Skip to main content

Ph.D. viva-voce examination of Mr. Siddhartha Paul

Name: Mr. Siddhartha Paul, Roll No. 10i19007, (Teaching Assistantship category)

Department: Industrial Engineering and Operations Research

Ph.D. Supervisor: Prof. J. Venkateswaran

Venue and Time: Friday, 30-11-2018 at 11.00 A.M. in IEOR Seminar Room.

Title of Thesis: "Medicine Supply Chain Management in Response to an Infectious Disease Outbreak".

Abstract: The overall goal of this dissertation is twofold, (i) to model and analyse the effect
of medicine supply chain on the dynamics of infectious diseases (a.k.a. epidemics); (ii)
improving the drug supply chain management to minimize the impact of an epidemic.

The effect of drug shortages on the infectivity parameter estimation of antiviral-treatable disease epidemics is evaluated using an illustrative dataset. Simulation-based analyses show that a given outbreak can be caused by either (i) a high infectivity parameter with a sufficient and timely supply of medicines or (ii) a low infectivity parameter and poor supply of medicines. Also, the use of a stand-alone epidemic model is found to overestimate the disease transmissibility. A compartmental epidemic model is integrated with multi-echelon supply chain models to further investigate the impact of medicine supply chain on the epidemic dynamics. In integrated models, medicine demands for the supply chain are generated from the disease model, and the medicine supply rate controls the recovery rate of patients in the disease model. It is found that supply chain aspects have a significant effect on the epidemic dynamics. Some improvement schemes for supply chain management are also highlighted.

All plausible behaviours of the integrated epidemic-supply chain model, under deep parameter uncertainty, are explored using exploratory modelling and analysis. The experiment is conducted for six different categories of epidemics, classified based on the disease transmissibility. Further, scenario discovery analysis has been used to identify the key input parameters’ combination and their respective ranges responsible for a specific worst-case scenario. Finally, a couple of robust inventory management policies are proposed to alleviate an epidemic.

A generic framework is developed, comprising a hospital, forecasting, and an inventory management module for medicine stock management of any regional level hospital during both regular and epidemic season. The performance of the proposed dynamic disease diffusion models with parameter update is compared against a traditional naïve forecasting policy. The disease diffusion forecasting models are found to outperform the naïve policy in terms of epidemic alleviation and inventory savings. Further analyses are carried out on choosing suitable ordering policies, quantifying the effect of key factors like safety stockpile, epidemic declaration threshold on epidemic dynamics. The findings of this research are summarized and the managerial implications are derived for practical applications.

News Category