Coffee shop menu item recipe with ingredient details

This Dataset integrates two datasets to develop a new one, designed to simulate the monthly workflow of a local coffee shop and optimize inventory and revenue generation per menu item. The first dataset selected for the current study is a list of coffee shop menu items and their corresponding recipes, presented on the Kaggle database under the “Food Ingredients and Recipes Dataset with Images” description [1]. The noted dataset is created by scraping from the Epicurious Website and contains a Comma Separated Value (CSV) file consisting of 13,582 images and data rows, respectively. The data columns are food names, ingredients, cooking recipes, food images, and the ingredients after being processed and cleaned. The second dataset utilized in this study is the monthly distribution of demand for food and drinks sales of a coffee shop which is presented in the Kaggle database under the description of “Bakery Sales for Food & Drinks” [2]. This dataset replicates a real-world scenario in which a coffee shop aims to maximize sales profit while accounting for the current market demand for its products. Building on these datasets, a new dataset was specifically developed for the purposes of providing more details for future optimization problems. The newly constructed dataset includes 34 unique ingredients and 40 products commonly offered on a local coffee shop menu. It covers various product categories, such as espresso-based drinks, teas, cakes, milkshakes, smoothies, and other beverages, along with their corresponding ingredient amounts. The methodology for creating this dataset involved several key steps. First, the original datasets were filtered to include only items relevant to a typical coffee shop setting. Products were selected based on their popularity and practicality for daily sales, ensuring a comprehensive range of beverages and cakes. The corresponding ingredients were then identified, grouped to avoid redundancy, and refined down to 34 essential ingredients. Each product’s ingredient list includes specific amounts, facilitating accurate inventory management. Finally, the dataset was aligned with market demand trends from the sales data to ensure that product selection would reflect real-world preferences and maximize relevance. This newly created dataset provides a clearer understanding of the relationship between menu items and their ingredients, aiding in the improvement of inventory management and revenue optimization. By offering insights into the specific amounts of ingredients tied to each product, it enables better forecasting of inventory needs and helps maximize sales by aligning product offerings with ingredient availability and customer demand. [1] S. Goel, A. Desai, and Tanvi, “Food Ingredients and Recipes Dataset with Images,” Kaggle, 2021. https://www.kaggle.com/datasets/pes12017000148/food-ingredients-and-recipe-dataset-with-images (accessed Jun. 09, 2023) [2] J. Alexander, “Bakery Sales for Food and Drinks,” Kaggle, 2019. https://www.kaggle.com/datasets/alexanderjohn/bakery-sales-forfooddrink (accessed Jun. 09, 2023)本数据集整合两份现有数据集以构建全新数据集，旨在模拟本地咖啡店月度运营流程，并优化各菜单单品的库存管理与营收表现。 本研究选用的首份数据集为咖啡店菜单单品及其对应配方列表，该数据集在Kaggle平台以“带图片的食品配料与配方数据集（Food Ingredients and Recipes Dataset with Images）”为名发布[1]。该数据集由Epicurious网站爬取得到，包含13582条数据行与13582张图片，以逗号分隔值（Comma Separated Value, CSV）格式存储。其数据字段包括食品名称、配料成分、烹饪配方、食品图片以及经过处理与清洗后的配料信息。 本研究使用的第二份数据集为某咖啡店食品与饮品月度销售需求分布数据，该数据集在Kaggle平台以“烘焙店食品与饮品销售额（Bakery Sales for Food & Drinks）”为名发布[2]。该数据集还原了真实商业场景：咖啡店在兼顾当前市场对其产品的需求的同时，力求实现销售利润最大化。 基于上述两份数据集，本研究专为未来优化问题的细节补充开发了全新数据集。新构建的数据集涵盖本地咖啡店菜单中常见的34种核心配料与40款产品，覆盖浓缩基饮品、茶饮、蛋糕、奶昔、冰沙及其他饮品等多个产品品类，并附带各产品对应的配料用量信息。 该数据集的创建方法包含以下关键步骤：首先，对原始数据集进行筛选，仅保留与典型咖啡店运营相关的单品；基于日常销售的受欢迎程度与实用性挑选产品，确保饮品与蛋糕品类的覆盖全面性。随后，识别各产品对应的配料，进行归组以避免冗余，并精简至34种核心配料。每款产品的配料列表均标注具体用量，便于实现精准库存管理。最后，将数据集与销售数据中的市场需求趋势对齐，确保所选产品能够反映真实消费偏好，提升数据集的相关性。 本全新数据集可更清晰地呈现菜单单品与其配料之间的关联，助力优化库存管理与营收提升。通过提供各产品绑定的具体配料用量信息，该数据集能够辅助更精准地预测库存需求，并通过匹配产品供应、配料可得性与消费者需求，助力实现销售收益最大化。 [1] S. Goel、A. Desai与Tanvi，“带图片的食品配料与配方数据集（Food Ingredients and Recipes Dataset with Images）”，Kaggle，2021年。https://www.kaggle.com/datasets/pes12017000148/food-ingredients-and-recipe-dataset-with-images（2023年6月9日访问） [2] J. Alexander，“烘焙店食品与饮品销售额（Bakery Sales for Food & Drinks）”，Kaggle，2019年。https://www.kaggle.com/datasets/alexanderjohn/bakery-sales-forfooddrink（2023年6月9日访问）