炖菜类菜品预订偏好数据

炖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，这些数据使餐饮企业能够识别顾客对各种炖菜（如炖肉、炖汤、煲仔等）的喜好，从而优化炖菜菜品的菜单设置，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，餐饮企业能够预测炖菜食材的需求趋势，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炖菜菜品来增加销量，或通过特价优惠来提升低预订率炖菜菜品的吸引力。1.数据抽取和预处理：（1）从公司订单系统抽取炖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函数计算所有炖菜类菜品近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/所有炖菜类菜品近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/所有炖菜类菜品近30-60日间的预订总数量×100%。
3.输出近30日预订率排前三的炖菜类菜品：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”。

Booking preference data for stew dishes is critical for catering enterprises and their supply chain management. First, such data enables catering businesses to identify customer preferences for various stew dishes (such as stewed meat, soup stews, clay pot dishes, etc.), thereby optimizing the menu configuration of stew dishes to meet market demand and improve customer satisfaction. Second, by analyzing booking rates and their changes, catering enterprises can predict the demand trends of stew ingredients, adjust procurement plans accordingly, reduce inventory overstock and waste, and improve inventory management efficiency. In addition, booking preference data can also provide a basis for marketing campaigns, such as promoting high-booking-rate stew dishes to increase sales, or using special offers to enhance the attractiveness of low-booking-rate stew dishes.
1. Data extraction and preprocessing:
(1) Extract booking data of stew dishes from the company's order system, including dish name, dish code, booking order number, booking date, booking time, and booking quantity.
(2) Perform data cleaning to remove invalid or erroneous records and ensure data quality.
2. Calculation of the booking rate of the subject dish in the past 30 days and the period from 30 to 60 days ago:
(1) Based on historical data, use the SUM function to calculate the total booking volume of all stew dishes in the past 30 days and the period from 30 to 60 days ago.
(2) Use the SUMIFS function to calculate the total booking volume of this specific dish in the past 30 days and the period from 30 to 60 days ago.
(3) Booking rate of this dish in the past 30 days = (Total booking volume of this dish in the past 30 days / Total booking volume of all stew dishes in the past 30 days) × 100%; Booking rate of this dish during the period from 30 to 60 days ago = (Total booking volume of this dish during the period from 30 to 60 days ago / Total booking volume of all stew dishes during the period from 30 to 60 days ago) × 100%.
3. Output the top three stew 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 and visually output the top three dishes with the highest booking rates.
4. Calculation of 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 during the period from 30 to 60 days ago.
6. Judgment of the booking preference trend of this dish: If the change value > 0, it is "Preference Improved"; if the change value < 0, it is "Preference Declined"; if the change value = 0, it is "Preference Unchanged".