INVESTMENT DECISION OF BIOMEDICAL IMPLANT PRODUCTION UNDER UNCERTAINTY CONDITION: A MONTECARLO SIMULATION APPROACH

Suganta Handaru Setiawan, Erman Sumirat, Raden Aswin Rahadi

Abstract


PT. Langit Biru make investment project plan in biomedical implant production to grab the attractive market potential of biomedical implant and to diversify the business footprint in medical segment. Investment decision is made by considering the impacts of discrete risks using scenario analysis, and montecarlo simulations analysis for exploring the consequences of continuous risk to the project NPV.Based on the analysis, the project is feasible and exhibits a relatively low risk  under discrete and continuous risk where based on scenario analysis  NPV in worst case scenario is positive with value USD 433.621 (93% lower than the base case ) and montecarlo simulation expose that the project has 100% probability of positive NPV from 1000 simulation with NPV mean USD 6.224.042 that very close to the base NPV  USD 6.252.653 (-0,46% lower).  

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References


Damodaran, A. (2015). Applied Corporate Finance. ohn Wiley & Sons, Inc.

Dimian, A. C., Bildea, C. S., & Kiss, A. A. (2014). Economic evaluation of projects. In Computer aided chemical engineering (Vol. 35, pp. 717–755). Elsevier.

Gitman, L. J. (2015). Principles of Managerial Finance. The British Accounting Review.

Kuppens, T., Rafiaani, P., Vanreppelen, K., Yperman, J., Carleer, R., Schreurs, S., Thewys, T., & Van Passel, S. (2018). Combining Monte Carlo simulations and experimental design for incorporating risk and uncertainty in investment decisions for cleantech: a fast pyrolysis case study. Clean Technologies and Environmental Policy, 20(6), 1195–1206.

O’Donoghue, T., & Somerville, J. (2018). Modeling risk aversion in economics. Journal of Economic Perspectives, 32(2), 91–114.

Pasqualino, R., Demartini, M., & Bagheri, F. (2021). Digital transformation and sustainable oriented innovation: A system transition model for socio-economic scenario analysis. Sustainability, 13(21), 11564.

Salling, K. B., & Leleur, S. (2017). Transport project evaluation: feasibility risk assessment and scenario forecasting. Transport, 32(2), 180–191.

Sartori, D., Catalano, G., Genco, M., Pancotti, C., Sirtori, E., Vignetti, S., & Del Bo, C. (2014). Guide to cost-benefit analysis of investment projects. Economic Appraisal Tool for Cohesion Policy, 2020.

Suganta Handaru, R. A. (2023). FINANCIAL BUSINESS FEASIBILITY OF BIOMEDICAL IMPLANTPRODUCTIONUSING ADDITIVE MANUFACTURING PROCESS. COSTING:Journal of Economic, Business and Accounting, 2709-2714

Xiong, J., Ng, T. S. A., & Wang, S. (2016). An optimization model for economic feasibility analysis and design of decentralized waste-to-energy systems. Energy, 101, 239–251.

Zio, E. (2018). The future of risk assessment. Reliability Engineering & System Safety, 177, 176–190.




DOI: http://dx.doi.org/10.31000/dmj.v8i1.10588

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