蔬菜类菜品预订偏好数据

蔬菜类菜品预订偏好数据对于餐饮企业及其供应链管理至关重要。首先，这些数据使企业能够识别顾客对各类蔬菜菜品（如绿叶菜、根茎类、食用菌等）的偏好，从而优化蔬菜菜品的菜单设置，满足市场需求，提升顾客满意度。其次，通过分析预订率及其变化，企业能够预测蔬菜食材的需求量，进而调整采购计划，减少库存积压和浪费，提高库存管理效率。此外，预订偏好数据还能为营销活动提供依据，比如通过推广高预订率的蔬菜菜品来增加销量，或通过特价优惠来提升低预订率蔬菜菜品的吸引力。1.数据抽取和预处理：（1）从公司订单系统抽取蔬菜类菜品的预订数据，包括菜品名称、菜品代号、预订单号、预订日期、预订时间、预订数量。（2）通过数据清洗去除无效或错误记录，确保数据质量。 2.计算本菜品近30日及近30-60日间的预订率：（1）基于历史数据，利用SUM函数计算所有蔬菜类菜品近30日及近30-60日间的预订总数量。（2）使用SUMIFS函数计算本菜品近30日及近30-60日间的预订总数量。（3）本菜品近30日预订率=本菜品近30日预订总数量/所有蔬菜类菜品近30日预订总数量×100%；本菜品近30-60日间的预订率=本菜品近30-60日间的预订总数量/所有蔬菜类菜品近30-60日间的预订总数量×100%。 3.输出近30日预订率排前三的蔬菜类菜品：使用数据透视表对历史积累的预订率数据进行汇总和排序，使用RANK函数筛选出预订率最高的前三名菜品并进行可视化输出。 4.计算本菜品预订率变化值：本菜品预订率变化值=本菜品近30日预订率-本菜品近30-60日间的预订率。 6.本菜品预订偏好趋势判断：若变化值＞0，则为“偏好提升”，若变化值＜0，则为“偏好下降”，若变化值＝0，则为“偏好不变”。Data on booking preferences for vegetable dishes is critical for catering enterprises and their supply chain management. First, such data allows enterprises to identify customer preferences for various vegetable dishes (e.g., leafy greens, root vegetables, edible fungi, etc.), thereby optimizing the menu setup of vegetable dishes to meet market demand and improve customer satisfaction. Second, by analyzing booking rates and their fluctuations, enterprises can forecast the demand for vegetable ingredients, adjust procurement plans, reduce inventory backlog and waste, and enhance inventory management efficiency. In addition, booking preference data can also provide a basis for marketing activities, such as promoting vegetable dishes with high booking rates to increase sales, or using special offers to boost the attractiveness of vegetable dishes with low booking rates. 1. Data extraction and preprocessing: (1) Extract booking data of vegetable dishes from the company's order system, including dish name, dish code, order number, booking date, booking time, and booking quantity. (2) Clean the data to remove invalid or erroneous records to ensure data quality. 2. Calculate the booking rates of this dish in the past 30 days and between the past 30 and 60 days: (1) Based on historical data, use the SUM function to calculate the total booking quantity of all vegetable dishes in the past 30 days and between the past 30 and 60 days. (2) Use the SUMIFS function to calculate the total booking quantity of this dish in the past 30 days and between the past 30 and 60 days. (3) The booking rate of this dish in the past 30 days = (Total booking quantity of this dish in the past 30 days / Total booking quantity of all vegetable dishes in the past 30 days) × 100%; The booking rate of this dish between the past 30 and 60 days = (Total booking quantity of this dish between the past 30 and 60 days / Total booking quantity of all vegetable dishes between the past 30 and 60 days) × 100%. 3. Output the top three vegetable dishes by booking rate in the past 30 days: Use a pivot table to summarize and sort the accumulated historical booking rate data, and use the RANK function to filter out the top three dishes with the highest booking rates for visual output. 4. Calculate the booking rate change value of this dish: Booking rate change value of this dish = Booking rate of this dish in the past 30 days - Booking rate of this dish between the past 30 and 60 days. 6. Judgment of booking preference trend for this dish: If the change value > 0, it is "Preference Improvement"; if the change value < 0, it is "Preference Decline"; if the change value = 0, it is "Preference Unchanged."

搜集汇总

数据集介绍

特点

该数据集包含538条蔬菜类菜品的预订偏好数据，每日更新，用于餐饮企业的菜单优化、库存管理和营销活动。数据结构详细，包含预订率、偏好趋势等关键指标，并通过算法规则进行计算和分析。