Optimizing The Public Distribution System Supply Chain In India

Abstract

Food is an essential human requirement, and food availability and security are socioeconomic concerns closely related to comprehensive growth and human wellness. In India, this is accomplished through the Public Distribution System (PDS) that was in place since before independence. A thorough review of the literature showed that the existing Supply Chain (SC) of PDS is both inefficient and inadequate with enormous costs associated with carrying food grains from the source to the targeted recipients. Regarding the operation of Fair Price Shops (FPS), the current study makes an effort to identify the different challenges that are connected to these FPS operations and the issues that customers encounter in FPSs. It also examines the impact of PDS policy changes in the light of PDS reforms and novel alternatives to PDS. Due to leaks, corruption, ineffective Government operations, the lack of food grains in the FPS, long waiting queues in FPS and poor management, the PDS has drawn a large amount of criticism from the general public during the delivery of goods. This thesis looks into the matters mentioned above, and the following are some of its key contributions. We demonstrate an efficient queue management system in FPS by employing the M/M/C queueing model. The primary objective is to lower wait times for customers in FPS while using the fewest servers possible. It shows the implementation of simulation modelling as an approach for optimizing the number of servers in a particular FPS in Kerala, India. The developed model presents the outcomes of system elements such as arrival rate, waiting period, and server utilization. Later we concentrated on the challenges faced by Indian PDS during pandemic in handling uncertainties in demand and market trends. We presented a modelling and forecasting technique used for identifying rice sales demand in a specific FPS in Kerala, India by applying a time series method. Additionally, we demonstrated how past demand information can be utilized for forecasting demand in the future