肉类菜品预订偏好分析数据

肉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肉类菜品（如牛肉、猪肉、羊肉等）的偏好，从而优化肉类菜品的菜单设置，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，企业能够预测肉类食材的需求量，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肉类菜品来增加销量，或通过特价优惠来提升低预订率肉类菜品的吸引力。1.数据抽取和预处理：（1）从公司订单系统抽取肉类菜品的预订数据，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函数计算所有肉类菜品近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/所有肉类菜品近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/所有肉类菜品近30-60日间的预订总数量×100%。
3.输出近30日预订率排前三的肉类菜品：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 meat dish booking preferences is critical for catering enterprises and their supply chain management. First, this data enables enterprises to identify customers' preferences for various meat dishes (such as beef, pork, lamb, etc.), thereby optimizing the menu configuration for meat dishes to meet market demands and enhance customer satisfaction. Second, by analyzing booking rates and their variations, enterprises can forecast the demand for meat ingredients, adjust procurement plans accordingly, reduce inventory overstocking and food waste, and improve inventory management efficiency. In addition, booking preference data can also provide a solid basis for marketing campaigns, such as boosting sales by promoting meat dishes with high booking rates, or enhancing the appeal of under-performing meat dishes via special offers.
1. Data extraction and preprocessing:
(1) Extract booking data for meat dishes from the company's order management system, including dish name, dish code, booking order number, booking date, booking time and booking quantity.
(2) Conduct data cleaning to remove invalid or erroneous records and ensure data quality.
2. Calculation of booking rates for the target dish in the past 30 days and during the 30–60 days prior period:
(1) Based on historical datasets, use the SUM function to calculate the total booking volume of all meat dishes in the past 30 days and during the 30–60 days prior period.
(2) Use the SUMIFS function to calculate the total booking volume of the target dish in the past 30 days and during the 30–60 days prior period.
(3) Booking rate of the target dish in the past 30 days = (Total booking volume of the target dish in the past 30 days / Total booking volume of all meat dishes in the past 30 days) × 100%; Booking rate of the target dish during the 30–60 days prior period = (Total booking volume of the target dish during the 30–60 days prior period / Total booking volume of all meat dishes during the 30–60 days prior period) × 100%.
3. Output the top 3 meat dishes by booking rate in the past 30 days: Use a pivot table to summarize and sort the historically accumulated booking rate data, then use the RANK function to filter the top three dishes with the highest booking rates and generate visual outputs for them.
4. Calculation of the booking rate change value for the target dish: Booking rate change value for the target dish = Booking rate of the target dish in the past 30 days – Booking rate of the target dish during the 30–60 days prior period.
6. Judgment of the booking preference trend for the target dish: If the change value > 0, the trend is marked as "Preference Improvement"; if the change value < 0, the trend is marked as "Preference Decline"; if the change value = 0, the trend is marked as "Preference Unchanged."

搜集汇总

数据集介绍

特点

该数据集为肉类菜品预订偏好分析数据，包含641条记录，每日更新，数据格式为xlsx。数据集通过分析预订率及其变化，帮助餐饮企业优化菜单、预测需求、提升库存管理效率，并支持营销活动。