Data-Driven Retail Excellence: Machine Learning for Demand Forecasting and Price Optimization

Authors

  • Vinit Taparia Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India
  • Piyush Mishra Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India
  • Nitik Gupta Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India
  • Hitesh Chandiramani Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India

DOI:

https://doi.org/10.13052/jgeu0975-1416.1213

Keywords:

Demand Forecasting, Price optimization, Inventory, Linear Regression, price elasticity, feasibility index

Abstract

Demand forecasting and price optimization are critical aspects of profitability for retailers in a supply chain. Retailers need to adopt innovative strategies to optimize pricing and increase profitability. This research paper proposes a price optimization approach for retailers using machine learning. The approach involves using linear regression to forecast demand incorporating price as an input, followed by price optimization taking into account inventory and perishability costs. The feasibility of using linear regression for price optimization for Stock Keeping Units (SKUs) is assessed using a feasibility index. The linear regression can predict the demand more accurately (23% Mean Absoulute Percentage Error (MAPE)) compared to exponential smoothing with optimised smoothing constant (47.09% MAPE) for 1000 SKUs. Also, the feasibility index can segregate the SKUs with an accuracy of 99%. The machine learning-based demand forecasting can assist retailers in accurately predicting customer demand and improving pricing decisions, while the feasibility index enables retailers to identify SKUs that require alternative pricing strategies.

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Author Biographies

Vinit Taparia, Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India

Vinit Taparia did his B.Tech in Mechanical Engineering from Malaviya National Institute of Technology, Jaipur, Rajasthan. Currently he is working on Gas and Power Projects. His research interests include supply chain management, demand planning, inventory management, and renewable energy sources.

Piyush Mishra, Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India

Piyush Mishra completed his B.Tech in Mechanical Engineering from Malaviya National Institute of Technology Jaipur, Rajasthan. He is currently employed in Reliance Industries in FCC unit of DTA Refinery. His research interests include supply chain management, demand planning, inventory management and planning.

Nitik Gupta, Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India

Nitik Gupta completed his B.Tech in Mechanical engineering from Malaviya National Institute of Technology, Jaipur, Rajasthan. He is currently employed in Fernweh Group, a private equity firm as an Analyst focusing on Industrials sector and its sub-sectors. His research interests include supply chain management, demand planning & inventory management.

Hitesh Chandiramani, Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, India

Hitesh Chandiramani completed his B.Tech in Mechanical engineering from Maharishi Arvind Institute of Engineering and Technology, Jaipur, Rajasthan. He completed his Master’s in Industrial Engineering and Management from UD, RTU, Kota. His research interests include Reliability Engineering, Operations Research, and Applied Statistics.

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Published

2023-11-20

How to Cite

Taparia, V., Mishra, P., Gupta, N., & Chandiramani, H. (2023). Data-Driven Retail Excellence: Machine Learning for Demand Forecasting and Price Optimization. Journal of Graphic Era University, 12(01), 37–52. https://doi.org/10.13052/jgeu0975-1416.1213

Issue

2024: Vol 12 Iss 1

Section

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