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An Application of Pareto Analysis and Cause-Effect Diagram 
for analysis of the adversities that influence the reverse 
logistics of e-commerce 
 
Leonardo Santos Camargo1, Matheus Henrique Rossini1, Rodrigo Pereira Macedo1 
1 
Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Itapeva Experimental Campus, São Paulo, Brazil. 
 
Abstract: The technology is advancing each time more, and many sectors that were previously manual, now 
become computerized, such as trades. Previously, everything was done manually, face to face. However, now all 
this happens virtually, using the internet, without the consumer having physical contact with the product they are 
purchasing. However, with the many benefits that this can bring forward, there are also new problems to be 
solved, such as purchases processing, exchange, order return, among other issues to be analyzed. From this, you 
can use reverse logistics concepts and quality control tools such as Pareto diagram, Ishikawa diagram, to solve 
problems and improve this new form of commerce, called e-commerce, specifically addressing a store online of 
sports uniforms. 
Keywords: E-commerce, Reverse Logistic, Pareto Analysis, Quality, Root Cause 
 
I. Introduction 
 
 The emergence of the Internet has provided a new approach where a computer connected to a network, 
brought a new way to conduct trade. The advent of this new technology, took place in the 1990s in the United 
States, having a rapid spread to Europe and other populations already more developed around the world. 
 Because of its great potential, e-commerce has developed very quickly, and before long, it was an 
essential technology for all, and of course, already had a highly competitive market for retailers. Thus, to 
establish in the online market, retailers must always be renewing their processes to increase their profits, cut their 
losses, and cultivate their reputation with the virtual world. 
 Thus, in this paper will treated to some studies based on quality control tools to analyze the reverse 
logistics of this online trading process to its improvement and an effective improvement. 
______________________________________________________________________________________ 
II. Background Of The Study 
2.1 E-commerce 
Since the moment men begin to perform the exchange of services and goods, for cash, the word 
commerce first comes up. Increasingly, the way these trades happen, have been renewed, and follow the 
technological development of society. According to Novaes (2007), formerly, control of the payment of a product 
or service, was done manually, but the realization of such transaction by electronic means is currently growing 
increasingly. As this new form of commerce, called "e-commerce". 
The big growth of this market can be confirmed analyzing the following graphic: 
 
Graphic 1: E-consumers growth in Brazil since 2013. 
 
 
According to Fernandes et al. (2011), the definition of e-commerce is linked to the computer and the 
Internet, however, 30 years ago, a commercial transaction carried out by means of electronic equipment was 
already considered e-commerce, making the meaning of the word changes, only adapting to the current context. 
Kotler, cited Scandiuzzi (2011) defines e-commerce as commerce purchases and sales made online, and 
shows seven ways to be successful in this environment: conduct business research, provide information about the 
product or service, promote debate, offer training, and offer products and services through bits (online). 
The following chart shows the increase in online sales amount. 
Graphic 2: Online sales growth (consumer goods) in Brazil from the first half of 2011. 
 
 
2.2 Reverse logistics 
Reverse logistics, is a logistics concept that can be considered recent. The first studies and activities of 
the area are dated back to the 70 and 80. At first, the focus was directed to return goods with the purpose of use 
for recycling thereof (Hernández et al., 2012). 
According to Rogers and Tibben-Lembke (1998), the reverse logistics concept includes the same scope of 
activities of direct logistics; the difference is that it covers such activities in a inverse way. 
However, over the past decades, reverse logistics has been covered to new interests, such as creating 
and/or recovery value, after sales returns and also returns after consumption (Leite, 2012). 
 
2.3 Reverse logistics influence in e-commerce 
 As the E-bit report: 
When making a purchase, consumers feel more secure and confident when you know you can 
exchange or return the product in case of problems. In electronic commerce, the availability of 
these services and the ease of using them are important, especially because the contact 
between customer and store is not face by face (E-BIT, 2014, p. 22). Thus, reverse logistics 
can be considered an essential variable in the case of electronic marketing scenario, which the 
client does not have physical access to the product prior to purchase, which may increase the 
chances of intent to exchange / return the same when this comes in the consumer's hands. 
 Thus, you can check the level of importance that reverse logistics has in e-commerce, as it will 
influence whether the customer will or will not continue to make purchases, or will directly affect the loyalty and 
reputation of the company. 
 
 
III. Methodology 
 
This study makes use of quality tools to minimize reverse logistic effects. It presents the theoretical 
ideas about reverse logistic effects and quality tools specially Pareto Analysis and Cause-Effect diagram. The 
case study research conducted on the selected e-commerce company of sportive uniforms “iSports”. This step 
includes the understanding about the quality control system of the selected business and how this could be 
improved. The conceptual development includes solutions analysis that could minimize these effects, then will 
be related to their respective major areas. Finally, the last step consists in a theoretical and math analysis about 
the quality control on ecommerce logistic. Steps involved in the study: 
 
Step 1: Company Selection 
After gathering information, we made contact with some sportive articles companies that entered the 
ecommerce recently, to help in their development applying our knowledge on quality control. Then, we have 
selected a particular sportive uniforms company in São Paulo, Brazil. 
 
 
 
Step 2: Conducting of Case Study 
Finally we conduct our research work in a particular sportive uniforms company named “iSports” 
established in 2010 which situates in São Paulo, Brazil. The demography of the situated organization is 
presented in Table 1. 
 
Table 1. Demography of “iSports” 
 
Company Name iSports 
Location São Paulo, Brazil 
Established 2010 
Product type Sportive uniforms 
Number of production line 1 
Total worker 20 
Production capacity per day 5000 units 
Working hour per day 10 Hours (maximum) 
Buyers Brazil 
 
Step 3: Gather Information 
In this step, we have gathered information on the Quality Control system of the logistic section 
of the selected ecommerce company. Here we have collected data of reasons for products return through 
reverse logistic provided by the management which is used for the Analysis purpose of the study. 
 
Step 5: Identify the Problem 
Identification of the major return reasons to minimize the reverse logistic effects was next step. 
According to the observation and using management data, we detected some common reasons for products 
return. Therefore, we tried to do our research work on this section, which is our major concern. 
 
Step 6: Analysis and Suggestions 
In this step Pareto Analysisis performed which is important to identify the principal products 
return reasons. After that, we constructed a Cause- Effect Diagrams for principal reasons. Then we have 
provided some respective suggestions to minimize those frequency. 
 
 
IV. Findings And Data Analysis 
In the previous section we discussed the identification of possible reasons that lead to the reverse 
logistics phenomenon. From our observation and data provided by the company management, we have seen 
that there is a reasonable amount of products return to the company for various reasons, causing waste of time 
and human resources, and increase the company's costs. Based on this, we try to focus on the main reasons in 
order to minimize the amount of products returned. For this, we use Pareto analysis to identify the main 
reasons for consumer dissatisfaction, and then built a cause-effect diagram to analyze the cause of these 
problems. Finally, a list of suggestions has been developed that can help to minimize this problem. 
 
4.1 Process Overview 
We conducted our study in an e-commerce company of sports uniforms called "iSports" established in 
2010 in São Paulo, Brazil. Our research was conducted in all business processes, considering that it does not 
have a production line, but only a distribution center for the rest of the country. In this center we have the 
processing of claims and payments, checking stock, product packaging, and distribution. Figure 1 shows a 
general outline of the process. 
 
Figure 1: Flowchart of process paths 
 
 
4.2 Data Collection 
 For our research work, we collected data from the past five months, except current, comprising the 
period from May to September 2016. The Customer Service of the company provided the data for this study. 
Each product that returns is recorded in a Quality Control Check Sheet shown in Figure 2. Supervisors meet this 
sheet with the customer name, order number, the specification of returned/canceled/changed product, the amount 
of this product and the reason for the return/cancellation/exchange. The company on which made this study uses 
codes to identify the reasons for return/cancellation/exchange of a product. From the data analysis, we identified 
7 different reasons from 12 issues of the process. This different reasons and their codes are given in Table 2. The 
data relating various reasons with the process issues are presented in Table 3. 
 
 
Figure 2: Sample of a Check Sheet 
 
 
Table 2: Process defects with their corresponding codes 
Sl No. Defect Type Defect Codes 
1 Wrong Model A 
2 Wrong Dimensions B 
3 Wrong Amount C 
4 Product Damaged D 
5 Product Exchange E 
6 Delivery Problems F 
7 Waiver G 
 
 
 
Table 3: Five months combined data of reasons and process issues. 
 
Defect Type → 
A B C D E F G TOTAL 
Defect Position ↓ 
Choice of Product 
Missing photo 4 6 2 0 0 0 0 12 
Incomplete description 8 5 0 0 0 0 0 13 
Wrong information provided by the customer 32 38 25 0 37 0 0 132 
Wrong information provided by the store 43 29 27 0 54 0 0 153 
Order processing 
Wrong product identification 3 2 7 0 4 0 0 16 
Stock organization issues 27 31 43 0 53 0 0 154 
Wrong package picked 5 2 1 0 4 0 0 12 
Shipping 
Missing address information 0 0 0 0 0 17 2 19 
Wrong address information 0 0 0 0 5 9 6 20 
Distributor overloaded 0 0 0 0 0 4 1 5 
Unsecure packages 0 0 0 0 11 7 0 18 
Customer absent at delivery 0 0 0 0 0 13 8 21 
Total 122 113 105 0 168 50 17 575 
 
Table 3. Shows the combined data of the reasons with the process issues, collected in the last five months, except 
current. Red cells represent the process problems and green represent different reasons. 
 
4.3 Pareto Analysis 
 We have carried out our Pareto analysis based on the last five months by combining the return reasons of 
products with the process problems. From this analysis, we identify the major points in the process that influence 
the return of products. The analysis is shown in Figure 3, where the horizontal axis represents the process 
problems and vertical axes of the right and left, respectively represent the percentage and quantity of occurrence 
of the problems. 
 
 
Figure 3: Pareto Analysis for top defects types 
 
4.3.1 Observations from Pareto Analysis for Top Defects Positions 
 1. Stock organization issues is the most frequent defect with 26,8% of the total. 
 2. Wrong information provided by the store is the second most frequent defect with 26,6% of the total, 
follow wrong information provided by the customer within 23% of the total. 
 3. These three defect types area the “vital few” representing 76,3% of total defects occurred. 
 4. We must to improve further Pareto Analysis on those top defect position to identify and characterize 
the vital few defect types that are responsible for maximum amount of defect. 
 
4.3.2. Further Pareto Analysis for Defect Types 
We have performed further Pareto Analysis for Stock Organization issues and Wrong information 
provided by the store and customer. From these analysis we identified news “vital few” defect types for each 
defect type out of the process defects. 
Pareto Analysis for Stock Organization issues is represented in Figure 4. 
 
Table 4: Stock organization issues defect data 
 
Stock organization issues 
Defect Types Defect Codes Defect Amount 
Product in wrong department E 12 
Product missing C 8 
Not enough amount of the product C 3 
Wrong product identification E 67 
Stock count out of date C 64 
TOTAL 154 
 
 
Figure 4: Pareto Analysis of Stock organization issues 
 
Observation from the Analysis: 
 Wrong product identification is the defect that has de more frequent data out of the total of 
defects descendant from Stock Organization issues, with 43,5% 
 Stock count out of date contribute with 41,6% 
 These two defects combine, represent 85,1% of the total 
 
Pareto Analysis for Wrong information provided by the store is shown in figure 5 
 
 
Table 5: Wrong information provided by the store defect data 
 
Wrong information provided by the store 
Defect Types Defect Codes Defect Amount 
Color did not match A 58 
Size did not match B 75 
Brand did not match A 9 
Silk did not match A 4 
Product out of stock C 7 
TOTAL 153 
 
 
Figure 5: Pareto Analysis of Wrong information provided by the store 
 
 
Observation from the Analysis: 
 Wrong size did not match is the defect that has the more frequent data with 49% of the total. 
 Color did not match has a frequent 37,9%. 
 These two defects combine, represent 86,9%. 
 
Pareto Analysis for Wrong information provided by the Customer is shown in figure 6 
 
Table 6: Wrong information provided by the customer defect data 
 
Wrong information provided by the customer 
Defect Types Defect Codes Defect Amount 
Size unities issues B 49 
Chose wrong color A 12 
Chose wrong model A 11 
Chose wrong silk A 13 
Forgot to remove from cart C 33 
Chose wrong brand A 14 
TOTAL 132 
 
 
 
Figure 6: Pareto Analysis Wrong information provided by the customer 
 
Observation from the Analysis: 
 Size unities issues typed is the defect that has the more frequent data with 37,1%.. 
 Forgot to remove from cart has a frequent 25%. 
 These two defects combine, represent 62,1%. 
 
Major Concerning Areas: 
 
Figure 7: Major Concerning Areas 
 
4.4 Result of the Pareto Analysis 
 After Pareto Analysis, it was found seven types of defect that represent the Major Concerning areas from 
the company “iSports”. The defect types and the corresponding Process Defects within their respective defect 
amount areshow in table 8. 
 
Table 7: Total Amount of Defects in Major Concerning Areas 
 
 
 
4.5 Analysis of data. 
 
Table 8: Percentage of defects in major concerning area 
 
Total number of defects 
575 
Total number of defects in major 
concerning area 
439 
Percentage of defects in major concerning 
area 
76,35% 
 
Table 9: Percentage of major concerning area 
 
Total number of concerning areas 12 
Total number of major concerning area 3 
Percentage of major concerning area 25% 
 
 By analyzing the Tables 10 and 11, we can see that if we focus our efforts on 25% of key process 
problems, we could reduce errors that lead to 76,35% of reasons of return/cancellation/change of products. 
 
4.6 Hierarchy of Cause and Cause-Effect Diagram 
 By Pareto Analysis we have identified the process problems and analyzing more sink was possible to 
identify three main. The main ones are: Stock organization issues, Wrong information provided by the costumers 
and Wrong information provided by the store. These process issues occur due to some specific causes. From our 
observation and data provided by Customer Service supervisors, we identified the causes for each type of 
problem. These causes were arranged in a hierarchy according to frequency of occurrence. The hierarchy of 
causes is shown in Tables 10, 11, and 12. Then it was built one cause-effect diagram for each process issue using 
4M (Man, Machine, Materials and Methods) bones. The diagrams are shown in Figures 8, 9 and 10. 
 
 
Table 10: Hierarchy of Causes for Stock organization issues. 
 
SL. NO. CAUSES FREQUENCY 
1 Inefficient operator 27 
2 Product with wrong label 14 
3 Product stored in the wrong place 08 
 
Table 11: Hierarchy of Causes for Wrong information provided by the customers. 
 
SL. NO. CAUSES FREQUENCY 
1 Typo 29 
2 Error on selection of specification 17 
3 Polluted layout 08 
4 Error on the website 05 
 
Table 12: Hierarchy of Causes for Wrong information provided by the store. 
 
SL. NO. CAUSES FREQUENCY 
1 Typo 15 
2 Server error 06 
 
 
 
 
Figure 8: Cause-Effect Diagram for Stock organization issues 
 
 
 
 
Figure 9: Cause-Effect diagram for Wrong information provided by the costumers 
 
 
 
 
Figure 10: Cause-Effect diagram for Wrong information provided by the store 
 
 
4.7 Suggestions to reduce top defects percentage 
 From our observations, literature review and consultation with the company's employees, we proposed 
some solutions to reduce the percentage of errors. 
 
Table 13: Suggested Solutions for Stock organization issues 
Causes Types Causes Suggested Solutions 
Man 
Inefficient operator 
Provide adequate training to the 
operators 
Product stored in the wrong place 
Create an identification system in 
stock which allows the separation 
of the products by sectors 
Machine Product labeled wrong 
Integrating the system of arrival 
of products to the identification 
system, allowing you to create 
proper identification labels for 
each product that comes to stock 
 
 
Table 14: Suggested Solutions for Wrong information provided by the costumers 
Causes Types Causes Suggested Solutions 
Man 
Typo 
Make website simple and more 
practical for providing user 
informations Error on selection of specification 
Machine Error on the website 
Contact the developer or change it 
to minimize errors on the website 
Method Polluted layout 
Wipe the website layout to make 
the information more visible 
 
 
 
 
 
 
 
 
Table 15: Suggested Solutions for Wrong information provided by the store 
Causes Types Causes Suggested Solutions 
Man Typo 
Improve and simplify 
information integration platform 
for the site 
Machine Server error 
Hiring company that makes 
periodic server maintenance 
 
4.7 Suggested Additional Features for Distribution Center 
 
 - Constant maintenance of website 
 - Keep organization in the workplace 
 - Do not change the location of stock products without prior authorization 
 - To maintain synchronization between stock and system 
 - Create a system of communication between employees 
 - System of communication between consumer and employees 
 - Ask for suggestions to customers 
 
 By following the above topics, along with the suggestions made above, the process can be improved by 
reducing the reverse logistics, and consequently satisfying its customers 
 
4.8 Result 
 
 We have found that up 76,35% defect can be reduced by concentrating only on 25% areas. We have 
provided some suggestions related to those defect types. It is almost impossible to achieve zero defect. But by 
taking effective measure it is possible to reach near zero defect. So the more successfully those suggestions can 
be applied, the more the defects can be minimized. 
 
 
 
V. Conclusion 
 Within an increasingly competitive market, companies must always be conducting a continuous 
improvement in its processes. The quality tools are those of the utmost importance for this development. Among 
the various quality tools, we made use of the Pareto diagram, and also Ishikawa diagram, which were crucial to 
find the roots of the virtual store “iSports” problems. 
 After several data analysis and Pareto diagram realization, it was found that 76.35% of problems exist 
due to 25% of causes. So, from all defects that were analyzed, the sectors in which they were being generated 
were found, thus we were able to solve the problem directly from where it came. 
 However, as we find out which sectors were generating 76.35% of problems, from the Ishikawa 
diagram, it was possible to verify if the causes of these problems were by people, machines, methods or 
materials. 
 Thus, by bringing together all cause-effect analysis, the suggestion of improvements to 25% of causes 
for 76.5% of the webshop was possible. 
 Therefore, it can be concluded that quality tools are extremely efficient for solving the store's 
problems, along with reverse logistics, which made the whole process of the company much more clear, so we 
were able to analyze and improve them, helping to keep the reputation of store in the virtual environment, by 
reducing problems involved in RL (keep buyers). It also valuable to note the importance of reducing costs for a 
company, and only carry out the expansion of profit. 
 
 
VI. References 
 
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[3] FERNANDES, F. J. M., SIMÕES, J. S. P., PÁDUA T. P. M., BARROS, E. S. Compras Virtuais: Como a 
Logística tem se firmado como Componente Essencial para o E-commerce? VIII Convibra Administração – 
Congresso Virtual Brasileiro de Administração, 2011. 
 
[4] HERNÁNDEZ, C. T.; MARINS, F. A. S.; CASTRO, R. C. Modelo de gerenciamento da logística reversa. 
Gestão & Produção, São Carlos, v. 19, n. 3, p. 445-456, 2012. 
 
[5] LEITE, P. R. Direcionadores estratégicos em programas de logística reversa no Brasil. Revista Alcance: 
Eletrônica, v. 19, n. 02, p. 182-201, 2012. 
 
[6] NOVAES, A. G. Logística e Gerenciamento da Cadeia de Distribuição. Elsevier. Rio de Janeiro, 2007. 
 
[7] ROGERS, D. S.; TIBBEN-LEMBKE, R. S. Going backwards: reverse logistics trends and practices. 
Nevada: Reverse Logistics Executive Council, 1998. 
 
[8] SCANDIUZZI, F., OLIVEIRA, M. M. B., ARAÚJO, G. J. F. A logística no e-commerce B2C: Um estudo 
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retrieval: May 10, 2012)