Optimization Of An Inbound Logistics Network: An Automotive Case Study
Open Access
- Author:
- Pamaraju, Aishwarya
- Graduate Program:
- Industrial Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- None
- Committee Members:
- Arunachalam Ravindran, Thesis Advisor/Co-Advisor
- Keywords:
- logistics
inbound
network
cost
delivery time
transportation
inventory
automotive
shipping
goal programming - Abstract:
- The design, execution and coordination of a logistics network are essential activities of any supply chain. Although outbound logistics is very important to ensure high responsiveness for customer satisfaction, improper planning of the inbound process could cause disruptions further down the supply chain and affect the overall performance. The two most important factors to be considered for inbound logistics planning are: overall costs and the delivery time. This thesis aims at building an optimization model to aid an Indian automotive OEM in determining the most suitable option to ship material into their plant. The firm currently receives a variety of material at the plants via direct shipments from remotely located suppliers leading to high inventory holding costs. To address the issues of limited plant capacity and increased delivery lead times, including a warehouse and distributors located closer to the plant is proposed. The bi-criteria mixed integer linear program is developed to help determine if: (1) the plants should receive material from the suppliers by direct delivery or (2) from the distributors. The objectives of the model are to minimize overall costs (inventory and transportation) and lead time. The model is solved using Non Pre-emptive Goal Programming by assigning a set of weights to either objective. The model determines the most suitable option to ship material to the plant in the shortest time and at the lowest cost to the company. A scenario analysis is also performed to consider the uncertainty in demand. The most frequently selected shipping option is selected as the best strategy for a given material. The model is illustrated using a combination of real and simulated data from the OEM.