International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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An Application of Machine Learning in Retail for Demand Forecasting

  • Muhammad Umer Farooq (Department of Computer Science and Information Technology, NED University of Engineering and Technology) ;
  • Mustafa Latif (Department of Software Engineering, NED University of Engineering and Technology) ;
  • Waseemullah (Department of Computer Science and Information Technology, NED University of Engineering and Technology) ;
  • Mirza Adnan Baig (Department of Computer Science, IQRA University Karachi Pakistan) ;
  • Muhammad Ali Akhtar (Department Of Computer And Information Systems Engineering, NED University of Engineering and Technology) ;
  • Nuzhat Sana (Department of Computer Science and Information Technology, NED University of Engineering and Technology)
  • Received : 2023.09.05
  • Published : 2023.09.30
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Abstract

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

Keywords

References

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