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 t