about data management. We can divide the data work of this module into two parts. One is to use data to assist daily work and make the selection and judgment in daily work more standardized. This is done with data. The other is to evaluate work performance through data, so that the management of related work is more standardized, which is managed with data. Let's look at the things first. Products generally go through the following processes in retail e-commerce companies:
From the beginning of the purchase to the final sale, the products follow the product process, but the sales and returns of the products in turn affect the purchase choices of the products. There are different things to do at each node (the transportation node, in many cases, the transportation is the responsibility of the supplier, so the retailer may have nothing to do), this article will discuss the procurement module first.
What to do about purchasing:
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Select commodity categories and plan category structure. For most companies, the choice of commodity categories directly determines the company's retail strategy, and does not need to be decided at the procurement stage.
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Selecting the product brands in the category is an important part of purchasing decisions. This work can also be divided into two parts, introducing brands and eliminating brands.
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When selecting suppliers, the same brand may also have multiple channel distributors. Selecting suitable suppliers is also the key work of procurement.
And how we can make the above work easier through data, let me give two examples.
When choosing a commodity brand, one is to introduce and the other is to eliminate it: From a theoretical point of view, the introduction of a commodity brand needs to be judged after considering the brand's familiarity, quality, consumer purchase desire, brand expectations and uniqueness, etc., but the above content is very important. It is difficult to quantify, and it is difficult to judge the reference. In fact, the above items can be reflected by the sales situation, and whether the product is worth introducing can be judged by the sales situation of the product.
The above figure is a simple example. We use another reference data set to find products with better sales, and judge whether the product is worth introducing by checking the gross profit margin, sales volume and other information of the product. According to the actual situation, the table can have more changes. Different comparison sets and different formats can have more indicators to judge. This figure only provides a simple idea. (Don't ask me how to obtain the comparison set data, there are many comparison sets to choose from, and there are also many ways to obtain data)
In addition to the introduction of good products, it is also necessary to eliminate junk brands. This part is even simpler. Select the category to pull out the inventory and sales of each brand in the modified category, and calculate the number of days that the inventory can be maintained. The best selling situation, and judge whether the product needs to be eliminated.
Some people may say that different products cannot be compared together. Some brands sell 1 piece a month, and some brands sell 10 pieces, which is also unsalable. This is not a comprehensive consideration. What I want to say is, what do people use for it? There are 10 steps in total, 7 steps of data are completed, and the rest is left to people. Data can make people's judgments have a more comprehensive basis and make more reasonable judgments, instead of making judgments directly. I believe in one sentence: if you want to make things perfect, most of them can't be done. The same is true for data analysis here. Balance the relationship between data and people, and don’t think too much about data.
After completing the brand selection, you may be faced with the choice of suppliers. Click the product name to directly call up the supplier information of the product, and list the supplier-related indicator data, such as batch purchase price, selling price, inventory, and sales volume. , On-time delivery rate, etc.
After introducing the data application in the daily work of procurement, let's look at management.
The purpose of management is to find out the problems in the process by reviewing and analyzing the work performance over a period of time, and to urge the relevant responsible persons to do better. The method is generally to display the relevant indicators of procurement, or to rank and compare according to the indicator data, so as to drive the relevant person in charge to complete the work better. The more complicated point is the comprehensive monitoring from the whole to the individual to the node, through the visual display of the data, to achieve a more intuitive effect.
For the procurement module, we can divide the indicators into two parts, one is the process indicator, and the other is the result indicator.
Process indicators: procurement frequency, procurement cost, number of expanded suppliers, new product introduction rate, commodity elimination rate, new product arrival rate, procurement brand matching degree, price matching degree, and model matching degree.
Outcome indicators: sales of purchased goods, gmroi of purchased goods, gross profit margin of goods, ratio of sales to inventory, and rate of purchase and sale of goods. .
From the above, we can see that the result indicators that reflect the purchase value need to be reflected from the sales results of the goods. Therefore, commodity analysis can be used as a comprehensive module. All the purposes are to sell better commodities and bring higher profits. Whether it is procurement, inventory, or sales, it is all for this goal, so I am not targeting individual products here. The purchasing module has made a report demo. (The above content only provides you with certain analysis ideas. A really easy-to-use data platform must be combined with actual scenarios)
Text | jiago king
The article comes from: Zhihu column " Look at the data "