吉林省区域内门店预测下季度销量分析数据

本单位在每一个市内的若干个区县开有终端门店用于销售塑料食品袋和纸盒（各市有不同数量的门店），每个市也均设置有一个仓库，通过每个终端门店上季度的销量，来预测到每个终端门店下季度的销量数据，总而得到该市下季度的总销量预测数据，对本行业所有企业可起到三个作用：第一针对该市下季度的总销量预测数据M，本行业所有企业可以合理安排生产计划和调度资源，以确保按时交货并满足该市的市场供货需求。第二针对该市内的每个终端门店，Y1和M1、Y2和M2、Y3和M3等数据可以帮助本行业所有企业合理进行库存管理策略，在该市内进行合理的库存周转。第三本行业所有企业可以制定销售策略和市场推广计划。例如，根据预测结果，企业可以确定目标市场、制定定价策略、设计促销活动，并与销售渠道合作，以提高产品的市场份额和销售业绩。1.数据采集：采集每天门店销量数据。2.数据处理：针对该市第一家门店，汇总该季度每个月的销量Y1a、Y1b、Y1c，以及该季度总销量Y1，对于第二家门店，汇总该季度每个月的销量Y2a、Y2b、Y2c，以及该季度总销量Y2，对于第n家门店，汇总该季度每个月销量Yna、Ynb、Ync，以及该季度总销量Yn。3.预计下季度销量：第一家门店下季度预测销量M1 = （Y1b/Y1a+Y1c/Y1b）*0.5*Y1（最终数值取整数），第二家门店下季度预测销量M2= （Y2b/Y2a+Y2c/Y2b）*0.5*Y2，针对该市内第n家门店，第n家门店下季度预测销量Mn= （Ynb/Yna+Ync/Ynb）*0.5*Yn。4.数据分析：上季度该市总销量Y=Y1+Y2+....Yn，下季度该市预测总销量M=M1+M2+....Mn，令P=M-Y，不同省份根据P值的不同采取不同生产计划和库存管理（因为不同的省份仓库库存量不同），如果P＞7000，则可加大生产计划加大库存量；如果P＜4000，则可减缓生产计划加小库存量；如果4000≤P≤7000则可保持原生产计划原库存量。

Our company operates terminal retail stores in multiple districts and counties under each city for selling plastic food bags and paper cartons (each city has a different number of stores), and each city is also equipped with a dedicated warehouse. Based on the sales volume of each terminal store in the previous quarter, we predict the sales volume of each terminal store in the next quarter, thereby obtaining the total sales forecast data for the entire city in the next quarter. This dataset can provide three key benefits for all enterprises in this industry:
1. For the total sales forecast data M of the city in the next quarter, all enterprises in the industry can reasonably arrange production plans and allocate resources to ensure on-time delivery and meet the local market supply demand.
2. For each terminal store in the city, data pairs such as Y1 and M1, Y2 and M2, Y3 and M3 can help all enterprises in the industry formulate rational inventory management strategies and carry out reasonable inventory turnover within the city.
3. All enterprises in the industry can develop sales strategies and marketing promotion plans. For example, based on the forecast results, enterprises can identify target markets, formulate pricing strategies, design promotional activities, and cooperate with sales channels to enhance product market share and sales performance.

The workflow of this dataset is as follows:
1. Data Collection: Collect daily sales data of all terminal stores.
2. Data Processing: For the first store in the city, summarize the monthly sales volumes Y1a, Y1b, Y1c for each month of the current quarter, as well as the total quarterly sales volume Y1. For the second store, summarize the monthly sales volumes Y2a, Y2b, Y2c for each month of the current quarter, as well as the total quarterly sales volume Y2. For the nth store, summarize the monthly sales volumes Yna, Ynb, Ync for each month of the current quarter, as well as the total quarterly sales volume Yn.
3. Next Quarter Sales Forecast: The forecasted next quarter sales volume of the first store M1 = (Y1b/Y1a + Y1c/Y1b) * 0.5 * Y1 (the final value is rounded to an integer). The forecasted next quarter sales volume of the second store M2 = (Y2b/Y2a + Y2c/Y2b) * 0.5 * Y2. For the nth store in the city, the forecasted next quarter sales volume Mn = (Ynb/Yna + Ync/Ynb) * 0.5 * Yn.
4. Data Analysis: The total sales volume of the city in the previous quarter Y = Y1 + Y2 + ... + Yn. The forecasted total sales volume of the city in the next quarter M = M1 + M2 + ... + Mn. Let P = M - Y. Different provinces will adopt different production plans and inventory management strategies based on the value of P, as the inventory volumes of provincial warehouses vary. If P > 7000, enterprises can expand production plans and increase inventory volumes; if P < 4000, enterprises can slow down production plans and reduce inventory volumes; if 4000 ≤ P ≤ 7000, enterprises can maintain the original production plans and inventory volumes.