2022 Asia-Pacific Computer Technologies Conference (APCT)
2022 Asia-Pacific Computer Technologies Conference (APCT) | 978-1-6654-8345-2/22/$31.00 ©2022 IEEE | DOI: 10.1109/APCT55107.2022.00011
Design and Application of Real-Time Analysis
Model for Accounts Receivable Based on PowerBI
Jing Cheng*
Xiaocheng Gu
Department of Finance and Accounting
Anhui Institute of International Business
Hefei, Anhui, China
e-mail: chengjing4567@qq.com
Department of Accounting
Anhui Business & Technology College
Hefei, Anhui, China
e-mail: 214712727@qq.com
Abstract—With the wide application of new technologies
such as big data and artificial intelligence, more and more
traditional industries tend to be intelligent, and the traditional
enterprise financial management work is also moving toward
the direction of intelligence. MicrosoftPowerBI is a data
analysis and visualization tool launched by Microsoft, which
can extract data from various data sources, organize and
analyze the data, and then generate beautiful charts. This
paper introduces the use of MicrosoftPowerBI, a business
intelligence analysis tool, to build a real-time analysis model of
accounts receivable, which realizes the real-time fetching,
analysis and presentation of enterprise accounts receivable
and helps to improve the management ability of accounts
receivable of related enterprises.
and the concept of big data should be introduced to explore
the contribution of accounting data cleaning, conversion and
processing to the application of accounting information
systems in the context of big data [5]. According to Zhou
Jian (2015), a senior data analyst, enterprises must be on a
special platform for data visualization, because on the one
hand, the software of statistics is slow to calculate under the
huge data volume, and it is difficult to deal with statistical
data across years, accounts and systems, and on the other
hand, the functionality of graphical controls within software
is often weak, making it difficult to analyze data charts
based on statistical software, which is where the need for
visualization platform research lies [6].
II. SOFTWATE INTRODUCTION
Keywords—accounts receivable, PowerBI, analytical model
Microsoft PowerBI is a new generation of Microsoft
business intelligence analysis tools, including four modules:
Power Query, Power Pivot, Power Map and Power View [7].
It is based on the processing of enterprise business data and
relies on four technologies: data cleaning, data modeling,
data calculation and data visualization, which are used to
solve the quantitative, systematic and automated problems of
data analysis.
I. RESEARCH BACKGROUND
With the accelerated iteration of new technological
innovations such as big data, artificial intelligence, mobile
Internet, cloud computing, Internet of Things, blockchain,
etc., the digital transformation of the economy and society
has been fully opened, and the digital application scenarios
have posed new challenges and provided new opportunities
for accounting informatization practices and theories [1].
The advantages of Power BI in solving data processing
problems are mainly reflected in its systematic, real-time and
monitoring nature. It can connect more than one hundred
and twenty data sources, simplify data preparation, complete
statistical analysis of data instantly, and generate rich
interactive visual reports that can be published to web and
mobile devices for relevant personnel to access anytime and
anywhere in order to detect the operational status of various
businesses of the enterprise [8]. For example, if data is
collected through data file import or direct database
connection, as long as the model of data calculation is
established, the real-time changes of front-end file and
database data will be reflected in the final results and
presented in real-time through visual reports. Compared with
Excel, Power BI is still the four major steps of inputting data,
cleaning data, calculating data and outputting data, but
around a larger amount of more multi-dimensional financial
and business data, the input has changed from copy and
paste to import from database and Excel files, data cleaning
has changed from various small tricks and functions of
Excel to a special data cleaning tool Power Query,
calculation has changed from a pivot table to a more
powerful DAX language, and presentation has changed from
printing to paper to a visual interactive object that can be
interacted with and drilled into, making Power BI fully
adapted to the growing scale of data in the era of digital
economy.
The essence of digitization is actually to convert various
behaviors and movements in the physical society into
numbers to be recorded and to use these data for analysis. In
the past, the formation of data relied on traditional means,
such as using notes and entering with keyboards, and the
cost of acquiring data was relatively high and the scale of
data was limited. In the digital era, with the development of
infrastructure and technologies such as IoT sensors to collect
data and mobile Internet to deliver data, the ability to acquire
data has increased significantly. For example, the source of
revenue data can no longer be limited to the only
information in the accounting entries, but can be obtained
from the sales department for each revenue occurrence time,
transactions, purchasers, amount and other business-level
information in more detail. This has inevitably driven a shift
in finance towards the integration of business and
finance,and have the ability to efficiently analyze data.
Maria Brigida Ferraro (2015) argues that most of the
traditional data analysis is presented in the form of bar charts,
pie charts, etc., which leads to the fact that many data cannot
be displayed on a single graph, and it is more difficult to
analyze their intrinsic logical relationships [2], and simply
using the traditional way to organize and analyze data is not
only time-consuming and labor-intensive, but also difficult
to achieve the desired results [3]. According to Pan Jian
(2017), on the basis of China's overall move into the era of
big data, the financial personnel of enterprises cannot be
limited to bookkeeping and financial statements anymore [4],
978-1-6654-8345-2/22/$31.00 ©2022 IEEE
DOI 10.1109/APCT55107.2022.00011
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number, invoice date, amount, tax amount, price and tax
total, corresponding contract number, contract A party,
salesman and other related information. Each invoice
number represents an invoice issued independently.
III. CONSTRUCTION OF A REAL-TIME ANALYSIS MODEL OF
ACCOUNTS RECEIVABLE
A. Business scenarios
Accounts receivable is a claim that is formed along with
credit sales, specifically, it refers to the amount that an
enterprise should collect from the purchasing unit or the
receiving unit for the business activities such as selling
goods or providing services[9], mainly including the price of
goods or services and the advance payment for packaging
and transportation costs. The reason is that, in order to
increase sales volume and improve sales in a competitive
market environment, enterprises adopt the credit sales
method to sell, which in turn generates accounts receivable,
which increases revenue and also increases the risk of
management, and if a large number of accounts receivable
cannot be collected in time, enterprises will have the risk of
capital chain breakage[10]. Therefore, enterprises should
strengthen the daily supervision and management of
accounts receivable and conduct follow-up analysis.
The "Collection Status Table" contains voucher
number, contract number, collection time, amount, contract
A party, salesman and other related information. The
collection status table uses the voucher number as an
independent and unique primary key, representing the
bookkeeping voucher generated for each collection.
After analysis, the three tables have the typical
characteristics of a fact table with independent and unique
fields contract number, invoice number, and voucher
number as the primary keys in their respective tables, and
then record information, mainly time and amount.
b) Determine the dimension table
The business classification table, the main business of
the enterprise is divided into a total of 4 major categories
(primary subjects) such as engineering consulting; 8 subcategories (secondary subjects) such as planning consulting;
and 21 specific business details (tertiary subjects) such as
special planning. In order to be able to drill down, the table
is converted into a one-dimensional table form, i.e., the data
can be drilled down hierarchically.
In the enterprise practice, the management of accounts
receivable will have problems such as untimely monitoring
and lack of responsibility. The reason is that there is a lack
of communication between the relevant departments within
the enterprise, the sales department and the finance
department work independently, the information is not
smoothly transmitted, the sales department is focused on the
pursuit of performance, lack of awareness of the control of
accounts receivable, the finance department can not get the
relevant data of the sales department in a timely manner, the
formation of accounts receivable time, amount,
correspondent unit, sales person and other information can
not be real-time monitoring, which is not conducive to the
management of accounts receivable and the supervision of
the sales person accounts receivable return. Even though
some enterprises have deployed ERP information
management software, they cannot achieve real-time visual
monitoring and management. This model is used to achieve
real-time monitoring and analysis management of accounts
receivable data.
In order to better explain the relevant information,
tables such as department information table, salesperson
information table, customer information table, and business
classification completion table are also identified as
dimension tables.
2) Data organization
Import the Excel table file into Power BI, open Power BI,
import the data table file, check all the fact tables and
dimension tables, and click the "Convert Data" button to
enter Power Query.
Check the tables in Power Query, organize and clean the
parts with data problems, for example, delete the redundant
columns in the "Contract Status Table"; adjust the format of
the "Invoicing Status Table" "Ticket Number The format of
the "Invoicing" field is text; the "Department Information"
and "Customer Information" tables, upgrade the first row
title, etc.
B. Data collection
1) Data sources
The data source of enterprise accounts receivable-related
information is generally stored in the sales business system
of the enterprise's ERP management software. In some small
and medium-sized enterprises that do not use ERP
management systems, the sales department also uses Excel
for the storage and calculation of sales data. Here is an
example of the data source of HG Design Ltd, a company
whose main business is to carry out architectural design
consulting.
C. Modeling
1) Relationship construction
After importing the data table to Power BI, under the
"Model" view, establish a one-to-many relationship between
the primary key "Salesperson" field of the "Salesperson
Information" table and the "Salesperson" field of the three
fact tables. The "Salesperson" field of the "Salesperson
Information Table" is linked to the "Business Name" field of
the three fact tables, and the "Business Detail" field of the
"Business Category Completion Table" is linked to the
"Business Name" field of the three fact tables. "The main
key "Contract A" field of the "Customer Information" table
is linked to the "Contract A" field of the three fact tables.
The primary key "Business Department" of the "Department
Information Table" is linked to the field "Department" of the
"Salesperson Information Table". The final model is shown
in Figure 1.
a) Determination of the fact sheet
The fact sheet is mainly a form for recording business
data. HG has three fact sheets in total, namely, "Contract
Status Sheet", "Invoicing Status Sheet" and "Receipt Status
Sheet".
The "Contract Status Table" records the contract
number, contract signing time, amount, project name,
contract A, salesman and other related information. The first
column of the contract status reflects the contract number,
which is the primary key of the table.
The "Invoicing Status Table" contains the invoice
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Fig. 2. Creating a conversion table
to
Modify the most basic amount metric in this example
Invoicing amount = SUM('1 Invoicing Status'[total
price tax])/SELECTEDVALUE('2 Unit conversion table'
[unit value],1)
Fig. 1. data model
Amount receipt = SUM('1 Collections'[Amount])
/SELECTEDVALUE('2 Unit conversion table'[unit value],1)
In this way, a data model consisting of three factual
tables, contract, collection and invoicing, as the core, and
four other dimensional tables around it is constructed.
Contract amount= SUM('1 Contract Status'[contract
amount])/SELECTEDVALUE('2 unit conversion table'[unit
value],1)
2) Writing metric values
a) Base metric
In order to better analyze contracts, invoicing and
collections, their amounts should be aggregated and two new
base metrics should be created, which are
That is, the base metric is set to the original metric
/SELECTEDVALUE('unit table'[unit value],1) so that when
the filter filters the corresponding unit, the associated
number is automatically divided by the unit value
corresponding to the unit.
Invoicing amount = SUM('1 invoicing status'[total
price tax]).
3) Visualization design of real-time analysis model for
accounts receivable
The analysis of accounts receivable needs to be
considered in conjunction with the actual business. The
model needs to reflect the amount of accounts receivable for
each accounting period in real time, the opening, current
period occurrence, and closing numbers, and it needs to be
able to monitor the return of accounts receivable in real time
and find the business personnel with whom it is interfaced,
so the model is built according to the following steps.
Amount receipt= SUM('1 Collections'[Amount]).
b) Accounts receivable metric
Current period debit of accounts receivable (Increase)
= [Invoicing amount]
Current period credit of accounts receivable (Decrease)
= [Amount receipt]
Closing balance of accounts receivable = Accounts
receivable beginning of period + [Invoicing amount] [Amount receipt]
a) Set the date horizontal slicer
In the visual object generated by [Filter], drag the
"Year" field in the date table into the [Field], select the list
form, and in the [Format] tab [Direction] drop-down list box,
select "Vertical".The date style is shown in Figure 3.
Accounts receivable beginning of period should be
equal to the sum of all occurrences up to the date of
screening, so it is written as
Accounts receivable beginning of period =
CALCULATE(
[Invoicing amount] - [Amount receipt],
DATESBETWEEN(
'2 Date Table'[Date],
date(2001,1,1),
FIRSTDATE( '2 Date Table'[Date])
)
)
Fig. 3. Date Slicer Style
b) Increase in accounts receivable movement card
Insert four card charts, insert four fields of [Accounts
receivable beginning of period], [Invoicing amount],
[Amount received], [Closing balance of accounts receivable],
filter the unit to million, and insert the operation symbols,
the effect is as shown in Figure4.
The first level uses CALCULATE, the expression of
which is (formula + filter condition).
The
secondlevel
filtering
condition
DATESBETWEEN, whose expression is (date table, start
date, end date)
Fig. 4. Accounts Receivable movement card
=DATESBETWEEN('2 Date Table' [Date], date
(2001,1,1), end date).
c) Create a focus list
In order to monitor the return of accounts receivable
in real time and to find the business people with whom they
are in contact, a key focus list is designed to list the
companies with high accounts receivable on their books and
the business people who are in contact with them.
The third level end date FIRSTDATE('2 Date
Table'[Date]), displays the first day of the filtered month.
c) Set unit conversions
Create a new unit conversion table with units in
dollars and tens of thousands, as shown in the Figure2.
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First, ranking of closing balance of accounts
receivable = rankx(all('2 customer information'),[closing
balance of accounts receivable]).
Second, the new [table] visualization object, the
customer information table in the "contract A" field dragged
into the line, the end of the period ranking dragged into the
value, sorted from lowest to highest order, to get the ranking
results.
Third, Drag and drop the closing receivables into the
row area to form a more complete table, as shown in the
Figure5, while ranking the results by the closing balance of
accounts receivable.
Fig. 6. Ranking of persons with uncollected accounts receivable
Fifth, the final "Real-time Analysis Model of
Company's Accounts Receivable" was created as shown in
Figure 7.
The model can filter the opening, closing, and current
debit and credit amounts of accounts receivable in any
month, and show the eight companies with the top accounts
receivable balances in the month and the accounts receivable
indicators of the relevant salesmen, so that finance and
business departments can understand the outstanding
situation in a timely manner, and guide the salesmen to
adopt corresponding methods to supervise the collection and
improve the accounts receivable turnover rate.
Fig. 5. Accounts Receivable Balance Ranking
Fourth, construct the salesperson accounts receivable
balance table in the same way, where the metric is the
closing balance of accounts receivable from sales staff=
rankx(all('2 salesperson information'),[closing balance of
accounts receivable]), as shown in Figure 6.
When new business data is generated, just add the data
source file, the new data will be automatically imported into
the model, and the analysis table data will be updated
automatically, forming a real-time monitoring mode, which
also truly allows managers to understand the situation of
accounts receivable anytime, anywhere and intuitively.
Fig. 7. Model final display effect
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(No.2018jxtd120).
IV. CONCLUSION
In the information age, data is everywhere, and the
effectiveness of information is also very important. The realtime analysis model of accounts receivable in this example
means that it can show the full picture of the amount of
accounts receivable, and it can also pass the data to finance
at the first time when the sales business occurs, and clarify
the person responsible for collection to achieve real-time
monitoring, which can greatly improve the efficiency of
accounts receivable management. Based on this model,
enterprise managers can analyze customer receivables data,
grade customers' credit levels and set corresponding credit
limits. They can also use the real-time payback situation in
the model as the basis for assessing sales personnel, such as
giving early warning to sales personnel who cause overdue
accounts, deducting their salaries for sales personnel who
cause bad debt losses, etc. In general, the model is useful for
enterprise managers to grasp the information of accounts
receivable and improve the efficiency of accounts receivable
recovery through practical testing.
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ACKNOWLEDGMENT
This research was supported by Anhui University
Excellent Top Talent Cultivation Funding Project
(No.gxbjZD2021039) and Anhui Quality Project - High
Level Teaching Team of Financial Accounting
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