Statistical Methods for Analyzing Supplier Performance and Evaluating Vendor Reliability in Procurement
Keywords:
information processing theory, procurement analytics, procurement environment, vendor reliability, statistical methods applicationAbstract
Purpose:
This study investigates how statistical method application enhances vendor reliability under varying procurement environment conditions within multinational procurement systems. It examines whether descriptive statistical analysis, inferential statistical analysis, predictive statistical modelling, and multivariate statistical techniques improve vendor reliability and whether procurement environment conditions strengthen these relationships.
Methodology:
The study employs a balanced longitudinal panel dataset comprising 527 firm-year observations from 50 Fortune Global 500 corporations covering the period 2015 to 2025. Secondary data were integrated from internationally recognised corporate and institutional databases and analysed using descriptive statistics, panel unit root tests, diagnostic and specification tests, construct validation procedures, fixed effects panel regression, moderated regression analysis, and robustness assessments to establish reliable causal relationships.
Results/Analysis:
The empirical findings reveal that all four dimensions of statistical methods application exert significant positive effects on vendor reliability. Procurement environment conditions significantly strengthen these relationships through enhanced regulatory compliance, technological infrastructure, data quality, organisational procurement maturity, and competitive market conditions. The results remain robust across multiple econometric diagnostics and specification tests, confirming the stability and validity of the estimated relationships.
Originality/Value:
The study advances Information Processing Theory and Contingency Theory by conceptualising Statistical Methods Application as a higher-order organisational capability whose effectiveness depends on contextual procurement conditions. It provides internationally relevant evidence demonstrating that integrating advanced statistical capability with supportive procurement environments strengthens supplier governance, improves procurement resilience, and enhances analytics-driven decision-making across global supply networks.
How to cite this article:
Kumar A D, Mishra A K, Vasuki M, Celestin M, Harif B M, Zulu L, Mishra S. Statistical Methods for Analyzing Supplier Performance and Evaluating Vendor Reliability in Procurement. J Adv Res Oper Markt Mgmt 2026; 9(2): 127-166.
DOI: https://doi.org/10.24321/2582.5399.202634
References
Mishra K, Bahadur KC, Aithal PS. Association of number of bidders and minimum bid ratio (AEr) with effect of E-bidding of different project. International Journal of Management, Technology, and Social Sciences (IJMTS). 2020 Oct 30;5(2):201-15.
Mishra, A. K. (2019). Implementation status of value management in project management practice in Nepal. International Journal of Management Studies, 6(1), 92-108.
Mishra, A. K. (2019). Status of Collusive Bidding in World Bank Funded Road Projects of Nepal. CERN European Organization for Nuclear Research-Zenodo.
Celestin M, Mishra A K. E-ProcurementGovernance and Fraud Detection in DigitallyIntegrated Public Institutions. Int J Hum CompInter Data Min. 2026; 9(2): 1-23
Mishra, A. K., Khoteja, S. & Aithal, P. S. (2024). Impact of Procurement Practice Adoptedon Budgetary Utilization. Poornaprajna International Journal of Management, Education& Social Science (PIJMESS), 1(1), 144-170. DOI: https://doi.org/10.5281/zenodo.12807511
Mishra, A.K., Project Management: Theory and Practice from Different Countries (May 13, 2020). PROJECT MANAGEMENT: THEORY AND PRACTICE FROM DIFFERENT COUNTRIES, DK International Research Foundation, Tamilnadu, pp. 345 (978-81-945468-4-9), Available at SSRN: https://ssrn.com/abstract=4366829
Bahrami, M., Shokouhyar, S., & Seifian, A. (2022). Big data analytics capability and supply chain performance: The mediating roles of supply chain resilience and innovation. Modern Supply Chain Research and Applications, 4(1), 62 to 84. https://doi.org/10.1108/MSCRA-11-2021-0021
Kalaitzi, D., & Tsolakis, N. (2022). Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage. International Journal of Production Economics, 248, 108466. https://doi.org/10.1016/j.ijpe.2022.108466
Patrucco, A. S., Frattini, F., & Di Benedetto, A. (2022). Characteristics of supplier performance measurement systems in collaborative innovation projects: The role of the purchasing department. Supply Chain Management: An International Journal, 27(2), 207 to 231. https://doi.org/10.1108/SCM-11-2020-0551
Harju, A., Hallikas, J., Immonen, M., & Lintukangas, K. (2023). The impact of procurement digitalization on supply chain resilience: Empirical evidence from Finland. Supply Chain Management: An International Journal, 28(7), 62 to 76. https://doi.org/10.1108/SCM-08-2022-0312
Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023). Artificial intelligence and big data analytics for supply chain resilience: A systematic literature review. Annals of Operations Research, 327(2), 605 to 632. https://doi.org/10.1007/s10479-022-04983-y
Cui, L., Li, Y., & Zhang, C. (2024). How does data driven supply chain analytics capability affect supply chain agility in the digital era? International Journal of Production Economics, 277, 109405. https://doi.org/10.1016/j.ijpe.2024.109405
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