Effect of Supply Chain Risk Management Strategies on the Operational Performance of Manufacturing Firms in Dangote Cement Plc, Obajana Plant, Kogi State
Authors
PhD Procurement Management Student, Nasarawa State University, Keffi (Nigeria)
PhD Procurement Management Student, Nasarawa State University, Keffi (Nigeria)
Institute of Governance and Development Studies, Nasarawa State University, Keffi (Nigeria)
Article Information
DOI: 10.47772/IJRISS.2026.10100337
Subject Category: Social science
Volume/Issue: 10/1 | Page No: 4361-4376
Publication Timeline
Submitted: 2026-01-19
Accepted: 2026-01-24
Published: 2026-02-05
Abstract
The operational performance of Dangote Cement Plc, Obajana Plant, a flagship manufacturing hub in Nigeria’s cement industry, is critical for sustaining productivity, reliable deliveries, and cost efficiency in an increasingly volatile operating environment. Supply Chain Risk Management (SCRM), conceptualised in this study through three core dimensions, Risk Identification, Risk Assessment, and Risk Mitigation strategies is central to cushioning the plant against high-impact disruptions in raw material sourcing, production, and outbound logistics. This study examined the effect of SCRM strategies on operational performance, measured through productivity, delivery reliability, and cost efficiency. A cross-sectional survey design was adopted, and 210 copies of questionnaires were distributed to supply chain and logistics staff, procurement and inventory officers, and production or operations supervisors using stratified proportionate allocation based on Bowley’s formula. A total of 156 copies of questionnaires were retrieved and found usable, yielding a response rate of 74.3%. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results revealed that Risk Identification (β = 0.398, p = 0.000), Risk Assessment (β = 0.258, p = 0.002), and Risk Mitigation strategies (β = 0.289, p = 0.001) each exert a positive and statistically significant effect on operational performance, with the three constructs jointly explaining 76.1% of its variance (R² = 0.761). The findings demonstrated that robust SCRM capabilities substantially enhance productivity, strengthen delivery reliability, and improve cost efficiency at the Obajana Plant. The study recommends deeper institutionalisation of systematic risk identification, analytics-driven risk assessment, and proactive mitigation measures, supported by digital technologies and strategic supplier partnerships, to consolidate operational resilience in Nigeria’s cement manufacturing sector.
Keywords
Risk Identification, Risk Assessment, Risk Mitigation, Supply Chain Risk Management
Downloads
References
1. Afifa, D., & Santoso, A. (2022). Proactive risk mitigation strategies and supply chain resilience in the food industry: A mini-review. International Journal of Supply Chain Management, 11(4), 112–120. [Google Scholar] [Crossref]
2. Alkhatib, H., & Momani, A. (2023). Global supply chain vulnerabilities and performance resilience: A post-pandemic analysis. Journal of Supply Chain Risk Management, 8(2), 55–70. [Google Scholar] [Crossref]
3. Al Majali, K. (2023). Operational performance measurement and organizational efficiency: An analytical review. International Journal of Operations and Logistics, 12(1), 44–58. [Google Scholar] [Crossref]
4. Aliu Ogbaini, M. (2025). Supply chain management strategies and firm performance in the Nigerian oil and gas sector. Nigerian Journal of Logistics and Operations Management, 9(1), 77–93. [Google Scholar] [Crossref]
5. Andeobu, L. O., et al. (2015). Risk management and supply chain performance. International Journal of Supply Chain Studies, 6(3), 145–160. [Google Scholar] [Crossref]
6. Ankhi, N. (2017). Supply chain risk management of liquefied natural gas in Australia. Journal of Energy Economics and Management, 3(2), 56–72. [Google Scholar] [Crossref]
7. Animah, I., & Shafiee, M. (2020). A systematic review of risk analysis applications in the LNG industry. Reliability Engineering & System Safety, 193, 106675. [Google Scholar] [Crossref]
8. Arıcan, U., & Ünal, O. (2025). Risk assessment in LPG/LNG maritime operations using Delphi and fault-tree analysis. Journal of Maritime Risk and Safety, 18(1), 20–38. [Google Scholar] [Crossref]
9. Arndt, D. (2025). Risk mitigation strategies in industrial supply networks: A strategic perspective. Global Journal of Industrial Management, 14(2), 55–70. [Google Scholar] [Crossref]
10. Asika, C., et al. (2024). Risk management practices in the Nigerian oil and gas sector: The case of SPDC. African Journal of Energy Management, 6(1), 101–119. [Google Scholar] [Crossref]
11. Asikhia, O. U., Makinde, G., Akinlabi, H., & Olawore, A. (2022). Supply chain risk management and business performance of oil and gas marketing firms in Lagos State: Moderating role of firm size. Journal of Business and Supply Chain Management, 15(3), 1–18. [Google Scholar] [Crossref]
12. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. [Google Scholar] [Crossref]
13. Bowley, A. (1926). Measurements of precision: An introduction to sampling theory. Royal Statistical Society. [Google Scholar] [Crossref]
14. Buzinkay, M. (2024). Operational performance frameworks in modern manufacturing. Journal of Manufacturing Systems, 52, 215–230. [Google Scholar] [Crossref]
15. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum. [Google Scholar] [Crossref]
16. de Oliveira, M. (2025). Managing operational performance in large manufacturing firms. International Journal of Production and Operations, 19(1), 1–18. [Google Scholar] [Crossref]
17. Enumah, E. (2025). Supply chain disruptions in Nigerian cement manufacturing. Journal of Manufacturing Logistics in Africa, 7(2), 88–104. [Google Scholar] [Crossref]
18. Ekpudu, J., Eze, R., & Acha, I. (2022). Operational performance measurement in Nigerian manufacturing firms. Nigerian Journal of Management Sciences, 13(1), 120–133. [Google Scholar] [Crossref]
19. Faizal, M., & Palaniappan, S. (2014). Delivery reliability and supply chain performance. International Journal of Logistics Research, 7(4), 351–366. [Google Scholar] [Crossref]
20. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. [Google Scholar] [Crossref]
21. Fozia, R. (2022). Foundations of risk identification in industrial supply chains. International Journal of Risk and Contingency Planning, 10(3), 77–90. [Google Scholar] [Crossref]
22. Golobrodska, T. (2024). Global manufacturing supply chains and systemic risk exposure. Journal of Industrial Economics, 72(1), 33–49. [Google Scholar] [Crossref]
23. Hair, J. F., Hult, G., Ringle, C., & Sarstedt, M. (2019). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage. [Google Scholar] [Crossref]
24. Hair, J. F., Hult, G., Ringle, C., & Sarstedt, M. (2022). PLS-SEM: Updated guidelines for advanced researchers. Sage. [Google Scholar] [Crossref]
25. Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V., & Tuominen, M. (2004). Risk management processes in supplier networks. International Journal of Production Economics, 90(1), 47–58. [Google Scholar] [Crossref]
26. Harju, M., et al. (2024). Risk management in IT procurement: A practice-based view. Journal of Procurement and Technology Management, 11(2), 65–84. [Google Scholar] [Crossref]
27. Hatami-Marbini, A., et al. (2024). Supply chain risks and institutional weaknesses in African manufacturing. African Journal of Industrial Development, 9(1), 109–126. [Google Scholar] [Crossref]
28. Henseler, J., Ringle, C., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based SEM. Journal of the Academy of Marketing Science, 43(1), 115–135. [Google Scholar] [Crossref]
29. Hu, Y., Xu, X., & Zhang, W. (2024). Big data analytics for supply chain risk monitoring. International Journal of Production Economics, 261, 108874. [Google Scholar] [Crossref]
30. James, F., & Renjith, R. (2021). Fuzzy LOPA risk assessment for LNG regasification terminals. Process Safety and Environmental Protection, 147, 140–151. [Google Scholar] [Crossref]
31. Jahin, A., et al. (2023). Adoption of artificial intelligence in risk-informed supply chains. Journal of Supply Chain Innovation, 5(3), 22–41. [Google Scholar] [Crossref]
32. Kaka, A., Yusuf, A., & Sharif, M. (2024). Risk assessment models in industrial operations. Operations Risk Management Journal, 8(1), 45–59. [Google Scholar] [Crossref]
33. Khosrow-Pour, M. (2018). Productivity in modern manufacturing. International Journal of Industrial Productivity, 9(2), 87–101. [Google Scholar] [Crossref]
34. Kiarie, J., Wanyoike, D., & Muturi, W. (2017). Risk identification strategies and supply chain performance of manufacturing firms in Kenya. International Journal of Economics, Commerce and Management, 5(6), 1–15. [Google Scholar] [Crossref]
35. Kilic, H., et al. (2023). Quantitative prioritisation of supply chain risks. Journal of Supply Chain Analytics, 12(2), 144–160. [Google Scholar] [Crossref]
36. Ngii, P. (2017). Effects of supply chain risk management on organisational performance: A study of Accelar Global Logistics. International Journal of Logistics and Transport, 4(1), 55–72. [Google Scholar] [Crossref]
37. Nunnally, J., & Bernstein, I. (1994). Psychometric theory (3rd ed.). McGraw-Hill. [Google Scholar] [Crossref]
38. Nurwin, G. (2022). Procurement risk identification and supply chain performance in Kenya’s construction industry. East African Journal of Logistics, 3(2), 102–118. [Google Scholar] [Crossref]
39. Nyamah, E., et al. (2023). Cost efficiency and supply chain performance in African manufacturing. African Journal of Operations Research, 15(1), 65–82. [Google Scholar] [Crossref]
40. Obi, I., & Fadun, O. (2025). Supply chain risk management and organisational resilience in developing economies. International Journal of Risk Studies, 13(1), 33–51. [Google Scholar] [Crossref]
41. Okegbemi, A. C. (2024). Economic environment factors and how they suppress growth and development in Nigeria. Retrieved from https://www.academia.edu/122486898/Economic_Environment_Factors_and_Ho w_They_Suppress_Growth_and_Development_in_Nigeria [Google Scholar] [Crossref]
42. Okoye, P., et al. (2023). Technological strategies for supply chain resilience. Journal of Industrial Technology Systems, 44(2), 130–146. [Google Scholar] [Crossref]
43. Omoruyi, E., & Quayson, M. (2023). Risk-sharing, supplier commitment, and procurement performance in South Africa’s public health supply chain. Journal of Public Procurement, 23(1), 1–20. [Google Scholar] [Crossref]
44. Onoh, A., et al. (2025). Risk assessment and performance of manufacturing companies in North-Central Nigeria. Journal of Manufacturing Research, 17(1), 55–72. [Google Scholar] [Crossref]
45. Owusu, E., & Ihunwo, B. (2019). Risk identification and sales performance of petroleum marketing firms in Nigeria. West African Journal of Business Studies, 10(2), 88–104. [Google Scholar] [Crossref]
46. Paul, S. (2023). Procurement risk management practices and procurement performance: The case of RUWASA Dodoma. Tanzanian Journal of Procurement and Supply, 6(1), 22–38. [Google Scholar] [Crossref]
47. Peteraf, M. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14(3), 179–191. [Google Scholar] [Crossref]
48. Priem, R., & Butler, J. (2001). Is the Resource-Based View a useful perspective for strategic management research? Academy of Management Review, 26(1), 22–40. [Google Scholar] [Crossref]
49. Rahimian, H. (2020). Risk management strategies for procurement and supply in project-based organizations. Journal of Project Procurement, 8(2), 112–127. [Google Scholar] [Crossref]
50. Renault, M., et al. (2016). Supply chain risk identification methods in industrial operations. International Journal of Industrial Risk, 9(1), 76–92. [Google Scholar] [Crossref]
51. Ritchie, B., & Brindley, C. (2007). Supply chain risk management and performance. International Journal of Production Economics, 112(1), 301–313. [Google Scholar] [Crossref]
52. Rusliadi, F., Parung, J., & Pahlavi, R. (2024). Risk mitigation for public works procurement in Enrekang Regency. Indonesian Journal of Public Sector Management, 5(1), 55–70. [Google Scholar] [Crossref]
53. Saptarini, R., & Nainggolan, B. (2022). Risk management in oil and gas field development: A case of East Kalimantan. Journal of Petroleum Operations, 4(3), 201–217. [Google Scholar] [Crossref]
54. Song, Y., et al. (2025). Economic impacts of equipment failure in heavy manufacturing. Journal of Industrial Maintenance Economics, 12(1), 33–48. [Google Scholar] [Crossref]
55. Sulaiman, A., & Ganiyu, B. (2024). Operational efficiency and supply chain performance in Nigerian manufacturing. Journal of Operations and Supply Chain Performance, 9(1), 55–69. [Google Scholar] [Crossref]
56. Sukdeo, N. (2017). Operational performance and competitiveness in industrial firms. South African Journal of Industrial Engineering, 28(3), 42–56. [Google Scholar] [Crossref]
57. Tullio, S. (2024). Proactive mitigation in complex supply networks. International Journal of Supply Chain Strategies, 6(2), 118–140. [Google Scholar] [Crossref]
58. Teece, D., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. [Google Scholar] [Crossref]
59. Tran, T., et al. (2018). Hazard assessment models for industrial operations. Journal of Risk Engineering, 2(2), 66–81. [Google Scholar] [Crossref]
60. Um, J., & Han, J. (2021). Supply chain diversification and resilience in global manufacturing. International Journal of Production Research, 59(7), 2070–2087. [Google Scholar] [Crossref]
61. Wernerfelt, B. (1984). A Resource-Based View of the firm. Strategic Management Journal, 5(2), 171–180. [Google Scholar] [Crossref]
62. Wu, T., et al. (2006). Supply chain risk management framework. International Journal of Production Research, 44(12), 2787–2811. [Google Scholar] [Crossref]
63. Wawire, A., et al. (2022). Effects of risk identification on supply chain performance in Western Kenya county governments. African Journal of Procurement and Supply Chain, 5(2), 50–67. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- The Impact of Ownership Structure on Dividend Payout Policy of Listed Plantation Companies in Sri Lanka
- Urban Sustainability in North-East India: A Study through the lens of NER-SDG index
- Performance Assessment of Predictive Forecasting Techniques for Enhancing Hospital Supply Chain Efficiency in Healthcare Logistics
- The Fractured Self in Julian Barnes' Postmodern Fiction: Identity Crisis and Deflation in Metroland and the Sense of an Ending
- Impact of Flood on the Employment, Labour Productivity and Migration of Agricultural Labour in North Bihar