Discipline: Technology and Engineering
Subcategory: Civil/Mechanical/Manufacturing Engineering
Niegel Middleton - Savannah State University
Unlike traditional supply chains, cold chain management adds an additional level of complexity in the form of temperature and humidity control that’s necessary for the products to be uncompromised. These are also referred to as cold chain management. The information in the form of temperature and humidity status for every certain period of time has to recorded in order to verify if the product/shipment is within the specifications dictated by Food and Drug Administration. The temperature, and humidity of the pharmaceutical materials or food, and finished product must be sustained in a way that ensures the quality and stability of the products. A slight change in temperature and humidity of the product during transit or storage could result in complete rejection of the shipment, or discarding the product. Often times the information is not passed on to the corresponding party that needs to take action in case of a lapse in temperature or humidity, which implies information is decentralized, thus resulting in too late to take action and thus shipment rejection. This causes additional burden on the supply chain, and prompts for increased inventory storage thus resulting in overall cost increase of the supply chain. The objective of this research is to focus on the implications of decentralized (local) versus centralized (global) information sharing. Finding the optimal level of inventory at each stage of the supply chain in presence of decentralized and centralized information sharing for a given service level. An objective function is developed for the multi-echelon which is a function of cost of ownership and information sharing. A simulation optimization technique is utilized to determine optimal inventory level under decentralized and centralized information sharing. The multi-echelon cold chain optimization is studied under stochastic demand and probabilistic information sharing, and constant capacity. The results provide results of optimal target stock levels at various stages of multi-echelon under various scenarios, for a target service level, with aforementioned stochastic conditions. Dynamic equations for network are developed and a MontiCarlo Simulation based Optimization using ARENA and Opt Quest is conducted to obtain the results. The future research will focus on providing optimal stock level by accounting for stochastic capacity and lead time between each echelon.
Funder Acknowledgement(s): This study was supported, in part, by a grant from NSF awarded in 2015 to Jonathan Lambright, Dean of College of Sciences and Technology and Suman Niranjan, Associate Professor for Operations Management, Director for Interdisciplinary Transportation Studies, Savannah State University, Savannah, GA.
Faculty Advisor: Suman Niranjan, email@example.com
Role: Conducting simulations for various scenarios, partially developing mathematical equations.