An empirical investigation of supplier-driven operational risks in manufacturing systems using multivariate statistical analysis

Abstract

Introduction:

The outsourcing of manufacturing activities results in operational uncertainties such as supplier delay, instabilities of raw materials, inconsistencies of product quality, cultural difference, regulatory pressure and digital integration issues. There is inadequate empirical research that explores the interrelationship of these operational risks driven by suppliers in the Indian context of outsourcing. The current study focuses on identifying, measuring and categorizing the major operational risks of manufacturing operations driven by suppliers.

Methods:

A quantitative research approach was utilized for the research. Data were collected from manufacturing professionals involved in supplier coordination, quality control, logistics, production planning and operational management. The responses were analyzed using SPSS through frequency analysis, descriptive statistics, correlation analysis, KMO and Bartlett’s test and exploratory factor analysis. Parallel analysis and Velicer’s MAP test were also utilized.

Results:

The results show that raw material availability was the most critical risk factor with the highest mean value of 4.58, followed by supplier-side disruption at 4.53 and inadequate real-time data sharing at 4.42. Product reliability linked with supplier performance recorded a mean of 4.33 while customer satisfaction decline due to supplier delays recorded a mean of 4.25. Correlation analysis showed strong associations between communication gaps and business ethics misalignment (r = 0.687) and between product specification variation and supplier work practice differences (r = 0.675). The KMO value of 0.861 and Bartlett’s test significance at p < 0.001 confirmed suitability for factor analysis. Parallel analysis and Velicer’s MAP test supported a final three-component solution explaining 52.449% of total variance. The three components were labelled supplier coordination, compliance and performance risk operational quality, regulatory and process-control risk and supply and production-continuity with digital-readiness risk.

Discussion and conclusion:

The study concludes that supplier-driven operational risk is multidimensional and requires integrated supplier monitoring, digital coordination, quality alignment, compliance tracking and production-continuity planning. The study is limited to questionnaire-based responses. Future research may use longitudinal data, SEM or machine-learning models to validate causal risk pathways.

1 Introduction

Outsourced automotive assembly-line operations in India are highly dependent on fragmented supplier networks, multi-level sourcing and increasing production pressure. These conditions make manufacturing systems more vulnerable to operational disruption. The Indian automotive sector faces major risks related to supplier delay, supplier performance, raw material instability, management gaps and machinery-related failures (Surang et al., 2024; Gautam et al., 2018). Outsourcing further increases this risk because OEMs have limited control over supplier-side coordination and process execution (Pandey and Sharma, 2017). Supplier-related issues contribute more than 50% of overall risks in Indian manufacturing systems (Kumar Pradhan and Routroy, 2014). Weak senior management support and regulatory changes further increase operational fragility (Deshpande et al., 2023). Quality failures may also spread rapidly across interdependent sub-assembly lines when outsourced components show lower consistency than in-house production (Agrawal et al., 2017). Since India is becoming a major outsourcing base for global automotive manufacturing, supplier-driven operational risk needs focused empirical investigation (Gautam et al., 2025). Outsourced automotive assembly lines in India face a cluster of high-severity operational risks, primarily driven by supplier dependence, process variability, equipment failures, and complex multi-tier supply networks. The evidence below synthesis the risks most consistently highlighted across Indian automotive manufacturing studies.

Table 1 shows that supplier-driven risk is not limited to delivery failure. It also includes quality loss, process variation, equipment failure and weak information flow. Supplier failure and material shortage directly disturb assembly continuity. The risk of delay is amplified in the case of outsourcing since production relies heavily on external scheduling and coordination in logistics. Poorly disciplined processes increase the number of defects produced in outsourcing processes. Moreover, machine degradation, lack of maintenance and hazardous working environments also reduce the reliability of the line. Delays in information flow in the multi-level automobile industry supply chain in India have the potential to cause dependency chain disruption within no time. Risk exposure can also be increased due to shocks from external sources such as pandemics, disasters and importing constraints. Inadequate ICT and poor data integration are still important risks for outsourced firms.

Cultural aspects also influence how supplier risks are recognized, reported and managed in outsourced automotive supply chains. Hierarchy and power distance in the hierarchical organizational structures prevailing in Indian manufacturing organizations may constrain the communication process upwards and minimize the early reporting of risks and issues in outsourcing (Mathew and Taylor, 2019). Organizational culture plays an important role in how risks are responded to in organizations. Organizations with flexible cultures cope better with disruption, while those with rigid cultures adopt mitigation measures slowly (González-Zapatero et al., 2024). Trust in outsourcing relationships is another critical issue since cultural dissimilarities affect how conflicts are handled and the extent of commitment between supplier and OEM (Ndubisi, 2011).

Table 2 presents the human and organizational behavioral factors that contribute to supply-driven risks. For instance, strong hierarchical structure may cover up any early warning signs associated with delay, deviated quality, and process failure. Collaboration helps identify any risks quickly and implement corrective measures together. Trust and commitment strengthen the relationship, while value misalignment hinders coordination. Cross-cultural communication gaps may also delay problem solving and increase operational uncertainty. Thus, cultural alignment becomes necessary for transparency, joint planning and shared mitigation in outsourced automotive supply chains.

Table 3 indicates that outsourced manufacturing risk is multidimensional. It includes supplier delay, quality inconsistency, raw material instability, poor planning, unsafe practices, machine breakdown, logistics disruption, supplier capability gaps and digital coordination problems. Prior studies have ranked and described these risks in different contexts. However, most studies do not statistically classify supplier-driven risks into integrated empirical dimensions. This creates a clear need for a multivariate study that connects supplier behaviour, operational disruption, cultural alignment, compliance pressure and technological capability in Indian outsourced manufacturing systems.

Supplier performance has a direct role in operational risk formation because outsourced automotive assembly depends on timely component flow, raw material stability and supplier-side process control. Supplier delay, supplier quality and raw material availability are major risk factors in Indian automotive supply chains (Fartaj et al., 2020; Surange and Bokade, 2022b). This risk becomes stronger in fragmented supplier systems where tier-2 and tier-3 suppliers may have limited technology, weak infrastructure and lower process maturity (Basu et al., 2023). In outsourced assembly, supplier reliability directly affects production continuity because any failure in parts delivery can disturb the full manufacturing flow. Machine failure, labour shortage and logistics issues at the supplier end can further affect production performance in component-intensive assembly (Surange and Bokade, 2023; Kamble et al., 2021). Supplier risks are also linked with manufacturing, logistics and customer-facing performance. Supplier delay and raw material shortage increase cycle time, reduce output and increase operational cost (Surange et al., 2022; Aman et al., 2024). These risks become more severe during local or global disruptions. The COVID-19 crisis slowed Indian OEM production and created inventory shortages because suppliers failed to deliver required components on time (Butt, 2021; Chen and Liu, 2021). In outsourced assembly, this problem becomes stronger because suppliers manage important processes and influence yield and quality under uncertainty (Kach and Pournader, 2026). Strong supplier integration, effective communication and supplier relationship management reduce the effect of supply disruption on organizational performance (Sumrit and Jongprasittiphol, 2025). Supplier collaboration also improves upstream readiness in the automobile sector (Bade et al., 2025). Therefore, outsourcing requires structured supplier development and monitoring. Excessive supplier dependence without control mechanisms may weaken competitiveness and increase operational fragility.

Digital integration is central to supplier-driven operational risk because outsourced manufacturing depends on the timely movement of both materials and information. Industry 4.0 maturity, OT/IT integration, digital-thread continuity and digital-twin readiness improve visibility across supplier networks by enabling real-time data sharing, traceability, predictive monitoring and faster disruption response (Margherita and Braccini, 2021; Elyasi et al., 2026; Lin and Fan, 2024; Mohammed et al., 2025). However, when suppliers lack automation capability, compatible digital platforms or real-time data exchange, digital systems can become an additional source of operational risk rather than only a mitigation tool.

Existing studies mostly examine supplier risk, outsourcing risk and automotive supply-chain disruption as separate issues. Limited empirical evidence explains how supplier delay, quality inconsistency, cultural mismatch, compliance pressure and digital integration gaps jointly affect outsourced manufacturing performance in India. This study addresses this gap by identifying and measuring major supplier-driven operational risk factors. It further classifies these risks into key dimensions using descriptive statistics, correlation analysis and exploratory factor analysis.

2 Methodology

2.1 Research design

The study adopted a quantitative research design to examine supplier-driven operational risks in outsourced manufacturing systems. A structured questionnaire was used to collect responses from manufacturing professionals. The study focused on key risk variables such as supplier disruption, raw material fluctuation, logistics interruption, quality inconsistency, cultural differences, regulatory compliance, digital integration, defect rate and customer satisfaction. The research design was suitable because the study required numerical measurement of respondent perceptions and statistical analysis of relationships among multiple operational risk variables. Data analysis involved the use of SPSS through frequency analysis, descriptive statistics, correlation and exploratory factor analysis. To establish the suitability of data analysis through factor analysis, KMO and Bartlett’s Test were used. The research instrument consisted of questions that measured supplier-induced operational risks associated with outsourcing in manufacturing operations. Items were developed based on risk dimensions widely discussed in the relevant literature, including supplier disruption, raw material availability, logistics disturbance, inconsistent quality, cultural compatibility, regulatory compliance, technology integration and operational performance.

Measurement of perceptions toward each item used a five-point Likert scale, where one indicated strong disagreement and five indicated strong agreement. The item design covered upstream and downstream aspects of the concept under investigation. The questionnaire examined supplier-induced risks as multidimensional constructs. Some items focused on supplier performance and its implications, such as delivery delays, raw material variations, supplier quality assurance procedures, product specification variation, communication gaps, compliance penalties, digital integration, automation and real-time information exchange.

2.2 Data collection

Primary data were collected through a structured questionnaire survey from manufacturing professionals having experience in outsourced manufacturing, supplier coordination, quality assurance, logistics, production planning and operational management. The respondents were selected because they had direct exposure to supplier delays, raw material issues, quality variations, compliance-related issues and coordination problems in manufacturing operations. A total of 507 respondents completed the questionnaire. After screening for missing values, the number of valid responses differed across variables. Missing values were treated using item-wise exclusion during frequency analysis so that each variable was analyzed according to its valid response count without altering the original response structure. Ethical approval for the study was obtained from the Research Ethics Committee (REC) of MIT Art, Design and Technology University, Pune. The REC approval reference number is RDC/2026/7, and the approval letter was dated 11 Ma y 2026, with the approval validity period from 22 April 2026 to 31 December 2026. The study involved human participants in the form of manufacturing professionals responding to an academic research survey. Informed consent was obtained from all participants before data collection. Participation was voluntary, and respondents were informed of their right to withdraw at any stage without penalty. No personally identifiable information was collected, and confidentiality and anonymity were maintained throughout the study. The collected data were used only for academic and research purposes and were reported in aggregate form.

2.3 Sampling strategy

The study used a purposive sampling strategy because the research required responses from professionals having direct exposure to outsourced manufacturing operations and supplier-driven operational risks. The sampling frame consisted of manufacturing professionals working in supplier coordination, production planning, quality assurance, logistics, procurement, compliance monitoring and operational management. Respondents were drawn from OEM-linked manufacturing units and supplier-side firms, including Tier-1, Tier-2 and Tier-3 suppliers. The sector mix included automotive assembly support, auto-component manufacturing, plastic and polymer components, metal and fabricated parts, electrical/electronic sub-assemblies, logistics support and quality-control functions. The sample included respondents from large firms as well as SME/MSME supplier units to capture operational-risk perceptions across different firm-size categories. Geographically, the survey focused on Indian automotive manufacturing and supplier clusters where outsourced assembly-line and supplier-dependent manufacturing activities are prominent. Participants were contacted through professional networks, industry contacts, email circulation and direct communication with manufacturing professionals. Approximately 800 questionnaires were circulated among eligible manufacturing professionals. A total of 507 completed and usable responses were retained after screening, giving an approximate usable response rate of 63.4%. This sampling approach was considered appropriate because the study aimed to collect informed responses from professionals having practical experience with supplier delays, raw material fluctuation, quality inconsistency, compliance issues, digital coordination gaps and operational disruptions. The questionnaire contained 23 Likert-scale items across seven constructs, including supply-chain disruptions, quality-control issues, cultural differences, regulatory compliance, technological integration, defect rate and customer satisfaction.

2.4 Instrument development

The questionnaire initially contained 23 Likert-scale items across seven constructs, supply-chain disruptions, quality-control issues, cultural differences, regulatory compliance, technological integration, defect rate and customer satisfaction. Before the final multivariate analysis, the item set was screened for conceptual overlap, item clarity, analytical suitability and adequacy of representation within each construct. Three items were removed during this screening stage: supplier dependency increases operational risk, frequent regulatory changes create operational uncertainties, and customer complaints frequently arise due to supply-chain or quality issues. These items were removed because their content overlapped with stronger retained indicators or was represented more directly by other retained variables. The final PCA/EFA was therefore conducted using 20 retained variables coded V1-V20. The same variable key is now used consistently across the descriptive statistics, communalities table, rotated component matrix, reliability analysis, correlation appendices and Supplementary Appendix D.

2.5 Statistical analysis

The collected data were analyzed using SPSS. Frequency distribution was used to examine item-wise response patterns for each operational risk variable. Descriptive statistics were used to calculate mean and standard deviation values. This helped identify the most strongly perceived supplier-driven risk factors. Correlation analysis was applied to examine the strength of association among key operational risk variables. The Kaiser-Meyer-Olkin test and Bartlett’s test of sphericity were used to check data suitability for exploratory factor analysis. Exploratory factor analysis was then performed to identify the major underlying dimensions of supplier-driven operational risk. Principal component analysis and varimax rotation were used for factor extraction and interpretation.

2.6 Factor extraction and interpretation

This process first identified an eigenvalue-based four-component structure. However, because the Kaiser criterion can over-retain components, the final retention decision was checked using parallel analysis and Velicer’s MAP test. These tests supported a three-component solution. The final extracted components were interpreted as supplier coordination, compliance and performance risk operational quality, regulatory and process-control risk and supply and production-continuity with digital-readiness risk.

3 Frequency analysis

The analysis of the frequency distribution reveals supplier-related disruptions as a major type of operational risk for the examined manufacturing firms. There is a clear connection between product reliability and supplier performance based on Figure 1. The percentage of respondents who strongly agree is 45.3%, while 44.6% agree with the statement.

FIGURE 1

Supplier-side disruptions became a critical indicator of risk, as seen in Figure 2. A total of 60.2% of the respondents strongly agree that these risks lead to operational problems. Another 35.0% believe that supplier-side disruptions occur and affect operations.

FIGURE 2

Transportation and logistics disruptions act as a moderate risk factor according to Figure 3. Only 38.6% of the respondents chose the agree option, while 12.4% selected the strongly agree option. Meanwhile, 38.4% remained neutral about the issue under discussion.

FIGURE 3

Availability of raw materials emerged as the strongest indicator of production continuity, as indicated by Figure 4. More than 62.5% strongly agreed and about 34.3% agreed that raw material variability has a negative impact on the production schedule. This implies that material volatility poses an immediate risk to production planning in manufacturer-supplier dependent systems.

FIGURE 4

Quality risks revealed both weak and strong response patterns, as seen in Figure 5. Rework arising from supplier quality issues recorded 36.1% agreement and 9.4% strong agreement. A considerable neutral response of 33.3% also indicates that supplier-induced rework does not affect all businesses with equal intensity. This may relate to the nature of inspection, supplier maturity and product maturity.

FIGURE 5

Supplier quality checks also emerged as another significant supplier-related risk, as illustrated by Figure 6. More than 36.7% agreed and about 27.1% strongly agreed that supplier quality checks do not comply with manufacturing requirements.

FIGURE 6

Variation in product specification had a wide range of response compared to other variables, as seen in Figure 7. The neutral response had the highest percentage at 27.2% while the agreement had 23.4% and disagreement at 22.2%. The distribution indicates that specification variation between batches exists however, the degree of variation varies among different firms.

FIGURE 7

These results show that supplier-driven operational risk is shaped by delivery disruption, material instability, supplier quality inconsistency and specification variation. The strongest risks are supplier delay and raw material fluctuation, while logistics interruption and specification variation show more firm-specific effects.

3.1 Cultural and compliance risks

Cultural and collaboration-related responses indicate that supplier risk also develops through communication gaps, work practice differences and value misalignment. Communication gaps due to cultural differences received strong agreement from respondents, with 35.9% strongly agreeing and 34.5% agreeing. Figure 8 indicates that cultural communication barriers create coordination problems between firms and suppliers. This result shows that operational risk is not only technical or material-based. It is also shaped by communication quality and inter-organizational understanding.

FIGURE 8

Supplier work practice differences showed a moderate but relevant impact on workflow. Figure 9 shows that 28.2% of respondents agreed and 17.5% strongly agreed that differences in supplier employee work practices affect operational flow. A neutral response of 28.0% also indicates that this issue varies across firms. This variation may depend on supplier training, process standardization and alignment with buyer-side work systems.

FIGURE 9

Business ethics and value misalignment showed a stronger response pattern than work practice differences. As presented in Figure 10, 37.3% of the participants chose agree, while 36.3% strongly agreed with the statement that misalignment in ethics or values affects collaboration effectiveness. This finding highlights the importance of trust, transparency and shared working philosophy in manufacturing outsourcing.

FIGURE 10

A moderate response pattern appeared among the respondents regarding regulatory compliance. Figure 11 shows that 37.2% of the respondents chose neutral, while 32.8% agreed that regulation creates problems in domestic and international compliance requirements. This finding indicates that compliance issues exist but do not affect all companies equally. This may depend on export exposure, business sector, supplier certification stage and compliance maturity level.

FIGURE 11

A different response pattern appeared for penalties and warnings related to compliance issues. Figure 12 shows that 45.4% of the respondents agreed, while 32.0% strongly agreed with the statement that suppliers frequently receive penalties or warnings related to compliance. This finding implies that poor supplier compliance creates operational risk for the buyer company.

FIGURE 12

Environmental and safety standards had a moderate level of difficulty in the survey. According to Figure 13, 34.1% of participants chose the neutral option, while 25.0% answered in the affirmative, and 9.1% strongly agreed that complying with environmental and safety standards is difficult. The finding implies that compliance with environmental and safety standards is an operational risk, but it is not as severe as supplier delays, raw material fluctuations, and digital integration challenges.

FIGURE 13

The results regarding culture and compliance reveal that operational risks in outsourcing manufacturing depend on human, organizational, and regulatory aspects. In particular, communication gaps and ethical business conduct mismatch are more prominent than general regulatory difficulties. Compliance penalties imposed on suppliers also pose serious risks for organizations. The above data suggest that supplier risk management must involve culture fit, supplier education, compliance evaluation, and communication management in addition to the traditional quality and delivery control practices.

3.2 Digital and performance risks

Concerning technology, digital integration is a critical aspect of operational risk in outsourcing manufacturing systems where suppliers play a crucial role. The difficulty associated with integrating digital platforms attracted considerable agreement among respondents. As shown in Figure 14, 38.6% of participants agreed that it is hard to integrate with the suppliers’ digital platforms, while 27.7% of them strongly agreed with the statement.

FIGURE 14

Supplier automation ability emerged as another key technology risk. Figure 15 shows that 48.0% of respondents agreed and 31.6% strongly agreed that suppliers do not have the necessary technology abilities or automation processes. This trend suggests that most suppliers operate in a low digital maturity environment. It can negatively influence process control, tracking, responsiveness and product flexibility.

FIGURE 15

Real-time data sharing emerged as another important risk factor with high levels of agreement among respondents. Figure 16 presents results showing that 51.1% of respondents strongly agreed and 42.3% agreed that real-time data sharing between the firm and suppliers remained lacking. This confirms the importance of information delay risks in outsourced production. Lack of information exchange may increase uncertainty in production planning, logistics planning, quality assurance and scheduling.

FIGURE 16

Technological changes received mixed response patterns. Figure 17 illustrates that 24.1% disagreed, 23.9% remained neutral, 22.9% agreed and 18.7% strongly agreed that new technology implementation causes operational delays. This result suggests that the effect of technology change on business processes is not uniform. It depends on multiple factors including training and implementation strategies.

FIGURE 17

Defects associated responses revealed that supplier performance impacts quality results. As depicted in Figure 18 below, 34.3% respondents agreed while 33.5% strongly agreed that defects rates depend significantly on supplier performance. This means that supplier performance has some impact on manufacturing quality and inspection results.

FIGURE 18

Responses to inspection rejections were mixed, ranging from moderate to high. As illustrated in Figure 19 below, 31.4% respondents indicated neutrality, while 31.0% agreed and 17.2% strongly agreed that rejected parts happen often. The findings suggest that rejection risks exist, although there may be variations among different companies depending on various factors such as quality systems of suppliers, level of inspection toughness, etc.

FIGURE 19

Customer satisfaction showed a strong downstream effect of supplier delay. Figure 20 shows that 44.6% of respondents strongly agreed and 40.7% agreed that customer satisfaction decreases when supplier delays affect delivery timelines. This result indicates that supplier-driven risks do not remain limited to internal operations. They also influence customer-facing performance through delivery failure, delayed order fulfillment and reduced service reliability.

FIGURE 20

The digital and performance-related results show that supplier-driven operational risk extends from technological weakness to quality outcomes and customer satisfaction. Inadequate real-time data sharing and lack of automation appeared as the strongest digital risk indicators. Defect rate and inspection rejection confirmed the quality effect of supplier performance. Customer satisfaction results further establish that supplier delay creates downstream market-level consequences. These findings show that supplier risk mitigation should include digital integration, automation support, real-time information exchange and quality-linked supplier monitoring.

3.3 Descriptive statistics and correlation analysis

The descriptive statistics of the operational risk variables are presented in Figure 21. Raw material availability recorded the highest mean value of 4.58 with a standard deviation of 0.588. This shows that raw material fluctuation is the most strongly perceived operational risk. Supplier-side disruption showed the second highest mean value of 4.53, followed by inadequate real-time data sharing with a mean value of 4.42. Product reliability linked with supplier performance also recorded a high mean value of 4.33, while customer satisfaction reduction due to supplier delays recorded a mean value of 4.25. These results confirm that supplier delay, material instability and weak information sharing are the major operational risks in outsourced manufacturing systems.

FIGURE 21

Figure 21 also shows that lack of automation or Industry 4.0 capability, compliance penalties and business ethics misalignment recorded high mean values of 4.08, 4.07 and 4.02 respectively. In contrast, product specification variation recorded the lowest mean value of 2.86. Technological upgrades causing delays showed the highest standard deviation of 1.429. This indicates higher variation in respondent perception across firms.

The correlation analysis is presented in Figure 22. Communication gaps and business ethics misalignment showed the strongest correlation coefficient of 0.687. Product specification variation and supplier work practice differences also showed a strong correlation of 0.675. These results indicate that cultural alignment and work practice consistency are closely linked with operational stability.

FIGURE 22

Business ethics misalignment showed a positive correlation with compliance penalties at 0.598. Supplier work practices showed a correlation of 0.568 with customer satisfaction impact. The defect rate also correlated positively with inspection rejects with a correlation of 0.462. Generally, descriptive and correlation results show that supplier-driven operational risks are multifaceted and dependent on supplier delay, instable raw material, digital deficiency, incompatible culture, and quality factors.

3.4 Factor analysis

KMO and Bartlett’s test checked the sampling suitability and correlation adequacy for EFA, as illustrated in Table 4. The KMO value of 0.861 indicates strong sampling adequacy. Bartlett’s test showed statistical significance with χ² = 3,624.658 and p < 0.001. This confirms that the correlation matrix remained suitable for factor extraction.

Table 5 present the component-retention result. The initial eigenvalue-greater-than-one criterion suggested four components, however, parallel analysis and Velicer’s MAP test supported retaining three components. The final three-component solution explained 52.449% of total variance. Component 1 explained 29.578%, Component 2 explained 15.021%, and Component 3 explained 7.851%. As shown in Figure 23, the three-component PCA solution explains 52.449% of the total variance, indicating an interpretable reduction of the 20 operational-risk indicators. Component 1 contributes 29.578% and, as illustrated in Figure 24, is dominated by ethics misalignment, communication gaps, defect-rate influence, compliance penalties, customer impact, digital integration and supplier-quality checks. This pattern shows that supplier risk is primarily structured through coordination, governance and performance consequences rather than isolated technical failures. Component 2 explains 15.021% and combines work-practice differences, specification variation, regulatory challenges, inspection rejection, technological delays, environmental requirements, rework and logistics interruption. It therefore represents the operational-control mechanisms through which supplier variability becomes quality and compliance exposure. Component 3 adds 7.851% and integrates supplier disruption, raw-material availability, real-time data sharing, equipment breakdowns and automation capability, linking physical continuity with digital readiness.

FIGURE 23

FIGURE 24

3.5 Reliability, communalities and rotated component matrix

Reliability and factor-validity analyses were conducted for the 20 retained variables included in the final three-component solution as shown in Table 6. The overall Cronbach’s alpha value was 0.870, indicating good internal consistency of the retained measurement scale. Factor-wise Cronbach’s alpha values were also calculated using the items primarily loaded on each extracted component. Component 3 shows combined supply-continuity and digital-readiness items.

Reliability analysis confirmed good internal consistency for the overall retained 20-item scale and acceptable reliability for the first two components. The third component is retained because the component-retention tests support the three-component structure, but it is interpreted cautiously due to its mixed supply-continuity and digital-readiness content.

Communalities were examined to determine how well each retained variable was represented by the three-component solution as shown in Table 7. The extraction communalities ranged from 0.331 to 0.705, indicating that the retained variables contributed meaningfully to the final component structure.

The rotated component matrix was used to identify the dominant loading pattern of each retained variable after Varimax rotation with Kaiser normalization as shown in Table 8. Only loadings of 0.30 or higher are reported. Supplementary Appendix A presents the full pairwise Pearson correlation matrix for the 20 retained operational-risk variables. The matrix shows that supplier-driven risks are interrelated rather than isolated. Stronger correlations are observed among cultural, quality, compliance and performance-related variables. This supports the multidimensional nature of supplier-driven operational risk in outsourced manufacturing systems. Supplementary Table Appendix B reports the valid sample size used for each pairwise correlation. The pairwise valid N values confirm that correlations were calculated using available responses for each variable pair. This approach avoids unnecessary loss of data caused by listwise deletion. The table improves transparency by showing the exact respondent base used for each correlation coefficient. Supplementary Appendix C table provides the two-tailed significance values for the full pairwise correlation matrix. The significance results help identify which relationships among supplier-risk variables are statistically meaningful. Several relationships show statistically significant associations, confirming that many risk indicators move together. This strengthens the empirical basis for using multivariate analysis in the study. Supplementary Appendix D provides the variable codes, full item descriptions, valid N, mean and standard deviation values. This table helps readers interpret the abbreviated variable labels used in the correlation matrix. It also summarizes the descriptive profile of each retained operational-risk item. Therefore, it improves the clarity and reproducibility of the supplementary correlation analysis.

3.6 Factor extraction and retention

Principal component analysis (PCA) was selected because the objective of the study was to reduce a set of interrelated operational-risk indicators into a smaller number of interpretable empirical dimensions as shown in Table 9. The purpose was not to estimate a latent causal measurement model, but to classify dominant patterns of supplier-driven operational risk. Therefore, PCA was considered appropriate for summarizing shared variation among the retained risk variables. Principal axis factoring (PAF) and maximum likelihood (ML) extraction were not used because the study primarily aimed at component-based risk classification, and ML additionally requires stronger distributional assumptions. The initial eigenvalue-greater-than-one criterion suggested a four-component solution. However, as the Kaiser criterion can sometimes overestimate the number of components, parallel analysis and Velicer’s minimum average partial (MAP) test were applied to strengthen the factor-retention decision. Parallel analysis indicated that only the first three actual eigenvalues exceeded the corresponding random eigenvalues, while the fourth actual eigenvalue did not exceed the random threshold. Velicer’s MAP test reached its minimum value at the three-component solution as shown in Table 10. Therefore, the final retained structure to a three-component solution, explaining 52.449% of the total variance as shown in Table 11.

3.7 Common-method bias assessment

Common-method bias was assessed using Harman’s single-factor test because the data were collected through a self-reported questionnaire. All 20 retained measurement items were entered into an unrotated principal component analysis using listwise deletion. The analysis was based on 450 valid cases after missing-value exclusion. The first unrotated component produced an eigenvalue of 5.916 and explained 29.578% of the total variance. Since this value is below the commonly accepted 50% threshold, no single factor accounted for the majority of covariance among the measurement items.

Supplier-driven operational risk in outsourced manufacturing converges on three linked themes Industry 4.0 maturity, digital twins/digital supply chain twins, and OT/IT integration/digital thread as enabling capabilities for resilience and visibility (Mittal et al., 2018). Assessment and maturity-model studies argue that firms need structured readiness tools and staged transformation roadmaps before implementing Industry 4.0, because many organizations are still at relatively immature digital levels (Zamora Iribarren et al., 2024). Case-based work similarly treats readiness as a prerequisite for effective transformation and operational improvement (Çınar et al., 2021). Industry 4.0 adoption is associated with stronger supply chain resilience and performance, especially when firms can translate digital capability into visibility, coordination, and risk sensing (Qader et al., 2022). Related studies also report that barriers to adoption can constrain these benefits, meaning that digital tools alone are not enough without implementation capability and organizational support (Chauhan et al., 2021). Reviews of COVID-era resilience research reinforce this point by linking Industry 4.0 with resilience-building in disrupted supply networks (Spieske and Birkel, 2021). Digital twins as tools for real-time monitoring, simulation, predictive analytics, and decision support in supply chains and production systems (Alguirat et al., 2025). Digital supply chain twin research frames them as mechanisms for managing disruption risks and improving resilience under uncertainty. Production-distribution systems and maintenance settings, showing resilience-oriented uses across multiple operational contexts. The digital thread/OT-IT integration layer is presented as the backbone that makes these capabilities usable across outsourced ecosystems. A shipyard framework explicitly combines digital twin and digital thread to improve traceability and continuity of information across operations (Bott and Milkau, 2015). Digitalization and supplier relationships suggest that trust, governance, data quality, skills, cybersecurity, and collaboration shape whether digital transformation actually improves resilience (Ngo et al., 2025). Industry 4.0 maturity enables OT/IT integration and digital thread continuity, which in turn allows digital twins to convert supplier data into visibility, disruption sensing, and resilience in outsourced manufacturing systems (Mittal et al., 2018).

4 Discussion

The three-component solution shows that supplier-driven operational risk in outsourced manufacturing is organized around three broad and interrelated dimensions. The first component, supplier coordination, compliance and performance risk, explained the largest share of variance (29.578%). This component grouped supplier quality checks, cultural communication gaps, business ethics/value misalignment, supplier compliance penalties, digital-platform integration difficulty, defect-rate influence and customer satisfaction impact. The second component, operational quality, regulatory and process-control risk, explained 15.021% of variance and grouped logistics interruption, rework, product-specification variation, supplier work-practice differences, regulatory-compliance difficulty, environmental/safety standards, technology-upgrade delay and inspection rejection. The third component, supply and production-continuity with digital-readiness risk, explained 7.851% of variance and grouped product reliability, supplier-side disruption, raw-material availability, lack of automation/Industry 4.0 capability and inadequate real-time data sharing. Together, these three components explained 52.449% of total variance and provide a more consistent interpretation of supplier-driven operational risk.

The findings confirm prior Indian automotive and manufacturing risk studies. Surange and Bokade’s risk-ranking work identified delay risks, management risks and supplier risks as severe concerns in Indian automotive manufacturing supply chains. The present descriptive results support this emphasis because raw-material availability, supplier-side disruption and inadequate real-time data sharing had the highest mean values. This confirms that practitioners perceive supply continuity, supplier delay and information availability as immediate operational threats. Similarly, studies on automotive supply-chain risk and quality have identified supplier production problems, internal machine breakdown and raw-material fluctuation as critical risks, which is consistent with the high descriptive salience of supplier delay, raw-material instability and product reliability in the present study.

The results also connect with Sharma and Bhat’s work on supply-chain risk management dimensions in the Indian automobile industry. Their SCRM dimensions emphasized avoidance, supplier development, flexibility, risk pooling, redundancy, integration and control strategies. The present study does not simply replicate those dimensions because it measures perceived supplier-driven operational risks rather than risk-mitigation strategies. However, the component structure is compatible with their framework. Component 1 points toward integration, supplier development and control because cultural communication, supplier quality checks, compliance penalties, digital integration and customer impact all depend on coordinated buyer-supplier governance. Component 2 points toward control, standardization and process discipline because quality rework, specification variation, work-practice differences, compliance difficulty and inspection rejection all require operational-control mechanisms. Component 3 points toward redundancy, flexibility and supplier development because supply continuity, raw-material availability, automation capability and real-time data sharing determine whether a firm can maintain production during supplier-side disturbance.

The main new contribution is that cultural and collaboration-related risks emerged as the dominant underlying structure even though supply-side risks had the highest individual mean scores. Mean values show perceived severity or salience of individual risks. Factor analysis, by contrast, shows how variables move together and which underlying patterns explain common variance across respondents. Raw-material availability and supplier-side disruption were rated very highly, but their high means and relatively concentrated responses suggest broad agreement that these are severe risks. Because many respondents rated them similarly, these items were highly salient but less able to differentiate patterns among respondents. In contrast, communication gaps, business ethics/value misalignment, compliance penalties, supplier quality checks, digital-platform integration, defect-rate influence and customer-satisfaction impact varied together more strongly. These items therefore formed the first component and explained the largest portion of shared variance.

This divergence between mean-based salience and variance-based structure is one of the most important findings of the study. It indicates that managers may recognize material shortage and supplier delay as the most visible risks, but the deeper structure of supplier-driven operational risk is shaped by coordination quality, cultural alignment, compliance behaviour, information integration and performance consequences. In practical terms, a firm may know that raw-material fluctuation is severe, but whether such disruption becomes manageable or damaging depends on supplier communication, shared standards, compliance discipline, data visibility and joint problem-solving. Thus, the study extends prior risk-ranking work by showing that the most highly rated risks are not necessarily the same as the risks that organize the broader risk system.

4.1 Digital manufacturing implications

The digital-manufacturing findings reinforce this interpretation. Digital integration was not retained as an isolated technological factor in the final three-component solution. Instead, digital-platform integration difficulty loaded with supplier coordination, compliance and performance risk, while lack of automation/Industry 4.0 capability and inadequate real-time data sharing loaded with supply and production-continuity with digital-readiness risk. This means that Industry 4.0 maturity, OT/IT integration, digital-thread continuity and digital-twin readiness should be understood as risk-visibility capabilities embedded in supplier governance and production-continuity management. Without compatible supplier platforms and real-time data sharing, firms cannot trace quality issues, anticipate delay, monitor supplier capacity or build reliable digital-twin models for disruption planning.

The managerial implication is that supplier-risk mitigation should not focus only on the highest-mean risks such as raw-material availability and supplier delay. Those risks are important, but the latent structure shows that firms also need to strengthen the coordination system that determines how supplier problems are detected, communicated and controlled. Supplier evaluation should therefore include cultural and communication alignment, compliance behaviour, quality-check consistency, digital-platform compatibility, real-time data-sharing capability and the supplier’s ability to support traceability. This integrated approach is more consistent with outsourced manufacturing realities, where material flow, information flow, quality performance and relationship governance operate together.

The study confirms prior evidence that delay, supplier-side disruption, raw-material instability and quality problems are central risks in Indian automotive-linked manufacturing. It adds that these risks are embedded in a broader coordination and governance structure. The finding that cultural/collaboration items dominate the first component while supply-side items dominate mean rankings clarifies the difference between visible operational severity and underlying risk-system structure. This contribution helps move the literature from ranking individual risk factors toward understanding how supplier-driven risks cluster and reinforce one another in outsourced manufacturing systems.

4.2 Recommendations and future scope

Supplier risk management in the form of integration of monitoring system should be adopted for outsourced manufacturing companies. Supplier assessment must not be restricted to the areas of cost, deliveries and quality alone. Stability of material supply, digital integration, compliance, communication process and automation of suppliers should also be considered during assessment process. Dashboarding is recommended for real time monitoring of suppliers. Real time dashboarding can monitor the status of materials available, delay in deliveries, quality deviation, and inspection rejection. Real time monitoring will aid early identification of supplier problems and prevent disruptions in production activities. Digitalization should be accorded high importance as real time digital information sharing was found to be one of the most significant supplier risks identified in the study. Suppliers should be enabled through the use of common digital platform, automatic reporting and industry 4.0 program. Such measures will facilitate better information visibility, reduced delay in coordination and prompt corrective action. Supplier quality and compliance control should be improved using regular audit programs, standardized quality norms and supplier joint improvement program. Considering the strong association between supplier practices and deviation from specifications, standard operating procedures (SOPs) should be harmonized in the buyer and supplier organization. Supplier risk assessment should consider domestic, international, environmental and health and safety regulations to minimize disruptions caused by penalties on suppliers. Future researches may build upon the current study by incorporating sector wise comparative analysis in the automotive, electronics, and heavy industries. Another area future researchers may focus on is to incorporate structural equation modelling or machine learning techniques to establish the relationship between supplier risk factors.

5 Conclusion

The present study demonstrates that supplier-driven operational risks in outsourced manufacturing systems are not caused by a single disruptor. Rather, they arise from the combined effects of supplier delays, raw-material instability, limited digital integration, cultural misalignment, quality inconsistency and compliance pressure. The empirical findings show that raw-material availability was the most strongly perceived risk, with the highest mean value of 4.58, followed by supplier-side disruption (4.53) and inadequate real-time data sharing (4.42). Product reliability linked with supplier performance recorded a mean of 4.33, and customer satisfaction reduction due to supplier delays recorded a mean of 4.25. The correlation results further confirm that supplier-driven risks are interrelated. The strongest relationship was observed between communication gaps and business ethics/value misalignment (r = 0.687), followed by product specification variation and supplier work-practice differences (r = 0.675). The factor-retention analysis supported a three-component solution explaining 52.449% of total variance. The components were interpreted as supplier coordination, compliance and performance risk, operational quality, regulatory and process-control risk and supply and production-continuity with digital-readiness risk. The study therefore establishes that supplier-risk management should move beyond conventional vendor evaluation based only on cost, delivery and quality. It should also include communication and cultural alignment, digital integration, real-time information sharing, quality alignment, compliance monitoring, supplier development and collaborative governance. Such an integrated approach can improve production stability, reduce inspection rejection, control defect rates and protect customer satisfaction in supplier-dependent manufacturing systems. The sample used in this study was automotive-linked but multi-sector in composition. It included professionals from OEM-linked manufacturing units and supplier-side firms across automotive assembly support, auto-component manufacturing, plastic and polymer components, metal and fabricated parts, electrical/electronic sub-assemblies, logistics support and quality-control functions. Therefore, the findings should be interpreted as evidence from automotive-linked outsourced manufacturing and supplier networks rather than from OEM-only automotive plants. The study is limited to questionnaire-based responses, and future research may use longitudinal operational data, structural equation modelling or machine-learning approaches to validate causal relationships among supplier-risk dimensions.

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