Syntax Literate:
Jurnal Ilmiah Indonesia p–ISSN: 2541-0849 e-ISSN: 2548-1398
Vol. 9, No.
7, Juli 2024
LARGS
PERFORMANCE INTEGRATIONS IN A LAUNDRY SUPPLY CHAIN
Rafly Galih Saputra1, Evieana R. Saputri2, Hermala Kusumadewi3
Magister Teknik
Industri, Universitas Islam Indonesia, Yogyakarta, Indonesia1
Accounting,
Politeknik YKPN, Yogyakarta, Indonesia2
Tax Accounting,
Politeknik YKPN, Yogyakarta, Indonesia3
Email: [email protected]1,
[email protected]2,
[email protected]3
Abstract
This research is conduct with the object to
observe whether that lean, agile, resilience, green, and sustainable can be
applied into a laundry supply of supply chain management. Literature review are
reviewed to build a foundation regarding SCM to achieve the objectives. Model
of lean, agile, resilience, green, and sustainable are built on the theoretical
review of literature. The novelty of this research is to classify scheme of a
laundry supply chain paradigm in SCM was developed. The result indicates that
lean, agile, resilience, green, and sustainable have a important role to
achieve successful performance, and customer satisfaction.
Keywords:
LARGS, SCM, laundry
Supply chain management (SCM) is very dynamic in
seeing an increase or decrease in a distribution system that aims to create an
effective and efficient service, product and low cost (Basuki, 2021). In its implementation which has been
implemented for several decades, supply chain management (SCM) has experienced
several challenges both internally and externally. External challenges can be
related to matters relating to the environment/nature and society (Dahlmann &
Roehrich, 2019; Dey et al., 2019; Tasdemir & Gazo, 2018); customers’ demand uncertainty (Lotfi & Saghiri,
2018): technological disruptions with a shorter
product life cycle (Carvalho &
Voigtländer, 2014) and global sourcing (Parkouhi et al., 2019). Anvari (2021) all obstacles make the supply chain (SC)
ineffective, unstable, unable to adapt, and shaken (S. Azevedo et al.,
2013; Centobelli et al., 2020; Lotfi & Saghiri, 2018).
Several researchers compiled by Hosseinet al. (2018), Sharma et al. (2021), and Chavez et al. (2024) focuses on the importance of various
emerging practices and theories and suggests restructuring traditional
management philosophies such as lean, agile to stay in business. Azevedo et al.
(2016), Luthra et al. (2016), Anvari (2021), Dey et al. (2019), and Izadyar et al. (2020), discusses the important role of various
variables that arise or are integrated with these variables such as lean,
agile, tough, and green (LARG), green with sustainability, and lean, green with
agility and resilience in this highly competitive supply chain environment. In
recent years, research on integrating various combinations of lean, agile,
resilient, green and sustainable (LARGS) paradigms in the SC domain has
received considerable attention from academic researchers and practitioners.
However, no research studies have addressed how much integration of the
aforementioned paradigms is possible. Also, how is research based on this
paradigm evolving in the supply chain domain? Previous research studies have
addressed the synergies and differences between these paradigms and their
attributes, considering a few at a time. Mason-Jones et al. (2000), Bruce et al. (2004), and Agarwal et al. (2006); discussing the interrelationships of lean
and agile (LA) paradigms; Christopher and Peck (2004) discuss the interconnectedness of the
agile and resilient paradigms, and Lartey et al. (2020) discussed the link between lean and green
paradigms.
All companies that produce both services and finished
products, require good supply chain management to create an effective and
efficient business process, in this case even small companies, especially
laundries, in running a laundry business need to have supply chain management
in order to run their business. effectively and efficiently for the sake of the
resilience and development of the company. In addition to the business that
needs to be optimized in a laundry company, it is also necessary to pay
attention to the processing of production waste, there are several production
wastes that occur during the production process in laundry. That is; heat,
water use, and detergent waste. Therefore, we need a theory that can solve and
describe how to deal with and manage a laundry business. And the LARGS theory
was chosen which in this theory includes lean, agile, tough, and green. What
helps the company to manage well economically is based on the health of the
surrounding environment. This research is conduct with the object to observe
whether that lean, agile, resilience, green, and sustainable can be applied
into a laundry supply of supply chain management.
The object of this study are the
owner and laundry employees around Universitas Islam Indonesia, consist of 30
people. This research was conducted around the UII Campus by distributing
questionnaires and data collection was carried out in November 2022. This study
uses a structural equation modeling (SEM) approach because it can analyze the
relationship between LARGS criteria to improving SCM performance. This project
study uses five types of exogenous latent variables (ξ) and one endogenous
variable (ε) along with their indicators which are described as follows:
Table 1. Criteria to Measure the Performance
|
Criteria |
|
Sub-Criteria |
|
P1 |
Operational Performance |
The level of influence of Operational Performance
on the overall performance of the laundry business |
|
P2 |
Economic Performance |
The level of influence of Economic Performance on
the overall performance of the laundry business |
|
P3 |
Environmental Performance |
The level of influence of Environmental
Performance on the overall performance of the laundry business |
Table 2. Criteria to Measure LARGS
|
Criteria |
|
Sub-Criteria |
|
Explanation |
|
T1 |
Leanness
in SC |
T11 |
Timely
Production |
Daily
scheduled processing time |
|
T12 |
Supplier
Communication |
Effective
and efficient communication with suppliers |
||
|
T13 |
Number
of Defects |
The
amount of laundry results is less clean or smelly |
||
|
T2 |
Agility
in SC |
T21 |
Speed
in customer response |
Speed
of responding to customers |
|
T22 |
Flexibility
in producing values |
Flexible
in making products |
||
|
T23 |
Ability
to change at production time |
Ability
to respond to sudden changes |
||
|
T3 |
Resilience
in SC |
T31 |
Flexibility
in production according to inventory and supply conditions |
Real
time data inventory level |
|
T32 |
Waiting
time |
Long
waiting time for consumers |
||
|
T33 |
Distribution
of product on demand |
Accuracy
of distribution of laundry results |
||
|
T4 |
Greenness
in SC |
T41 |
Reduce
the variety of materials used |
The
use of environmentally friendly detergents |
|
T42 |
Cooperation
of suppliers to reduce environmental impacts |
Level
of Cooperation reduces environmental impact with suppliers |
||
|
T5 |
Sustainability
in SC |
T51 |
Economic
Approach (cost reduction, high profitability, inventory management) |
Application
of an economic approach in the laundry business |
|
T52 |
Environmental
factors (fuel reduction, greenhouse gases, waste) |
The
level of influence of the laundry business on environmental factors |
||
|
T53 |
Social
factors (health and safety, law and regulation) |
The
level of influence of social factors on the laundry business |
The type of data in this study
uses primary data, where the researcher's data is obtained from the results of
the questionnaire distribution. The questionnaire measurement process is
carried out by providing a Likert scale level or measurement value using an
interval scale as follows:
Table 3. Supply Chain Indicators
|
Criteria |
Sub-Criteria |
Explanation |
Scale |
|
T1 |
T11 |
Daily scheduled processing time |
Higher is better |
|
T12 |
Effective and efficient communication with
suppliers |
Higher is better |
|
|
T13 |
The amount of laundry results is less clean or
smelly |
Lower is better |
|
|
T2 |
T21 |
Speed of responding to customers |
Higher is better |
|
T22 |
Flexible in making products |
Higher is better |
|
|
T23 |
Ability to respond to sudden changes |
Higher is better |
|
|
T3 |
T31 |
Real time data inventory level |
Higher is better |
|
T32 |
Long waiting time for consumers |
Lower is better |
|
|
T33 |
Accuracy of distribution of laundry results |
Higher is better |
|
|
T4 |
T41 |
The use of environmentally friendly detergents |
Higher is better |
|
T42 |
Level of Cooperation reduces environmental impact
with suppliers |
Higher is better |
|
|
T5 |
T51 |
Application of an economic approach in the laundry
business |
Higher is better |
|
T52 |
The level of influence of the laundry business on
environmental factors |
Lower is better |
|
|
T53 |
The level of influence of social factors on the
laundry business |
Lower is better |
The collected data were taken from
the population using a Likert 1-5 scale questionnaire as a data collection
tool. SEM research uses the Likert scale, where the Likert scale is ordinal
data, that is, data that has sequential categories (Ghozali,
2015). In this study, the number of
samples taken was 30 people, taking into consideration that if the missing data
can be deleted as long as the amount of data lost does not exceed 10% (Hair et al.,
2018).
In this study, it used the
Structural Equation Modeling (SEM) method with SPSS AMOS 24 software and
Generalized Least Square correction as an alternative to the data used for
estimating abnormal structural models.
Hypothesis testing observes three
variables, namely operational, economic, and social. Performance variables are
also observed which are used to assess lean, agile, robust, green, and
sustainable construction in the laundry supply chain.
The relationship between variables
that are considered successful is estimated with successful performance
required more than 0.20, which is then acceptable. The table below shows that
each variable manages to do more than 0.20 which is why lean, agile, resilient,
eco-friendly and sustainable in the laundry supply chain has an immediate
positive effect.
Table 4. Research Hypothesis Testing
|
No |
Hypothesis |
Factor Loading |
|
1 |
Leanness is critical to
the successful performance of a supply chain |
0.29 |
|
2 |
Agility is critical to
successful supply chain performance |
0.34 |
|
3 |
Resilience is critical to
successful supply chain performance |
0.28 |
|
4 |
Greenery is critical to
successful supply chain performance |
0.22 |
|
5 |
Sustainability is critical
to successful supply chain performance |
0.30 |
In this study, the influence of
lean, agile, resilience, green and sustainable in the laundry supply chain on
successful performance has been studied. It can be seen that by using the PLS
technique, the effect of each variable is observed by considering the effect of
the variables simultaneously. The model of the PLS technique using Amos
software and t-statistics is displayed to assess the significance of the output
relationship shown in the figure below.
The image below shows the output
from the Amos software. In the figure below, the influence of the five
variables of leanness, agility, resilience, green, and sustainability on the
success variable of the laundry supply chain performance is observed. It shows
the strength of each relationship between hidden factors or variables and
variables that can be observed by factor loading. Factor assignment has a value
range between zero and one. If the factor loading is less than 0.3, it is a
weak relationship and should be ignored. If the factor loading is between 0.3
and 0.6, it is an acceptable relationship. If it is greater than 0.6, it is a
highly desirable relationship. Therefore, according to the factor loading
coefficients, all the coefficients are within the specified range.
Calculation of the t-statistic to
measure the significance of the relationship between variables is shown in the
C.R value in the Amos software below. The t-statistic value among all variables
is greater than 1.96. Therefore, based on the results of the general model it
can be concluded that the technique learned plays a decisive role in the
success of the performance.
Figure 1. PLS Technique Model of Separated Model Using Amos
Software
Figure 2. T-Statistic of Model of Separated Model Using Amos
Software
Finally, in this section, using
the PLS technique, the overall effect of the different performance techniques
is investigated in terms of a general model. The final structural model is
shown in the figure below, and the t-statistic to assess the significance of
the relationship is shown in figure below.
The strength of the relationship
between LARG with engineering and sustainable supply chain performance was
obtained at 0.78, which indicates a high correlation. Moreover, the calculated
t-value is 33.619, which is higher than 1.96. Therefore, based on the results
of the general model it can be concluded that LARG and sustainable supply chain
techniques have an important role in the success of the show.
The strength of the relationship between
LARG and sustainable supply chain techniques and satisfaction is 0.72, which
indicates a high correlation. Furthermore, the t-value is 5.070, which is
higher than 1.96. Therefore, according to the general model results it can be
concluded that LARG and sustainable supply chain techniques have an important
role in achieving satisfaction.
The strength of the relationship
between the performance dimensions and satisfaction is 0.76, which indicates a
high correlation. Furthermore, the calculated t-value was 23.975, which is
higher than 1.96. Therefore, based on the results of the general model, it can
be concluded that the performance dimension has an important role in achieving satisfaction.
Figure 3. PLS Technique Model of General Model Using Amos
Software
Figure 4. T-Statistic of Model of General Model Using Amos
Software
In this study, the main
performance criteria and sub-criteria were first ranked using AHP. In this
step, based on the weight of the identified criteria, the existing techniques
are prioritized using the VIKOR technique. LARG rating and engineering
sustainable supply chain is based on performance and satisfaction indicators.
Table 5. Final Priority Ranking of Criteria Using AHP
Technique
|
Criteria |
Weight |
Rank |
|
Operational Performance |
0.363 |
2 |
|
Economic Performance |
0.373 |
1 |
|
Environmental Performance |
0.263 |
3 |
Table 6. Final Priority Ranking of LARGS Using AHP Technique
|
LARGS
Factor |
Weight |
Rank |
|
Leanness |
0.202 |
3 |
|
Agility |
0.237 |
1 |
|
Resilience |
0.195 |
4 |
|
Greenness |
0.153 |
5 |
|
Sustainability |
0.209 |
2 |
As it can be observed in Table 6,
determining the importance degree and ranking of LARGS factors through PLS
technique is supported by VIKOR technique. These results indicate that the
development of LARGS model is highly reliable.
In this study, is done to develop LARGS in a laundry supply chain,
which customer satisfaction factors including time, quality, cost, and service
level are observed. IT is found that the effective factors of LARGS in the
supply chain have an important role in achieving successful performance. Key
factors of customer satisfaction, LARGS, and performance criteria affect the
SCM. The cause-and-effect relation pattern among variable was evaluated and
ranked. The results indicate that agility is the most prioritize factors of
LARGS followed by sustainability, leanness, resilience and in the last place is
greenness according to 30 questionnaire answerers.
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Copyright holder: Rafly Galih Saputra, Evieana R. Saputri, Hermala Kusumadewi (2024) |
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First publication right: Syntax Literate: Jurnal Ilmiah Indonesia |
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