Volume 3, No. 10
October 2024 - (2268-2281)
p-ISSN 2980-4868 | e-ISSN 2980-4841
https://ajesh.ph/index.php/gp
The Role of
Information Processing and Digital Supply Chain Technology in Supply Chain
Resilience through Supply Chain Risk
(Management
in Manufacturing Companies in DKI Jakarta)
Azizah1*, Wahyungningsih
Santosa2, Triwulandari Satitidjati Dewayana3
Universitas Trisakti,
Indonesia
E-mail: 122012301052@std.trisakti.ac.id1, wahyuningsih@trisakti.ac.id2
ABSTRACT
The
2019 COVID-19 pandemic highlighted the importance of supply chain resilience in
the face of global disruptions. Supply chain disruptions caused by isolation
measures during the pandemic triggered market turmoil, logistical uncertainty,
and reduced production capacity, which sometimes led to shutdowns. In this
context, research on supply chain resilience is crucial to prepare companies
for future disruptions. This research aims to examine the impact of supply
chain resilience on supply chain continuity in manufacturing companies in DKI
Jakarta, focusing on the role of digital supply chains, information processing
capabilities, and supply chain risk management. This research method uses a
quantitative approach. Data were collected from 300 respondents in the basic
and chemical industry, miscellaneous industries, and consumer goods sectors
through purposive sampling techniques. The analysis was conducted using
Structural Equation Modeling (SEM) to identify the relationship between
variables. The results showed that supply chain resilience has a significant
positive influence on supply chain continuity. In addition, digital supply
chains, information processing capabilities, and effective supply chain risk
management were shown to support supply chain resilience in the studied
sectors. The implication of this research is that it is important for companies
to invest in digital technology, develop better information processing
capabilities, and implement effective risk management to strengthen their
supply chain resilience and continuity in the face of future global
uncertainty.
Keywords: Information
Processing Capability, Digital Supply Chain, Supply Chain Risk Management,
Supply Chain Resilience, Supply Chain Performance.
INTRODUCTION
Based on the data reported
supply chain Indonesia, the 2019 covid pandemic dealt a great blow not only to
social life but also to the global economy. The nationwide closure of access or
isolation carried out by several countries to prevent the transmission of
Covid-19 has had an impact on slowing down or even temporarily stopping the
flow of raw materials and finished products (Rahman et al., 2021). This disrupts demand and supply stability, increases
uncertainty in the supply chain, and causes disruptions in logistics and supply
chains that can affect the continuity of the supply chain.
In terms of supply chains,
the industry faces disruptions and internalities as well as externalities of
market turbulence and logistical uncertainty from the movement of products by
land, air, and sea (Fajarini et al., 2020), resulting in reduced capacity utilization which causes
obstacles to key activities such as procurement of raw materials, and
import/export of key components as well as restrictions on the movement of
goods, services, and labor that in some cases stop production.
The most affected
manufacturing industries are those that rely heavily on global supply chains,
international workforce, and export-intensive operations (Raihansyah et al., 2024). The problem is made worse for manufacturers who have
not diversified their suppliers and who rely on low-cost foreign suppliers to
avoid expensive regional suppliers. In addition, manufacturers who only
consider their direct suppliers are also affected without monitoring the status
of their lower-level suppliers. Supply-side shocks thus become inevitable,
resulting in unwanted economic turmoil on the demand side, which is also
reflected in a decline in income and disposable savings (ZUHRO, 2022).
Before the 2019 COVID
pandemic occurred, many companies focused more on creating competitive
advantages but neglected their companies' supply chain. After the 2019 covid
pandemic, the company shifted from efficiency to resilience to secure
sustainability. The goal of many companies today is to be agile, flexible,
collaborative, predictive, and focused on network development (Suprihadi & Kom,
2020).
Uncertainty in global supply
chains due to factors such as natural disasters, wars, demand and supply
uncertainties, and microeconomics and macroeconomics has prompted many
researchers to conduct studies on how supply chains can more effectively adapt
to change. This triggered the emergence of the term supply chain resilience,
which is a development of various fields of science, including technology,
psychology, sociology, risk management, and network theory. The term supply
chain resilience was actually known long before the 2019 Covid pandemic but was
widely discussed during and after the pandemic because it was considered a
critical aspect of the company's sustainability (Supply Chain Indonesia, 2023).
Previous research conducted
by (Shi et al., 2023) offline
and online through the WIX platform with 96 valid questionnaires out of a total
of 116 questionnaires run in Chinese manufacturing found that digital
technology has a positive effect on supply chain resilience with a standardized
weight of 0.405 which indicates that the influence is significant. Followed by
a research by (Rashid et al., 2024) conducted on 215 respondents working in the
manufacturing industry in Pakistan found that information processing capability
with the dimensions of disruptive orientation and visibility, digital supply
chain, has a positive effect on supply chain risk management, and supply chain
risk management has a positive effect on supply chain resilience.
Previous research by (Rashid et al., 2024) had limitations that the data collected only from
producers in Pakistan by (Rashid et al., 2024) recommended that further research be expanded by
collecting data from developing countries as well as other developed countries
so that previous researchers could conduct comparative studies. The following
is recommended to cover various industries for a more in-depth, informative
understanding and to produce a comprehensive assessment of supply chain risk
management and supply chain resilience.
According to (Wamba et al., 2020) and (Rosenzweig & Roth,
2007), supply chain performance is very important for all
organizations. Supply chain sustainability is evaluated using resources such as
inventory, the cost of using various resources, and returns on investment such
as customer satisfaction output, sales volume, profits, and flexibility such as
new products, supply, mix, and volume flexibility (Beamon, 2019), (Wamba et al., 2020) revealed that supply chain risks could have a negative
impact on supply chain sustainability. Previous research by (Xiao & Khan, 2024) found that supply chain resilience positively influences
supply chain performance.
Based on the above
background, the research aims to examine factors that can affect supply chain
performance such as supply chain resilience which is influenced by information
processing capabilities with the dimensions of disruptive orientation and visibility,
digital technology, digital supply chain, and supply chain risk management and
researchers will also examine the mediating effect of supply chain risk
management through information processing capabilities and digital supply chain
on supply chain resilience. Thus, the benefits in this research are to provide
empirical contributions to the development of theories related to supply chain
management, especially in terms of supply chain resilience which is influenced
by information processing capabilities, digital technology, and supply chain
risk management. This research also provides practical insights for decision
makers in companies, especially in implementing effective digital technology
and risk management strategies to improve supply chain resilience and
performance. In addition, the results of this research are expected to assist
companies in designing more comprehensive strategies to deal with disruptions
and risks that may arise in their supply chain operations, so as to improve the
competitiveness and sustainability of the company in an increasingly competitive
global market.
RESEARCH METHOD
The following research design uses a non-probability sampling technique,
i.e. respondents in the population do not have the same opportunity to be
selected as sample subjects. The technique used is purposive sampling, namely
samples are determined based on criteria set by researchers (Sekaran &
Bougie, 2016). The respondent criteria in this research
are middle/senior managers/executives who have worked for more than 5 years in
managerial experience in the basic and chemical industry companies,
miscellaneous industries, and Indonesia's consumer goods industry in DKI
Jakarta. This research collected primary data obtained directly from the
primary sources (Sekaran &
Bougie, 2016). Data collection began from July 10 to July
20, 2024, with a sample of 300 respondents with a valid response of 300
respondents. The number of respondent is in accordance with the statement of
Hair that the sample should be 10 times the number of indicators for each
variable or at least 200 respondents (Hair Jr et
al., 2019). The indicators in the research are 30, so
the number of respondents used is 300 respondents according to Hair's
statement. The questionnaire was created using a Google form and shared through
social media.
Table 1. Respondent
Profile (N=300)
|
Variable |
Description |
Frequency |
Percent |
Valid
Percent |
|
Middle/senior
managers/executives who have worked for more than 5 years in the company |
Yes |
300 |
100,0 |
100,0 |
|
Total |
300 |
|
100,0 |
|
|
Gender |
Man |
159 |
53,0 |
53,0 |
|
Woman |
141 |
47,0 |
47,0 |
|
|
Total |
300 |
|
100,0 |
|
|
Domicile |
West Jakarta
City |
63 |
21,0 |
21,0 |
|
Central Jakarta
City |
56 |
18,7 |
18,7 |
|
|
South Jakarta
City |
67 |
22,3 |
22,3 |
|
|
East Jakarta
City |
73 |
24,3 |
24,3 |
|
|
North Jakarta
City |
41 |
13,7 |
13,7 |
|
|
Total |
300 |
|
100,0 |
|
|
Corporate
Sector |
Basic Industry
and Chemical Sector |
98 |
32,7 |
32,7 |
|
Miscellaneous
Industrial Sectors |
105 |
35,0 |
35,0 |
|
|
Consumer Goods
Industry Sector |
97 |
32,3 |
32,3 |
|
|
Total |
300 |
|
100,0 |
|
|
Goods and
Chemical Industry Sector |
Cement Sector |
12 |
4,0 |
4,0 |
|
Porcelain and
Glass Ceramics Sector |
17 |
5,7 |
5,7 |
|
|
Metal Sector
and Similar |
13 |
4,3 |
4,3 |
|
|
Chemical Sector |
11 |
3,7 |
3,7 |
|
|
Plastic and
Packaging Sector |
17 |
5,7 |
5,7 |
|
|
Animal Feed
Sector |
8 |
2,7 |
2,7 |
|
|
Timber Sector
and Its Processing |
10 |
3,3 |
3,3 |
|
|
Pen and Paper
Sector |
10 |
3,3 |
3,3 |
|
|
Total |
98 |
|
100,0 |
|
|
Miscellaneous
Industrial Sectors |
Machinery and
Heavy Equipment Sector |
20 |
6,7 |
6,7 |
|
Automotive and
Components Sector |
21 |
7,0 |
7,0 |
|
|
Textile and
Garment Sector |
17 |
5,7 |
5,7 |
|
|
Footwear Sector
|
10 |
3,3 |
3,3 |
|
|
Electronics
Sector |
26 |
8,7 |
8,7 |
|
|
Cable Sector |
11 |
3,7 |
3,7 |
|
|
Total |
105 |
|
100,0 |
|
|
Consumer Goods
Industry Sector |
Food and
Beverage Industry Sector |
28 |
9,3 |
9,3 |
|
Cigarette
Sector |
19 |
6,3 |
6,3 |
|
|
Pharmaceutical
Sector |
24 |
8,0 |
8,0 |
|
|
Cosmetics and
Household Goods Sector |
9 |
3,0 |
3,0 |
|
|
Home Appliances
Sector |
17 |
5,7 |
5,7 |
|
|
Total |
97 |
|
100,0 |
Data
Testing Methods
The data testing
method in this research was carried out using the Structural Equation Model
(SEM) and processed using the Analysis of Moment Structures (AMOS)
application. SEM can check for
measurement errors. This technique can also be used to analyze the influence of
one variable on another variable or a structural equation.
Validity Test
Validity tests are
carried out to find out if the instrument actually measures what it is supposed
to measure. The criterion is to look at the factor loading value, which depends
on the sample size (Sekaran & Bougie, 2016). With a sample size of 300 respondents, the
factor loading value used is 0.35, as written by (Hair Jr et al., 2019). Based on the results of the validity test
attached to Table 2, all indicators of the research are said to be valid
so that they can be used in research.
Reliability Test
In this research,
the reliability test uses the internal consistency reliability method with
Cronbach's alpha output as a quality reference for a research instrument by
Taber (2018). The higher the value of Cronbach's alpha output, the more
homogeneous or simultaneously consistent these indicators are in measuring
variables. The following is the basis for reliability test decision-making
according to (Sekaran & Bougie, 2016):
a.
The
research instrument is said to be reliable if Cronbach's alpha value ≥
0.60.
b.
The
research instrument is said to be unreliable if Cronbach's alpha value <
0.60.
The results of the
reliability test attached to Table 2 show that all of the research's indicators
are reliable enough to advance to descriptive statistical tests.
Descriptive Statistical Test
The descriptive
statistical test measured the average value, standard deviation, minimum value,
and maximum value to determine the respondents' perceptions and responses to
the variables used in the research.
Based on Table
2, the results of the descriptive
statistical test show that the average respondent applies information
processing capability, digital technology, digital supply chain, and supply
chain risk management, which leads to the strength and sustainability of the
supply chain in manufacturing companies in basic and chemical industries,
miscellaneous, and consumer goods in DKI Jakarta. The standard deviation value
of the overall respondents was around 0.7, which showed that the respondents'
answers related to this variable were quite diverse.
Table 2. Results of
Validity, Reliability, and Descriptive Statistics Tests
|
Items |
Factor Loadings |
Cronbach's α |
Mean |
Std. Deviation |
|
|
Information Processing Capability
(IPC) |
|
0.949 |
|
|
|
|
How
much do you agree with the following statement: "Companies must conduct
a thorough analysis of supply chain disruptions." |
0.914 |
|
4.02 |
0.765 |
|
|
How
much do you agree with the following statement: "Companies must develop
a plan to avoid supply chain disruptions." |
0.905 |
|
4.06 |
0.747 |
|
|
How
much do you agree with the following statement: "Supply chain
disruptions will always haunt." |
0.920 |
|
4.07 |
0.751 |
|
|
How
much do you agree with the following statement: "Supply chain
disruptions provide opportunities for growth." |
0.895 |
|
4.06 |
0.741 |
|
|
How
much do you agree with the following statement: "Companies
should always be on the lookout for possible supply chain disruptions." |
0,919 |
|
4.05 |
0.759 |
|
|
How
much do you agree with the following statement: "Companies
should engage in search and tracking through barcodes." |
0.944 |
|
3.98 |
0.751 |
|
|
How much do you agree with the following statement: "Companies must improve information integration." |
0.944 |
|
4.01 |
0.741 |
|
|
Digital Technology (DT) |
|
0.932 |
|
|
|
|
How
much do you agree with the following statement: "It is important for
company employees to have high digital technology skills." |
0.939 |
|
4.04 |
0.744 |
|
|
How
much do you agree with the following statement: "Artificial intelligence
technology has a high impact on employee performance efficiency." |
0.942 |
|
4.09 |
0.728 |
|
|
How
much do you agree with the following statement: "Digital predictive
analytics tools have a great influence on transaction speed." |
0.934 |
|
4.09 |
0.726 |
|
|
Digital Supply Chain (DSC) |
|
0.950 |
|
|
|
|
How
much do you agree with the following statement: "Companies must adopt
the use of digital technology." |
0.910 |
|
4.00 |
0.761 |
|
|
How
much do you agree with the following statement: "The company transacts
with most suppliers using digital technology." |
0.903 |
|
4.03 |
0.738 |
|
|
How
much do you agree with the following statement: "The company uses
digital technology to transact with suppliers." |
0.928 |
|
4.02 |
0.752 |
|
|
How
much do you agree with the following statement: "The company uses
digital technology to transact with customers." |
0.886 |
|
4.06 |
0.743 |
|
|
How
much do you agree with the following statement: "High-volume corporate
transactions with customers are carried out with digital technology." |
0.935 |
|
4.03 |
0.740 |
|
|
Supply Chain Risk
Management (SCRM) |
|
0.937 |
|
|
|
|
How
much do you agree with the following statement: "The company's
operational risks have recovered (with clear responsibilities, contingency
plans)." |
0.922 |
|
4.02 |
0.752 |
|
|
How
much do you agree with the following statement: "The company responds to
risks (alternative transportation, capacity, buffer stock, suppliers)." |
0.921 |
|
4.09 |
0.745 |
|
|
How
much do you agree with the following statement: "The company must detect
risks (tracking, inspection, external and internal monitoring)." |
0.908 |
|
4.04 |
0.759 |
|
|
How
much do you agree with the following statement: "The company takes
precautions against risks (precautions, safety procedures, reliable
inventory)." |
0.917 |
|
4.07 |
0.751 |
|
|
Supply Chain Resilience
(SCR) |
|
0.937 |
|
|
|
|
How
much do you agree with the following statement: "Companies must be able
to meet demand without deviating from the set goals." |
0.912 |
|
4.00 |
0.747 |
|
|
How
much do you agree with the following statement: "The company must continue
the supply chain after an outage." |
0.928 |
|
4.07 |
0.759 |
|
|
How
much do you agree with the following statement: "Companies should
quickly reconfigure resources after an outage." |
0.915 |
|
4.01 |
0.743 |
|
|
How
much do you agree with the following statement: "The company must be
able to anticipate and overcome disruptions." |
0.914 |
|
4.07 |
0.764 |
|
|
Supply
Chain Performance (SCP) |
|
0.963 |
|
|
|
|
How
much do you agree with the following statement: "Your
company's supply chain already has the ability to modify products quickly to
meet customer needs." |
0.898 |
|
4.02 |
0.749 |
|
|
How
much do you agree with the following statement: "Your
company's supply chain allows your company to quickly introduce new products
to the company's intended market." |
0.882 |
|
4.04 |
0.762 |
|
|
How
much do you agree with the following statement: "The
shorter the length of a company's supply chain process, the better." |
0.909 |
|
4.03 |
0.760 |
|
|
How
much do you agree with the following statement: "Your
company is satisfied with the speed of the existing supply chain
process." |
0.907 |
|
4.05 |
0.761 |
|
|
How much do you agree with the following statement: "A
company's knowledge of supply chain processes, is directly proportional to
the company's performance efficiency assessment." |
0.925 |
|
4.01 |
0.745 |
|
|
How
much do you agree with the following statement: "Your
company's supply chain has an outstanding record of on-time delivery." |
0.901 |
|
3.91 |
0.743 |
|
|
How
much do you agree with the following statement: "Your
company's supply chain already provides satisfactory customer service." |
0.914 |
|
4.03 |
0.744 |
Goodness of Fit Test
Table 3. Goodness of Fit
Table
|
Measurement Type |
Measurement |
Value |
Recommended
Admission Limits |
Conclusion |
|
Absolute fit measures |
p-value |
0,000 |
≥ 0.05 |
Poor Fit |
|
RMSEA |
0,119 |
≤ 0.08 |
Poor Fit |
|
|
GFI |
0,749 |
≥ 0.90 |
Poof Fit |
|
|
Incremental fit measures |
NFI |
0,881 |
≥ 0.90 |
Marginal fit |
|
TLI |
0,882 |
≥ 0.90 |
Marginal fit |
|
|
RFI |
0,858 |
≥ 0.90 |
Marginal fit |
|
|
CFI |
0,901 |
≥ 0.90 |
Goodness of Fit |
|
|
IFI |
0,902 |
≥ 0.90 |
Goodness of Fit |
|
|
Parsimonius fit measures |
AGFI |
0,675 |
≤ GFI |
Goodness of Fit |
Based on the results of the goodness-of-fit
test, it can be seen that the type of measurement of absolute fit measures, the
measurement values of the probability values of P-value, RMSEA, and GFI show
poor fit values. For the type of measurement of incremental fit measures,
measurements from NFI, TLI, and RFI get marginal fit values, while CFI and IFI
get goodness-of-fit values. For the type of parsimonius fit measures, by
looking at the value from AGFI and showing a value that meets the criteria
below the GFI value, it is declared good-of-fit. According to Hair, Black et
al. (2019), if one of the criteria of goodness-of-fit is met, then the research
model can be continued to the hypothesis test stage.
RESULT AND
DISCUSSION
Table 4. Hypothesis Test
Table
|
|
|
Hypothesis |
Estimate |
P-Value |
Decision |
|
H1 |
: |
Digital technology has a positive effect on the digital supply chain. |
0,870 |
0,000 |
H1 supported |
|
H2 |
: |
Information processing capability has a positive effect on supply chain risk management. |
0,566 |
0,000 |
H2 supported |
|
H3 |
: |
Digital supply chain has a positive effect on supply chain risk management. |
0,435 |
0,000 |
H3 supported |
|
H4 |
: |
Supply chain risk management has a positive effect on supply chain resilience. |
0,950 |
0,000 |
H4 supported |
|
H7 |
: |
Supply chain resilience has a positive effect on supply chain performance. |
0,961 |
0,000 |
H7 supported |
H1: There is a positive
influence of digital technology on the digital supply chain.
Based on the results of
the hypothesis test in this research, the influence of digital technology on
the digital supply chain gets a p-value of 0.000 or has a significance value of
< 0.05 with an estimated value of 0.870. This result implies that digital
technology has a positive effect on the digital supply chain. This shows that Managers/Middle/Senior level who have worked for
more than 5 years in managerial experience in the basic and chemical industry,
miscellaneous industry, and Indonesia consumer goods industry in DKI Jakarta Those who apply digital
technology in their companies will experience an increase in the digital supply
chain.
H2: Information processing
capability has a positive influence on supply chain risk management.
Based on the hypothesis test results in this research, the
influence of Information processing capability on supply chain risk management
gets a p-value of 0.000 or has a significance value of < 0.05 with an
estimated value of 0.566. These results imply that information processing
capability has a positive effect on supply chain risk management. This shows
that middle/senior managers/executives who work for more than 5 years in
managerial experience in the basic and chemical industry, miscellaneous
industries, and Indonesia's consumer goods industry in DKI Jakarta who apply
information processing capabilities in their companies will experience an
increase in supply chain risk management. The results of the following research
are in line with the results of previous research by (Rashid et al., 2024), which found that information processing
capability has a positive effect on supply chain risk management.
H3: There is a positive influence of digital supply chains on supply
chain risk management.
Based on the hypothesis test results in this research, the
influence of the digital supply chain on supply chain risk management gets a
p-value of 0.000 or has a significance value of < 0.05 with an estimated
value of 0.435. These results imply that digital supply chains have a positive
effect on supply chain risk management. This shows that middle/senior
managers/executives who work for more than 5 years in managerial experience in
the basic and chemical industry, miscellaneous industries, and Indonesia's consumer
goods industry in DKI Jakarta who implement digital supply chain in their
companies will experience an increase in supply chain risk management. The
following research results are in line with the results of a previous research by (Rashid et al., 2024), which found that digital supply chains have a
positive effect on supply chain risk management.
H4: Supply chain risk management has a positive influence on supply chain
resilience.
Based on the hypothesis test results in this research, the
influence of supply chain risk management on supply chain resilience gets a
p-value of 0.000 or has a significance value of < 0.05 with an estimated
value of 0.950. These results imply that supply chain risk management has a
positive effect on supply chain resilience. This shows that middle/senior
managers/executives who work for more than 5 years in managerial experience in
the basic and chemical industry, miscellaneous industries, and Indonesia's consumer
goods industry in DKI Jakarta who implement supply chain risk management in
their companies will experience increased supply chain resilience. The
following research results are in line with the results of previous research by
(Rashid
et al., 2024) which found that supply chain risk management
has a positive effect on supply chain resilience.
H7: Supply chain resilience has a positive influence on supply chain
performance.
Based on the hypothesis test results in this research, the influence of
digital supply chain resilience on supply chain performance gets a p-value of
0.000 or has a significance value of < 0.05 with an estimated value of
0.961. This result implies that supply chain resilience has a positive effect
on supply chain performance. This shows that managers/middle/senior executives
who work for more than 5 years in managerial experience in the basic and
chemical industry, miscellaneous industries, and Indonesia's consumer goods
industry in DKI Jakarta who implement supply chain resilience in their
companies will experience an increase in supply chain performance. The
following research results are in line with the results of previous research by
(Xiao
& Khan, 2024) which also found that
supply chain resilience has a positive impact on supply chain performance.
Testing the
Indirect Influence Hypothesis with Moderator Variables
Figure 2. (Model 1) The
Effect of Supply Chain Risk Management Mediation
on Information
Processing Capability and Supply Chain Resilience
Figure 3. (Model 2) The
Effect of Supply Chain Risk Management Mediation
on Digital Supply Chain
and Supply Chain Resilience
Table 5. Results of
Hypothesis 5 and 6 Tests
|
Hypothesis |
Estimate |
P-value |
Result |
|
Figure 2 |
|||
|
H5:
Supply chain risk management positively mediates the relationship between
information processing capability and supply chain resilience. |
0,538 |
0,000 |
H5
supported |
|
Figure 3 |
|||
|
H6:
Supply chain risk management positively mediates the digital relationship
between supply chain and supply chain resilience. |
0,413 |
0,000 |
H6
supported |
Based on Table 5, in model 1, hypothesis 5 with
an estimated value of 0.538 means that there is an effect of information
processing capabilities on supply chain resilience mediated by supply chain
risk management with a p-value of 0.000 <0.05 so it can be concluded that
there is a significant positive effect between information processing
capabilities on supply chain resilience mediated by supply chain risk
management.
These results are
in line with previous literature that shows the important role of information
processing capabilities in enhancing supply chain resilience through effective
risk management. For example, research (Saeidi et al., 2019) confirms that the utilization of advanced
information technology strengthens firms' ability to detect and respond to
risks, thereby improving supply chain resilience. In addition, research (Fan et al., 2016) also supports these results by showing that
strong risk management, supported by efficient information processing, can
mitigate the impact of supply chain disruptions more effectively, in line with
the results obtained in this study.
On the other hand,
this finding also corroborates the literature showing that the integration of
digital supply chain with supply chain risk management results in significant
supply chain resilience, as evidenced by (Zouari et al., 2021). Thus, the results of this study support the
existing literature and strengthen the evidence that information processing
capabilities and risk management play an important role in strengthening supply
chain resilience.
Hypothesis 6 in model
2, with an estimated value of 0.413, means that there is an influence of
digital supply chain on supply chain resilience mediated by supply chain risk
management with a p-value of 0.000 < 0.05 so that it can be concluded that
there is a significant positive influence between digital supply chain on
supply chain resilience mediated by supply chain risk management.
The findings of
this study are in line with existing literature on the positive relationship
between digital transformation, supply chain risk management, and supply chain
resilience. Previous research has shown that digital technologies, such as
advanced information processing tools, play an important role in improving
supply chain resilience, especially in mitigating risks and improving
operational efficiency. For example, research shows that supply chain
resilience is strengthened through digital supply chain integration, including
the use of digital tools for real-time data processing and better
decision-making during disruptions (Yuan et al., 2024).
CONCLUSION
Based
on the research findings, it can be concluded that digital technology
positively influences the adoption of digital supply chains, especially in
industries with managers and executives who have more than five years of experience.
The basic and chemical, miscellaneous industries, and consumer goods sectors in
DKI Jakarta show a strong tendency to adopt digital supply chains thanks to the
application of digital technology. The research also reveals that information
processing capabilities improve supply chain risk management, where companies
are better able to manage risks effectively when executives use advanced
information processing tools. The link between digital supply chain adoption
and risk management is also clear, with companies adopting digital solutions
showing stronger risk management capabilities. This research underscores the
importance of investing in digital technologies and improved information
processing to strengthen supply chain resilience. Nonetheless, this study has
limitations as it focuses on a specific industry sector and specific variables,
so future research could expand the scope to gain a more comprehensive
understanding.
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Azizah,
Wahyungningsih Santosa, Triwulandari Satitidjati Dewayana (2024) |
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First publication right: Asian Journal of Engineering, Social and Health (AJESH) |
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