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.

中文翻译：

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数据、机器学习和深度学习在餐厅需求预测中的价值：大型连锁餐厅的见解和经验教训


餐饮业在采用供应链、运营和需求预测分析方面进展缓慢，对该行业的研究有限。 COVID-19 大流行对受灾最严重的行业之一的餐饮业产生了重大影响，凸显了对数字技术和高级分析来管理供应链和制定运营决策的需求。本文介绍了与美国最大的连锁餐厅之一的合作研究，强调了高级数据分析在预测餐厅需求方面的价值。该研究深入探讨了将外部数据（包括宏观经济和流行病相关因素）整合到需求预测中的好处。它探索传统的机器学习算法和最先进的深度学习架构，评估它们在餐饮业背景下的有效性。本文进一步讨论了利用先进预测模型的影响，为餐饮业面对供应链中断和流行病提供了宝贵的见解。