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Post-script-Retail forecasting: Research and practice.

Robert Fildes1, Stephan Kolassa2, Shaohui Ma3

  • 1Lancaster Center for Marketing Analytics and Forecasting, University Management School, Lancaster, United Kingdom.

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|October 11, 2022
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Summary
This summary is machine-generated.

This study updates retail forecasting research, incorporating COVID-19 impacts and new machine learning (ML) algorithms. It provides updated conclusions and challenges for retail demand forecasting practices.

Keywords:
COVID-19DisruptionInstabilityMachine learningOmni-retailingOnline retailStructural change

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Area of Science:

  • Business Analytics
  • Operations Research
  • Artificial Intelligence

Background:

  • The 2019 review "Retail forecasting: Research and practice" provides foundational knowledge.
  • The COVID-19 pandemic significantly disrupted retail operations and demand patterns.
  • Recent advancements in machine learning offer new tools for forecasting.

Purpose of the Study:

  • To update the 2019 review on retail forecasting.
  • To incorporate the impact of the COVID-19 pandemic.
  • To analyze the role of machine learning in retail demand forecasting.

Main Methods:

  • Literature review and synthesis.
  • Analysis of recent research trends in retail forecasting.
  • Evaluation of machine learning algorithm applications in retail.

Main Results:

  • Significant shifts in retail demand forecasting due to the pandemic.
  • Demonstrated effectiveness of machine learning algorithms in improving forecast accuracy.
  • Identification of new research gaps and practical challenges.

Conclusions:

  • Retail forecasting requires adaptation to pandemic-induced volatility.
  • Machine learning presents a transformative opportunity for retail demand prediction.
  • Future research should focus on hybrid models and explainable AI in retail contexts.