Abstract
Business management is concerned with organizing and efficiently utilizing resources of a business, including people, in order to achieve required goals. One of the main aspects in this process is planning, which involves deciding operations of the future and consequently generating plans for action. Computational models, both theoretical and empirical, help in understanding and providing a framework for such a scenario. Statistics and probability can play an important role in empirical research as quantitative data is amenable for analysis. In business management, analysis of risk is crucial as there is uncertainty, vagueness, irregularity, and inconsistency. An alternative and improved approach to deterministic models is stochastic models like Monte Carlo simulations. There has been a considerable increase in application of this technique to business problems as it provides a stochastic approach and simulation process. In stochastic approach, we use random sampling to solve a problem statistically and in simulation, there is a representation of a problem using probability and random numbers. Monte Carlo simulation is used by professionals in fields like finance, portfolio management, project management, project appraisal, manufacturing, insurance and so on. It equips the decision-maker by providing a wide range of likely outcomes and their respective probabilities. This technique can be used to model projects which entail substantial amounts of funds and have financial implications in the future. The proposed chapter will deal with concepts of Monte Carlo simulation as applied to Business Management scenario. A few specific case studies will demonstrate its application and interpretation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arend MG, Schäfer T (2019) Statistical power in two-level models: a tutorial based on Monte Carlo simulation. Psychol Methods 24(1):1
Dornheim T, Groth S, Sjostrom T, Malone FD, Foulkes WMC, Bonitz M (2016) Ab initio quantum Monte Carlo simulation of the warm dense electron gas in the thermodynamic limit. Phys Rev Lett 117(15):156403
Gholami H, Rahimi S, Fathabadi A, Habibi S, Collins AL (2020) Mapping the spatial sources of atmospheric dust using GLUE and Monte Carlo simulation. Sci Total Environ 138090
Klein SR, Nystrand J, Seger J, Gorbunov Y, Butterworth J (2017) STARlight: a Monte Carlo simulation program for ultra-peripheral collisions of relativistic ions. Comput Phys Commun 212:258–268
Liu Y, Wang W, Sun K, Meng ZY (2020) Designer Monte Carlo simulation for the Gross-Neveu-Yukawa transition. Phys Rev B 101(6):064308
Rillo G, Morales MA, Ceperley DM, Pierleoni C (2018) Coupled electron-ion Monte Carlo simulation of hydrogen molecular crystals. J Chem Phys 148(10):102314
Wang R, Lin TS, Johnson JA, Olsen BD (2017) Kinetic Monte Carlo simulation for quantification of the gel point of polymer networks. ACS Macro Lett 6(12):1414–1419
Zhou J, Aghili N, Ghaleini EN, Bui DT, Tahir MM, Koopialipoor M (2020) A Monte Carlo simulation approach for effective assessment of flyrock based on an intelligent system of neural network. Eng Comput 36(2):713–723
Zhu Z, Du X (2016) Reliability analysis with Monte Carlo simulation and dependent Kriging predictions. J Mech Des 138(12)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Appendix
Appendix
Part of a table of random numbers
61424 | 20419 | 86546 | 00517 |
90222 | 27993 | 04952 | 66762 |
50349 | 71146 | 97668 | 86523 |
85676 | 10005 | 08216 | 25906 |
02429 | 19761 | 15370 | 43882 |
90519 | 61988 | 40164 | 15815 |
20631 | 88967 | 19660 | 89624 |
89990 | 78733 | 16447 | 27932 |
Generating random numbers using R software
Generating random numbers through MS EXCEL
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Prasad, S. (2021). Computational Aspects of Business Management with Special Reference to Monte Carlo Simulation. In: Patnaik, S., Tajeddini, K., Jain, V. (eds) Computational Management. Modeling and Optimization in Science and Technologies, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-030-72929-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-72929-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72928-8
Online ISBN: 978-3-030-72929-5
eBook Packages: Business and ManagementBusiness and Management (R0)