Ethical Challenges in Demand Prediction: A Case Study in the Wholesale Grocery Sector

Abstract

Artificial Intelligence (AI) has emergedas a transformative tool in inventory management and demand prediction within the wholes ale grocerysector. By leveraging machine learning algorithms, businesses can analyze historical sales data, market trends, and seasonal variations to optimize inventory levels, reducing overstock and stockouts. AI-drivendemand prediction models provide accurate forecasts, enabling whole salers to anticipate customer needs and streamline supply chain operations. Thisarticle examines the ethical challenges associated with developing and implementing AI-driven demand prediction models in the wholesale grocery sector. As businesses seek to optimize their operations through artificial intelligence, significant ethical concerns arise that must be addressed to ensure responsible and fair implementation. This case study highlights the main ethical challenges identified in a grocery wholesaler, focusing on issues such as transparency, accountability, fairness, and human control. Through the analysis of aspecific demand prediction model, we discuss how these ethical concerns not only influence user acceptance of the model but also impact operational efficiency and customer satisfaction. The article aims to contribute to the ongoing dialogue on ethics in data science, providing insights and recommendations for companies looking to adopt predictive technologies ethically.