Demand forecasting in restaurants using machine learning and statistical analysis

Tanizaki, Takashi; Hoshino, Tomohiro; Shimmura, Takeshi; Takenaka, Tohru

doi:10.1016/j.procir.2019.02.042

Cited by 52 publications

(23 citation statements)

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“…The comparison of ML techniques to forecast curated restaurant sales is a common research question and can be seen in several recent works [14][15][16][17]. Two additional recent, non-restaurant forecasting with ML problems are also examined [11,18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first important difference in researched methods is the forecasting horizon window used. Many papers either used an unclear horizon window or made forecasts of only one time step at a time [14][15][16][17][18]. Only one paper increased the forecast horizon beyond one time step [11], so we consider forecasting one week of results the main contribution of this research.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The premiere difference, which we aim to highlight in this work, are the models selected for testing. Some papers test only traditional models such as linear regression, decision trees, or support vector machines [15][16][17] with no mention of recurrent neural networks (RNN). One paper only includes RNN models without any other family of statistical or ML models for comparison [11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some features are easily mined from the raw data, some features are generated from summary statistics, and some features come from outside the dataset-such as special holiday events. It is of note that we do not use weather metrics as feature labels in favor of testing holidays and summary statistics on their own merit, although this is generally accepted as a very good method for increasing forecast accuracy [14][15][16]18]. We define or otherwise summarize all features in Table 1.…”

Section: Feature Definitionsmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning Based Restaurant Sales Forecasting

Schmidt

Kabir

Hoque

2022

MAKE

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To encourage proper employee scheduling for managing crew load, restaurants need accurate sales forecasting. This paper proposes a case study on many machine learning (ML) models using real-world sales data from a mid-sized restaurant. Trendy recurrent neural network (RNN) models are included for direct comparison to many methods. To test the effects of trend and seasonality, we generate three different datasets to train our models with and to compare our results. To aid in forecasting, we engineer many features and demonstrate good methods to select an optimal sub-set of highly correlated features. We compare the models based on their performance for forecasting time steps of one-day and one-week over a curated test dataset. The best results seen in one-day forecasting come from linear models with a sMAPE of only 19.6%. Two RNN models, LSTM and TFT, and ensemble models also performed well with errors less than 20%. When forecasting one-week, non-RNN models performed poorly, giving results worse than 20% error. RNN models extended better with good sMAPE scores giving 19.5% in the best result. The RNN models performed worse overall on datasets with trend and seasonality removed, however many simpler ML models performed well when linearly separating each training instance.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Feature Definitionsmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Based Restaurant Sales Forecasting

Schmidt

Kabir

Hoque

2022

MAKE

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“…• Sales forecasting (Lasek et al 2016;Xinliang and Dandan 2017) and revenue management (Noone and Maier 2015) • Demand prediction and production planning in quick service restaurants (Noone and Coulter 2012) • Demand forecasting in restaurants (Tanizaki et al 2019) However, the abilities of AI and robots in performing human tasks varies depending on the specific task and the required skills for mastering the activities of this task. The potential of AI technologies should not be assessed by looking at the occupation level.…”

Section: Automation Potentialmentioning

confidence: 99%

AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry

Blöcher

Alt

2020

Electron Markets

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The restaurant technology market is rapidly evolving and is transforming the restaurant business as a significant sector of tourism and hospitality. Enabled by artificial intelligence (AI), mobile apps, kiosks and chatbots revolutionize the guest experience and robots automate restaurant operations. Despite the increasing interest, the use of AI and robotics in restaurants is still in its early stage and restaurant managers are seeking guidance to leverage these technologies for service excellence. In this high-contact service sector, emotional skills need to be balanced with the possible automation potentials. The present research analyzes the current state of AI and robotics in the restaurant sector and proposes a systematic identification of process innovation potentials. For this purpose, a market analysis of the European AI and robotics market for restaurant operations is conducted, which yields a first knowledge base for future research and conceptual work. Besides detailed empirical data, a reference process is developed for leveraging new technologies for process innovation.

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Realising Newspaper Sales by Using Statistic Methods

Doğan

Gürcan

2020

Advances in Intelligent Systems and Computing

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Demand forecasting in restaurants using machine learning and statistical analysis

Cited by 52 publications

References 0 publications

Machine Learning Based Restaurant Sales Forecasting

Machine Learning Based Restaurant Sales Forecasting

AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry

Realising Newspaper Sales by Using Statistic Methods

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