干货配送超市金额数据

通过配送管理系统软件采集本公司干货配送超市订单信息，将不同的配送订单以各超市来划分，从而计算各个超市每个月的配送金额数据，再计算出12个月的配送金额的方差大小，方差大小用来评价各个超市的配送情况，从而用于评估所有超市的采购和配送策略，所有企业可以通过该数据了解干货在不同超市的配送行情，以平衡采购和配送干货数量。例如对于超平稳型超市，本行业内企业无需时时关注该超市的行情，可每月保持好干货的采购和配送数量；对于一般平稳型超市，本行业内企业需每季度关注该超市的行情，适当调整干货的采购和配送数量；而对于波动型超市，本行业内企业需每月时时关注该超市行情，了解该超市的配送行情变化，以适应大幅度调整干货的采购和配送数量变化。其次，本数据还能为配送行业的相关企业（如物流公司、农贸市场等）提供整体性参考，从而有效洞察市场趋势，更好地做出科学的营销决策。步骤1：通过配送管理系统软件采集本公司干货配送超市的订单信息，将不同的配送订单以各超市来划分归类，从而将各年度每个月配送至超市的金额，从一月至十二月依次用y1,y2...y12表示，并汇总计算得到各个超市当年的总配送金额y。步骤2：计算各超市的月平均配送金额y̅=各个超市当年的总配送金额y/12。步骤3：根据方差公式计算方差s2={(y1-y̅)2+(y2-y̅)2+(y3-y̅)2+…+(y12-y̅)2}/12，从而得到各个超市的月配送金额的方差s2大小。步骤4：当方差s2小于0.001评价该超市为超平稳型超市。当方差s2大于等于0.001并且小于等于0.003评价该超市为一般平稳型超市。当方差s2大于0.003评价该超市为波动型超市。

Order information of dry goods distribution supermarkets under our company is collected via distribution management system software. Different distribution orders are classified by individual supermarkets, so as to calculate the monthly distribution amount data for each supermarket, then compute the variance of the 12-month distribution amounts. The variance is used to evaluate the distribution performance of each supermarket, assisting in assessing the procurement and distribution strategies of all supermarkets. Enterprises can utilize this dataset to understand the dry goods distribution trends across different supermarkets, thereby balancing the procurement and distribution quantities of dry goods.

For example, for ultra-stable supermarkets, enterprises in this industry do not need to closely monitor their market trends, and can maintain consistent dry goods procurement and distribution quantities on a monthly basis. For generally stable supermarkets, enterprises need to track their market trends quarterly and make appropriate adjustments to the procurement and distribution quantities of dry goods. For volatile supermarkets, enterprises must closely monitor their market trends monthly, grasp changes in their distribution conditions, and make substantial adjustments to the procurement and distribution quantities of dry goods accordingly.

In addition, this dataset can also provide holistic reference for relevant enterprises in the distribution industry (such as logistics companies, agricultural wholesale markets, etc.), enabling them to effectively identify market trends and make more evidence-based marketing decisions.

Step 1: Collect order information of dry goods distribution supermarkets under our company via distribution management system software. Classify different distribution orders by individual supermarkets, denote the monthly distribution amounts to each supermarket from January to December as y₁, y₂, ..., y₁₂ respectively, and aggregate to calculate the total annual distribution amount y for each supermarket.
Step 2: Calculate the monthly average distribution amount ȳ for each supermarket, where ȳ = y / 12, with y representing the total annual distribution amount of the supermarket.
Step 3: Calculate the variance s² of the monthly distribution amounts for each supermarket using the standard variance formula: s² = [(y₁ - ȳ)² + (y₂ - ȳ)² + (y₃ - ȳ)² + … + (y₁₂ - ȳ)²] / 12.
Step 4: Classify each supermarket based on its calculated variance s²:
- Ultra-stable supermarket: s² < 0.001
- Generally stable supermarket: 0.001 ≤ s² ≤ 0.003
- Volatile supermarket: s² > 0.003

搜集汇总

数据集介绍

特点

该数据集记录了干货配送至各超市的月度金额及统计分析结果，通过方差评估超市配送稳定性（分三类），用于优化采购配送策略和行业市场分析，含3129条企业数据，每年更新。