Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
This paper presents an optimization model for the placement of safety stocks in multi-echelon supply networks using the Guaranteed-service Model. Our model handles complex network topologies and multiple products while examining the impact of service level and service time on total costs, formulated with mixed-integer linear programming. We utilize a unique network dataset acquired through data mining of financial databases to generate scenarios that reflect the complexities of real-world supply networks of five major automotive corporations. Experimental results validate the effectiveness of a dynamic-programming based solver in obtaining optimal solutions within large general network topologies. Furthermore, a sensitivity analysis reveals a negative correlation between safety stock costs and the maximum allowed service and a positive correlation between safety stock costs and the service level.
Recommended Citation
Rolf, Benjamin; Lavassani, Kayvan; Lang, Sebastian; and Reggelin, Tobias, "Optimizing Safety Stock Placement in Large Real-World Automotive Supply Networks Using the Guaranteed-Service Model" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 4.
https://aisel.aisnet.org/hicss-57/da/digital_twins/4
Optimizing Safety Stock Placement in Large Real-World Automotive Supply Networks Using the Guaranteed-Service Model
Hilton Hawaiian Village, Honolulu, Hawaii
This paper presents an optimization model for the placement of safety stocks in multi-echelon supply networks using the Guaranteed-service Model. Our model handles complex network topologies and multiple products while examining the impact of service level and service time on total costs, formulated with mixed-integer linear programming. We utilize a unique network dataset acquired through data mining of financial databases to generate scenarios that reflect the complexities of real-world supply networks of five major automotive corporations. Experimental results validate the effectiveness of a dynamic-programming based solver in obtaining optimal solutions within large general network topologies. Furthermore, a sensitivity analysis reveals a negative correlation between safety stock costs and the maximum allowed service and a positive correlation between safety stock costs and the service level.
https://aisel.aisnet.org/hicss-57/da/digital_twins/4