Critical Success Factors for Supplier Selection and Performance Enhancement in the Medical Device Industry: An Industry 4.0 Approach

Abstract

Supplier selection in the medical device manufacturing (MDM) industry significantly affects quality, operational efficiency, and overall organizational performance. Due to the industry’s dependence on advanced technologies and rigorous regulatory standards, identifying critical success factors (CSF) for selecting suppliers is essential. This study aims to analyze relationships among critical success factors (CSF) influencing supplier selection and their influence on supplier quality and the performance outcomes of MDM companies. A structured survey was conducted among MDM companies in Mexico, and the collected data were analyzed through exploratory and confirmatory factor analysis. Structural equation modeling (SEM) was used to quantify the relationships identified. Results indicate that information technology, reliable delivery, Industry 4.0 adoption, resilience, and environmental and social responsibility positively influence supplier quality, which subsequently enhances MDM firm performance. Supplier quality emerges as a critical mediator between supplier selection factors and company performance. Findings emphasize that prioritizing supplier quality, reinforced through Industry 4.0 technologies and resilient practices, ensures operational continuity, enhances competitive advantage, and supports sustainability. Companies incorporating these critical success factors into their supplier selection processes are better equipped to manage supply disruptions, achieve consistent quality, and sustain performance in highly regulated environments.

1. Introduction

Due to the high level of globalization and increased expectations of customers, enterprises are strengthening their supply chain management (SCM) ability to constantly shorten product development time, improve quality, reduce cost, and enhance process efficiency [1]. Increasing uncertainties in the supply chains (SC) have caused more attention to be paid to SC risk management approaches [2], including the COVID-19 pandemic, which affected various aspects of the SC, such as operations, design, manufacturing, reverse logistics, and waste management [3]. The study of global SC during the pandemic has identified challenges and imbalances, leading to the adoption of measures such as supplier diversification and internal capacity development [4] and turning supplier selection and evaluation into a strategic step in SCM [5] that requires the consideration of various requirements and criteria due to its complexity [6].

Strategic supplier selection directly improves the buyer’s competitive performance capabilities [7], thereby increasingly making SC leaders aware that they must develop and implement complex tools and metrics to compare suppliers before awarding business [8]. Several critical success factors (CSF) for supplier selection have been identified in different industries. In the construction industry, these CSF include building metric magnitude, process optimization, and procurement transparency [9], while, in the hospital industry, they include product cost, product quality, delivery, service, supplier background, and information technology [10]. On the other hand, in the textile manufacturing industry, factors such as quality, delivery time, cost, technology, payment due date, flexibility, and corporate reputation are considered when selecting suppliers [11]. For social commerce microenterprises, the CSF identified include service assurance, delivery, price, quality, flexibility, and relationships [12]. The variety of CSF for supplier selection involves a combination of factors related to the quality of the product/service, delivery, cost, and technology, among other things.

Medical device manufacturing (MDM) companies and their suppliers sit at the top of the healthcare supply chain (HSC). These companies are driven by innovation and technological advancements. The medical device supply chain presents several unique challenges for manufacturers and distributors, primarily due to its complexity and the criticality of the products involved. These challenges include issues related to demand forecasting [13], regulatory compliance [14], and supply chain resilience [15], which can significantly impact patient care and operational efficiency. According to ref. [16], regulatory compliance and quality control measures ensure that suppliers meet safety and efficacy standards, thereby improving supply chain reliability. This reduces the risks associated with outsourcing, ultimately improving efficiency and accelerating the time to market for medical devices. Therefore, the decision to select the right supplier has a significant impact on healthcare profitability, the total cost of medical devices, and the quality of healthcare services provided [17,18].

Despite the importance of the MDM industry, little information is available on the CSF the industry considers when selecting suppliers and their impact on two relevant aspects: first, the quality the industry perceives from suppliers; and, second, the performance of organizations in the MDM industry, especially in emerging countries such as Mexico. This highlights a gap in the literature. Therefore, the objective of this research is, first, to evaluate the relationship between CSF for supplier selection in the MDM industry and supplier quality, and then to assess their impact on the performance of organizations in this industry. This is carried out in the Mexican context, which constitutes an important market for MDM companies seeking to supply the healthcare sector in the U.S.A. and globally. The study proposes a statistically validated model using SEM and describes the proposed relationships. This topic is particularly relevant today due to the growing challenges in global supply chains, stricter regulatory oversight in the healthcare sector, and the crucial role played by supplier partnerships, especially in high-risk sectors like the MDM industry. The sections of this document are organized as follows: First, we present a literature review. Second, we describe the methodology. Then, we have the results and discussion sections. Finally, we conclude the study with the conclusions and future research directions.

2. Literature Review

2.1. Critical Success Factors in Supplier Selection for MDM Companies

For any business, CSF are a limited number of areas in which results, if satisfactory, will ensure successful competitive performance for the organization [19]. For ref. [20], ‘critical success factor’ refers to what needs to go well to ensure the success of an organization and, therefore, to the area where more attention should be paid to achieve better performance. CSF vary depending on the given scope and purpose [21], but they are qualitative variables whose application can be determined based on location or industry [22]. Strategic supplier selection has been identified as a promising source of competitive advantage [7] whose main objectives are to reduce purchasing risk, maximize overall value for the buyer, and develop close and long-term relationships between buyers and suppliers [23]. For ref. [9], supplier selection is a common phenomenon in modern purchasing processes that must be adapted to keep up with an increasingly demanding industry, mainly because it affects the total performance of each company [24] as well as the entirety of the SC [25].

Partnering with suppliers is crucial for quality control, so quality-oriented selection, among other practices, positively impacts product quality, process quality, and inventory performance in manufacturing companies [26]. Identifying causal relationships between supplier selection factors, both beneficial and non-beneficial, can help determine supplier performance and ensure the quality of the final product [27] because the selection process plays an important role in reducing costs, improving profits, and, above all, the quality of products [28]. The medical device industry is a highly regulated and sensitive sector where the quality of suppliers directly impacts patient safety and product efficacy. In this regard, we formulated the following question:

RQ1. What are the CSF that impact supplier quality in the MDM industry?

Selecting a reliable supplier is increasingly crucial in the healthcare sector [17], particularly when it comes to minimizing risks [29]. The Global Model Regulatory Framework (GMRF) for medical devices requires manufacturers to establish and maintain a quality management system to ensure that devices are designed and manufactured to meet safety, performance, and quality requirements during their life cycle [30]. For supplier quality, this study considers dimensions such as timely supply, high product quality, compliance with regulatory standards, effective risk management, and robust supplier monitoring and auditing processes to ensure the safety and performance of medical devices [14]. Furthermore, it considers consistent adherence to product quality specifications (materials, dimensions, design, durability), as well as to production processes, the quality, and the continuous improvement management system [23].

Delivery is among the most reported CSF for supplier selection in the literature. In this regard, ref. [31] pointed out that the quality and delivery of suppliers significantly impact product quality, highlighting that quality is the main criterion in the selection of suppliers, while reliability in delivery is crucial to guaranteeing customer satisfaction. Integrating reliable suppliers into the supply chain is relevant to product reliability, as the weakest link can determine the overall performance of the system [32]. Reliable delivery ensures that products reach their destination on time, minimizing costs and maintaining quality [33]. Effective communication and collaboration between suppliers and manufacturers are essential to maintaining quality standards and regulatory compliance, which are vital for patient safety [16]. Regarding reliable delivery, we defined the following hypothesis:

H1. 

Reliable delivery (RD) has a positive effect on supplier quality in the MDM industry.

The integration of supplier information technologies is related to the quality of organizations, by improving communication, transparency, and operational efficiency. The use of information technology (IT) has been highlighted as a key element for analysis and improvement purposes due to the importance of having adequate information along the supply chain [34] and the capability of information integration and operational coordination to affect organizational agility [35]. Moreover, the integration of IT enables material and information flows to be combined for analysis, supporting the planning and control of logistics activities [34]. Key technologies include enterprise resource planning (ERP), which is considered a major breakthrough information technology that re-shaped the manufacturing industry [36] and predictive health data analysis and technology for remote monitoring of inventories, supporting the organizational capabilities of a resilient HSC [37]. Supplier IT improves organizational quality by improving operational efficiency, transparency, and collaboration. They facilitate real-time communication, centralized data sharing, and data-driven decision-making, fostering trust and alignment with common goals, ultimately leading to improved supply chain performance and sustainability [38]. Regarding IT, we defined the following hypothesis:

H2. 

Information technology (IT) has a positive effect on supplier quality in the MDM industry.

The use of Industry 4.0 technologies has been identified as a factor that supports suppliers to improve quality [39] and the healthcare supply chain’s performance [40]. According to ref. [41], the application of I4.0 technologies enables improvements in the performance of individual supply chain processes, such as purchasing, production, inventory management, and retail, by enabling the integration, digitalization, and automation of processes, and by generating new analytical capabilities. For [39], the technological capabilities of suppliers improve the production volume and the quality of the products of the purchasing companies. For his part, ref. [42] pointed out that technologies such as IoT allow continuous and real-time monitoring of operations along the SC. Additional technologies include artificial intelligence and simulation [43], RFID [44,45], automated guided vehicles [34], blockchain [46], and smart contracts [47], among others. Some impacts of I4.0 technologies on supplier selection are greater visibility and transparency. Today, digital SC require the selection of competent suppliers based on their responsiveness, resilience, sustainable practices, and digital innovation [48]. In this context, we formulated the following hypothesis:

H3. 

Industry 4.0 technologies (IND) have a positive effect on supplier quality in the MDM industry.

Environmental and social responsibility (ESR) has been identified as leading to the better performance of customers in SC [49,50,51]. An organization with ESR has a significant impact on its sustainable supply chain management practices, which, in turn, improves operational performance, including quality. Relational capital with suppliers further strengthens this influence, especially in terms of flexibility and delivery compliance [52]. According to ref. [53], suppliers with ESR have a significant impact on the quality of organizations by improving operational performance and fostering positive organizational behaviors. Therefore, organizations are increasingly incorporating environmental and social factors into supplier selection processes, emphasizing the importance of sustainability in decision-making. In this regard, we defined the following hypothesis:

H4. 

Environmental and social responsibility (ESR) has a positive effect on supplier quality in the MDM industry.

Resilience (RES) is considered a critical element of supply chains. It is characterized by the capacity to absorb, adapt to, and restore after disruptions [54]. The resilience concept includes both resistance (proactive approach) and recovery (reactive approach) [55,56]. Recently, the phenomenon of nearshoring, i.e., attracting production lines to places closer to the end customer [57], has been associated with the resilience of the global SC by reducing dependence on distant suppliers, offering proximity advantages, and mitigating the risks associated with interruptions [58]. For ref. [59], disruptions significantly affect supplier quality, suggesting a relationship between supplier resilience and perceived quality. In this sense, higher-performing suppliers experience fewer and shorter disruptions, which can improve their perceived quality and resilience in the supply chain context. Furthermore, the integration of supply chain resilience with quality management improves organizational outcomes, suggesting a relationship in which resilient suppliers tend to perceive and deliver higher quality, thereby ensuring customer satisfaction and loyalty through effective risk management and quality assurance. This integration offers synergistic benefits that contribute to operational efficiency, market competitiveness, and improved brand reputation [60]. Taking these arguments into account, we formulated the following hypothesis:

H5. 

Resilience (RES) has a positive effect on supplier quality in the MDM industry.

2.2. The Relationship Between Quality as a Critical Factor in Supplier Selection and Organizational Performance in MDM Companies

Companies constantly seek to improve productivity, especially in an environment of globalization, intense competition, and technological advances. Supplier performance positively influences business performance [61], including quality and delivery [62]. Particularly, supplier quality impacts organizational performance in several ways. According to ref. [63], supplier quality in the context of the supply chain refers to the reliability and performance of suppliers that provide essential components, raw materials, or services for manufacturing processes, thereby ensuring compliance with required standards of quality, reliability, and performance. This concept is crucial to maintaining the stability and efficiency of supply chains as it directly impacts product quality, customer satisfaction, and overall business success. On the other hand, according to ISO 13485:2016 [64], which establishes the requirements for a quality management system (QMS) specific to medical devices, in the context of suppliers, the term “quality” refers to the ability to supply products and services that meet the regulatory, technical, and performance requirements necessary to ensure the safety and efficacy of medical devices. Therefore, supplier quality based on the QMS involves meeting clear requirements for the validation of production processes, identification, and traceability, as well as the preservation of customer property, including intellectual property and product integrity.

For ref. [65], supplier quality has a significant impact on organizational performance, while, for refs. [66,67], supplier quality has an indirect positive impact on organizational performance. According to ref. [68], selecting suppliers based on quality will lead to an improvement in the quality of the purchasing company. Based on this information, we pose the following research question:

RQ2. Does supplier quality affect the performance of MDM companies?

Quality is considered one of the leading measures of organizational performance and directly affects total quality management practices [69]. In this sense, ref. [70] pointed out that Quality 4.0 helps companies achieve greater competitive performance through improvements. Similarly, for ref. [71], total quality management has a significant influence on operational performance. After this argument, we propose the next research hypothesis:

H6. 

Supplier quality (SQ) has a positive effect on the performance of MDM companies.

Supplier selection and its relationship to organizational performance represent an area of interest for researchers, as evidenced by the extensive literature on the topic. However, there is a clear gap in the application of these analyses to MDM companies, particularly in the context of Mexico. This study aims to address this gap by offering valuable insights into the strategic practices that influence organizational performance in this highly regulated and innovation-driven sector. The proposed hypotheses are structured within a conceptual framework, as illustrated in Figure 1, to systematically examine the variables and their interrelationships.

2.3. The Medical Device Manufacturing (MDM) Industry in Mexico

The MDM industry focuses on designing, developing, manufacturing, and distributing a wide range of devices, equipment, and instruments used in healthcare, ranging from simple diagnostic tools to advanced surgical equipment. Factors such as the increasing prevalence of lifestyle diseases, economic development in emerging markets, and technological advancements such as artificial intelligence are driving the growth of the medical devices industry globally. The revenue of this market is projected to reach USD 509.9 Bn by 2024 [72]. Furthermore, the market volume is expected to reach USD 673.10 Bn by 2029. The five countries with the highest income in this sector are the U.S. (USD 164.1 Bn), Germany (USD 33.98 Bn), Japan (USD 32.65 Bn), China (USD 31.45 Bn), and France (USD 17.72 Bn) [72].

The MDM industry in Mexico is strategically important due to its high international competitiveness and potential impact on the quality of medical care [73]. Mexico has been actively participating in this industry, particularly in manufacturing, but there is an increasing emphasis on design trends to improve user experience and product innovation [74]. The country has medical device clusters, such as Baja California, which is known for its quality products and services offered at a competitive cost [75], leading to market growth [76]. Therefore, identifying CSF for supplier selection and their relationship with quality and performance can play a big role. The present study addresses these topics, and the research steps are described in the methodology.

3. Materials and Methods

This section details the stages carried out to test the research hypotheses. Figure 2 presents a flowchart summarizing the procedural steps used in this study, adapted from ref. [77].

A survey design was used to collect data on CSF for supplier selection and their impact on the performance of the Baja California medical device industry. The survey development and validation followed common approaches [78], comprising three stages which are described below. The survey design and validation procedure comprised three stages, with the structural model being constructed and validated in stage 3. Each stage is described in detail below.

3.1. Survey Development and Sampling

The first step involved defining the constructs and conducting an extensive literature review using four databases: Elsevier, Emerald, Springer, and Google Scholar. We searched for studies on CSF for supplier selection published in English from 2014 to 2024 using keywords such as “critical success factors”, “supplier selection”, “organizational performance”, and “medical device”. As the literature regarding CSF for supplier selection in the MDM industry is scarce, we also identified CSF from other manufacturing industries. However, to validate and contextualize these factors, we consulted five industry experts from the MDM sector in Mexico. These experts were selected based on a minimum of three years of experience working directly with suppliers and procurement processes, as well as their familiarity with relevant regulatory standards. They were approached via professional networks and invited to participate in the validation process through direct, personalized communication. Their input helped confirm the relevance of the following CSF: supplier quality (SQ), reliable delivery (RD), information technology (IT), environmental and social responsibility (ESR), resilience (RES), and Industry 4.0 (IND).

Because critical success factors (CSF) represent latent constructs that cannot be directly observed, it was necessary to operationalize them through measurable indicators [79]. Based on the conceptual definitions presented in

Table 1. Conceptual definitions of the CSF.

Supplier quality is the degree to which a set of product features meets customer requirements [23,82]. It is the ability to provide products and services that meet necessary standards and requirements, thereby ensuring the safety and proper functioning of medical devices [63].

Reliable delivery is the ability to meet specific delivery schedules, including lead times, punctuality, fulfillment rate, returns management, and location and transportation [23], while minimizing costs and maintaining quality [33].

Information technology refers to systems compatibility, ease of communication, information exchange, and information technologies [83]; technological capacity and the ability to acquire new technologies and technical resources for research and development practices and processes [23]; and the sum of all the knowledge of a company in support of technological innovation [82].

Environmental social responsibility is the responsible use of natural resources, minimizing damage and ensuring that these resources are available for future generations [23].

Resilience is the capacity to absorb, adapt to, and restore after disruptions [54].

Industry 4.0 refers to the use and/or integration of advanced technologies in processes to improve efficiency, quality, and innovation [84,85].

, we identified key dimensions from the literature that characterize each factor. Drawing on this foundation, we formulated original survey items that reflect these dimensions in a practical and context-appropriate way. While the items were developed specifically for this study, they are rooted in established theoretical and empirical frameworks [80]. The instrument employed a five-point Likert scale ranging from “never” (1) to “always” (5), allowing respondents to indicate the extent to which each statement applied to their supplier evaluation practices [81].

For content validity, the survey was reviewed by a panel of nine experts, seven senior professionals from the medical device industry, each with at least five years of experience in supplier evaluation, procurement, or quality assurance, and two academics with extensive expertise in research methodology and instrument design. These experts assessed the relevance of each item, the clarity and appropriateness of the wording, the alignment with industry terminology, and the estimated completion time. In addition, they were asked to identify any redundant, unclear, or missing items. Based on their feedback, adjustments were made to improve the precision and completeness of the instrument.

The final structure of the instrument comprises four sections: the first provides an overview of the survey’s goals, the second collects general information on the MDM industry, the third evaluates the CSF for supplier selection, and the fourth gathers data on the performance of the medical devices industry. The research instrument was administered via the Alchemer platform, with distribution channels including the Baja California medical device cluster directory, direct meetings with companies within the industry, and LinkedIn. Survey items related to the constructs of this study can be found in

Table A1. Items by constructs.

Construct	Item
Supplier Quality
SQ1	To what extent do suppliers demonstrate a robust quality system?
SQ2	To what extent do suppliers ensure that their processes are of quality?
SQ3	To what extent do suppliers have a quality philosophy aligned with my company’s quality philosophy?
SQ4	To what extent do suppliers have a system for evaluating their suppliers’ performance that allows them to select them better?
Reliable Delivery
RD1	To what extent do suppliers meet delivery schedules on time?
RD2	To what extent do suppliers deliver in full according to what is established in the order?
RD3	To what extent are suppliers performing to an established compliance rate?
RD4	To what extent do suppliers demonstrate adequate handling and conservation processes for the products/services required?
RD5	To what extent do suppliers demonstrate a product identification and traceability system?
RD6	To what extent do providers offer greater benefits that can be reflected in costs, prices, and care?
Information technology
IT1	To what extent do suppliers have cutting-edge and updated technology in their production processes?
IT2	To what extent do suppliers have the technological capacity to meet the needs and/or requirements of my company?
IT3	To what extent do suppliers use Information Technology (IT)-based support for exchanging shipping and delivery information?
IT4	To what extent do suppliers use IT for inventory management and/or reporting their warehouse stocks?
IT5	To what extent do suppliers share information in real-time to work on common demand forecasts?
Environmental and social responsibility
ESR1	To what extent do suppliers show commitment to the environment in the design of their products?
ESR2	To what extent do suppliers have environmental policies?
ESR3	To what extent do suppliers implement recycling programs (for relevant materials and/or resources)?
ESR4	To what extent do suppliers have activities that have a social impact inside and outside their facilities?
Resilience
RES1	To what extent can suppliers keep us alert of any situation at all times?
RES2	To what extent can suppliers cope with the changes brought about by SC disruption?
RES3	To what extent can suppliers recover normal operations quickly after SC disruption?
RES4	To what extent do suppliers offer flexibility to changes or modifications to product and/or process requirements?
Industry 4.0
IND1	To what extent do suppliers use artificial intelligence?
IND2	To what extent do providers use automation?
IND3	To what extent do providers use simulation?
IND4	To what extent are providers using remote sensing?
IND5	To what extent are suppliers using collaborative robot systems?
IND6	To what extent are suppliers using 3D printing/additive manufacturing?
Performance
PER1	Considering the operational efficiency of the last year, to what extent does my company comply with production plans?
PER2	Considering operational efficiency over the past year, to what extent does my company have a program for developing new products to meet customer needs?
PER3	Considering cost competitiveness over the past year, to what extent can my company compete on price within the market?
PER4	Considering cost competitiveness during the past year, to what extent has my company managed to reduce production costs due to innovation in production processes?
PER5	Considering cost competitiveness over the past year, to what extent does my company offer competitive prices as a result of product innovation?
PER6	Considering the responsiveness over the past year, to what extent is my company able to satisfy customers in terms of volume and delivery time?

of Appendix A.

The target population of this study included those in middle and senior management roles such as directors, managers, engineers, buyers, and supervisors of departments related to the selection and/or evaluation of suppliers, such as the quality, procurement, SC, operations, management, and engineering departments of Mexican manufacturing companies.

3.2. Statistical Validation of the Survey

Validating a survey involves two key tests: reliability and validity. For reference, ref. [86] used factor analysis to assess the reliability and validity of indirectly observable variables. In this sense, four critical components of survey validation were examined: missing data, outliers, univariate and multivariate normality assumptions, and multicollinearity [87]. This study conducted statistical analyses using IBM SPSS^® Statistics version 23, supplemented with Analysis of Moment Structures (AMOS^®). To avoid missing data, only fully completed surveys were included in the analysis. The dataset was then evaluated for outliers using Mahalanobis distance [77]. The univariate normality of each variable was assessed based on skewness and kurtosis, in accordance with ref. [88].

Multivariate normality was determined using Mardia’s test, which compares the Mardia coefficient to a value computed from the formula p(p + 2), where p is the number of observed variables [86]. This computed value was then compared to results from SPSS^® AMOS^®. To check for multicollinearity, variance inflation factors (VIFs) were calculated. Variables with VIF values exceeding 10 were considered redundant [89].

An exploratory factor analysis (EFA) was then performed on the correlation matrix to identify latent dimensions, establishing the validity of each construct. According to ref. [90], instrumental validity is defined as the degree to which an instrument measures what it purports to measure. Maximum likelihood estimation was used in the factor analysis to extract components, and Varimax rotation was applied to minimize correlated components [91].

The Kaiser–Meyer–Olkin (KMO) index was calculated to measure sampling adequacy, and Bartlett’s test of sphericity was used to confirm the suitability of the factor analysis. Items with loadings of at least 0.4 on their associated factors were retained, as suggested by ref. [92].

After EFA, a confirmatory factor analysis (CFA) was conducted using SPSS^® AMOS^®. The data were re-evaluated for univariate and multivariate normality, outliers, and multicollinearity. Goodness-of-fit indices and tests for construct validity were used to validate the measurement model. According to ref. [89], the validation process should include the minimum discrepancy coefficient/degrees of freedom (CMIN/DF), root mean square error of approximation (RMSEA), standardized root mean residual (SRMR), Tucker–Lewis index (TLI), comparative fit index (CFI), and parsimony normed fit index (PNFI) for comparing models of varying complexity.

Convergent validity was assessed using the Average Variance Extracted (AVE) index, which must exceed 0.5, ensuring that a set of items collectively represents a specific construct [93]. Internal consistency was evaluated using Cronbach’s Alpha. Nunnally [94] recommends a minimum reliability of 0.7 for early research and 0.8 for basic research. Discriminant validity was confirmed following the method proposed by ref. [95]. This was achieved by ensuring that the square root of the AVE for each construct exceeded its correlation coefficient with any other construct, thereby affirming their independence.

3.3. Structural Equation Modeling (SEM)

Following the evaluation of the measurement model, a structural model was created and validated to determine the effect of CSF for supplier selection on the performance of the medical devices industry. To analyze the proposed structural model, we employed covariance-based structural equation modeling (CB-SEM) using maximum likelihood estimation. This method was selected because the primary objectives of the study are theory testing and model validation rather than prediction or exploration. CB-SEM is particularly well suited for confirming theoretically grounded models, especially when constructs are measured reflectively [96], as is the case in this study. Additionally, the sample size satisfies the recommended thresholds for CB-SEM, allowing for robust estimation of model parameters and overall fit [77]. This approach also enables the assessment of goodness-of-fit indices, providing a more rigorous evaluation of the model structure. Ultimately, we evaluated the statistical significance of the correlations and validated the hypotheses by analyzing the direct, indirect, and total effects of the variables.

4. Results

4.1. Application of the Survey

The research was carried out among companies of the Baja California medical device cluster, which is oriented toward the external market [97]. In total, 180 completed surveys were collected. All participants were informed of the anonymity and confidentiality of their responses. Moreover, the research adhered to the Declaration of Helsinki [98] and followed all ethical guidelines for human research, including compliance with Mexican legal requirements. The Baja California medical device industry cluster approved this procedure.

4.2. Data Analysis and Results

Both refs. [77,89] emphasize the importance of addressing four critical issues before performing SEM analysis: outliers, univariate and multivariate normality, and multicollinearity.

Table 2. Assumptions results.

Issues	Results	Recommended Values
Outliers	162 significant responses.	Mahalanobis distance, with a statistical significance level of p < 0.001 [89].
Univariate Normality	Kurtosis (−0.999, 0.936), skewness (−0.747, 0.206).	Kurtosis: range of ±3 [88]. Skewness: range of ±2 [99].
Multivariate Normality	Multivariate kurtosis 159.078, obtained through SPSS® AMOS ® version 23.	A value lower than that derived from the formula p(p + 2), where p represents the number of measured variables in the model [100], resulting in a value of 1295.
Multicollinearity	Correlation coefficients below the recommended maximum value.	The correlation coefficient between pairs of measured variables >0.85 [100].
	VIF’s maximum calculated value: 4.913.	Variance inflation factor (VIF) with values >10 [89].

summarizes the analysis results, demonstrating that the basic assumptions for SEM analysis were satisfied after 18 outliers were removed because they did not meet the level of statistical significance recommended by ref. [89]. In the end, 162 responses were included, representing a response rate of 36%.

4.2.1. Results of the Exploratory Factor Analysis (EFA)

To assess sample adequacy, we conducted the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity. According to ref. [101], KMO values should exceed 0.9, and the p-value from Bartlett’s test should be less than 0.01. Our research yielded a KMO of 0.905 and a p-value of <0.000, confirming the suitability for factor analysis.

Following the exploratory factor analysis (EFA), seven constructs and 35 observed variables with significant loadings were identified, collectively explaining 75.59% of the total variance. According to Hair et al. [77], the significance of factor loadings is influenced by sample size. In studies with over 150 samples, loadings greater than 0.4 are generally considered acceptable. Given our sample of 162 valid responses, the observed factor loadings exceeded this threshold and were statistically significant. Additionally, all extracted components had eigenvalues greater than 1, confirming the factor structure and supporting construct validity. These results are summarized in

Table 3. EFA and CFA results.

EFA										CFA
	Factors							Eigenvalues	Cronbach’s Alpha	Standardized Loading	AVE
	1	2	3	4	5	6	7
RD4	0.817							13.989	0.923	0.932	0.656
RD5	0.789									0.834
RD2	0.789									0.818
RD3	0.755									0.788
RD6	0.697									0.741
RD1	0.592									0.731
IND5		0.900						3.724	0.913	0.907	0.658
IND6		0.807								0.772
IND4		0.797								0.842
IND1		0.689								0.708
IND3		0.684								0.839
IND2		0.671								0.785
PER3			0.853					3.170	0.899	0.867	0.536
PER5			0.835							0.776
PER4			0.722							0.658
PER6			0.717							0.744
PER1			0.691							0.722
PER2			0.568							0.594
ESR2				0.791				1.598	0.932	0.910	0.778
ESR1				0.748						0.867
ESR4				0.722						0.901
ESR3				0.697						0.849
IT3					0.792			1.327	0.910	0.910	0.667
IT4					0.769					0.922
IT5					0.615					0.779
IT1					0.547					0.733
IT2					0.518					0.716
RES2						0.694		1.179	0.905	0.919	0.717
RES3						0.658				0.845
RES4						0.644				0.837
RES1						0.537				0.781
SQ2							0.725	1.107	0.866	0.807	0.620
SQ1							0.581			0.755
SQ3							0.555			0.803
SQ4							0.478			0.784

.

Although some authors recommend a minimum sample size of 200 for covariance-based structural equation modeling (CB-SEM) [102,103,104], sample adequacy also depends on model complexity, the strength of indicator loadings, and the chosen estimation method. Considering the robustness of our factor loadings and the theoretical grounding of the model, the use of CB-SEM is justified. The empirical evidence supports the reliability of the analysis and the appropriateness of the method in the context of this study.

4.2.2. Results of the Confirmatory Factor Analysis (CFA)

After completing the EFA, we re-evaluated the data for univariate and multivariate normality, outliers, and multicollinearity. Any issues with normality were discarded, ensuring that the sample size remained unchanged for the subsequent analyses.

Next, confirmatory factor analysis (CFA) was performed using SPSS^® AMOS^® 23 software. As shown in

Table 4. Goodness-of-fit indices of the models.

Goodness-of-Fit Statistics	Measurement Model	Structural Model Results	Recommended Values
CMIN/DF	1.644	1.664	<3 [105]
CFI	0.924	0.923	>0.9 [77]
TLI	0.915	0.915	>0.9 [77]
RMSEA	0.063	0.063	<0.08 [77]
SRMR	0.075	0.079	<0.08 [93]
PNFI	0.743	0.749	≥0.5 [87]

, the goodness-of-fit indices indicated acceptable values, confirming that the constructs are suitable for evaluating CSF in supplier selection within the MDM industry. These indices suggest a good fit, with a CMIN/DF below 3. The CFI and TLI values exceed 0.9, while the RMSEA and SRMR are below 0.08. Additionally, a PNFI rating of 0.743 indicates an appropriate level of model complexity.

Figure 3 graphically represents the constructs of the proposed measurement model.

4.2.3. Results of the Construct Validity

Construct validity encompasses convergent, discriminant, and nomological validity. Convergent validity assesses the extent to which multiple items measure the same concept [106]. AVE values greater than 0.5 typically indicate good convergent validity [77] (Table 3). Since all values exceeded 0.5, we determined that the theoretical model has sufficient convergent validity.

Discriminant validity evaluates whether one construct is different from another [107]. The results summarized in

Table 5. Discriminant validity of constructs.

Construct	RD	IND	PER	ESR	IT	RES	SQ
RD	0.810 a
IND	0.283	0.811 a
PER	0.177	0.048	0.732 a
ESR	0.634	0.432	0.225	0.882 a
IT	0.632	0.513	0.102	0.667	0.817 a
RES	0.695	0.463	0.234	0.672	0.677	0.847 a
SQ	0.692	0.511	0.131	0.678	0.697	0.741	0.788 a

^a Square root of AVE.

indicate that all constructs possessed discriminant validity, as the square root of the AVE for each construct was greater than its correlation coefficient with any other construct, thus confirming their independence. Finally, nomological validity was determined by examining the positive and significant correlations between constructs within the measurement theory.

4.3. Evaluating Hypothesized Relationships Using SEM

For the SEM analysis, the study hypotheses describing the connections between latent variables or constructs were tested [87], as illustrated in Figure 1.

Table 6. Hypothesis test results.

Hypotheses	Path			SRW	SE	CR	p	Results
H1	RD	→	SQ	0.265	0.100	2.662	0.008 *	Supported
H2	IT	→	SQ	0.131	0.078	1.667	0.095 ***	Supported
H3	IND	→	SQ	0.106	0.047	2.266	0.023 **	Supported
H4	ESR	→	SQ	0.106	0.063	1.672	0.094 ***	Supported
H5	RES	→	SQ	0.254	0.094	2.696	0.007 *	Supported
H6	SQ	→	PER	0.144	0.081	1.786	0.074 ***	Supported

* Significant at a 0.01 level, ** significant at 0.05 level, *** significant at a 0.1 level.

presents the results, including standardized regression weights (SRW), the critical ratio (CR), and p-values (p), which help confirm if the model effectively fits the data. Our findings indicate that all six hypotheses have statistical significance. The final model is depicted in Figure 4.

5. Discussion

The objective of this study was to evaluate the relationships among the CSF for sup-plier selection, supplier quality, and the performance of medical device manufacturing (MDM) companies. The results of the structural model support the mediating role of supplier quality in the relationship between the identified critical success factors (CSFs) and the performance of medical device manufacturing (MDM) firms. As shown in Table 6, the paths from reliable delivery, information technology, Industry 4.0 technologies, resilience, and environmental and social responsibility to supplier quality (H1–H5) were all statistically significant (p < 0.05). Additionally, supplier quality had a significant positive effect on firm performance (H6), suggesting that the influence of CSFs on organizational performance is channeled through improvements in supplier quality. These findings confirm the conceptual framework and highlight supplier quality as a key mediating construct in the model.

5.1. Theoretical Implications

In the context of this study, the theoretical implications of using CSFs for supplier selection in medical device manufacturing organizations suggest that they can improve product quality and operational efficiency. By integrating CSFs into the supplier selection process, organizations can systematically evaluate suppliers based on criteria that directly impact medical device quality. This approach not only aligns suppliers’ capabilities with organizational objectives but also promotes a more robust supply chain.

The results indicate that information technology has a positive effect on supplier quality, which positively affects the performance of MDM companies. This is similar to previous studies highlighting the importance of information along the supply chain [34] and operational coordination [35] as a critical element for analysis and improvement. Key technology includes enterprise resource planning (ERP), which is considered a breakthrough IT that re-shaped the manufacturing industry [36]. Additional technologies include predictive health data analysis and remote monitoring of inventories, supporting the organizational capabilities of a resilient HSC [37]. Concerns such as safety, storage, information technology use, and costs are factors to consider in the healthcare supply chain [108].

The study first confirmed a significant direct effect of reliable delivery on supplier quality, indicating that delivery performance contributes meaningfully to how firms perceive the reliability and capability of their suppliers. Reliable delivery entails not only timely shipments but also consistency and trust, which are particularly critical for high-risk products with short life cycles [6,109]. Building on this, we also confirmed a positive indirect effect of reliable delivery on the performance of MDM companies, mediated by supplier quality. This finding reinforces the strategic importance of ensuring delivery precision as part of broader supply chain performance. Furthermore, digital platforms now play a central role in monitoring and ranking suppliers based on delivery performance. Complementary tools such as demand forecasting systems and contract management solutions enable simulation and optimization of delivery logistics [37], thereby supporting data-driven decision-making in supplier management.

The results confirmed the positive effect of Industry 4.0 technologies on supplier quality, which positively affects the performance of MDM companies. Our finding aligns with previous studies indicating that the I4.0 technology capabilities of suppliers improve quality [39] and directly affect the healthcare supply chain’s performance [40]. A related term is Healthcare Supply Chain 4.0 (HSC4.0) [110], which conveys the use of Industry 4.0 technologies in the HSC, including sensors and RFID to monitor product quality, lead time, and inventory [44,45] and additive manufacturing for low-volume production, high flexibility, customized output, and complex geometric designs at a lower cost [111]. Moreover, technologies such as IOT [112], big data analytics [113], and artificial intelligence have been reported to optimize supply chains, enhance production efficiency, and ensure quality control [114]. Additional technologies related to HSC4.0 include the use of automated guided vehicles [34], blockchain [46], and smart contracts [47], among others.

The supplier’s environmental and social responsibility (ESR) plays an important role in the manufacturer’s performance through the perceived quality of the supplier. This is in line with previous studies suggesting that ESR leads to better customer performance in SC [51,115,116], either reducing resource waste or eliminating adverse impacts on the environment and society, which ultimately improves the overall performance [117]. Environmental and social responsibility, including sustainable initiatives, positively influences customer engagement, trust, and satisfaction, fostering a stronger relationship between the company and the customer [118]. Moreover, HSCs have a distinctive social relevance as their reliability and stability directly impact public health [37]; thus, MDM companies pay more attention to suppliers aligned with these principles. Identified factors that affect the implementation of ESR include industry type, firm size, export orientation, regulations, top management commitment, and employee training [117].

The study confirmed that supplier resilience positively affects the manufacturer’s performance through the perceived quality of the supplier. Similar to other industries, MDM companies look for a resilient supply chain. In countries such as Mexico, this is particularly relevant in the context of nearshoring, i.e., attracting manufacturing processes close to the end consumer, such as the U.S.A., thus reducing dependence on distant suppliers, offering proximity advantages, and mitigating the risks associated with interruptions [58]. In addition, resilient supply chains are characterized by the capacity to absorb, adapt to, and restore after disruptions [54]. The resilience concept includes both resistance (proactive approach) and recovery (reactive approach) [55,56]. To resist disruptions and reduce their impact, companies need redundancy (safety stock and additional production capacity) and flexibility (alternative suppliers, transportation depots, and modes for delivery) [119]. To recover from disruptions, similar tactics include the use of backup suppliers for sourcing, the use of buffer stock, and redundant capacity for continuing production [120]. Particularly in the healthcare sector, a resilient performance plays out in several ways, such as in the use of similar, redundant medical materials [121] and the sharing of workers, materials, and technologies between healthcare organizations [122], as well as collaborative planning, forecasting, and replenishment practices across the tier levels [47]. Regarding quality, our results align with previous studies [17,18] confirming the importance of supplier quality for the performance of customers.

5.2. Practical Implications

The use of CSF for supplier selection in medical device manufacturing organizations has important practical implications for improving overall quality. By implementing structured supplier evaluation frameworks, organizations can ensure the selection of suppliers that meet strict quality standards, ultimately resulting in increased product safety and reliability. As ref. [123] points out, a supplier selection framework improves cost optimization, establishes clear performance expectations, and fosters collaboration, ultimately resulting in improved quality of supplies and services, which positively impacts patient care in healthcare organizations. On the other hand, while CSF frameworks can significantly improve supplier selection processes, they can also introduce complexity and require continuous adjustments to remain relevant in a dynamic market. This requires a balance between structured evaluation and flexibility to adapt to industry demands.

This study demonstrated that supplier quality significantly impacts the performance of MDM organizations. Effective supplier relationships and rigorous quality management practices are essential to ensuring that medical devices meet safety and efficacy standards. Effective supplier controls improve product reliability, reduce risks, and optimize the overall performance of MDM organizations, thereby driving innovation and market competitiveness [16]. While focusing on key success factors for supplier selection can lead to improved quality outcomes, it is critical to recognize that an overemphasis on specific criteria can inadvertently overlook other critical factors, such as supplier innovation and flexibility, which are also vital to long-term success in the dynamic medical device industry [124].

According to ref. [33], supplier quality directly impacts manufacturing performance in the MDM industry, as reliable suppliers ensure compliance with regulatory standards, improve product quality, and optimize delivery times, ultimately fostering trust and efficiency in operations management and customer satisfaction. Furthermore, our study demonstrates that, in addition to delivery reliability, other key success factors, such as information technology, Industry 4.0, environmental and social responsibility, and resilience, influence perceptions of supplier quality. It is worth noting that it is advisable to contrast these findings with other similar studies and research.

5.3. Challenges and Limitations

In line with refs. [125,126], our results suggest that the accelerated technological development for healthcare requires the continuous adaptation of suppliers and the whole HSC to ensure high-quality products and efficient processes. Therefore, the ability to use and integrate technological advances at a seamless pace represents an important challenge for suppliers. In addition, different challenges for HSC have been identified, including the lack of resilience, lack of visibility, cost management, integration and interoperability [127], fragmentation, complexity, and disruption [128]. As healthcare organizations continue to face unprecedented price and specific volume growth, implementing strategies to make SC more efficient is critical to success [128]. In this regard, innovative digital tools facilitate the HSC to become more resilient [43] by minimizing crises due to supply interruptions while preventing adverse scenarios due to supply chain failures and market failures [129]. Our results identified information technology and Industry 4.0 technology as boosters of quality. Both rely on data; therefore, ensuring the interoperability among technologies and the protection of sensitive healthcare data remain as key challenges for the HSC.

As for the limitations of this study, it was conducted within the context of the Mexican medical device manufacturing (MDM) industry, which may restrict the generalizability of the findings. Future research would benefit from comparative analyses across different countries or regions to validate and expand upon these results.

6. Conclusions

Our study demonstrates that five critical success factors—information technology, reliable delivery, adoption of Industry 4.0 technologies, supplier resilience, and environmental and social responsibility—play a significant role in shaping supplier quality within the medical device manufacturing (MDM) industry. These factors should be prioritized by organizations seeking to strengthen supply chain performance in a highly regulated and competitive environment.

One of the key contributions of this research is the identification of supplier quality as a mediating element between these success factors and the overall performance of MDM firms. High-performing suppliers identified through structured evaluation frameworks based on these criteria help improve operational outcomes, ensure regulatory compliance, and support long-term competitiveness. This underscores the strategic value of supplier selection in achieving operational excellence, reducing risk exposure, and delivering consistent product quality.

The findings also point to the growing relevance of Industry 4.0 technologies and supplier resilience. Manufacturers that engage technologically advanced and resilient suppliers are better positioned to manage disruptions and maintain operational continuity—both of which are essential in an industry where product reliability and patient safety are non-negotiable.

In addition, the integration of environmental and social responsibility into supplier evaluation practices contributes not only to the ethical and sustainable positioning of firms, but also to enhancing the long-term integrity and reputation of their supply chains on the global stage.

By identifying these five critical success factors in the context of the Mexican MDM industry, this study offers a novel and practical framework for supplier evaluation. In a country that must balance investment attraction with compliance to standards such as ISO 13485 [64] and FDA regulations, these insights provide timely and relevant guidance. The adoption of Industry 4.0 technologies, in particular, stands out as a transformative recommendation, redefining these tools not merely as operational enablers, but as essential criteria in supplier strategy. This approach is driving Mexican manufacturers toward more agile, technology-enabled production models that are capable of adapting to international regulatory demands and global supply chain disruptions, ultimately strengthening their position in the international market.

Research Recommendations

This study addresses a topic that has been underexplored in the Mexican context. Therefore, replicating the proposed model with a larger sample, as well as applying it to other industries, could significantly enrich the existing literature on CSF and their relationship to organizational performance. Furthermore, the use of alternative statistical methodologies would confirm the findings or, where appropriate, reveal different results not considered in this research. Finally, comparing the results with those of similar studies conducted in other countries would identify opportunities for improving the methodology used, as well as incorporating new factors and relationships not initially considered.