Production
https://prod.org.br/article/doi/10.1590/0103-6513.20220121
Production
Research Article

Cost at Risk (CaR): a Methodology for Costing under Uncertainty

Victor Jiménez Carabalí; Paulo Afonso

Downloads: 0
Views: 162

Abstract

Paper aims: This paper proposes Cost at Risk (CaR), a concept and a methodology that allows the computation of the risk of cost estimations within a costing system by means of the Monte Carlo Simulation, considering a predefined level of confidence and considering the worst expected result in terms of cost in a certain period.

Originality: Traditionally, researchers and practitioners have been focused on deterministic costing models without recognizing and managing cost uncertainty. The proposed methodology is based on five steps that go from the determination of the parameters that generate uncertainty to the estimation of the risk.

Research method: A Design Science Research (DSR) approach was followed based on mathematical modeling and computer simulation.

Main findings: The model was applied to the imaging area of a hospital allowing to identify and quantify the risk of the most relevant costs and therefore, supporting the design and implementation of both operational and strategic decisions.

Implications for theory and practice: The main contribution is the inclusion in costing systems of the uncertainty inherent in the estimation of costs, particularly in complex environments.

Keywords

Costing Systems, Activity Based Costing, Risk management, Uncertainty, Monte Carlo Simulation, Cost at Risk

References

Abed, E., Safadi, E., Adrot, O., & Flaus, J.-M. (2015). Advanced Monte Carlo Method for model uncertainty propagation in risk assessment. IFAC-PapersOnLine, 48(3), 529-534. http://dx.doi.org/10.1016/j.ifacol.2015.06.135.

Afonso, P., & Jiménez, V. J. (2016). Costing systems for decision making under uncertainty using probabilistic models. In D. J. Jakóbczak. Analyzing risk through probabilistic modeling in operations research (pp. 221-245). Pensilvânia: IGI Global.

Akoka, J., Comyn-Wattiau, I., Prat, N., & Storey, V. C. (2022). Knowledge contributions in design science research: Paths of knowledge types. Decision Support Systems, 166, 113898. http://dx.doi.org/10.1016/j.dss.2022.113898.

Anderson, T. W., & Darling, D. A. (1954). A test of godness of fit. Journal of American Statiscal Association, 49(268), 765-769. http://dx.doi.org/10.1080/01621459.1954.10501232.

Baskerville, R. L., Kaul, M., & Storey, V. C. (2015). Genres of inquiry in design-science research: Justification and evaluation of knowledge production. Management Information Systems Quarterly, 39(3), 541-564. http://dx.doi.org/10.25300/MISQ/2015/39.3.02.

Bhattacharjee, P., & Ray, P. K. (2016). Simulation modelling and analysis of appointment system performance for multiple classes of patients in a hospital: a case study. Operations Research for Health Care, 8, 71-84. http://dx.doi.org/10.1016/j.orhc.2015.07.005.

Calvi, K., Chung, S. H., Havens, R., Economou, M., & Kulkarni, R. (2019). Simulation study integrated with activity-based costing for an electronic device re-manufacturing system. International Journal of Advanced Manufacturing Technology, 103(1-4), 127-140. http://dx.doi.org/10.1007/s00170-019-03429-3.

Coronel-Brizio, H. F., & Hernández-Montoya, A. R. (2010). The Anderson-Darling test of fit for the power-law distribution from left-censored samples. Physica A, 389(17), 3508-3515. http://dx.doi.org/10.1016/j.physa.2010.03.041.

Datta, P. P., & Roy, R. (2010). Cost modelling techniques for availability type service support contracts: A literature review and empirical study. CIRP Journal of Manufacturing Science and Technology, 3(2), 142-157. http://dx.doi.org/10.1016/j.cirpj.2010.07.003.

Díaz, H., Teixeira, A. P., & Soares, C. G. (2022). Application of Monte Carlo and Fuzzy Analytic Hierarchy Processes for ranking floating wind farm locations. Ocean Engineering, 245, 110453. http://dx.doi.org/10.1016/j.oceaneng.2021.110453.

Durán, O., & Afonso, P. (2021). Physical asset risk management. In D. G. Garrido, & L. Almeida. Risk management: an overview (pp. 45-82). New York: Nova Science Publishers, Inc.

Durán, O., & Durán, P. A. (2018). Activity Based Costing for wastewater treatment and reuse under uncertainty: a fuzzy approach. Sustainability (Basel), 10(7), 1-15. http://dx.doi.org/10.3390/su10072260.

Durán, O., Afonso, P. S., & Durán, P. A. (2019). Spare parts cost management for long-term economic sustainability: Using fuzzy activity based LCC. Sustainability (Basel), 11(7), 1835. http://dx.doi.org/10.3390/su11071835.

Esmalifalak, H., Albin, M. S., & Behzadpoor, M. (2015). A comparative study on the activity based costing systems: traditional, fuzzy and Monte Carlo approaches. Health Policy and Technology, 4(1), 58-67. http://dx.doi.org/10.1016/j.hlpt.2014.10.010.

Fei, Z. Y., & Isa, C. R. (2010). Factors influencing activity-based costing success: a research framework. International Journal of Trade, Economics and Finance, 1(2), 144-150. http://dx.doi.org/10.7763/IJTEF.2010.V1.26.

Glasserman, P. (2004). Monte Carlo methods in financial engineering (Vol. 53). Springer.

Gosselin, M. (2006). A review of activity-based costing: technique, implementation, and consequences. In C. S. Chapman, A. G. Hopwood, & M. D. Shields (Eds.), Handbooks of management accounting research (Vol. 3, pp. 641–671). Amsterdam: Elsevier Ltd. http://dx.doi.org/10.1016/S1751-3243(06)02008-6

Govindan, K., & Jepsen, M. B. (2016). ELECTRE: a comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1-29. http://dx.doi.org/10.1016/j.ejor.2015.07.019.

Grenyer, A., Erkoyuncu, J. A., Zhao, Y., & Roy, R. (2021). A systematic review of multivariate uncertainty quantification for engineering systems. CIRP Journal of Manufacturing Science and Technology, 33, 188-208. http://dx.doi.org/10.1016/j.cirpj.2021.03.004.

Gupta, A., & Maranas, C. D. (2003). Managing demand uncertainty in supply chain planning. Computers & Chemical Engineering, 27(8-9), 1219-1227. http://dx.doi.org/10.1016/S0098-1354(03)00048-6.

Hazır, Ö., & Ulusoy, G. (2020). A classification and review of approaches and methods for modeling uncertainty in projects. International Journal of Production Economics, 223, 107522. http://dx.doi.org/10.1016/j.ijpe.2019.107522.

Hellowell, M. (2013). PFI redux? Assessing a new model for financing hospitals. Health Policy (Amsterdam), 113(1–2), 77-85. http://dx.doi.org/10.1016/j.healthpol.2013.09.008. PMid:24138730.

Henri, J.-F., Boiral, O., & Roy, M.-J. (2016). Strategic cost management and performance: The case of environmental costs. The British Accounting Review, 48(2), 269-282. http://dx.doi.org/10.1016/j.bar.2015.01.001.

Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. Management Information Systems Quarterly, 28(1), 75-105. http://dx.doi.org/10.2307/25148625.

Jahan-Shahi, H., Shayan, E., & Masood, S. (1999). Cost estimation in flat plate processing using fuzzy sets. Computers & Industrial Engineering, 37(1-2), 485-488. http://dx.doi.org/10.1016/S0360-8352(99)00124-2.

Jang, D., Kim, J., Kim, D., Han, W. B., & Kang, S. (2022). Techno-economic analysis and Monte Carlo simulation of green hydrogen production technology through various water electrolysis technologies. Energy Conversion and Management, 258(March), 115499. http://dx.doi.org/10.1016/j.enconman.2022.115499.

Jiang, C., Xu, R., & Wang, P. (2023). Measuring effectiveness of movement-based three-way decision using fuzzy Markov model. International Journal of Approximate Reasoning, 152, 456-469. http://dx.doi.org/10.1016/j.ijar.2022.11.010.

Jiao, L., Yang, H., Liu, Z., & Pan, Q. (2022). Interpretable fuzzy clustering using unsupervised fuzzy decision trees. Information Sciences, 611, 540-563. http://dx.doi.org/10.1016/j.ins.2022.08.077.

Jiménez, V., & Afonso, P. (2016). Risk assessment in costing systems using costing at risk (CaR): An application to the Coffee production cost. In Proceedings of the 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1315-1319). USA: IEEE.

Jiménez, V., Duarte, C., & Afonso, P. (2015). Cost system under uncertainty: a case study in the imaging area of a hospital. In P. Cortés, E. Maeso-González & A. Escudero-Santana (Eds.),Enhancing synergies in a collaborative environment (pp. 325-333). Cham: Springer.

Jorion, P. (2000). Value at risk: The benchmark for controlling market risk (2 ed.). New York: McGraw-Hill.

Kroese, D. P., Brereton, T., Taimre, T., & Botev, Z. I. (2014). Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics, 6(6), 386-392. http://dx.doi.org/10.1002/wics.1314.

Kropivšek, J., Jošt, M., Grošelj, P., Kuzman, M. K., Kariž, M., Merela, M., & Bučar, D. G. (2021). Innovative model of the cost price calculation of products from invasive non-native wood species based on the ftdabc method. Forests, 12(11), 1519. http://dx.doi.org/10.3390/f12111519.

Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov test for normality with mean and variance unknown. Journal of the American Statistical Association, 62(318), 399-402. http://dx.doi.org/10.1080/01621459.1967.10482916.

Liu, W., Lai, Z., Bacsa, K., & Chatzi, E. (2022). Physics-guided Deep Markov Models for learning nonlinear dynamical systems with uncertainty. Mechanical Systems and Signal Processing, 178, 109276. http://dx.doi.org/10.1016/j.ymssp.2022.109276.

Liu, Y., & Ralescu, D. A. (2017). Value-at-risk in uncertain random risk analysis. Information Sciences, 391, 1-8.

Lueg, R., & Storgaard, N. (2017). The adoption and implementation of Activity-based Costing: a systematic literature review. International Journal of Strategic Management, 17(2), 7-24. http://dx.doi.org/10.18374/IJSM-17-2.1.

Magnacca, F., & Giannetti, R. (2023). Management accounting and new product development: a systematic literature review and future research directions. The Journal of Management and Governance, 1-35. http://dx.doi.org/10.1007/s10997-022-09650-9.

Massey Junior, F. J. (1951). The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association, 46(253), 68-78. http://dx.doi.org/10.1080/01621459.1951.10500769.

Mestre, A. M., Oliveira, M. D., & Barbosa-Póvoa, A. P. (2014). Location-allocation approaches for hospital network planning under uncertainty. European Journal of Operational Research, 240(3), 791-806. http://dx.doi.org/10.1016/j.ejor.2014.07.024.

Mo, J., Wang, L., Qiu, Z., & Shi, Q. (2019). A nonprobabilistic structural damage identification approach based on orthogonal polynomial expansion and interval mathematics. Structural Control and Health Monitoring, 26(8), 1-22. http://dx.doi.org/10.1002/stc.2378.

Myrodia, A., Kristjansdottir, K., & Hvam, L. (2017). Impact of product configuration systems on product profitability and costing accuracy. Computers in Industry, 88, 12-18. http://dx.doi.org/10.1016/j.compind.2017.03.001.

Nachtmann, H., & Needy, K. L. (2001). Fuzzy activity based costing: a methodology for handling uncertainty in activity based costing systems. The Engineering Economist, 46(4), 245-273. http://dx.doi.org/10.1080/00137910108967577.

Nachtmann, H., & Needy, K. L. (2003). Methods for handling uncertainty in activity based costing systems. The Engineering Economist, 48(3), 259-282. http://dx.doi.org/10.1080/00137910308965065.

Namazi, M. (2009). Performance-focused ABC: a third generation of activity-based costing system. Cost and Management, 23(5), 34.

Nguyen, T., Duong, Q. H., Nguyen, T. Van, Zhu, Y., & Zhou, L. (2022). Knowledge mapping of digital twin and physical internet in Supply Chain Management: a systematic literature review. International Journal of Production Economics, 244, 108381. http://dx.doi.org/10.1016/j.ijpe.2021.108381.

Oehmen, J., Locatelli, G., Wied, M., & Willumsen, P. (2020). Risk, uncertainty, ignorance and myopia: their managerial implications for B2B firms. Industrial Marketing Management, 88, 330-338. http://dx.doi.org/10.1016/j.indmarman.2020.05.018.

Ostadi, B., Mokhtarian Daloie, R., & Sepehri, M. M. (2019). A combined modelling of fuzzy logic and Time-Driven Activity-based Costing (TDABC) for hospital services costing under uncertainty. Journal of Biomedical Informatics, 89, 11-28. http://dx.doi.org/10.1016/j.jbi.2018.11.011.

Page, K., Barnett, A. G., Campbell, M., Brain, D., Martin, E., Fulop, N., & Graves, N. (2014). Costing the Australian National Hand Hygiene Initiative. The Journal of Hospital Infection, 88(3), 141-148. http://dx.doi.org/10.1016/j.jhin.2014.06.005. PMid:25092619.

Parker, L. D. (2016). From scientific to activity based office management: a mirage of change. Journal of Accounting & Organizational Change, 12(2), 177-202.

Rinaldi, M., Murino, T., Gebennini, E., Morea, D., & Bottani, E. (2022). A literature review on quantitative models for supply chain risk management: can they be applied to pandemic disruptions? Computers & Industrial Engineering, 170, 108329. http://dx.doi.org/10.1016/j.cie.2022.108329. PMid:35722204.

RiskMetrics Group. (1999). Corporate metrics: technical document. New York: RiskMetrics Group.

Rivero, E. J. R., & Emblemsvåg, J. (2007). Activity-based life-cycle costing in long-range planning. Review of Accounting and Finance, 6(4), 370-390. http://dx.doi.org/10.1108/14757700710835041.

Rodríguez, A. E., Pezzotta, G., Pinto, R., & Romero, D. (2022). A framework for cost estimation in product-service systems: a systems thinking approach. CIRP Journal of Manufacturing Science and Technology, 38, 748-759. http://dx.doi.org/10.1016/j.cirpj.2022.06.013.

Saaty, T. L. (2016). The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making. In S. Greco, M. Ehrgott & J. Figueira (Eds.),Multiple criteria decision analysis (pp. 363-419). New York: Springer. http://dx.doi.org/10.1007/978-1-4939-3094-4_10.

Sarokolaei, M. A., Bahreini, M., & Bezenjani, F. P. (2013). Fuzzy Performance Focused Activity based Costing (PFABC). Procedia: Social and Behavioral Sciences, 75, 346-352. http://dx.doi.org/10.1016/j.sbspro.2013.04.039.

Seppälä, J., Basson, L., & Norris, G. A. (2001). Decision analysis frameworks for life‐cycle impact assessment. Journal of Industrial Ecology, 5(4), 45-68. http://dx.doi.org/10.1162/10881980160084033.

Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591. http://dx.doi.org/10.2307/2333709.

Soto, A. R., Sobrino, A., Trillas, E., & Alsina, C. (2020). Reflections on an old problem: that of preserving the logical forms and symmetry. Fuzzy Sets and Systems, 401, 150-162. http://dx.doi.org/10.1016/j.fss.2019.10.008.

Total Cost Management Division – CII. (2017). TCM Maturity Model. Retrieved in 2017, January 1, from http://taruncma.wixsite.com/ciitcm

Wang, J., Wang, Y., Zhang, Y., Liu, Y., & Shi, C. (2022). Life cycle dynamic sustainability maintenance strategy optimization of fly ash RC beam based on Monte Carlo simulation. Journal of Cleaner Production, 351, 131337. http://dx.doi.org/10.1016/j.jclepro.2022.131337.

Wu, Z., & Zhang, R. (2022). Central limit theorem and moderate deviation principle for stochastic scalar conservation laws. Journal of Mathematical Analysis and Applications, 516(1), 126445. http://dx.doi.org/10.1016/j.jmaa.2022.126445.

Yazdi, M., Kabir, S., & Walker, M. (2019). Uncertainty handling in fault tree based risk assessment: State of the art and future perspectives. Process Safety and Environmental Protection, 131, 89-104. http://dx.doi.org/10.1016/j.psep.2019.09.003.

Zimlichman, E., Henderson, D., Tamir, O., Franz, C., Song, P., Yamin, C. K., Keohane, C., Denham, C. R., & Bates, D. W. (2013). Health Care–Associated Infections. JAMA Internal Medicine, 173(22), 2039-2046. http://dx.doi.org/10.1001/jamainternmed.2013.9763. PMid:23999949.

Zimmermann, H.-J. (2000). An application-oriented view of modeling uncertainty. European Journal of Operational Research, 122(2), 190-198. http://dx.doi.org/10.1016/S0377-2217(99)00228-3.
 


Submitted date:
11/21/2022

Accepted date:
09/18/2023

654b6b3aa9539512b342e863 production Articles
Links & Downloads

Production

Share this page
Page Sections