Analysis of E-Commerce Process in the Downstream Section of Supply Chain Management Based on Process and Data Mining

Analysis of E-Commerce Process in the Downstream Section of Supply Chain Management Based on Process and Data Mining

Ferra Arik TridalestariMustafid Budi Warsito Adi Wibowo Hanung Nindito Prasetyo 

Doctoral Program of Information System, School of Postgraduate Studies, Diponegoro University, Semarang 50241, Indonesia

Department of Digital Business, Faculty of Business, Universitas PGRI Yogyakarta, DI Yogyakarta 55182, Indonesia

Department of Statistics, Diponegoro University, Tembalang, Semarang 50275, Indonesia

Department of Informatics, Diponegoro University, Tembalang, Semarang 50257, Indonesia

Department of Information System Diploma, School of Applied Science, Telkom University, Bandung 40257, Indonesia

Corresponding Author Email: 
ferraarik@upy.ac.id
Page: 
81-91
|
DOI: 
https://doi.org/10.18280/isi.270110
Received: 
15 November 2021
|
Revised: 
28 January 2022
|
Accepted: 
12 February 2022
|
Available online: 
28 February 2022
| Citation

© 2022 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

OPEN ACCESS

Abstract: 

Most businesses today use ecommerce stores and/or ecommerce platforms to carry out online marketing and sales activities. The rapid increase in the volume of E-commerce sales transactions normatively causes various problems that occur, especially in this case the buyer or consumer. Consumers expressed dissatisfaction in their e-commerce delivery experience. Customers often complain to sellers in the marketplace about the delay in sending the ordered package. This paper proposes a research model that is proposed in analyzing the datasets generated from the Downstream Supply Chain Management process, especially the process of selling and shipping E-Commerce goods to end customers. The mechanism used is collaborating process mining and data mining so that the resulting analysis becomes more powerful and better information is obtained compared to only analyzing separately. The results of the analysis in the case study of the E-commerce Costumer to Customer (C2C) marketplace show that process mining related to shipping goods can be explained by adding the results of data mining analysis from the datasets obtained, especially the processes in the Downstream Supply Chain Management Section.

Keywords: 

downstream supply chain management, E-commerce, process mining, data mining

1. Introduction

The Covid-19 Pandemic period has become one of the factors in accelerating digitalization at every line and level, starting from government, education, work behavior, and the most influential, namely in the trade sector [1]. In a trading company, Supply Chain Management has a very important function and role, because it relates to the company's business processes starting from the process of providing raw materials, processing raw material products into finished products, to how the process of distributing these products or services can be accepted by consumers [2]. To be able to increase the company's competitiveness is to make efficiency in terms of supply chains and apply the right information technology. Supply chain management that is assisted by using information technology will provide long-term benefits so that relevant parties in the supply chain can work together and be able to share information. With this Pandemic condition, the company must be sensitive in carrying out the supply chain management process. The need for demand, speed and customer satisfaction is very important for companies to always improve the service processes that are run [3]. No exception in this case are E-commerce companies in Indonesia. Currently, E-Commerce companies in Indonesia are very dominant in trading in national and international markets [4]. In the last decade, E-Commerce Startup companies have been born that are able to reach the Unicorn and Decacorn Levels in the Southeast Asia Region originating from Indonesia such as Tokopedia, Bukalapak and so on [5].

E-commerce is all buying and selling activities carried out through electronic media. Although the facilities include television and telephone, now e-commerce is more common through the internet [6]. In general, customers in C2C e-commerce only retail. The medium for such transactions to occur is known as the marketplace. Marketplace is one of the e-commerce models, where it serves as an intermediary between sellers and buyers [7]. Business models in E-commerce are very diverse, so in this paper the context used is the type of Customer to Customer (C2C) in the E-Commerce Marketplace. In this type of business model, where E-Commerce marketplace users can act as customers and sellers at the same time [8]. Users who sell on the marketplace only need to meet the needs of buyers. All other activities such as managing the website have been taken care of by the platform.

The increase in the growth of sellers in the marketplace is in line with the ever-increasing market demand [9]. The increasing needs of people's lifestyles make the volume of business transactions in the marketplace increase rapidly [10]. The very rapid increase in transaction volume has also caused various problems, especially for the buyer. Most of the fear when shopping online is shipping [11]. Will the ordered goods be delivered immediately? Or worst of all, what if it turns out that the order never arrived at the intended address? Customers often complain to sellers in the marketplace about the delay in the delivery of the ordered package. This raises the question who is at fault, is it the seller or the shipper?

Delivery Process is still the biggest challenge for e-commerce. In a survey conducted by iPrice Group and Parcel Perform, 36% of consumers expressed dissatisfaction with their e-commerce delivery experience. Of course, this complaint cannot be underestimated, especially considering that Indonesia's e-commerce growth is quite high, at 10.3% over the last five years. Consumer dissatisfaction with e-commerce shipments was revealed in a survey by iPrice Group and Parcel Perform, an independent parcel tracking service that tracks more than 600 logistics operators globally. For this survey alone, it involved more than 80,000 consumers in Malaysia, Singapore, Indonesia, Vietnam, and Thailand [12]. The results of the survey show 35% of consumers still consider shipping as the biggest problem in e-commerce. In addition, more than 90% of complaints and negative feedback from customers, usually related to delays in delivery or poor communication about delivery status [13].

There are many studies related to Supply Chain Management, especially in the field of Information Systems. But in the last 5 years not much has been discussed about process mining and data mining. Process mining research in the supply chain was conducted in 2015. In this paper, the researcher tries to investigate the feasibility of applying process mining to model supply chain processes through real case studies. The researcher determined that supply chain business process management as part of the implementation of Business Process Management (BPM) [14]. Then still by the same researcher, the research concept was improved by discussing CRM in 2018. In this publication, the researcher presents how the implementation of process mining is carried out to analyze the process of customer needs. This research is expected to provide a foundation for companies to develop standard procedures, establish service level agreements and propose potential planning processes to better meet customer demands based on CRM systems [15]. In 2018 there was another research publication that presented the application of process mining in the purchasing process from the heavy manufacturing industry. The Event Log of this process is extracted from the company's Supply Chain Management System related to Shipbuilding Processing plans, then filtered and analyzed using process mining techniques. The results of the analysis can be used by companies to optimize and improve processes at manufacturing companies of naval and ship spare parts [16]. There is another interesting research, namely research that tries to improve the manual mechanism and replace it with a process mining approach. The research conducted is to develop a methodology to apply process mining to the internal logistics department for a mixed model assembly line. This methodology combines multi-dimensional process mining (MDPM) techniques with proven production streamlining principles and VSM [17]. Another recent study related to process mining in the supply chain was published in 2021. In this research, the researcher proposes and evaluates the use of the Open Trip Model (OTM) for Process mining. Inspired by the industry's current use of OTM for reporting and business intelligence, researchers believe that the OTM data model can be used for unified storage, integration, interoperability, and querying of logistics event data. Therefore, the OTM model data is mapped to a generic event log structure to meet the minimum process mining requirements [18]. Still in the same year, another study tried to integrate the SIX Sigma concept with process mining. This research aims to describe the practical implementation of the problem solving structure provided by the Define, Measure, Analyze, Improve and Control (DMAIC) framework in combination with the analytical concepts provided by process mining, to improve the quality of the supply chain of health care providers. The mechanism is carried out to identify the existing framework by combining six sigma with process mining [19].

What about Data Mining? In 2017, there was a research publication proposing a food safety warning system, adopting the use of data mining with an association approach and Internet of Things technology to monitor all data in real-time from the entire supply chain and also automatically provide advance warnings. A case study of a dairy producer was conducted and the results show that the proposed early warning system can effectively identify safety risks and accurately determine whether a warning should be issued, depending on expert analysis when an abnormality is detected by the system [20]. In 2019, another study put forward the idea of customer clustering and dynamic customer clustering. A customer clustering dataset is provided to supply the production ranking order. The results show that the customer clustering idea effectively reduces the difficulty in solving modeling. The concept used is able to increase the flexibility of customer grouping [21]. Supply Chain research using data mining was also conducted in the same year. The research conducted focuses on the role of the use of information and proposes a relational model that considers the integration of information in the supply chain with a data mining approach. Complete integration is achieved when all supply chain members or actors share a common goal. This becomes the basis for the integration modeling carried out [22]. In the same year, there were studies that put forward proposals based on extreme distance calculation methods in the field of data mining technology, new su

2. Material & Methods

2.1 Supply chain management

Supply Chain Management is a management activity from obtaining raw goods to finished goods to consumers. Supply Chain is divided into 3 main components, namely Upstream Supply Chain, Internal Supply Chain or called workflow, and Downstream Supply Chain [25].

2.2 Process mining & data mining

Process mining is a relatively new science in the last decade. Basically, process mining is a technique that helps organizations analyze and monitor internal processes to identify their activities and resources. In the past, organizations would manage this process through a series of interviews that produced an overall ideal summary of the business. Process mining allows companies to automatically monitor and track business processes in real-time without human intervention [26]. Another important aspect of process mining is that it enables business processes to identify process risks and bottlenecks, determine potential improvements that can improve efficiency, and monitor data to predict future events [27].

Figure 1. The relationship between process mining and data mining

While data mining is a process of extracting or collecting important information from a large data [28]. The data mining process often uses statistical, mathematical methods or utilizes artificial intelligence technology. Things that are done in data mining activities include data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation. Data mining has many functions. There are two main functions, namely descriptive function and predictive function [29]. To see the relationship between process mining and data mining as shown in Figure 1.

Figure 1 shows that process mining and data mining are connected through the exploratory concept of business process management [30]. So, in this case the use of process mining and data mining can be done simultaneously to find a more powerful analysis instead of using only one. The Data Mining concept used in this study is K-Mean Clustering with Davies Bouldin Analysis.

2.3 Methodology

The methodology used in this paper is as described in the following steps:

  1. Identification of data needs

For the needs of this paper, data was collected from one of the E-commerce that serves online sales. The seller uses an E-Commerce system associated with various expedition parties. The system has provided a sales data import feature from the transaction process that occurs.

  1. Generate e-commerce Downstream data

At this stage, it takes data based on a certain period. Data retrieval is in accordance with the needs of the analysis so that the attributes or variables are adjusted to the method of analysis carried out. The mechanism is shown as Figure 2.

Figure 2 shows that after the data retrieval process, the data is then converted into excel data to then be adjusted to the needs of the analysis and tools used in this study.

  1. Pre-processing data

At this stage, data preprocessing is carried out. The data that has been obtained is divided into two parts namely Numeric data & Timestamp Data.

  1. Discovery model process & data mining process

At this stage perform data processing for both timestamp data and numeric data. The tools used in this process are ProM and DISCO tools for measurement mechanisms in process discovery and Excel and Rapidminer tools for data mining mechanisms such as regression and K-Mean Clustering.

  1. Result analysis

This stage analyzes the results from the previous stage to evaluate the Downstream E-Commerce process that occurs.

Figure 2. Steps of e-commerce Downstream data collaboration analysis

3. Result & Discussion

3.1 Result

As the basis for the analysis is the Downstream SCM E-Commerce process as shown in Figure 3.

Figure 3 shows the processes that occur in Downstream Supply Chain Management. In the picture there are 3 processes involved, namely the ordering process from the customer, the approval process and packing the package from the seller and the shipping process by the expedition company.

The first mechanism is to obtain datasets taken from the running system according to the analysis needs to be used. The dataset is taken from three processes that occur as shown in Figure 3. The total attributes obtained are 24 with various data types such as numeric, character, category, date and time. The attributes obtained are as shown in Table 1.

The next step is to divide the table into 2 groups as shown in Table 2 and Table 3.

Figure 3. Process in Downstream supply chain management in B2C e-commerce

Table 1. Dataset attributes

Attribute

Data Type

ID Order

Character

Order Status

Category

Make order

Date & Time

No. receipt

Character

Product name

Category

Variation Name

Category

Initial Price

numeric

Amount

numeric

Total Product Price

numeric

Number of Products in Order

numeric

Total payment

numeric

Order Payment

Date & Time

Recipient's name

Character

Shipping address

Character

County town

Character

province

Category

Order Approved

Date & Time

Package Received Expedition

Date & Time

Shipping Warehouse

Date & Time

Package Shipping

Date & Time

Receiving warehouse

Date & Time

Package to Customer

Date & Time

Received by Customer

Date & Time

Done Approved

Date & Time

Table 2. Event Log dataset for data mining

Case ID

Events

Started

Finished

Duration (Minute)

Expedition Type

City

190710123661H8U

7

10.07.2019 11:36:00

13.07.2019 06:29:00

4013

Expedition A

City A

190720055566T5G

7

20.07.2019 04:55:00

24.07.2019 16:32:00

6457

Expedition A

City D

19072506336Q7QX

10

25.07.2019 05:33:00

27.07.2019 18:06:00

3633

Expedition B

City B

190725102165JA2

10

25.07.2019 09:21:00

29.07.2019 17:16:00

6235

Expedition B

City A

19073015476M26F

10

30.07.2019 14:47:00

02.08.2019 08:42:00

3955

Expedition B

City D

19073121496N31V

10

31.07.2019 20:49:00

02.08.2019 21:44:00

2935

Expedition C

City A

190803212265CA5

10

03.08.2019 20:22:00

08.08.2019 14:03:00

6821

Expedition B

City E

19081015286BQV6

10

10.08.2019 14:28:00

15.08.2019 17:16:00

7368

Expedition E

City A

19081119426KSPQ

10

11.08.2019 18:42:00

15.08.2019 21:52:00

5950

Expedition A

City C

19081311556NE7V

10

13.08.2019 10:55:00

20.08.2019 12:49:00

10194

Expedition B

City C

19082118596RDSF

10

21.08.2019 17:59:00

23.08.2019 06:05:00

2166

Expedition C

City A

19082219296HYY2

10

22.08.2019 18:29:00

24.08.2019 14:30:00

2641

Expedition B

City E

1908271408656K2

10

27.08.2019 13:08:00

28.08.2019 14:11:00

1503

Expedition B

City A

19083015266FP38

10

30.08.2019 14:26:00

01.09.2019 20:21:00

3235

Expedition B

City B

 

 

200129VNVBVB5K

10

29.12.2019 15:26:00

02.02.2020 05:23:00

49797

Expedition B

City B

Table 3. Event log proses e-commerce Downstream supply chain management

ID Case

Activity

Time Stamp

190710123661H8U

Make order

2019-07-10 11:36

190710123661H8U

Order Payment

2019-07-10 11:36

190710123661H8U

Order Approved

2019-07-10 11:36

190710123661H8U

Package Received Expedition

2019-07-10 22:24

190710123661H8U

Package Shipping

2019-07-12 11:28

190710123661H8U

Received by Customer

2019-07-12 18:28

190710123661H8U

Done Approved

2019-07-13 06:29

190720055566T5G

Make order

2019-07-20 04:55

190720055566T5G

Order Payment

2019-07-20 04:58

190720055566T5G

Order Approved

2019-07-20 04:58

190720055566T5G

Package Received Expedition

2019-07-22 22:17

190720055566T5G

Package Shipping

2019-07-24 10:39

190720055566T5G

Received by Customer

2019-07-24 13:39

190720055566T5G

Done Approved

2019-07-24 16:32

19072506336Q7QX

Make order

2019-07-25 05:33

19072506336Q7QX

Order Payment

2019-07-25 05:34

19072506336Q7QX

Order Approved

2019-07-25 05:34

19072506336Q7QX

Package Received Expedition

2019-07-25 10:29

19072506336Q7QX

Shipping Warehouse

2019-07-25 16:27

19072506336Q7QX

Package Shipping

2019-07-25 19:18

19072506336Q7QX

Receiving warehouse

2019-07-27 00:30

…..

…..

…..

3.1.1 Process mining mechanism

In this case, after getting the Event Log, the next step is to model the process using the mining process mechanism using the Inductive Miner Algorithm. The use of the Inductive Miner Algorithm is based on many process mining algorithms. Inductive Miner Algorithm is the most stable algorithm in process modeling [31]. The data description of the event log is shown in Table 4.

Table 4. Event log description

Component

Score

Events

972

Cases

99

Activities

10

Mean Case Duration

4.7 day

Start

10-7-2019 11:36

End

02-02-2020 05:23

Table 4 shows that the total number of events in the Event Log Dataset reached 972 with 9 cases. The dataset period used was from 10-7-2019 to 2-2-2020 with an average duration of each case being 4,7 days which means the length of service for each transaction. For more details, the spread of transactions can be seen in Figure 4. Figure 4 shows that near the end of the year, the transaction volume experienced a significant increase.

Using the ProM Visual Machine Learning application and the Inductive Miner Algorithm, Petri net was obtained from the Downstream E-Commerce process from the existing dataset as shown in Figure 5.

Process performance shows that bottlenecks occur in the shipping process area by expedition as shown in Figure 6 for activities that are colored red. This is as shown in Table 5.

Table 5. Activities that are bottlenecked

Bottleneck Activities

Waiting Time

Sink Package Shipping to Receiving Warehouse

20.37 hours

Receiving Warehouse

21.01 hours

Received by Customer

7.35 hours

Sink package to customer to Receive by Customer

1.38 day

Sink Received by Customer to Done Approved

20.49 hours

Done Approved

23.32 hours

Figure 4. Distribution of transactions in period

Figure 5. Petri net proses Downstream supply chain management e-commerce C2C

Figure 6. Measurement results of Downstream supply chain management process performance in C2C e-commerce

Table 5 shows the activities that experienced bottlenecks. Activities that experience bottlenecks are in the process of shipping goods carried out by expeditions. Activities that experienced a significant bottleneck were sending packages to the receiving warehouse, receiving packages at the warehouse, and sending packages to end customers. The activity that experienced the longest bottleneck condition was the activity of sending packages from the receiving warehouse to the end customer which took up to 1.38 days. This activity is known to be a mechanism for distributing packages to end customers in a city by couriers.

3.1.2 Data mining mechanism

The next step is the data mining mechanism on the selected dataset. As previously explained, the dataset used is a special event log dataset for data mining in tracing the delivery process that occurs. The data mining mechanism carried out in this process is to use clustering. The algorithms used are K-Mean Clustering and Davies Bouldin Approach.

With the Davies Bouldin Approach, Cluster selection is carried out by performing 7 iterations, the first iteration (Clustering (k-2)) selecting 2 clusters and then obtaining the Davies Bouldin value, then the second iteration (Clustering (k-3)) selecting 3 clusters until the fifth iteration (Clustering (k-6)) with the number of selected clusters 6.

Determination of the number of clusters is based on Davies Bouldin values, based on data processing using Rapidminer, Davies Bouldin values are obtained as Table 6.

Table 6. Davies Bouldin's value in K-means clustering iteration

No

Number of K

Davies Bouldin’s Value

1

2

0.016

2

3

0.031

3

4

0.270

4

5

0.327

5

6

0.361

6

7

0.383

7

8

0.327

Based on the Davies Bouldin value, the Davies Bouldin value drops back to K=8, meaning the number of selected clusters is 7 at the Davies Bouldin value of 0.383. The results of the cluster grouping are as shown in Table 7.

Obtained Cluster as Figure 7.

Table 7. Distribution of data on the 7 selected Clusters

Cluster

Items

0

33

1

1

2

1

3

9

4

33

5

15

6

17

7

23

Totally

251

3.1.3 Cluster division

Based on the data processing of the Cluster division based on indicators namely Duration, Expedition and Province, the order of the clusters is obtained, namely Cluster 0, Cluster 4, Cluster 6, Cluster 3, Cluster 5, Cluster 1, and Cluster 2. For the distribution of data in the cluster can be seen in the cluster grouping in Tables 8-14.

Table 8. Cluster 0

Average Duration

6310.70

Expedition

Q

Expedition A

21

Expedition B

4

Expedition C

3

Expedition D

4

Expedition E

1

Expedition F

0

Expedition G

0

Province

 

Province A

7

Province B

8

Province C

8

Province D

3

Province E

1

Province F

3

Province G

2

Province H

1

Province I

0

Figure 7. Graph of the determination of K-Mean clustering results on the dataset

Table 9. Cluster 4

Average Duration

6340.70

Expedition

Q

Expedition A

7

Expedition B

5

Expedition C

1

Expedition D

0

Expedition E

0

Expedition F

1

Expedition G

1

Province

 

Province A

5

Province B

2

Province C

1

Province D

1

Province E

1

Province F

0

Province G

1

Province H

2

Province I

2

Table 10. Cluster 6

Average Duration

6405.62

Expedition

Q

Expedition A

18

Expedition B

4

Expedition C

1

Expedition D

0

Expedition E

0

Expedition F

0

Expedition G

0

Province

 

Province A

4

Province B

3

Province C

5

Province D

1

Province E

4

Province F

1

Province G

3

Province H

1

Province I

1

Table 11. Cluster 3

Average Duration

7280.87

Expedition

Q

Expedition A

8

Expedition B

0

Expedition C

0

Expedition D

1

Expedition E

0

Expedition F

0

Expedition G

0

Province

 

Province A

2

Province B

3

Province C

0

Province D

1

Province E

1

Province F

1

Province G

0

Province H

1

Province I

0

Table 12. Cluster 5

Average Duration

6419.23

Expedition

Q

Expedition A

12

Expedition B

4

Expedition C

0

Expedition D

0

Expedition E

1

Expedition F

0

Expedition G

0

Province

 

Province A

5

Province B

4

Province C

4

Province D

2

Province E

1

Province F

1

Province G

0

Province H

0

Province I

0

Table 13. Cluster 1

Average Duration

155245

Expedition

Q

Expedition A

1

Expedition B

0

Expedition C

0

Expedition D

0

Expedition E

0

Expedition F

0

Expedition G

0

Province

 

Province A

1

Province B

0

Province C

0

Province D

0

Province E

0

Province F

0

Province G

0

Province H

0

Province I

0

Table 14. Cluster 2

Average Duration

49797

Expedition

Q

Expedition A

1

Expedition B

0

Expedition C

0

Expedition D

0

Expedition E

0

Expedition F

0

Expedition G

0

Province

 

Province A

0

Province B

0

Province C

0

Province D

1

Province E

0

Province F

0

Province G

0

Province H

0

Province I

0

3.2 Discussion

Based on the approach used in this study, namely the collaborative analysis of mining processes and data mining. The two approaches can reinforce each other in the results of the analysis. In the mining process can be seen the performance of the process and in data mining can be seen the pattern of data distribution. This relationship can be shown in Figure 8.

Figure 8. Collaboration point

The results of the analysis of the collaboration show that the problematic activities in the Downstream SCM process in the C2C pattern E-commerce are activities that are in the process of shipping goods by expeditions. Some processes experience bottlenecks as shown in the table. Activities that experienced a significant bottleneck were sending packages to the receiving warehouse, receiving packages at the warehouse, and sending packages to end customers. Then for the pattern of events of the process can be seen in the resulting cluster pattern. Expedition A as an entity that delivers goods carries out extreme delivery durations in clusters 2, 1, 3, and 5. Cities that can be of particular concern to Expedition A are provinces A, B, C, D, and E. Then the results of the regression analysis show that the number of activities in the process does not really affect the duration of delivery. The results of regression analysis were carried out to see the effect of the number of activities on the process on the duration of delivery. This is done because in the delivery process there are differences in the number of activities. There is a delivery process that does not go through the storage warehouse and is sent directly to the customer and there is a process that goes through the storage warehouse for delivery and the warehouse for receiving incoming goods.

4. Recommendation

Based on the results of the analysis and discussion, it is necessary to improve the process of shipping goods carried out by the expedition. The recommendations that are needed as notes in the evaluation are as follows.

  1. Receiving goods at the receiving warehouse should have a target time set by the shipping company.
  2. Delivery of goods to end-customers from the receiving warehouse should be added with a target time. If the target time is not achieved, then an evaluation of the personnel involved can be carried out. With a high volume of shipments, it can be considered to increase the number of courier personnel who distribute packages to end-customers.
  3. For provinces that are detected as experiencing bottlenecks in the process of sending goods or packages, the company can evaluate the processes that occur in that province.
  4. From the results of the regression analysis, it can be seen that the number of activities in the process does not really affect the duration of the delivery of packages or goods so that the number of activities can be ignored.
  5. Expedition A spread to almost all clusters because it dominated as the main expedition. However, Expedition A as the owner of the shipping process must at least improve and evaluate the duration of the package delivery process carried out by them, especially the delivery process carried out in provinces A, B, C, D, and E.
  6. With the results of the analysis, the delivery service company can evaluate the package delivery process as the company's core business. Delivery service companies have a very important role in online business because they are in direct contact with consumers. A big role is the issue of delivery time, security of goods, wider market reach, and another important thing is increasing customer loyalty.
5. CONCLUSION & FUTURE WORKS

The Downstream Process in Supply Chain Management is one of the important processes because it directly intersects with end-customers. In this study, using a collaborative approach between the mining process and data mining, it can be shown that in the three processes analyzed, namely the process of ordering goods, the process of packing goods and the process of shipping goods, bottleneck conditions occur in the delivery of goods, namely in the activity of sending packages to the receiving warehouse, receiving packages in warehouses, and sending packages to end customers. These activities have high waiting times. This is like the initial identification that customers often complain about the delay in the delivery of goods or packages. The process of delays in the delivery of goods does occur as a whole, but to find out which expedition or province is experiencing delays, using data mining, the results obtained are generally expedition an experienced delivery delays in several places, namely provinces A, B, C, D, and E. On this basis, the shipping company, especially Expedition A can improve and evaluate the ongoing process related to the delivery of goods or packages by taking into account the recommendations given in this study.

The collaborative approach in this research shows that in the future mining and data mining processes can strengthen each other in analysis by preparing dataset scenarios and the analysis to be carried out. Such an approach can open avenues of research in various fields that specialize in process analysis. Sometimes it is not enough to only perform process analysis but also to perform data mining to complement the resulting analysis. Although some studies sometimes separate process mining and data mining, it is very open to research that collaborates both. With the growth in the level of large volume of E-Commerce transactions, it can be associated with concepts such as Big Data and IoT technology that is currently developing very open to collaborating between process mining and data mining approaches.

Acknowledgment

This research was supported by the Doctoral Program in Information Systems at the Graduate School of Diponegoro University, Semarang. Without the support of the Supply Chain Management Laboratory in the Information Systems Doctoral program, access to international journals and applications owned by Diponegoro University, this research would not have happened.

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