Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions

Hamid Ahaggach (), Lylia Abrouk and Eric Lebon
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Hamid Ahaggach: LIB Laboratory, University of Burgundy, 21000 Dijon, France
Lylia Abrouk: LIB Laboratory, University of Burgundy, 21000 Dijon, France
Eric Lebon: Syartec, 13290 Aix-en-Provence, France

Forecasting, 2024, vol. 6, issue 3, 1-31

Abstract: In a dynamic business environment, the accuracy of sales forecasts plays a pivotal role in strategic decision making and resource allocation. This article offers a systematic review of the existing literature on techniques and methodologies used in forecasting, especially in sales forecasting across various domains, aiming to provide a nuanced understanding of the field. Our study examines the literature from 2013 to 2023, identifying key techniques and their evolution over time. The methodology involves a detailed analysis of 516 articles, categorized into classical qualitative approaches, traditional statistical methods, machine learning models, deep learning techniques, and hybrid approaches. The results highlight a significant shift towards advanced methods, with machine learning and deep learning techniques experiencing an explosive increase in adoption. The popularity of these models has surged, as evidenced by a rise from 10 articles in 2013 to over 110 by 2023. This growth underscores their growing prominence and effectiveness in handling complex time series data. Additionally, we explore the challenges and limitations that influence forecasting accuracy, focusing on complex market structures and the benefits of extensive data availability.

Keywords: sales forecasting; predictive analytics; machine learning; time series analysis; regression analysis; artificial intelligence (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
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