Introduction: Through data analysis, we find new problems that are difficult to find in the "one-time report" of data, and provide new references and ideas for the analysis and rectification of the company's own situation.
Four tools
1. Grasp the data of invoicing and sales inventory
To be able to clearly calculate the each department, each classification, monthly invoicing data, and the use of POS system and EOS system can quickly grasp the company's sales information and purchase information. In other words, if the invoicing and inventory information can be obtained in a more scientific way, it will bring great help to the operation.
2. Analysis of the composition ratio of classification
To manage a company's merchandise, you can't just know the turnover and interests of the whole store, nor can you only take into account the turnover and interests of the department. For example, not only to know what the turnover and profit of the beverage category are, but also to understand the composition ratio of its composition, in order to know where the weakness of sales is and how to improve it.
3. Gross profit margin analysis
Gross margin = Gross margin / Turnover×100
For each classification, it is also necessary to be able to calculate the gross profit margin, understand which classification has good profitability and which classification has poor profitability, and adjust the commodity structure or strengthen the weak classification.
4. Commodity turnover rate analysis
Commodity turnover rate = Turnover / Initial Inventory + Ending Inventory / 2×100
Number of days of merchandise rotation = 365 days / year commodity turnover rate
One of the key to the operation of supermarkets is to seek a fast product turnover rate, so the turnover rate of each classification must be calculated, and the faster the turnover rate, the better. Because the faster the turnover rate, the better the freshness of the goods, and the faster the recovery of funds; so as to form a virtuous circle, the operation will be outstanding.
Generally speaking, the number of rotations in supermarkets should be maintained at more than 20-22 times a year to meet the standard, and operators can check whether the number of rotations of their own company is within the standard, if it is outside the standard, it is necessary to work hard.
The purchasing department is the department where the supermarket creates performance, so if there is no complete procurement organization, the supermarket simply cannot exist, let alone want to make money, so the first step for the supermarket to talk about profits is to first organize a procurement combat team with full combat effectiveness, so strictly guard the first pass of goods in and out, it is possible to make the supermarket truly invincible.
The purchasing department, like the production sector of the manufacturing industry, is a unit that creates profits, and if this pass is properly guarded, only some goods that will make money can naturally ensure the basic performance of supermarkets. Generally speaking, the purchasing organization can be divided into two categories: decentralized procurement organization and centralized procurement organization, these two types of organizational models have their own advantages and disadvantages, and the industry can choose a model suitable for themselves according to the size and goals of the individual.
In the procurement process, data analysis is of extremely strategic importance and is the core brain of optimizing supply chain and procurement decisions. Therefore, doing a good job of data analysis is one of the most important links in the procurement process.
So how to do a good job of data analysis? The following is a list of 8 steps of data analysis, as well as 7 common analysis ideas. Before initiating data analysis, it is best to confirm the analysis process at each step with your supervisor or colleagues who have more experience in data.
Eight processes of data analysis
1, why analyze
First of all, you have to know why the analysis? Figure out the purpose of this data analysis. For example, what type of customer delivery is always delayed. All your analysis revolves around this why. Avoiding repeated rework that doesn't meet your goals can be painful.
2. Analyze who the target is
Keep in mind the analytical factors, whether the statistical dimension is the amount, or the product, or the competitive trend of the supplier industry, or the size of the supplier, and so on. Avoid counting the amount as a product and the product as an amount, and the result is very different.
3, what effect do you want to achieve
Find the real problem by analyzing the various dimensions of product type, company procurement cycle, purchase terms. For example, the weak link suppliers in this analysis, all centralized procurement, and maintaining the status quo are not in line with the principle of maximizing benefits. Through analysis, we find the real root cause of the problem and find that refined procurement management is very necessary.
4. What data is needed
The procurement process involves a lot of data, and what source data is needed? Total purchases? How competitive is the parts industry? Payment cycle? How often? Stock in stock? Customer geography factor? Customer size? and so on to list a table. Avoid adding new factors.
5. How to collect
Supplier information collection in the database, usually suppliers of various information entry, product characteristics entry, etc., do data analysis must have raw materials, otherwise it is difficult for qiao women to cook without rice.
6, how to sort out
Collating data is a technical activity. I have to admit that EXCEL is a powerful tool, the skillful use and skills of the pivot table, as a payment data analysis is essential, a variety of functions and formulas also need to be slightly understood, to avoid inefficient data collation. Spss is also a very good data processing tool, especially when the amount of data is relatively large, and when the field is composed of special characters, it is easier to use.
7, how to analyze
After sorting out, how to conduct comprehensive analysis and related analysis of the data? This is a test of logical thinking and reasoning ability. At the same time, in the process of analysis and reasoning, it is necessary to know the product well, understand the supplier well, and be familiar with the procurement process. What seems like a simple data analysis is actually the embodiment of various capabilities. The first is the technical level, the understanding and understanding of the extraction-conversion-loading principle of data sources; in fact, it is a global view, with a clear understanding of the business at the seasonal, company and other levels; and finally, the professionalism, which knows the process and design of the business. The power of practicing data analysis is not an overnight achievement, but a continuous growth and sublimation in practice. A good data analysis should be value-oriented, look at the big picture, be based on the business, and use data to drive growth.
8, how to show and output
Data visualization is also a science. How to behave with the right chart? What is the moral of each chart? Here are 8 commonly used charts:
(1) Line chart: Suitable for continuous data that changes over time, such as changes in income over time and changes in growth rate.
(2) Column chart: Mainly used to represent the difference between groups of data. There are mainly two-dimensional column charts, three-dimensional column charts, cylinder charts, cones and pyramid charts.
(3) Stacked column chart: The stacked column chart can not only show the size of each type of data in the same category, but also the size of the total amount.
(4) Line-bar chart: This type of chart can not only show the comparison of similarities, but also show the trend situation.
(5) Bar chart: similar to the horizontal bar chart, and the display effect of the bar chart is the same, mainly used for the comparison of various categories.
(6) Pie chart: mainly shows the proportion of each item. Pie charts are generally used with caution unless the difference in proportion is very pronounced. Because the naked eye is not intuitive to distinguish the proportion of the pie chart. And the items of the pie chart, generally do not exceed 6 items. After 6 items, it is recommended to use a column chart to be more intuitive.
(7) Composite pie chart: Generally the next step of analysis of a certain proportion.
(8) Mother and child pie chart: the composition and proportion of the project can be intuitively analyzed
Charts don't have to be too fancy, just a table says a problem. Saving the reader's time with friendly visualizations is also a respect for the reader.
There are some data, painstakingly sorted out and analyzed, and finally found that the conclusion output is not related, although a lot of work has been done, but can not be in order to reflect the workload and pile up data.
In the process of presentation, please indicate the source of the data, the time, the description of the indicators, and the algorithm of the formula, which not only reflects the professionalism of the data analysis, but also respects the report reader.
Seven ideas for data analysis
1. Simple trend
Get real-time access to trends to see suppliers on time. Such as product type, supplier area (traffic factor), purchase amount, purchase amount to supplier proportion.
2. Multidimensional decomposition
Decompose the indicators from multiple dimensions according to the needs of the analysis. For example, the amount of product procurement, the size of suppliers (to be quantified), the complexity of the product, and so on.
3. Conversion funnel
Analyze the overall and each step conversion with the help of a funnel model based on known conversion paths. Common conversion scenarios include trends in on-time delivery rates from different suppliers.
4. User grouping
In refined analysis, it is often necessary to analyze and compare the supplier groups with a specific behavior; data analysis needs to use multi-dimensional and multi-indicators as group conditions to optimize the supply chain in a targeted manner and improve the stability of the supply chain.
5. Check the path carefully
Data analysis can observe the trajectory of supplier behavior and explore the interaction process between suppliers and the company, and then identify problems, inspire or test hypotheses.
6. Retention analysis
Retention analytics explores the correlation between user behavior and return visits. Generally speaking, the retention rate we talk about refers to the proportion of "new suppliers" who "repeat behavior" over a period of time. Find the optimization point of the supply chain by analyzing the retention differences of different supplier groups and the retention differences of suppliers who have used different functions.
7. A/B test
A/B testing is the parallel testing of multiple schemes at the same time, but each scheme has only one variable that is different; and then the optimal scheme is selected by some kind of rule of superiority. Data analysis requires the selection of reasonable grouped samples, monitoring data indicators, post-event data analysis and evaluation of different scenarios in this process.
Not only the data analysis of the supplier's timely delivery, other data analysis processes and ideas are also applicable, but there are many dimensions and many combined dimensions, so it is necessary to have a clearer idea and a big picture to avoid falling into the data ocean.