Published Paper Details:

Shrinkflation--the practice of reducing product size or quantity while maintaining price--has become increasingly prevalent in the Fast-Moving Consumer Goods (FMCG) sector. With a focus on artificial intelligence (AI) technologies, this paper provides a comprehensive review of recent advancements in shrinkflation detection. Conventional methods of identifying shrinkflation, like manual comparisons and user feedback, usually fall short when confronted with minor variations and the vast volume of products. However, significant advancements in image recognition, natural language processing, and data analytics are made feasible by artificial intelligence (AI), which enables more accurate and timely detection of shrinkflation activities. From its early applications to the most recent developments that help businesses and consumers alike, the paper examines how AI has evolved in this setting. It also looks at the challenges AI faces, including as issues with data quality, the intricacy of product variations, and moral conundrums. Through case studies, the study illustrates how AI may be used effectively to detect shrinkflation in the FMCG sector. It also discusses potential future developments that could enhance these technologies even further. The findings suggest that even though AI has greatly enhanced the ability to detect shrinkflation, further advancements and a better integration with other technologies are required to maintain market openness and customer confidence.