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A Model for Cost Deviation Analysis and Prescriptive Analytics

  • Victor Jimenez
  • Paulo AfonsoEmail author
Conference paper
  • 17 Downloads
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

A cost variation within an acceptable range is considered an “event under control” and does not require investigation or action. Nevertheless, often, managers investigate cost variations based on historical data and subjective judgments or empirical rules. Both, positive and negative cost variations may represent a problem, for example, if service levels and quality standards are not achieved. This paper presents a model and a methodology that allows establishing the acceptable range for cost analysis towards a predictive analytics approach. Monte Carlo simulation is used to compute the inherent variability in the processes. This methodology was tested in a case study, obtaining, as a result, the range of standard values which serve as a basis for operational and tactical decisions in the organization. Potential applications and opportunities for further developments and research are also discussed. Namely, the use of simulation that allows obtaining results in a short time for day-to-day decisions within the organization.

Keywords

Cost deviation Costing systems Prescriptive analytics Monte Carlo simulation 

References

  1. 1.
    Niazi, A., Dai, J., Balabani, S., Seneviratne, L.: Product cost estimation: technique classification and methodology review. J. Manuf. Sci. Eng. 128(2), 563–575 (2006)CrossRefGoogle Scholar
  2. 2.
    Gosselin, M.: A review of activity-based costing: technique, implementation, and consequences. In: Chapman, C.S., Hopwood, A.G., Shields, M.D. (eds.) Handbooks of Management Accounting Research, vol. 2, pp. 641–671. Elsevier Ltd., Amsterdam (2007)Google Scholar
  3. 3.
    Matz, A., Othel, J., Frank, G.: Contabilidade de custos. Atlas, Brasil (1987)Google Scholar
  4. 4.
    Les, H., Pekin, O., Serge, M.: Cost Accounting. South-Western Publishing Co., Cincinnati (1992)Google Scholar
  5. 5.
    Barfield, J.T., Raiborn, C.A., Kinney, M.R.: Cost Accounting: Traditions and Innovations. South-Western Educational Publishing, Cincinnati (2000)Google Scholar
  6. 6.
    Petrou, S., Gray, A.: Economic evaluation using decision analytical modelling: design, conduct, analysis, and reporting. BMJ 342(1), d1766-1–d1766-6 (2011)Google Scholar
  7. 7.
    Nachtmann, H., Needy, K.: Methods for handling uncertainty in activity based costing systems. Eng. Econ. 48(3), 259–282 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of ValleCaliColombia
  2. 2.University of MinhoGuimarãesPortugal

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