• International Journal of Technology (IJTech)
  • Vol 5, No 1 (2014)

Probabilistic Risk Assessment of the Shipyard Industry using the Bayesian Method

Minto Basuki, Djauhar Manfaat, Setyo Nugroho, AAB Dinariyana

Corresponding email: mintobasuki@yahoo.co.id

Published at : 27 Jan 2014
Volume : IJtech Vol 5, No 1 (2014)
DOI : https://doi.org/10.14716/ijtech.v5i1.157

Cite this article as:
Basuki, M., Manfaat, D., Nugroho, S., Dinariyana, A., 2014. Probabilistic Risk Assessment of the Shipyard Industry using the Bayesian Method. International Journal of Technology. Volume 5(1), pp. 88-97

Minto Basuki Department of Shipbuilding Engineering, Faculty of Mineral and Marine Engineering, Institut Teknologi Adhi Tama Surabaya, Jalan Arief Rachman Hakim 100, Surabaya, Indonesia
Djauhar Manfaat Faculty of Marine Technology, Institut Teknologi Sepuluh Nopember Surabaya, Kampus ITS Sukolilo, Surabaya, Indonesia
Setyo Nugroho Faculty of Marine Technology, Institut Teknologi Sepuluh Nopember Surabaya, Kampus ITS Sukolilo, Surabaya, Indonesia
AAB Dinariyana Faculty of Marine Technology, Institut Teknologi Sepuluh Nopember Surabaya, Kampus ITS Sukolilo, Surabaya, Indonesia
Email to Corresponding Author


The shipbuilding industry is characterized by high-risk business activities; therefore, caution should be taken in its operational processes. From upstream to downstream, the shipbuilding industry depends on other industries. In this study, a risk assessment was conducted on the construction of new vessels using the Bayesian network approach; accordingly, the risk assessment was carried out using a probabilistic value at risk (VaR). The study was carried out by PT PAL Indonesia in association with the construction of a new tanker ship (building production codes M271 and M272). An analysis was conducted on three main components of new vessel construction—design components, material and production components, and sub-components of the previous two components. From the study, we could conclude that the probability of delay for new vessel construction caused by design delay is 0.05; the probability of delay caused by material delay is 0.65; and the probability of delay caused by production delay is 0.3. For delays caused by design factors, a yard plan is the sub-component that contributes predominantly to delays (i.e., probability of 0.3). For delays caused by material factors, the sub-component with the greatest impact is hull and machinery outfitting, with a probability of 0.3. For delays caused by production factors, the sub-component with the biggest impact is hull construction, with a probability of 0.39. Thus, we could conclude that a project delay would occur if the material component and the hull construction sub-components were not handled properly.

Bayesian network, probability, shipyard industry


Abdullah, T., Mateen, A., Sattar, A.R., Mustafa, T., 2010. Risk Analysis of Various Phases of Software Development Models. European Journal of Scientific Research, Volume 40(3), pp. 369-376

Bashiri, E., 2010. Statistical Analysis-driven Risk Assessment of Criteria Air Pollutants: A Sulfur Dioxide Case Study. World Academy of Science, Engineering and Technology, Volume 39, pp. 85-91

Basuki, M., Artana, K.B., Nugroho, S., Dinariyana, A.A.B., 2010. Shipbuilding Industry in Indonesia, a Risk Perspective (in Indonesian). In: Proceedings of National Seminar of Postgraduate Study 2010, ITS, Surabaya, 8-9 December, Indonesia

Basuki, M., Manfaat, D., Nugroho, S., Dinariyana, A.A.B., 2012. Improvement of the Process of New Business of Ship Building Industry. Journal of Economics, Business, and Accountancy/Ventura, Volume 15(2), pp. 187-204

Ben-Azher, J.Z., 2008. Development Program Risk Assessment based on Utility Theory, Risk Management, Volume 10, pp. 285-299 http://dx.doi.org/10.1057/rm.2008.9

Bonafede C.E., Giudici, P., 2007. Bayesian Networks for Enterprise Risk Assessment. Physica A: Statistical Mechanics and its Applications, Volume 382(1), pp. 22-28 http://dx.doi.org/10.1016/j.physa.2007.02.065

Kalantarnia, M., Khan, F., Hawboldt, K., 2009. Dynamic Risk Assessment using Failure Assessment and Bayesian Theory. Journal of Loss Prevention in the Process Industries, Volume 22, pp. 600-606 http://dx.doi.org/10.1016/j.jlp.2009.04.006

Kruizinga, A.G., Briggs, D., Crevel, R.W.R., Knults, A.C., 2008. Probabilistic Risk Assessment Model for Allergens in Food Sensitivity Analysis of the Minimum Eliciting Dose and Food Consumption. Food and Chemical Toxicology, Volume 46, pp. 1437-1443 http://dx.doi.org/10.1016/j.fct.2007.09.109

Lee, E., Shin, J.G., Park, Y., 2007. A Statistical Analysis of Engineering Project Risk in the Korean Shipbuilding Industry. Journal of Ship Production, Volume 23(4), pp. 223-230

Lee, E., Park, Y., Shin, J.G., 2009. Large Engineering Project Risk Management using a Bayesian Belief Network. Expert Systems with Application, Volume 36(3), pp. 5880-5887 http://dx.doi.org/10.1016/j.eswa.2008.07.057

Satoh, N., Kumamoto, H., Kino, Y., 2008. Viewpoint of ISO GMITS and Probabilistic Risk Assessment in Information Security. International Journal of Systems Applications, Engineering & Development, Volume 2(4), pp 237-244

Whitney, P., Thompson, S., Wolf, K., Brothers, A., 2009. Bayesian Assessment of likelihood, Consequence and Risk for Comparing Scenarios. In: Proceedings of the 18th Conference on Behavior Representation in Modeling and Simulation, Sundance, UT, 31 March - 2 April

Wreathall, J., Nemeth, C., 2004. Assessing Risk: the Role of Probabilistic Risk Assessment (PRA) in Patient Safety Improvement. Quality and Safety in Health Care, Volume 13, pp. 206-212 http://dx.doi.org/10.1136/qshc.2003.006056