An Integrated DEA–Porter Decision Support Framework for Enhancing Supply Chain Competitiveness in the Muslim Fashion Industry: Evidence from Indonesia

Abstract

Background: The competitiveness of Indonesia’s Muslim fashion industry requires evaluation through both internal efficiency and external strategic factors, yet existing approaches often assess these dimensions separately. Methods: This study develops a Weighted Efficiency Competitive Score (WECS) that integrates Data Envelopment Analysis (DEA) to measure operational efficiency and Porter’s Five Forces to capture market pressures. The weights of α and β were calibrated through sensitivity analysis under the constraint α + β = 1, with values ranging from α = 0.3 to 0.7 and β = 0.7 to 0.3, using data from 23 Muslim fashion businesses in Jakarta. Results: The analysis identified α = 0.6 and β = 0.4 as the most stable configuration, and only 30% of firms achieved both high efficiency and strong market positioning. Strategic leaders such as JT. Co and PM. Co demonstrated that digital transformation, disciplined cost structures, and strong supply chain partnerships foster sustainable competitiveness. Conclusions: The WECS framework offers a replicable method to quantitatively integrate micro and macro determinants of competitiveness, contributes to the literature by bridging efficiency and strategy evaluation, and provides practical guidance for managers and policymakers to enhance decision support systems in strengthening the Muslim fashion industry’s global positioning.

1. Introduction

The fashion sector is one of the most dynamic centers of activity in the world, facing fluctuations in demand, rapid technological advances, and fierce competition. In Indonesia, the fashion industry ranks at the top of the e-commerce sector [1,2]. Among its segments, the Muslim fashion industry has unique characteristics in terms of market opportunities and sociocultural impact. Indonesia is the second-largest Muslim fashion market in the world after Saudi Arabia, with an industry value of approximately USD 20 billion per year [3,4]. The higher religious quality of these products supports this growth as an expression of Islamic law and an increasing global demand for halal lifestyles.

However, this sector is facing both structural and strategic challenges. These challenges include high dependence on imported raw materials, which reached USD 1.62 billion in 2022. Other issues include inconsistent product quality, low technology adoption, and lack of market intelligence [5,6,7,8]. Fundamentally, industries face a fundamental tension: improving operational efficiency through cost control and resource optimization, while also bolstering market differentiation in a crowded, brand-focused market. Resolving this dichotomy necessitates an integrative, contextually nuanced assessment approach capturing internal prospects alongside external pressures.

The competitive strength of the fashion industry stems from how well the industries operate and external pressure from the market. Recent studies have highlighted the importance of combined methods for assessing efficiency and strategy [9,10]. These methods help researchers understand operational performance and market competitiveness, which are fundamental in emerging markets, such as Indonesia.

Most existing studies assess competitiveness from an operational perspective (using DEA for efficiency measurement) or a strategic perspective (using Porter’s Five Forces for competitive analysis). However, a significant research gap remains in this regard. No integrated framework combines these perspectives in the Muslim fashion industry. Previous studies [11,12,13,14,15,16] introduced partial frameworks. However, they did not create a single metric to compare industries based on internal and external competitiveness [17].

To address this gap, this study introduces a Weighted Efficiency Competitive Score (WECS. This framework combines DEA efficiency measurements with Porter-based competitiveness scores. Unlike in previous studies, the WECS is not a simple summation. It is an index calibrated based on sensitivity analysis and conducted through expert consensus (the Delphi method). This framework contributes academically through innovative integration methods and practical implications, by offering a functional classification of industries for strategic actions. Although this study focuses on Jakarta as the central hub, Muslim fashion industries have headquarters in Jakarta, and some have branches in other regions, such as Bandung, Surabaya, Bogor, Depok, and Makassar. Additionally, the inclusion of sustainability dimensions remains limited in competitiveness research, despite its growing importance in the global fashion industry. Although not included in this study, sustainability considerations are proposed as a future extension of WECS.

This study also demonstrates the feasibility and importance of the proposed integrated framework through an initial offline assessment, using historical data from 23 Muslim fashion industries in Jakarta. This study does not claim to provide a fully functional decision support system but presents a conceptual framework that can be replicated based on empirical evidence for further development. Integrating DEA and Porter’s Five Forces in strategic decision-making boosts supply chain competitiveness by assessing efficiency and competitive dynamics. Simultaneously, Porter’s Five Forces provide insights into the external competitive forces that influence supply chain sustainability and performance of the supply chain [18]. In recent years, research has integrated these two approaches to provide more holistic decisions in supply chain management.

2. Literature Review

The literature on competitiveness in the fashion industry is typically divided into two categories. One focuses on operational performance, whereas the other examines strategic positioning. This study uses Data Envelopment Analysis (DEA), as shown by [12,19,20,21]. It illustrates how industries can turn inputs into outputs to measure their efficiency. Although DEA has been widely used in manufacturing, banking, and supply chains, its use in the fashion industry, especially in the halal or Muslim markets, is limited. This study also highlights the external factors that influence competitiveness. Porter’s Five Forces [22] remain the main framework for examining industry dynamics, competition, and value creation. Some researchers have attempted to combine operational and strategic analyses [11,12,13], but combining DEA and Porter’s Five Forces is still uncommon. Research shows that collaboration is linked to competitive results in the apparel and textile industries, particularly in terms of sustainability [23,24]. These findings support the view that collaboration boosts efficiency and strategic positioning [25].

The economic potential of the fashion industry, specifically the Muslim fashion sector, has attracted increased attention [26], as stated in the Global Islamic Economy Report, 2022 [4]. Most research has focused on export barriers, design innovation, and consumer behavior [5,8]. This study addresses this gap by presenting a dual framework that integrates DEA and Porter’s to assess supply chain competitiveness in the Muslim fashion industry. The literature lacks systematic synthesis, especially regarding methodological strength, contextual relevance (Indonesia), and policy and decision support system implications. Although DEA has been used in various sectors to assess efficiency, Porter’s Five Forces is an important tool for strategic analysis [27]. The combination of these approaches has not been fully explored. This is especially true in industries with complex sociocultural dynamics, such as Muslim fashion. Studies, such as those by [11,12,13], have sought partial integration through multi-criteria approaches or strategic mapping. However, they did not create a combined index that links operational and strategic elements.

Few studies have approached this integration in developing economies and cultural contexts. A literature review indicated that few studies have combined this approach in these areas. This study fills this gap by offering a precise and repeatable method designed for the Indonesian Muslim fashion industry.

2.1. Supply Chain

The Islamic fashion supply chain is a significant research area because of the growing global Muslim population and demand for sharia-compliant clothing. Owing to market conditions, Islamic fashion SMEs face considerable supply chain risk [28]. Consumer knowledge affects hijab purchase intentions [29]. Risk assessment studies identify critical risks in Islamic clothing supply chains that require systematic mitigation strategies [30]. The influence of brand loyalty on consumer preferences in the hijab market has proved to be an important element [31]. As illustrated in [32], technology such as RFID can improve the halal supply chain integrity by developing better traceability and tracking systems. A strategic approach can assist the growth and reduce market entry obstacles for SMEs in this sector [33,34].

2.2. The Data Envelopment Analysis (DEA)

DEA is a non-parametric method used to measure the relative efficiency of various decision-making units (DMUs), which can be industries, divisions, or other entities. DEA compares the inputs and outputs of these units [35,36]. The unit with the best input–output ratio is seen as the most efficient. DEA aims to identify less efficient units and indicate how improvements can be made. Although DEA has been widely used in manufacturing and logistics [37,38], recent applications in the fashion sector demonstrate that this method can identify performance gaps between industries of different sizes and innovation levels [9]. This supports the use of DEA in industries with rapidly changing demand and limited resources. According to the DEA methodology, the dry-docking process relies on the inputs and outputs that measure performance [39]. Using a categorical DEA model can improve efficiency measurement and process research [40].

The assumption of constant returns to scale (CRS) [41] came from the input-oriented model suggested by DEA [42]. This assumption is valid only if all decision-making units (DMUs) operate completely. The ability of DMUs to operate at their optimal level may be hindered by factors such as inadequate funding and unfavorable competition. It is unclear whether the CRS assumption still applies to DMUs that do not operate at an ideal scale, because technological and scale efficiency are combined. The variable returns to scale (VRS) assumption, first introduced by [43], has replaced the CRS assumption due to its weaknesses. The main difference between VRS (BCC model) and CRS (CCR models) is that the former provides an overall efficiency assessment.

On the other hand, the second model can distinguish between scale efficiency and technical efficiency. The BCC model addresses the weaknesses of the CCR model if some of the DMUs do not operate at the optimal scale [44], whereas the CCR model is considered to meet the optimal scale. The main benefit of DEA in management is the selection of units that serve as benchmarks to measure efficiency. This selection helps to identify the root causes of inefficiency and offers potential solutions [45]. Each decision-making unit (DMU) that uses different inputs and outputs has a certain level of efficiency, which Data Envelopment Analysis (DEA) measures. DEA is a selection unit that serves as a benchmark for measuring efficiency. This selection helps to identify the root causes of inefficiency and offers potential solutions [45]. Each decision-making unit (DMU) that uses different inputs and outputs has a specific efficiency level, measured by Data Envelopment Analysis (DEA). The initial research by [46] introduced the idea of DEA. The CCR model proposed by [47], which assumes a constant return to scale (CRS), and the BCC model proposed by [43], which assumes a variable return to scale (VRS), are DEA models that can be employed [48]. DEA steps: (1) determine the DMUs to be analyzed, (2) identify relevant input and output variables, (3) use a mathematical program to calculate relative efficiency scores, and (4) construct an efficiency frontier that represents the best performance. Previous research by [47] provided the theoretical foundation for DEA, where they introduced the CCR model (Charnes, Cooper, and Rhodes), which is frequently used in many industrial and supply chain applications.

The efficiency measurement of the Muslim fashion industry using DEA is formulated as follows:

$$\begin{gathered} e_k={\displaystyle \frac{{\displaystyle {\sum }_r^R=1u_{rb}{y}_{rb}}}{{\displaystyle {\sum }_i^{I}1v_{ib}{x}_{ib}}}} \\ {\displaystyle \frac{{\displaystyle {\sum }_r^R=1u_{rb}{y}_{rb}}}{{\displaystyle {\sum }_i^{I}1v_{ib}{x}_{ib}}}}\le 1,\forall j,j=1,2,3\dots \dots N \end{gathered}$$

and $u_{rb}$,$v_{ib}$ ≥ for each r, i (r = 1, 2, 3…R and I = 1, 2, 3…I).

The DEA in this study uses CRS (constant returns to scale) and VRS (variable returns to scale). An analysis was performed to address potential bias due to the limited number of samples. Then, the efficiencies of the two methods were compared. This model considers the limitations of input–output data, which may affect the reliability of the results [49].

According to [50], measurements in Data Envelopment Analysis determine the weight of each output and input in the decision-making unit (DMU). It is assumed that each DMU has the freedom to choose the weight for each input and output variable, provided that the DMU satisfies the following requirements: (1) The weight cannot be negative. (2) The weight is universal, meaning that every DMU in the sample must estimate its ratio (total weighted output/total weighted input) using the same weighting device. (3) The ratio cannot be greater than one (total weighted output/total weighted input ≤ 1).

2.3. Porter’s Five Forces Analysis

Porter’s Five Forces is a model that looks at industry competition. It considers five factors that influence how intense the competition is and the potential for profit in an industry (Figure 1) [22,25,51]: (1) Threat of New Entrants: The ease or difficulty with which new industries may enter the market [52]; (2) Threat of Substitutes: The opportunity for other products or services to replace the existing ones [53]; (3) Bargaining Power of Buyers: How much influence buyers have on price and quality of the product [54]; (4) Supplier Power: The extent to which suppliers can influence the cost and quality of inputs; and (5) After we describe each dimension, the danger of new rivals is one potential worry. This danger restricts the quick establishment of new industries.

Profits are reduced as a result of increased competition when new competitors expand into the related industries. We also evaluated the degree of threat of new market entrants competing with the existing firms [52]. This proves that the Porter model is still useful to analyze fast-moving consumer markets.

Porter’s Five Forces Analysis helps assess the competitiveness of the planned business and the strength of its current position [56]. After reviewing each factor, we highlighted the threat of new competitors as a possible concern. This threat affects the ease with which new industries can be introduced. Profits decrease because of stronger competition from new rivals entering the related markets. We assessed how new entrants could compete with existing businesses to gauge their threat levels [52]. Apart from that, we examine the bargaining power of suppliers, or supplier bargaining power. Through this bargaining power, suppliers can provide poor-quality or expensive raw materials to their purchasers [53]. The company is at risk because it relies on its suppliers. Therefore, industries must choose the best supplier based on cost and quality. Buyers’ bargaining strength. This power evaluates the bargaining power or offering power of buyers/consumers: the more bargaining power buyers have in demanding lower prices or higher-quality products, the less profit the company will make [54].

This risk or barrier appears when customers come across cheaper options or better-quality products with low switching costs [57]. Consequently, the industry produces a wider variety of goods, which can affect a company’s profits. Competition between competitors or rivals between current rivals. According to this theory, competition from comparable rivals drives competitiveness. Businesses that compete in the same market have become increasingly fierce. Every component must improve its position through pricing, competitive tactics, and promotion. This includes services and client guarantees [58].

Porter’s model looks at competitiveness based on five factors. Each factor receives a score from 1 to 5. A score of 1 means very low impact, and a score of 5 means very high impact [22]. The competitive impact score Pj for a specific DMU j is calculated as follows:

$$\mathrm{Pj}={\sum }_{f=1}^{5}WfFfj$$

where:

Pj = Competitive impact score for DMU j

F_fj = Score for force f (industry rivalry, new entrants, supplier power) (buyer power, substitutes).

W_f = weight assigned to force f, with $Ʃ$W_f = 1

Weight W_f is determined based on expert input and industry analysis

Expert selection for Porter’s Five Forces scoring followed four strict criteria (Delphi Method) [59]: (1) ≥10 years of professional experience in the fashion or textile industry; (2) leadership position in Muslim fashion associations; (3) involvement in policy-making or government initiatives related to the creative economy, textile industry, or fashion industry; and (4) content knowledge of supply chain management, fashion industry, or textile study.

2.4. Integration of DEA and Porter’s Model

The difference between the DEA measurement of efficiency and Porter’s Five Forces analysis has sparked research seeking to combine these methods. Ref. [15] highlighted the need to blend operational and strategic perspectives when evaluating supply chains, especially in unstable and competitive markets. Some hybrid models have been suggested for logistics, banking, and energy, but there is limited research on the fashion industry or cultural-creative sectors such as Muslim fashion. Furthermore, current integration attempts often rely on random DEA and strategic score combinations that lack a clear weighting system. This reduces their reliability and usefulness in decision-making.

The integration of DEA and Porter’s Five Forces can be performed in two main ways: (1) Evaluating Efficiency in a Competitive Market: DEA measures internal efficiency of the supply chain [60], whereas Porter’s Five Forces evaluate external threats to competitiveness; and (2) Performance Improvement Strategy: After industries can use Porter’s information to formulate more appropriate strategies to address external challenges and maximize supply chain efficiency. This method can be combined with matrix analysis, where DEA efficiency results are matched with factors identified in Porter’s Five Forces to design appropriate strategies.

To integrate the DEA efficiency results with Porter’s competitive impact, a weighted efficiency-adjusted competitive score (WECS) was calculated [61]:

WECS = (α × Ej) + (β × Pj)

WECS = Final integrated competitiveness score for DMU j

Ej = Efficiency score from DEA for DMU j (normalized 0–1)

Pj = Competitive impact score from Porter’s Five Forces (scaled 1–5)

α = weight assigned to DEA efficiency (0.6).

β = Weight assigned to Porter’s competitive impact (e.g., 0.4)

Weights α and β are restricted such that α + β =1 for all DMUs, thus ensuring a standardized composite index. A sensitivity analysis varied α from 0.3 to 0.7 and β from 0.7 to 0.3. The criterion for selecting the best weighting scheme was the stability of the DMU rankings and the robustness indices across scenarios. This confirms that α = 0.6 and β = 0.4 were the most reliable.

Based on

Table 1. Sensitivity analysis of α and β in WECS.

Scenario	A (DEA Weight)	Β (Porter Weight)	Stability of the Company Ranking
1	0.3	0.7	Low stability
2	0.4	0.6	Moderate stability
3	0.6	0.4	High stability
4	0.7	0.3	Medium stability

, the sensitivity analysis: α and β varied from 0.3 to 0.7 and were tested across multiple weighting schemes. Stability of company rankings and robustness indices guided the selection of α = 0.6 and β = 0.4 (prioritizing operational efficiency).

Strategic Group Mapping:

The cut-off values for the WECS were adapted from [61] and validated through cluster analysis. Three groups of mice were established per group.

➢

WECS ≥ 2.00 (high competitiveness) = strategic leaders.

➢

WECS 1.7–2.00 (moderate) = efficiency focused or structurally stable.

➢

WECS < 1.7 (low) = underperforming industries needing intervention.

This classification enables actionable differentiation between efficient and inefficient industries under varying competitive pressures.

The values of α and β were determined through a sensitivity analysis to determine the optimal weighting for decision-making. Typically, DEA efficiency is prioritized (higher α) because operational performance directly affects competitiveness.

In contrast to many studies that compare DEA and strategic frameworks side-by-side, this study uses a composite metric, the Weighted Competitive Efficiency Score (WECS), which combines DEA efficiency scores (Ej) with strategic pressure scores (Pj) from Porter’s five forces. The integration variables, α and β, were adjusted through sensitivity analysis (Figure 2). Operational efficiency was emphasized when α was set to 0.6. This method ensures that industries are evaluated on the basis of their internal capabilities and positions in a competitive landscape.

Despite the growing number of studies that use DEA or Porter’s Five Forces to assess competitiveness, the literature shows that these methods are still fragmented and seldom combined into a single metric. Prior studies [11,12,13,14,15,16] have attempted partial integration, yet no decision-oriented index has been developed to simultaneously account for both internal efficiency and external strategic pressures.

To fill this gap, this study creates a weighted efficiency competitiveness score (WECS) framework that integrates micro-level factors, including operational efficiency, measured using DEA [62]. Macro-level factors involve external strategic pressures, captured by Porter’s Five Forces. Efficiency shows how well a company uses its internal resources. Strategic strength describes a company’s ability to respond to and benefit from market forces.

2.5. Previous Research Integrating DEA and Porter’s Five Forces

Several studies that integrate DEA and Porter’s Five Forces in the context of supply chains are shown (

Table 2. Comparative Review of Prior Studies Using DEA, Porter’s Five Forces, or Other Integration Attempts.

No	Author	Method	Focus	Focus Analytics	Integration DEA–Porter	Key Findings
1	Liang et al. (2006) [19]	DEA	Supply Chain	Operational efficiency	No	Not considering external pressures or industry competition.
2	Liu et al. (2011) [63]	DEA + Porter	Supply Chain	Improve efficiency	Yes	Developed a model that combines DEA to assess supply chain efficiency and Porter’s Five Forces to evaluate market competitive forces. They found that industries can formulate policies to improve efficiency in their supply chains by understanding the external factors that influence competition.
3	Cheng et al. (2014) [64]	DEA + Porter	Supply Chain	Decision making	Yes	Integrating these two methods to identify parts of the supply chain that need Improvement and to assess external risks that could affect operational performance. This study shows that combining these two approaches provides deeper insights into managerial decision-making.
4	Kuo and Yang (2015) [65]	DEA + Porter	Supply Chain Management	Efficiency internal	Yes	Applying DEA and Porter’s Five Forces in global supply chain management for the manufacturing industry. They found that industries with high internal efficiency are more ready to handle threats from competition and strong suppliers.
5	Nikfarjam et al. (2015) [11]	DEA + Scor Hybrid	Rantai Pasok	Logistics performance and operational efficiency	No	Not linked to external competitive pressures.
6	Taylan et al. (2016) [12]	Dea + Fuzzy MCDM	Textile Industry	Multi-criteria assessment for design performance	No	Not combining Porter-based strategic analysis.
7	Miller and Lee (2018) [66]	DEA + Porter	Supply Chain	Optimize supply chain	Yes	Developing a further integration framework to optimize the supply chain by considering not only internal efficiency through DEA, but also the impact of competitive forces identified in Porter’s Five Forces.
8	Dona et al. (2019) [67]	Porter’s Five Forces	Garment Industry	External environment analysis and business strategy	No	No internal efficiency measurement.
9	Alidrisi (2021) [13]	DEA + Promethee	Distribution and Location	Selection of the distribution center location	No	Does not target the fashion sector and has no competitive dimension.
10	Zhao and Chan (2020) [68]	DEA + Porter	Supply Chain	To assess efficiency and risk mitigation strategy	Yes	Using DEA to assess efficiency in different supply chain segments and connecting it to the strengths identified in Porter’s model helps design risk mitigation strategies in competitive markets.
11	Farid (2021) [69]	SCOR (Kualitatif)	Fashion Muslim (SME)	SCOR-based supply chain performance evaluation	No	Not using DEA or Porter as an integrative framework.
12	Ghasemi et al. (2023) [14]	DEA + Multi Criteria	Manufacturing	Operational resilience	Partial	Suggested hybrid DEA frameworks.
13	Tirkolaee et al. (2023) [15]	DEA + Sustainability Indicators	Textile/ Clothing	Green supply chain	No	Proposed efficiency model with sustainability.
14	Gracia and Martinez (2023) [24]	Collaboration + Competitiveness	Textile	Collaboration linked to performance	No	Did not quantify efficiency + competitiveness.
15	Weber et al. (2024) [16]	DEA + Industry 4.0	Apparel	Digital competitiveness	Partial	Emphasized digital transformation.

).

3. Methods

3.1. Research Design

This study uses a mixed-method design that combines quantitative efficiency measurement (Data Envelopment Analysis, or DEA) with qualitative strategic assessment (Porter’s Five Forces) within a decision-support framework [70]. The main objective is to create a practical and replicable tool called the Weighted Efficiency Competitive Score (WECS). This tool is designed to assess competitiveness and inform strategic interventions in the Muslim fashion industry. The methodological flow consisted of the following three steps:

• Data Envelopment Analysis (DEA) for operational efficiency benchmarking [71,72].
• Porter’s Five Forces for assessment through expert scoring.
• Weighted efficiency competitiveness score (WECS) for construction using sensitivity-based weighting.

3.2. Research Location and Sample Determination

This study was conducted in Jakarta, the capital of Indonesia, and its economic center. Jakarta was chosen as the starting point because it is Indonesia’s most prominent production and distribution hub, and a key player in fashion innovation. The fashion sector in Jakarta has experienced notable transformations in recent decades. The city has shifted from following global trends to developing its identity by combining traditional elements with modern city fashions. Jakarta is a fashion consumer, trend setter, and innovator, solidifying its dominance in the Southeast Asian fashion industry.

Sample Determination

Researchers gathered data on Jakarta’s Muslim fashion industry population and identified 30 Muslim fashion industries during the data collection. To determine the relevant sample size that met the research criteria while remaining representative and efficient, the Slovin formula (N = 30, e = 0.1) was used. Based on these calculations, the sample size was determined as 23 Muslim fashion companies. DEA studies generally recommend ≥20 DMUs for a robust analysis [42]. Therefore, 23 Muslim fashion businesses in Jakarta were selected as decision-making units for further analysis using DEA (Data Envelopment Analysis). These 23 samples are considered representative of the Muslim fashion sector in Indonesia because, in addition to their headquarters in Jakarta, some of these businesses have branches in Surabaya, Bandung, Bogor, and Makassar, with differences in business models, strategies, and cost structures that can influence competitiveness.

3.3. Data Analysis

This study used case study techniques with a mixed-methods approach that combined qualitative and quantitative research methodologies. Qualitative and quantitative research combine current techniques and interpret natural events [73]. Activities performed to understand social problems in real-life situations have been described in qualitative research [74], according to the results of computation using the Slovin technique. Thirty (30) units are located in the Jakarta area in the Muslim fashion sector, leading to twenty-three (23) units in the Muslim fashion industry survey. The study period was September 2023 to May 2025. This research utilized both primary and secondary data. Methods for gathering data involved documentation, interviews, and observations. Data analysis techniques used were DEA and Porter. Integration is operationalized using the Weighted Efficiency Competitive Score (WECS).

4. Results

4.1. Competitiveness Analysis Using Data Envelopment Analysis (DEA) Method

This study measures the relative efficiency of various business actors in the Indonesian fashion sector using data envelopment analysis (DEA). DEA is a fractional programming model that can handle many inputs and outputs without clearly defining the relationship between them or deciding the importance of each variable beforehand [44,75,76,77]. The DEA sets the input and output levels for the evaluated units and calculates a scalar efficiency measure. The model used is CCR (Charnes, Cooper, Rhodes), assuming constant returns to scale and input orientation [78]. This model assesses how DMUs (decision making units) can minimize inputs while maintaining a fixed output. Including capital, number of employees, monthly production, and operational duration, whereas the outputs are sales and profit. Based on the data analyzed using the Data Envelopment Analysis (DEA) method, 23 decision-making units (DMUs) were evaluated based on various performance indicators such as sales, profit, operational duration, number of employees, capital, production, price, exports, imports, and debt [79]. DEA was used to assess how each business unit (DMU) in the Muslim fashion industry utilized inputs to generate outputs. The DEA results were used to understand the efficiency level of each DMU, determine benchmarks, and identify improvement strategies for efficient DMUs. Appendix A presents detailed data for the 23 fashion industries.

The data for the 23 industries were processed using the RStudio software (R.4.5.1). The inputs and outputs were determined using RStudio. From the DEA results, the efficiency of the Muslim fashion industry was analyzed based on inputs and outputs.

• Input: Input orientation is the number of employees, capital, production per month, price, exports, imports, and debt.
• Output: The output orientation used is sales and profit.

Table 3. Result: DEA using RStudio.

NO	DMU	CRS	VRS
1	Ss. Co	0.69	1
2	LA. Co	1	1
3	Jt. Co	1	1
4	PM. Co	1	1
5	LF. Co	0.37	1
6	LS. Co	1	1
7	AH. Co	0.38	1
8	NN. Co	1	1
9	FS. Co	0.36	1
10	NS. Co	1	1
11	BAD. Co	1	1
12	RR. Co	1	1
13	SA. Co	1	1
14	CL. Co	0.86	0.99
15	DR. Co	0.91	0.99
16	NY. Co	0.12	0.99
17	IP. Co	0.79	0.99
18	YA. Co	1	1
19	Op. Co	0.79	0.99
20	EA. Co	0.47	0.99
21	WN. Co	0.67	0.99
22	TA. Co	1	1
23	HH. Co	1	1

presents the Data Envelopment Analysis (DEA) for 23 decision-making units (DMUs) within the Muslim fashion industry in Jakarta [74]. This study utilized two models, constant returns to scale (CRS) and variable returns to scale (VRS) [75], which assess total technical efficiency and pure technical efficiency, respectively, while ignoring the scale of operations.

The data indicate that 13 DMUs (56.5%) attained peak efficiency (score = 1) in the VRS model, whereas only 10 DMUs (43.5%) were efficient in the CRS model. This suggests that certain industries possess significant technical efficiency; however, their operational scales have not yet reached optimal levels. The score disparity between CRS and VRS offers a valuable understanding of the inefficiency arising from company size rather than ineffective resource use.

The industries that achieved optimal efficiency in both models (CRS = 1 and VRS = 1) were LA. Co, JT. Co, PM. Co, LS. Co, NN. Co, FS. Co, and NS. Co. These DMUs act as internal standards for other industries because they can optimize resources such as capital, labor, and production capacity to enhance output in sales and profits.

Conversely, DMUs, such as NY. Co (CRS = 0.12; VRS = 0.99) indicate high technical efficiency. However, they are limited by their small operational scale, which prevents them from achieving overall efficiency. However, industries such as FS. Co (CRS = 0.36; VRS = 1) show efficiency in their technical framework but need to increase their operational size to produce a suitable output.

This finding highlights the importance of business growth strategies based on market expansion and improved production capacities. This CRS–VRS analysis also acts as a basis for classifying industries using efficiency enhancement approaches, either via internal reorganization or optimization of the operational scale.

The results of the calculation of the competitiveness of the Muslim fashion industry using Data Envelopment Analysis and processed by the RStudio analysis application are shown (

Table 4. Efficiency score.

No	DMU	Score	Efficiency Level
1	PM. Co	100%	Efficiency
2	JT. Co	100%
3	NN. Co	100%
4	LA. Co	100%
5	LS. Co	100%
6	FS.Co	100%
7	NS. Co	100%
8	SS. Co	74.65%	Efficiency Potential
9	AH. Co	69.45%
10	LF. Co	66.38%

Source: author elaboration

).

Data analysis using DEA shows that the 23 Muslim fashion industries have seven efficient DMUs with an efficiency score of =1 (100%), indicating that they can optimally manage inputs to produce outputs. The most efficient DMUs were LA. Co (100%), PM. Co (100%), JT. Co (100%), LS. Co (100%), NN. Co, FS. Co, and NS. Co. Three industries still have the potential to increase efficiency: SA. Co (74.65%), LF. Co (66.38%), and AH. Co (69.45%) (Table 4).

Based on Figure 3, the processing results in RStudio and efficiency scores were obtained from 21 fashion units analyzed, ranging from 0.66 to 1.00. Seven DMUs showed perfect efficiency (score = 1), indicating they optimally managed their resources. Meanwhile, the rest showed relative inefficiency and required strategic improvement. Of the ten Muslim fashion industries, seven achieved perfect efficiency, and three still have the potential to increase efficiency.

Based on Figure 4, the top-performing industries are PM. Co and JT. Co, which are the two most efficient industries (the longest bars in the graph), followed by NN. Co and LA. Co. This indicates that they utilized their resources well to produce an optimal output.

Based on Figure 5, in the context of technical efficiency, DMUs such as PM exist: Co, LA.Co, and JT.Co as frontiers mark an essential benchmark for other DMUs. DMUs with perfect efficiency can optimize resources and have effective production and distribution structures. The CRS model efficiency graph for DMUS showed that one-third of the units operated below the adequate standard of 0.7 to 0.9. The remaining units were evenly distributed below at the 0.7 level. This indicates an issue in resource management among the units with different industrial production standards. DEA provides insights into the relative effectiveness of each DMU, identifying low-operating units that may benefit from efficient standards. The DEA model quantitatively measures efficiency and evaluates collective learning in the fashion industry. By examining the efficient practices of frontier DMUs, other sectors can modify their input combinations, such as workers and capital size, to generate optimal output. This principle aligns with the results of DEA research in the automotive sector in the DEA 1 journal, where efficient DMUs serve as a strategic benchmark for other dealers.

The DEA efficiency score table shows that DMUs such as LF. Co and SA. Co and CL. Co levels remained sub-optimal. This can be observed in an unbalanced input structure relative to the output produced, such as high capital but low profits. Using the input orientation in the DEA model, the evaluation focused on cutting unproductive inputs while maintaining the output results. This is a valuable opportunity for the fashion industry to improve its performance by reconsidering the use of resources. For example, decision-making units with many staff members but low productivity can consider training employees or changing their production processes. Efficiency is not merely about cost savings, but about generating the same or greater value through more innovative and measurable methods. The efficiency bar chart highlights the differences between the DMUs, clearly visualizing each business’s relative position.

The distribution of the VRS model efficiency scores provides additional information that most DMUs have potential efficiency if their operational scales are optimal. The VRS model allows a more flexible understanding of scale efficiency, which is relevant in dynamic creative industries like fashion. Some DMUs that appear inefficient in the CRS model show better scores in the VRS model, indicating that suboptimal operational scale is the primary source of inefficiency. For example, a business unit with locally distinctive creative products, but small-scale production, may experience technical inefficiency in the CRS model but be efficient in the VRS model. This provides a basis for arguing that developing production capacity and market access is key to improving efficiency. According to the logic of systems thinking, efficiency results from the interaction between internal structure and external pressures. Therefore, policies for developing fashion SMEs should focus on accelerating the scale and improving cost structure efficiency.

The DEA findings reveal three distinct groups among the 23 Muslim fashion industry participants [80]:

• High Efficiency (100%): This group comprises seven industries that optimize resources and maximize outputs. These industries exhibit efficient capital management, digital marketing strategies, and strong supply chain collaborations. Examples: PM. Co, JT. Co, LA. Co, NN. Co, FS. Co, NS. Co, and LS. Co. Key Factors for Efficiency: Digital transformation and e-commerce utilization, strong supplier relationships and cost control, strategic branding, and effective marketing campaigns.
• Moderate Efficiency (50–80%): This group includes three industries with room for improvement, showing potential in specific areas. Examples include SA. Co, LF. Co, and AH. Co. Areas for improvement include capital distribution and resource utilization, extending market reach through digital channels, supply chain adaptability, and cost-cutting measures.
• Low Efficiency (<50%): The remaining 13 industries face significant operational efficiency challenges, supply chain optimization, and strategic differentiation. The following challenges were identified from the interviews: lack of financial management and investment in technology, high dependency on imported raw materials, limited market differentiation, and weak brand positioning.

For inefficient industries to improve their competitiveness, several steps can be taken:

• Resource Optimization: This is performed by adjusting the number of workers according to the required production capacity and reducing unnecessary items in the supply and distribution chain.
• Cost and Capital Efficiency: This is completed by managing capital more effectively by utilizing technological developments for production automation and improving the efficiency of raw material use.
• Marketing Strategy Improvement: This is completed by providing a competitive pricing strategy that considers consumers’ purchasing power and uses digital platforms to expand the market at home and abroad.
• Collaboration in Supply Chain: Collaboration with raw material suppliers and distributors to improve logistics efficiency [81].
• Utilizing a decision support system (DSS) for data-driven supply chain decision making [82].
• Product Diversification and Exports: Industry players can boost exports to increase market share. They can also innovate products to keep up with global fashion trends and to remain competitive.

DEA is useful for assessing an industry’s performance by using data with multiple inputs and outputs. As most DMUs have more control over inputs, such as production capacity, human resources, and capital, than outputs, the latter being primarily controlled by the market, input-oriented models are appropriate for the fashion sector. Relative efficiency ratio-based measurements provide a fair platform to compare participants operating in the same industry. Additionally, combining the CRS and VRS models offers a deeper evaluation of the performance. The CRS model examines the overall technical efficiency.

By contrast, the VRS model focuses on purity and ignores the operational scale. This mix is crucial in fields driven by innovation, such as fashion. This method fits well with the DEA in the fashion industry, which also considers the capacity and market.

Reach differences. The results of the DEA indicate that some industries have reached optimal efficiency, whereas others still have room for improvement. This combination is vital in sectors that emphasize innovation, such as fashion. With appropriate strategies such as using resources wisely, reducing costs, improving marketing, and working together in the supply chain, these industries can become more competitive and boost their profits. Research in the fashion industry has often linked efficiency with supply chain management factors and design innovation [12].

4.2. Competitiveness Analysis Using Porter

Industries can use Porter’s Five-Forces Model (Figure 6), a business analysis tool, to analyze an industry’s competitive environment [83]. This model was based on five forces. These forces are the main factors that determine the strengths and weaknesses of the industry and influence profit potential.

Porter’s Five Forces Model assesses competitive pressures from the outside. This model includes five key aspects: (1) threat of new entrants, (2) threat of substitute goods, (3) suppliers’ bargaining power, (4) buyers’ bargaining power, and (5) competition intensity within the industry. Every dimension was assigned a score between 1 and 5, where 1 indicated minimal pressure and 5 denoted very high pressure. This evaluation was not based on DEA outcomes to prevent bias or circular reasoning. Instead, the scores were established using semi-structured interviews with five experts (the Muslim fashion industry association, enterprises, government, and academic institutions).

Delphi questionnaires were created based on the qualitative insights obtained, and the same experts participated in the two-round Delphi process [59]. Each expert assigned each force for each company (DMU) a numerical score ranging from 1 to 5 in the first round. The experts were allowed to update their scores in the second round after receiving feedback on group averages and standard deviations. Porter’s scores were determined through a Delphi study involving five expert panels selected using the following criteria: (a) ≥10 years of experience in the industry, (b) leadership in Muslim fashion associations, (c) government policy involvement, and (d) academic expertise in SCM, textiles, and fashion. Convergence criteria (standard deviation < 0.7 across responses) were used to assess the consensus. The final WECS formula was then subjected to sensitivity analysis to test various weighting scenarios for α (DEA) and β (Porter). As the results were more stable and interpretable across industries, the combination of α = 0.6 and β = 0.4 was chosen. They are combined methodologically in the next stage (Section 4.3), employing the WECS method, thus eliminating the bias caused by the interdependence of variables.

• Porter’s Five Forces can be implemented in five key steps:

The analytical procedure used a score range of 1–5, with extremely low to very high scores. Scoring was performed using a data-driven evaluation method that combined interviews with industry experts, market data, and the quantitative DEA results.

1.

Industry Rivalry

Rivalry is fierce, and there are many rival industries, particularly those that have been in business for a long time. Some consumers patronize companies in this sector because of their brand loyalty. An analysis of competition with similar competitors is presented in

Table 5. Industry rivalry.

Indicator	Description	Score
Operational efficiency	Efforts to maximize the company’s output by minimizing waste of cost, time, and resources	3.53
Supply chain strategy [85]	A plan that a company makes to manage the flow of goods and services	3.53
Optimized marketing	Efforts to increase marketing results through various techniques and strategies	3.53
Technology use	Used to improve efficiency, productivity, and quality	3.53
Cost structure	Formation of the total costs incurred by the company to support all of its business operations and management strategies	3.53
Average		3.53

.

Table 5 shows that competition in the Muslim fashion industry in Indonesia is intense. The average score is 3.53 across five key indicators. This suggests that industries must keep improving their operations to stay relevant. Integrated supply chain strategies and effective digital marketing are essential for this effort [86]. Efficient cost structures are necessary for leading industries like JT. Co and LS. Co.

The DEA results and internal competition scores link show highly efficient industries such as PM. Co and JT. Co. also managed cost structures and used technology well. Conversely, industries, such as SA. Co, LF. Co, and AH. Co, which exhibit low efficiency, are often burdened by high-cost structures and cannot adapt to digitalization.

2.

Threat of New Entrants

The risk for new competitors entering the market is low and requires little attention. As new players enter the market, the Muslim fashion sector has become more competitive. However, the challenges and barriers faced by newcomers in the Muslim fashion sector, which competitors must consider, threaten this industry. An analysis of the threats posed by the new entrants is presented in

Table 6. Threat of new entrants.

Indicator	Description	Score
New arrivals	The degree of rivalry between industries that are currently operating in the sector	4
Customer loyalty	Customer loyalty to existing industries	4
Capital required	Capital required to establish a company	4
Product differentiation	Differences between one product and another	4
Government policy	Government policy towards newly established industries	4
Average		4

.

Table 6 displays an average rating of 4.00, indicating that the threat from new entrants is perceived as low. The market segment is beautiful for new players. However, the company has problems such as high capital requirements, strong customer loyalty, and product differentiation, among the main characteristics of well-established competitors that make it challenging to obtain a competitive edge or survive.

This presents a tactical opportunity for existing industries to strengthen their market influence by promoting innovation and collaboration between upstream and downstream partners. Suggested strategies include leveraging local resources to lower production expenses and broadening distribution channels to increase reach.

3.

Threat of Substitute Products

Existing industry must take into account the threat of alternative products.

Muslim fashion industry. They must be able to do the following:

(1)

Design Innovation: Continuously improve innovation in design to create unique, fashionable, trend-following products.

(2)

Premium Quality: Focus on the quality of materials and stitching to add value to the product.

(3)

Strong Branding: Build a strong brand.

(4)

Personal Branding: Engage influencers or create engaging content.

(5)

E-commerce: Leverage e-commerce platforms to reach a broader market.

(6)

Collaboration: Collaborate with other designers, influencers, or brands to create unique products that appeal to consumers [81].

(7)

Competitive Pricing: Offer competitive pricing without sacrificing quality

In

Table 7. Analysis of threats of substitute products.

Indicator	Description	Score
Design innovation	Continue to innovate in design to create unique, fashionable products that align with the latest trends	3.8
Product quality	Focus on the quality of materials and stitching	3.8
Branding	Build a strong brand	3.8
e-commerce	Using e-commerce platforms to reach a boarder market	3.8
Collaboration	Collaborate with designers, influencers, or other brands to create unique products that appeal to consumers	3.8
Competitive price	Providing competitive prices without compromising on quality	3.8
Average		3.8

, the danger from substitute products is clear, with an average score of 3.8, suggesting that the company encounters considerable competition from its rival products. In the business world, factors such as design creativity, product quality, teamwork methods, and engagement in online commerce are crucial for maintaining a competitive edge. Industries that produce products with unique features and compelling branding stories are less susceptible to replacement threats. Ref. [87] shared this opinion. According to these studies, brand loyalty is created by emotional relationships with customers. Green design using non-renewable materials is well-founded, as is a company’s brand image.

4.

Bargaining Power of Suppliers

Supplier Power: In this situation, salient considerations are product quality and the cost of extending the product.

Table 8. Supplier bargaining power of suppliers.

Indicator	Description	Score
Lots of choices	The more product choices, the higher the consumer’s bargaining power	3.73
Technology	Using technology to improve efficiency	3.73
Price sensitivity	Muslim fashion consumers are generally price: sensitive, especially for necessities	3.73
Supplier product quality level	The degree of challenge faced by the industry in acquiring high-quality products from suppliers	3.73
Average		3.73

: Supplier bargaining power analysis is given in the table below.

• Many Alternatives: The more product options there are, the more power buyers have. Consumers can choose another brand if they are unhappy with the product or its prices.
• Easily Accessible Information: Information on products, prices, and consumer reviews can be found online. This allows consumers to compare products before deciding to buy them.
• Price Sensitivity: What is the purchasing company’s sensitivity to fluctuations in input.

Table 8 shows the average rating of 3.73. This indicates that suppliers have strong bargaining power, particularly regarding the quality and price of the raw materials. The industry’s reliance on imported raw materials is a significant weakness. This calls for strategic actions such as replacing imports or developing local supply chains. Possible strategies include building lasting partnerships with local suppliers, using technology to improve supply chain efficiency, and expanding the sources of raw materials to reduce dependence on a single supplier. This will stabilize production costs and boost competitiveness through improved logistics efficiency.

5.

Bargaining Power of Buyers

Every customer selects the goods and services that offer the highest quality at a reasonable cost. Customers will be happy and continue choosing the product if the business can satisfy them by providing reasonably priced, high-quality goods. Consumers of Muslim fashion brands are typically loyal to them. However, given the competition in this market, it may be worthwhile to provide a low price for an additional business.

Table 9. Bargaining power of buyer.

Indicator	Description	Score
Sustainability	Changes in consumer behaviour toward awareness of sustainability, climate change, and environmental impacts.	3.67
Consumer purchasing power	The degree to which customers rely on the services offered.	3.67
Digital marketing	A marketing strategy that leverages digital technology and the internet to effectively promote products or services to a specific target audience.	3.67
Lots of choices	The consumer’s bargaining power grows as the number of product options increases. Customers dissatisfied with the products or prices can easily switch to another brand.	3.67
Price sensitivity	Muslim fashion consumers are generally price-sensitive, especially for necessities.	3.67
Average		3.67

presents the analysis of buyer bargaining power.

(1)

Numerous Options: The consumers’ bargaining power increases with the number of options. Customers who are happy with the quality or pricing may quickly move to a different brand.

(2)

Easily Accessible Information: Information about products, prices, and consumers.

(3)

Reviews are available online. This allows consumers to compare products before deciding to buy them.

(4)

Price Sensitivity: Muslim fashion consumers are generally price sensitive, especially for necessities.

(5)

Sustainability: “Everything we do, from the production of our clothes to the efforts we undertake to safeguard the environment, is woven with sustainability.” The concept of “sustainability” to the company’s “long history of caring” for the environment (we ensure compliance with water quality standards and maintain a restricted substances list for all contracted manufacturing facilities), apparel workers (through the company’s code of conduct related to labor contracting), and women (by introducing the first blue jeans for women) [88,89].

Based on the information in Table 9, an average score of 3.67 indicates that buyers possess high bargaining power. Players in an industry are compelled to innovate and ensure consistent product quality through the availability of differentiated alternatives, access to information, and price sensitivity.

Correspondingly, the industry should focus on digital marketing concepts, enhancing sustainability themes, and improving the customer experience. Using sustainability principles, such as eco-friendly products and ethical business practices, is increasingly essential for differentiating the market and establishing consumer loyalty amid global competition.

DEA and Porter’s elements offer the opportunity to build a better model for assessing competitiveness. This model includes the technical and structural demands of the industry. This is imperative for a rapid universe of Muslim fashion. It provides cost effectiveness, poor world market trends, fashion, and the Internet revolution. The integration framework of the DEA-Porter model is discussed in the next section. The framework also defines an individual business entity’s competitive stance. It also guides the formulation of precise and sustainable strategies to boost competitiveness.

4.3. Integration of DEA and Porter (WECS)

The WECS integration serves as a guiding strategy. For example, industries with high operational efficiency and low Porter scores may need to reposition themselves in the market. In contrast, those with low operational efficiency and high Porter scores may require internal restructuring. This dual mapping method offers valuable insights for internal improvements and strategic adjustments. Consequently, the WECS goes beyond diagnosis and serves as the foundation for developing a decision support system (DSS) to enhance the fashion sector’s future competitiveness. WECS calculations were conducted for each of the 23 industries using the DEA results (Table 3) and Porter’s analysis. The WECS computations used the formula specified in Section 2.3, applying weights of α = 0.6 for DEA and β = 0.4 for Porter.

Table 10. Integration of DEA and Porter.

DMU	DEA Score	Porter Score	Results of DEA and Porter Integration (WECS)
JT. Co	1	3.74	2.096
LA. Co	1	3.74	2.096
PM. Co	1	3.74	2.096
Ss. Co	0.69	3.74	1.91
LF. Co	1	3.74	2.096
LS. Co	1	3.74	2.096
AH. Co	0.38	3.74	1.724
NN. Co	1	3.74	2.096
FS. Co	0.36	3.74	1.712
NS. Co	1	3.74	2.096
BAD. Co	1	3.74	2.096
RR. Co	1	3.74	2.096
Sa Co.	1	3.74	2.096
CL. Co	0.86	3.74	2.012
DR. Co	0.91	3.74	2.042
NJ. Co	0.12	3.74	1.568
In. Co	0.79	3.74	1.97
Ya. Co	1	3.74	2.096
Op. Co	0.79	3.74	1.97
AR. Co	0.47	3.74	1.778
WN. Co	0.67	3.74	1.898
Tt. Co	1	3.74	2.096
HH. Co	1	3.74	2.096

displays the ratings for the WECS.

Competitiveness of the fashion industry is assessed using a quantitative approach that combines DEA and Porter. The DEA Score (Ej) measures internal operational efficiency. The technical efficiency score using the Data Envelopment Analysis (DEA) method ranges from 0 to 1.

Porter Score (Pj): An approach to evaluate external pressure and strategic position. The strategic competitiveness score based on Porter’s Five Forces Framework is measured using a specific quantitative scale.

Based on Table 9, which was generated from 23 DMUs, classification into three groups was performed [61]:

• High (≥2.00): DMUs that are highly operationally efficient and have a strong strategic market position.
• Moderate (1.7–1.99): These DMUs have one or both scores needing improvement.
• Low (<1.7): DMUs in this group require comprehensive strategic intervention regarding their internal efficiency and market competitiveness.

Strategic recommendations for each WECS group are as follows:

• For the high WECS (strategic leaders) group with a DEA score of 1 and a Porter score > 3.5, the strategy should focus on maintaining efficiency and competitiveness, expanding into new markets, and investing in innovation and technology to maintain market position. Jt. Co, PM. Co, LA. Co. Best practice in digitalization, cost discipline, and partnership.
• For moderate efficiency-focused company (WECS) groups with DEA scores (0.6–0.9) and high Porter scores, the strategy to be implemented is to optimize the supply chain and operational cost efficiency, diversify products, penetrate new markets, and strengthen distribution partnerships and branding to improve market position. Examples include CL. Co and DR. Co
• For the low WECS (industry) group, with low DEA characteristics (<0.6) and low Porter scores, the strategy is to conduct comprehensive operational restructuring, adopt technology, review the business model, and reposition the competitive strategy. Example: AH. Co, FS. Co, and NJ. Co

The suggestions from the integration outcomes are as follows: (a) high WECS must increase innovation and market development as it is already efficient and competitive, (b) moderate WECS should focus on improving its operational efficiency, and (c) low WECS requires a complete transformation in operations and market strategy.

5. Discussion

The main feature of this study is the combination of the DEA and Porter’s Five Forces. This generates a single measure, the weighted efficiency competitiveness score (WECS). Other studies, such as [18], have utilized DEA to assess supply chain efficiency. In Ref. [12], on the other hand, the MCDM framework was used. This study relates internal efficiency to external strategy. The study formulates an applied WECS framework by combining DEA with Porter’s five methods. Recent research has shown that DEA can identify problems with resource allocation [9]. Porter’s model examines external market factors such as supplier bargaining power and new competitors [10]. When used with collaborative supply chain strategies [24] and modern DSS models [90], WECS provides valuable insights for the fashion industry. Applying this model to the culturally important and economically significant field of Muslim fashion in Indonesia offers a practical way to evaluate competition in expanding halal and creative industries. This study reveals that combining DEA and Porter provides a well-rounded view of the competition. The boom sector balances the strong branding, operational efficiency, and technological growth. This aligns with [19], who note the need to connect internal and external factors in supply chain assessments.

In contrast to [12], who examine design innovation and supplier performance, this study shows how Porter’s external strategic framework and the quantitative method link operational efficiency to market trends. For instance, industries in the strategic leaders group, such as JT. Co., achieved efficiency scores of CRS = 1 and VRS = 1. The company also differentiates itself through aggressive digital marketing campaigns and strong e-commerce adoption, similar to the PM. Co demonstrates cost discipline, long-term supplier partnerships, active participation in industry associations, reduced buyer bargaining power, and strengthened brand loyalty. These practices explain why both industries have emerged as strategic leaders and have provided replicable benchmarks for other industries. From a methodological viewpoint, the weighting process was validated through sensitivity analysis. Here, α ranged from 0.4 to 0.8, and β from 0.2 to 0.6; the final selection (α = 0.6, β = 0.4) provided the most stable classification of industries across scenarios. This increased the consistency and transparency of the WECS. Efficiency oriented industries (WECS = 1.7–1.99) like CL Co and DR. Co require diversification and branding efforts to balance strong operations with positioning in the market.

Distressed industries (WECS < 1.7), like AH. Co and FS. Co need to change their situation significantly. They must restructure and embrace new technologies and overhaul their business models. However, the use of Delphi scoring in Porter’s model introduces subjectivity. Future studies should include quantitative market indicators such as consumer survey data, brand recognition indices, and market share statistics. With a greater focus on international sustainability, the current framework’s lack of environmental and social indicators is a weakness. Incorporating variables such as using green materials, labor practices, and customer preferences for sustainability will align the framework with international industry trends and ESG goals. The DEA-Porter combination is a practical decision-making tool. This helps to identify industries that face overlapping vulnerabilities. This approach establishes a foundation for developing funding mechanisms, setting training priorities, and creating digital readiness strategies. It also tackles inefficiencies in local supplier networks by encouraging the sourcing of domestic raw materials and providing digital export support. In summary, the integrated DEA–Porter framework enhances academic understanding and offers actionable steps to improve competitiveness in the Muslim fashion industry.

5.1. Synthesizing DEA and Porter Insights

The DEA findings show differences in efficiency among various industries. In this regard, some industries have achieved high efficiency. Others still struggle to use resources, such as labor and capital, to produce business results. External factors, especially customer: negotiating power and competition, play a key role in Porter’s evaluation.

These findings indicate that industries in the underdeveloped quadrant, with low DEA and Porter, often experience moderate market pressure, but have trouble effectively using their resources. Simultaneously, organizations in the at-risk quadrant (low DEA, high Porter) encounter significant pressure from rivals or customers but lack the internal efficiency to react. For example, the considerable negotiating power of clients in these industries may indicate a lack of product distinctiveness or consumer loyalty. This factor exacerbates inefficiency as sectors are compelled to participate in price competition.

By contrast, industries in the strategic leader quadrant can operate business processes effectively and handle competitive challenges by enhancing brand positioning and innovating products. This highlights the significance of a two-fold approach: internal cost-effectiveness and external market flexibility.

5.2. Comparison with Prior Literature

This research enhances a framework initially suggested by [19,88], highlighting the importance of efficiency in the supply chain as a crucial factor for competitiveness. However, this research approach is unique because it integrates the dimension of strategic market pressure, which has not been discussed in detail in the operational efficiency literature. This study questions the linear approach by combining the DEA and Porter models. This shows that efficiency cannot be evaluated in isolation without considering the competitive context within the industry. The findings of [91] also support the assertion that alignment between internal processes and external pressures is key to success.

Furthermore, the findings of this study reinforce the adaptive approach in operational management and strategy suggested by [92] for responsive supply chains [93]. Industries in the Muslim fashion sector that perform well have low costs and can accurately understand the market dynamics and adjust their product strategies accordingly.

5.3. Contribution to Open Innovation Theory

This research contributes to the open innovation literature by (a) introducing hybrid metrics (WECS) that bridge the internal operation and external environments, (b) demonstrating how Porter scoring with Delphi helps to complement data-driven DEA results, and proposing practical foundations for decision support systems (DSS) that are sensitive to both market complexity and resource limits, especially in emerging industries.

6. Research Limitation

Therefore, this research provides a significant understanding of the competitiveness in the Muslim fashion sector by combining DEA with Porter’s Five Forces Model. Nonetheless, this research had certain restrictions that must be considered. (1) The geographic scope was confined to Jakarta, whereas Slovin’s formula justified a sample of 23 DMUs. Further studies should expand their coverage to Bandung, Surabaya, Solo, Kalimantan, Sulawesi, and Makassar to capture regional supply chain variation; (2) Reliance on the Delphi scoring system introduces subjectivity. Future studies could integrate quantitative indicators such as market share, consumer surveys, and brand equity indices to strengthen objectivity; (3) The research lacks consumer insights, and the study fails to address consumer opinions directly about preferences, brand familiarity, recognition, or purchasing behavior, which would have added more value to the analysis; (4) Despite the inclusion of technological factors and sustainability trends, the study does not quantify such things as digital transformation, automation, or supply chain solutions on efficiency improvements [94]; (5) Only external market forces have been mentioned. The study has not attempted to determine how global trade policies, consumer sentiment, foreign competition, or macroeconomic factors, such as inflation and exchange rates, can affect the competitiveness of the Muslim fashion industry; and (6) Sustainability dimensions, although discussed, were not formally integrated into the WECS. Future studies should embed environmental and social metrics to align with the global trends in sustainable supply chains.

7. Practical and Managerial Implications

This study aims to determine how stakeholders and policymakers, along with the industry, can improve the competitive edge of the Muslim fashion industry through supply chain management analysis. (a) Supply chain management in DEA: In the modern approach to supply chain management, participants must use lean practices. They must also improve collaboration in operational flexibility and cost-effective production; (b) Organizational Collaboration: Reducing reliance on imported raw materials through increased cooperation between suppliers will improve cost variability and delivery time performance; vertical and horizontal integration among manufacturers, designers, and retailers will facilitate more efficient production and distribution; (c) Digital Marketing: Effective consumer engagement and customer loyalty can be enhanced by strengthening brand positioning through digital marketing and influencer marketing [95]; (d) Branding: Sustainability in production, such as ethical sourcing and aggressive waste reduction, will address growing sustainability issues in fashion and increasing consumer interest in responsible fashion [71]; (e) Government programs: To foster this industry, the government need to issue grants, preemptive export programs, and even hands-on training for small enterprises to help them become competitive on the world stage; (f) Policymakers: results highlight the need for regional benchmarking, R&D support and fiscal incentives for companies investing in sustainable materials and innovation; (g) Researchers: The DEA-Porter integration opens avenues for exploring ecoefficiency measures and testing the framework across industries and geographies; (h) Practitioners: companies should balance efficiency improvements with differentiation strategies, primarily through sustainability, halal certification, and brand authenticity.

Based on the results of this study, a few essential management strategies for the Indonesian Muslim fashion industry can be outlined. Firstly, although internal productivity continues to be central to operations, it does not ensure a competitive edge between firms. Consequently, industries in the efficiency-driven quadrant should focus on enhancing external capabilities such as branding, fostering customer loyalty, and improving distribution channels to diminish buyer bargaining power.

Secondly, turnaround strategies must enhance process efficiency and adjust marketing strategies to alleviate external pressures for industries in the at-risk quadrant. In contrast, industries in the underdeveloped quadrant need significant help from internal sources, such as lean management, and external sources, such as strategic partnerships.

Finally, the industries in the strategic leader quadrant can serve as examples or models. They show that a mix of operational efficiency and the ability to withstand market pressures is crucial for building lasting competitiveness in fast-changing industries such as Muslim fashion.

8. Conclusions

Integrating DEA and Porter’s Five Forces provides a more holistic approach to supply chain management. While DEA offers insights into internal efficiency, Porter’s model assesses external factors influencing competition and competitiveness. Recent research shows that combining these two methods can result in more informed decisions and effective strategies for dealing with market dynamics. In addition, this study offers concrete strategies for industries to enhance their competitiveness. The managerial implications of these findings are as follows: (a) employees should be more goal-oriented in managing human resources, and managers must adhere to established budget constraints; (b) investing in technology and team member training will reduce the need for manual labor and improve workforce efficiency and productivity; (c) implementing sustainable production in the long term will minimize operational costs and the negative impact of production on the environment; (d) competitive pressure and demand for new substitute products will enhance brand innovation and the ability to offer diverse products; and (e) e-commerce distribution channels and social media will improve market scalability and demand for brand growth. This will involve optimizing the supply chain using digital technology, strengthening sustainability and product innovation to tackle challenges, and collaboration with the government and related organizations for training support and incentives. The Muslim fashion industry must proactively improve its operational efficiency, innovate products and marketing, and leverage external support to address its increasingly competitive challenges. With the right strategy, industries can survive and thrive in increasingly competitive, dynamic markets. Implementing these recommendations is key to enhancing competitiveness, achieving a competitive advantage, and driving sustainable growth in the Muslim fashion industry.

Future research directions for measuring competitiveness and improving the fashion industry supply chain using DEA and Porter’s approach include integrating green and sustainable industrial practices. Given the importance of sustainability, future research could focus on (a) integrating green industry supply chain practices into the DEA models. This could involve evaluating suppliers based on their social and environmental performance alongside conventional economic criteria. (b) Performance measurement systems: Another area to consider is the development of comprehensive performance measurement systems that incorporate intermediate measures and account for interdependencies among supply chain members. This method has proven successful in other sectors and can help to identify inefficient areas in the supply chain and areas requiring improvement. Previous research considered only a few variables that influence competitiveness in the fashion industry. Identifying and analyzing new relevant variables, such as sustainability and innovation.