Ismael Gómez-Talal, Lydia González-Serrano, J. Rojo-álvarez, Pilar Talón-Ballestero
{"title":"避免餐厅小票中的食物浪费:大数据管理工具","authors":"Ismael Gómez-Talal, Lydia González-Serrano, J. Rojo-álvarez, Pilar Talón-Ballestero","doi":"10.1108/jhtt-01-2023-0012","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThis study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.\n\n\nDesign/methodology/approach\nA system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.\n\n\nFindings\nThe study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.\n\n\nResearch limitations/implications\nThis study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.\n\n\nOriginality/value\nThe value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.\n","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Avoiding food waste from restaurant tickets: a big data management tool\",\"authors\":\"Ismael Gómez-Talal, Lydia González-Serrano, J. Rojo-álvarez, Pilar Talón-Ballestero\",\"doi\":\"10.1108/jhtt-01-2023-0012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThis study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.\\n\\n\\nDesign/methodology/approach\\nA system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.\\n\\n\\nFindings\\nThe study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.\\n\\n\\nResearch limitations/implications\\nThis study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.\\n\\n\\nOriginality/value\\nThe value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.\\n\",\"PeriodicalId\":51611,\"journal\":{\"name\":\"Journal of Hospitality and Tourism Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hospitality and Tourism Technology\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1108/jhtt-01-2023-0012\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HOSPITALITY, LEISURE, SPORT & TOURISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hospitality and Tourism Technology","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/jhtt-01-2023-0012","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
引用次数: 0
摘要
目的本研究旨在通过分析餐厅小票提供的顾客销售信息,获得指导易腐产品销售的宝贵见解,并根据顾客需求优化产品采购,从而解决全球餐厅食物浪费问题。设计/方法/途径创建了一个基于无监督机器学习(ML)数据模型的系统,以提供一个简单且可解释的管理工具。该系统基于两个要素进行分析:首先,它利用多成分分析、引导重采样和 ML 领域描述,整合并可视化从票据中提取的信息特征之间的相互关系和非琐碎关系。其次,它以彩色编码表格的形式呈现统计相关关系,为餐厅经理提供与食物浪费相关的建议。研究结果该研究确定了产品与特定月份客户销售额之间的关系。其他票据要素也有关联,如产品与日、小时或功能区,以及产品与产品(交叉销售)。大数据(BD)技术帮助分析了餐厅票据,并获得了产品销售行为信息。研究局限性/意义本研究利用 BD 和无监督 ML 模型解决了餐厅中的食物浪费问题。尽管在票据信息和产品细节方面存在局限性,但它为关系分析、交叉销售、生产力和深度学习应用提供了研究机会。原创性/价值这项工作的价值和原创性在于应用 BD 和无监督 ML 技术分析餐厅票据并获取产品销售行为信息。更好的销售预测可以根据客户需求调整产品采购,减少食物浪费,优化利润。
Avoiding food waste from restaurant tickets: a big data management tool
Purpose
This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.
Design/methodology/approach
A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.
Findings
The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.
Research limitations/implications
This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.
Originality/value
The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.
期刊介绍:
The Journal of Hospitality and Tourism Technology is the only journal dedicated solely for research in technology and e-business in tourism and hospitality. It is a bridge between academia and industry through the intellectual exchange of ideas, trends and paradigmatic changes in the fields of hospitality, IT and e-business. It covers: -E-Marketplaces, electronic distribution channels, or e-Intermediaries -Internet or e-commerce business models -Self service technologies -E-Procurement -Social dynamics of e-communication -Relationship Development and Retention -E-governance -Security of transactions -Mobile/Wireless technologies in commerce -IT control and preparation for disaster -Virtual reality applications -Word of Mouth. -Cross-Cultural differences in IT use -GPS and Location-based services -Biometric applications -Business intelligence visualization -Radio Frequency Identification applications -Service-Oriented Architecture of business systems -Technology in New Product Development