Assessment of Contingency Sum in Buying of Offshore Construction Materials and Its Impact on Total Project Cost to Improve the Inventory Management: A Case Study PT SATM

Authors

  • Veren Priscila Department of Industrial Engineering, Universitas Atma Jaya Yogyakarta, Indonesia
  • Rhea D. Casela Department of Industrial Engineering, Bulacan State University, Philippines
  • Nicole Joy M. Realingo Department of Industrial Engineering, Bulacan State University, Philippines

DOI:

https://doi.org/10.24002/ijieem.v7i2.10358

Keywords:

contingency fund management, influence diagram, level inventory, Monte Carlo simulation , offshore engineering

Abstract

This study examines the operational challenges faced by an offshore company that has specialized in rigs and floaters, repairs and upgrades, offshore platforms, and specialized shipbuilding over the past seven years. Despite steady growth, the company has encountered significant issues related to contingency fund management during the construction phase. To mitigate unpredictable risk exposure, the company applies a 20% contingency to the total cost estimate of every offshore construction material. However, this approach has led to a consistent 10% surplus, resulting in excessive costs and inventory. The research aims to evaluate the effectiveness of the current contingency allocation strategy and propose solutions to reduce surplus costs and excess inventory. By analyzing the company's data, the study identifies key inefficiencies and suggests optimized approaches to contingency fund management. The findings aim to provide actionable insights for enhancing financial and inventory management practices, ultimately improving the company's overall operational efficiency and profitability.

References

Bozarth, C.C., Handfield, R.B., & Chandrasekaran, A. (2020). Introduction to operations and supply chain management. Pearson.

Burroughs, S.E., & Juntima, G. (2004). Exploring techniques for contingency setting. AACE International Transactions, ES31.

Chen, C., Chen, M., & Zhao, X. (2019). Impact of lead time uncertainty and lead time reduction on the performance of inventory control policies. International Journal of Production Economics, 218, 214–223.

Chikán, A. (2009). Managing inventory: Concept, theories, and practices. Springer.

Davis, K., & Patterson, D. (2012). Ethics of big data: balancing risk and innovation. O’Reily Media.

England, A., & Moreci, J. (2012). Project cost estimating and control. AACE International.

Howard, R.A., & Matheson, J.E. (2005). Influence diagrams. Decicion Analysis, 2(3), 127-143.

Jimoh, R.A., & Adama, U.J. (2014). Assessment of contingency sum in relation to the total cost of renovation work in public schools in Abuja, Nigeria. International Journal of Managerial Studies and Research, 2(10), 55-63.

Kannan, V. R., & Tan, K. C. (2005). Just in time, total quality management, and supply chain management: Understanding their linkages and impact on business performance. Omega, 33(2), 153–162.

Mak, S., & Picken, D. (2000). Using risk analysis to determine construction project contingencies. Journal of Construction Engineering and Management, 126(2), 130-136.

Marquez, P.C., Agustina, D., & Amin, S.H. (2021). Integrated vendor-managed inventory and supply chain coordination models: A review. Computers & Industrial Engineering, 159, Article 107526.

Perera, H.S.C., Samarasinghe, G.D., & Samarasinghe, D. (2019). Predictive analytics and risk analysis in supply chain management. International Journal of Supply Chain Management, 8(2), 91–97.

Rubio, J. L., & Jiménez-Parra, J. F. (2018). A new Monte Carlo simulation approach for inventory management in retailing. Mathematics, 6(11), 248-260.

Rossetti, M.D. (2008). Simulation modeling and arena. John Wiley & Sons.

Shachter, R.D. (2019). Influence diagrams for representing and solving decision problems. Communications of the ACM, 32(5), 408–415.

Shah, R., & Shin, H. (2007). Relationships among information technology, inventory, and firm performance. Production and Operations Management, 16(5), 577–593.

Torra, V., Narukawa, Y., Aguilo, I., & Gonzalez-Hidalgo, M. (2018). Modeling decisions: Information fusion and aggregation operators. Springer.

Venkatesan, M., Prajapati, P., & Raj, T. (2019). Analysis of inventory turnover as a measure of inventory performance: An empirical study. Materials Today: Proceedings, 18, 1826–1831.

Wakjira, M.T. (2021). Application of Monte Carlo simulation for inventory management. American Journal of Operations Management and Information Systems, 6(1), 1–6.

Watt, A. (2012). Project management. B.C. Open Textbook Project.

Yang, X. (2020). Just-in-time inventory management system and business performance: The moderating role of demand uncertainty. International Journal of Production Economics, 230, Article 107861.

Zhao, T., & Tseng, C.L. (2003). Valuing flexibility in infrastructure expansion. Journal of Infrastructure Systems, 9(3), 89-97.

Downloads

Published

2025-12-28

How to Cite

Priscila, V., Casela, R. D., & Realingo, N. J. M. (2025). Assessment of Contingency Sum in Buying of Offshore Construction Materials and Its Impact on Total Project Cost to Improve the Inventory Management: A Case Study PT SATM. International Journal of Industrial Engineering and Engineering Management, 7(2), 139–147. https://doi.org/10.24002/ijieem.v7i2.10358

Issue

Section

Articles