dinesh Swamynathan, Mark L. Spearman, Li Sun
University of Louisville
This research develops a Dynamic Risk-Based Scheduling model (DRS) at Factory Physics, Inc. to better calculate production scheduling based on a set of policy parameters. The research aims to compare key performance measures in manufacturing systems between DRS and the traditionally used system MRP via simulations and analytical tools.
Many companies use static Materials Requirements Planning (MRP) systems for scheduling their jobs. In this research, researchers from the University of Louisville and Factory Physics, Inc. attempt to demonstrate via simulation that when there is high degree of variability in the system, it is beneficial to use dynamic scheduling strategies. A set of simulations with varying levels of uncertainty in forecast demand and different levels of variability in the system were developed to compare MRP and DRS systems.
* In the system with moderate variability, the DRS model had better performance than the MRP model
* In the system without variability, the DRS model had better performance than the MRP model in terms
* DRS model with CONWIP and recourse constraints yields better performance.
* Based on the results of this project, general companies can gage the limitations of static models and the benefits of dynamic models.
* We can understand the benefits of using analytical tools to quickly estimate key performance measures of alternative scheduling policies and then select the best policy among these.