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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.
International Journal of Forecasting
|October 11, 2022
Summary
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.
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.
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