There is a backlog of tens of millions of goods in the warehouse, and seasonal goods have missed the sales season. What should I do? It took a few years to digest the liquidated goods, but due to the backlog, the goods depreciated, increased costs, and decreased profits. Although the goods were eventually sold, it was a loss-making business. Excessive inventory can hurt the sales of seasonal products and close the store. So, how can we effectively avoid the high inventory crisis? Is it feasible to use BI software for sales trend analysis?
It is not only sales trend analysis, but now that BI software intelligent data visualization analysis is used, we should do systematic and visual data analysis for the entire sales-production-inventory chain. In the final analysis, high inventory is not caused by a single reason, but by the company's forward-looking grasp of consumer trends, all-round control of the supply chain, and in-depth insights into production capacity and consumer habits. Any loss of the chain in any of these three aspects may lead to business missteps and overestimation of sales capacity, resulting in warehouse stock far exceeding customer consumption capacity, resulting in high inventory.
Therefore, if you want to avoid a high inventory crisis, both online and offline apparel companies, manufacturing companies, etc. should conduct a full range of data visualization analysis for sales, production, inventory and other multi-link departments. Especially when BI software is available, it is even more important to implement intelligent data visualization analysis to various departments to realize the overall data visualization and intelligent analysis of the entire sales-production-inventory .
Aowei BI sales data visualization analysis
Visualized analysis of sales data: The sales trends of various products and the comparison of sales in the same period are implemented one by one, and the changes in sales trends, changes in consumption preferences and consumption levels are grasped in time, and the sales targets are set in a reasonable position more rationally and scientifically.
Visualized analysis of production data: Quality control and production capacity are all the points of focus. If quality control is not well done, subsequent negative reviews and returns remain high, which will also affect brand image and reduce brand value. The same is true for production capacity. Take Double Eleven pre-sales as an example. If the production capacity is overestimated, the pre-sales undertaken exceed the actual production capacity of the company and the delivery is slow, which will also invite a large number of bad reviews and returns.
Aowei BI production data visualization analysis
Visual analysis of inventory data: mainly analyze and monitor inventory structure, inventory rate, etc. Generally speaking, the inventory rate of the clothing industry is ideally controlled at 20%. It is necessary to avoid high inventory by quickly grasping the changes in inventory rate through inventory analysis and timely cooperating with sales to increase inventory turnover.
In the era of digital economy where the data volume is tens of millions and hundreds of millions, data analysis and data visualization can be completed in a short time intuitively and easily, helping companies quickly grasp key sales trends, inventory rates, production control, production capacity changes, etc. Information, I am afraid it is BI software.
Taking the BI software of Aowei BI series as an example, using ETL to build a data center, one-click docking of global data, lightning unified data analysis caliber, data second matching, second analysis, and even the second presentation of data analysis results. Not only that, Aowei BI software also pre-creates a systematic BI data visualization analysis solution for clothing retail, manufacturing and other industries. From sales analysis to inventory analysis, from production visualization to quality analysis, there are preset analysis models. Users only need to modify part of the ETL scripts for the data source system, and only need to do the necessary personalized design, they can build their own big data visualization analysis platform, realize enterprise-level intelligent data visualization analysis, and gain faster and more intuitive insight into data and improve overall The efficiency and level of decision-making of personnel.