This is the thirteenth article in the Fanruan FineBI series. We hope that this series of articles will help everyone understand BI more systematically.
In the previous article, we shared "Introduction to FineBI Finance: Cost Analysis. How to solve the problem of scattered data and difficult multi-table analysis?" 》, mainly explains how using BI to analyze financial expense data is better than Excel, that is, data acquisition is more convenient, multi-table analysis is more free, and multi-dimensional data linkage is more flexible. It also explains in detail how to use FineBI to analyze expense data.
Today, we will enter the second part of the FineBI financial analysis series, namely: analysis of accounts receivable. How to use data to dynamically track receivable risks and shorten the collection cycle from 150 days to 90 days?
01 Business background: What problems are occurring in accounts receivable?
The credit sales model is an effective means to clear inventory in a timely manner and promote sales. In the process of development, in order to expand the market and meet development needs, enterprises often adopt the credit sales model to enhance their competitiveness. This situation is especially common in the manufacturing industry.
However, the credit sales model is a double-edged sword. While it promotes sales, it also prevents companies from receiving payment in time after selling products, resulting in an excessive proportion of accounts receivable, reducing the company's capital turnover capacity, and affecting The cash flow of the enterprise may even endanger the normal operation of the enterprise.
The problem of accounts receivable can generally be summarized as follows:
The scale of accounts receivable is too large: relying on the credit sales model to obtain market growth, but there is no effective control over the risk of credit sales orders, and there is a risk of arrears due to sales staff in order to obtain sales growth. Customers still sell on credit.
Slow collection of accounts receivable: lack of dynamic tracking of accounts receivable, outdated supervision methods, neglect of aging analysis of accounts receivable, and failure to form differentiated management of accounts with different aging cycles.
High bad debt rate: There is a lack of collection mechanism to guide collection personnel to control overdue & bad debt risks in a timely and effective manner.
02 Analysis ideas: How to solve the problems of accounts receivable?
Accounts receivable is also an item on the balance sheet, and analysis of accounts receivable using Excel is usually done at the end of the month. Since it is difficult to conduct dynamic analysis of accounts receivable, it is often difficult to detect abnormal changes in accounts receivable and monitor capital risks in a timely manner.
In order to solve the above problems, this article will use FineBI to create a dynamic accounts receivable analysis report through the ledger data of the virtual company's receivable statement and actual receipt statement.
Accounts receivable turnover: Accounts receivable turnover is a key metric that indicates how quickly a business collects its receivables. Calculated by dividing annual sales revenue/average accounts receivable balance. Analyzing changes in your turnover ratio can help you determine whether you need to take action to improve your accounts receivable management.
Bad and doubtful debt analysis: It is important to check the proportion of bad and doubtful debts in accounts receivable. Bad debts are accounts that are unlikely to be collected, and doubtful debts are accounts that are overdue. By analyzing this data, it is possible to estimate potential losses and determine whether stricter credit policies or recovery measures are needed.
Aging analysis: divide accounts receivable into different age groups, such as 30 days, 60 days, 90 days or more, etc. This helps determine which accounts are past due and require action to recover them. Often, the sooner you take action, the easier it is to collect on overdue accounts.
Customer Credit Assessment: It is important to assess the credit risk of your customers. Establish a customer credit rating system to evaluate a customer's creditworthiness based on their credit history, payment history and financial situation. This helps determine whether credit should be extended to certain customers, and the size of the credit limit.
Accounts receivable and sales correlation analysis: Analyzing the correlation between accounts receivable and sales can help you determine whether there are collection delays or other issues. If sales are increasing but accounts receivable are increasing faster, it could be a sign that collections are slowing down.
Develop improvement strategies: Based on the results of the analysis, develop improvement strategies. This could include updating credit policies, improving customer communications, taking more aggressive debt collection measures or working with at-risk customers on repayment plans.
Monitoring and reporting: Analyzing accounts receivable is an ongoing process. Regularly monitors accounts receivable metrics and develops reports so management can understand financial status and take corrective action as needed.
Automation and technology tools: Leverage financial software and automation tools to track and manage accounts receivable. These tools can speed up the collection process and provide more accurate data analysis.
03 Practice link: How to practice it?
1. Analysis of the scale of accounts receivable
First, let’s evaluate the current scale of accounts receivable. Accounts receivable alone cannot provide us with much information. Generally, we will evaluate the current scale of accounts receivable in combination with operating income (or accounts receivable). Combining funds with total funds, the logic is the same). In the analysis process, we can introduce timing comparative analysis and peer competition analysis. Let’s take timing analysis as an example:
We count changes in operating income and accounts receivable on a quarterly basis. Generally speaking, the two will grow simultaneously. When we monitor the scale of accounts receivable, we will focus on analyzing major deviations between the two, such as accounts receivable. The increase is much larger than operating income (the black line is higher than the blue line, such as the third quarter of 21). At this time, it may be that we are forced by competition or our customers are in financial crisis, or it may be that we have sacrificed credit to expand revenue. Policies require timely identification and insight into causes.
In order to better locate the scale problem, we can also define a ratio indicator of accounts receivable to operating income for monitoring (red line in the figure), so that we can more quickly locate the scale problem of accounts receivable, or lower it. As an example in the figure, we can see that the trend is that the scale of accounts receivable has been declining, with only a slight increase in the third quarter of 21.
For the analysis of the scale of accounts receivable, in addition to the time series analysis here, we can also introduce some public financial report data to analyze and compare the scale of accounts receivable of the same industry and related competing products as our own reference. At this time, we can also The turnover rate indicator is introduced for evaluation and comparison. After the turnover rate is introduced, we can also conduct comparative analysis in conjunction with the inventory turnover rate. The space is limited and will not be discussed here.
2. Quality analysis of accounts receivable
After assessing the size of accounts receivable, we need to further analyze the quality of accounts receivable.
For the assessment of quality, we can introduce two indicators: aging and bad debts. First, the aging structure is displayed based on the current accounts receivable (different industries have their own standards for the analysis of the aging structure, here is only an illustration) :
Then we know that the smaller the age of the account, the lower the risk. For the above structure chart, we have no way to understand whether the current structure is healthy. For the same reason as analyzing the scale of accounts receivable, we can analyze different accounts through time series diagrams. Use the time series trend of age to judge whether the current structure is healthy.
Many friends here may not understand how to calculate account age, so we will add additional information on the calculation of account age:
The business documents and receivables financial documents of most enterprises can be matched. In this case, it will be relatively simple for us to calculate the aging. Based on the receivables table, we first mark which documents are outstanding, and then based on the expected time and The overdue time can be obtained by calculating the current time, and the aging distribution can be obtained by classifying the overdue time. This can be quickly achieved using FineBI, as shown in the figure below.
At that time, there were also some companies whose business documents and financial documents could not completely match due to various reasons. At this time, we could estimate the aging of accounts based on the first-in, first-out principle with the customer as the most granular level.
After having the aging structure, the next step is to estimate the amount of bad debts based on the company's bad debt accrual rules:
3. Accounts receivable customer analysis
After we have a clear judgment on the scale and quality of the company's own accounts receivable, the next step is to formulate actions to close the business loop and try to increase our recovery rate.
At this step, we generally consider analyzing from the perspective of customer risk warning. If we analyze in depth here, we can build a customer credit rating system. Due to limited space, here is just a simple illustration:
In this process we hope to identify the following two types of customers:
Category 1: Customers with excessively large accounts receivable
Category 2: Customers with acceptable accounts receivable and risk of collection: high proportion of accounts receivable; recent decline in collection ability
For these customers, we can quickly capture data through FineBI and identify the above customers:
The first is customers with high accounts receivable and customers with a high proportion of accounts receivable. We can directly filter through FineBI's conditional style. The top-ranked customers in the figure are the customers with higher receivables. At the same time, we can also Use line charts to mark high-proportion customers who need attention.
If we want to additionally analyze the customer's ability to collect money in the near future, we can also use FineBI's data linkage capabilities based on the above figure.
We have added a new payment trend chart. Through linkage, we can analyze the recent payment situations of different customers, so as to identify customers whose payment ability has declined recently: The final
analysis effect
is summarized:
This article mainly explains the three problems currently encountered by enterprises in accounts receivable and the corresponding solutions, and uses the FineBI tool to demonstrate how to analyze the data. Through the above analysis solutions, enterprises can do:
Based on the dynamic analysis and supervision system of accounts receivable, the company, organizations at all levels and sales personnel can timely grasp the current status of accounts receivable risks.
Measure risk levels from different account ages, customer types, overdue account amounts and proportions, focus on collecting orders from high-risk customers, and improve payment collection efficiency
A manufacturing company used this analysis solution. After 2 years, the proportion of accounts receivable in current assets dropped significantly, by 15%, and the turnover days of accounts receivable was shortened from the original 150 days to about 90 days.
It is worth mentioning that the development of domestic BI tools today has entered deep water. When companies are launched, it is far from just finding a visualization tool that can meet the basic drag and drop requirements. Many companies that previously chose to develop their own or purchase foreign BI tools Recently, they have also switched to FineBI.
Because FineBI's three major functions: data editing, topic model, def function, and the spider engine behind it, from function to performance, they support users to express every step in the analysis and thinking process through data, assisting users in depth Think about complex business problems and finally turn data into productivity.
You can try it for free by replying to BI in the background !