Predicting and Validating the Impact of Energy Price Fluctuations on Food Inflation: A Machine Learning-Based Approach

Abstract

Supply chain operations and the use of energy are inextricably interwoven. The supply chains, especially those operating in the food sector, which experience high competition, high implied demand uncertainty, low profit margin etc., are trying very hard to minimize their operational costs to become efficient in the marketplace. On the contrary, inflation has been one of the key issues in food supply chain operations. Irrespective of whether the food product is need-based or demand-based, inflation of essential commodities is experienced by consumers throughout the year. This paper examines how energy price fluctuations have impacted food supply chain prices in India. The time series data of energy and food resources have been modeled and validated using Machine Learning (ML)-based SARIMAX algorithm to find that high-speed Diesel (HSD) impacts food inflation the most, keeping aside coal. In push-need-based food supply chains, the impact of HSD is relatively higher than in push-demand-based food supply chains.