Vol. 1 No. 2 (2021): African Journal of Artificial Intelligence and Sustainable Development
Articles

AI-Enabled Demand Sensing and Forecasting in Retail: Integrating IoT and Big Data Analytics

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  • Sudharshan Putha
Sudharshan Putha
Independent Researcher and Senior Software Developer, USA
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Published 10-06-2021

Keywords

  • demand sensing,
  • IoT
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Sudharshan Putha, “AI-Enabled Demand Sensing and Forecasting in Retail: Integrating IoT and Big Data Analytics”, African J. of Artificial Int. and Sust. Dev., vol. 1, no. 2, pp. 300–341, Jun. 2021, Accessed: Nov. 06, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/139

Abstract

In the contemporary retail landscape, the rapid evolution of consumer expectations and market dynamics necessitates advanced methodologies for demand forecasting and inventory management. This paper delves into the transformative potential of Artificial Intelligence (AI) in demand sensing and forecasting, particularly through the integration of Internet of Things (IoT) technologies and big data analytics. The central thesis of this research is that AI-enabled demand sensing, augmented by IoT and big data analytics, offers a superior approach to enhancing the accuracy and responsiveness of demand forecasts in the retail sector.

AI technologies have progressively demonstrated their ability to refine demand forecasting by leveraging complex algorithms that analyze vast quantities of data. These algorithms, when trained on historical sales data, market trends, and real-time inputs, can discern intricate patterns and trends that traditional forecasting methods may overlook. The integration of AI with IoT further amplifies these capabilities by providing real-time, granular data from a myriad of sources such as smart shelves, point-of-sale systems, and consumer mobile applications. This real-time data collection is crucial for achieving accurate demand sensing, as it allows retailers to adapt swiftly to fluctuations in consumer behavior and market conditions.

The incorporation of big data analytics into this framework introduces another layer of sophistication. Big data analytics involves the aggregation and analysis of large and diverse data sets, which can include transactional data, social media interactions, and external economic indicators. By applying advanced analytical techniques to these extensive data sets, retailers can gain deeper insights into consumer preferences and market trends. This, in turn, enhances the precision of demand forecasts and facilitates more informed decision-making.

The synergy between AI, IoT, and big data analytics fosters a more dynamic and responsive approach to demand forecasting. AI algorithms can process real-time data from IoT devices to identify shifts in consumer demand as they occur, rather than relying solely on historical patterns. This capability is particularly valuable in a retail environment where rapid changes in consumer preferences and external factors can significantly impact inventory levels and sales performance.

Moreover, the integration of these technologies supports more effective inventory management. With accurate demand forecasts, retailers can optimize stock levels, reduce excess inventory, and minimize stockouts. This not only enhances operational efficiency but also improves customer satisfaction by ensuring product availability and timely replenishment.

The implementation of AI-enabled demand sensing and forecasting systems, however, is not without challenges. Retailers must navigate issues related to data quality, integration, and security. Ensuring the accuracy and reliability of data collected from IoT devices is paramount, as erroneous data can lead to flawed forecasts and operational inefficiencies. Additionally, integrating disparate data sources and maintaining data privacy and security are critical concerns that require robust governance frameworks.

Case studies from leading retailers demonstrate the practical applications and benefits of this approach. For instance, retailers that have adopted AI-driven demand forecasting systems report significant improvements in forecasting accuracy, operational efficiency, and overall profitability. These success stories highlight the potential for AI, IoT, and big data analytics to revolutionize demand forecasting and inventory management in the retail sector.

AI-enabled demand sensing and forecasting, when integrated with IoT and big data analytics, represents a paradigm shift in retail analytics. This approach enhances the accuracy of demand forecasts, improves inventory management, and enables retailers to respond more effectively to market changes. Despite the challenges associated with implementation, the benefits of adopting these advanced technologies are substantial. As the retail industry continues to evolve, the integration of AI, IoT, and big data analytics will play a pivotal role in shaping the future of demand forecasting and inventory management.

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