The IUP Journal of Applied Economics
Predicting and Validating the Impact of Energy Price Fluctuations on Food Inflation: A Machine Learning-Based Approach

Article Details
Pub. Date : July, 2023
Product Name : The IUP Journal of Applied Economics
Product Type : Article
Product Code : IJAE010723
Author Name : Pramod K Mishra, B Kamaiah, Chinmaya Behera, Pratap K Jena and B Raja Shekhar
Availability : YES
Subject/Domain : Economics
Download Format : PDF Format
No. of Pages : 21



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-needbased food supply chains, the impact of HSD is relatively higher than in push-demandbased food supply chains.


Supply chains provide an end-to-end solution to firms, irrespective of the nature of the product or service they deal with. The basic objective of Supply Chain Management (SCM) is to minimize system-wide costs while satisfying service-level requirements. The firms that have managed their supply chains efficiently are getting better benefits than the ones that have failed to do so and are experiencing the bullwhip effect. Food supply chains are mostly push-based (e.g., cereals and pulses), where the demand is relatively fixed, implying that the