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

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 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.