蒸煮类菜品预订偏好数据

蒸煮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不同蒸煮菜品（如清蒸鱼、煮饺子、蒸排骨等）的喜好，从而优化蒸煮菜品的菜单设置，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，餐饮企业能够预测蒸煮食材的需求趋势，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蒸煮菜品来增加销量，或通过特价优惠来提升低预订率蒸煮菜品的吸引力。1.数据抽取和预处理：（1）从公司订单系统抽取蒸煮类菜品的预订数据，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函数计算所有蒸煮类菜品近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/所有蒸煮类菜品近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/所有蒸煮类菜品近30-60日间的预订总数量×100%。 3.输出近30日预订率排前三的蒸煮类菜品：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 steamed and boiled dishes is critical for catering enterprises and their supply chain management. First, this data allows catering enterprises to identify customer preferences for different steamed and boiled dishes (e.g., steamed fish, boiled dumplings, steamed pork ribs, etc.), thereby optimizing the menu offerings of such dishes to meet market demand and improve customer satisfaction. Second, by analyzing booking rates and their changes, catering enterprises can predict demand trends for ingredients used in steamed and boiled dishes, 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 increasing sales by promoting steamed and boiled dishes with high booking rates, or enhancing the attractiveness of low-booking-rate steamed and boiled dishes through special offers. 1. Data Extraction and Preprocessing: (1) Extract booking data for steamed and boiled dishes from the company's order system, including dish name, dish code, booking order number, booking date, booking time, and booking quantity. (2) Remove invalid or erroneous records via data cleaning to ensure data quality. 2. Calculation of Booking Rates for the Current Dish in the Past 30 Days and During the 30-60 Day Period Prior: (1) Based on historical data, use the SUM function to calculate the total booking quantity of all steamed and boiled dishes in the past 30 days and during the 30-60 day period prior. (2) Use the SUMIFS function to calculate the total booking quantity of the current dish in the past 30 days and during the 30-60 day period prior. (3) Booking rate of the current dish in the past 30 days = (Total booking quantity of the current dish in the past 30 days / Total booking quantity of all steamed and boiled dishes in the past 30 days) × 100%; Booking rate of the current dish during the 30-60 day period prior = (Total booking quantity of the current dish during the 30-60 day period prior / Total booking quantity of all steamed and boiled dishes during the 30-60 day period prior) × 100%. 3. Output the Top 3 Steamed and Boiled Dishes by Booking Rate in the Past 30 Days: Use a pivot table to summarize and sort the historically accumulated booking rate data, use the RANK function to filter the top three dishes with the highest booking rates, and generate visual outputs. 4. Calculation of Booking Rate Change Value for the Current Dish: Booking rate change value of the current dish = Booking rate of the current dish in the past 30 days - Booking rate of the current dish during the 30-60 day period prior. 6. Preference Trend Judgment for the Current 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".