The value of data, machine learning, and deep learning in restaurant demand forecasting: Insights and lessons learned from a large restaurant chain

Abstract

The restaurant industry has been slow to adopt analytics for the supply chain, operations, and demand forecasting, with limited research on this sector. The COVID-19 pandemic's significant impact on the restaurant industry, one of the hardest-hit sectors, has underscored the need for digital technologies and advanced analytics for managing supply chains and making operational decisions. This paper presents a collaborative study with one of the largest restaurant chains in the United States, highlighting the value of advanced data analytics in forecasting restaurant demand. The study offers insights into the benefit of integrating external data, including macroeconomic and pandemic-related factors, into demand forecasting. It explores traditional machine learning algorithms and state-of-the-art deep learning architectures, evaluating their effectiveness in the context of the restaurant industry. The paper further discusses the implications of utilizing advanced forecasting models, providing valuable insights for the restaurant industry in the face of supply chain disruptions and pandemics.