Skip to main content
Log in

Managerial insights from service industry models: a new scenario decomposition method

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

The service industry literature has recently assisted to the development of several new decision-support models. The new models have been often corroborated via scenario analysis. We introduce a new approach to obtain managerial insights in scenario analysis. The method is based on the decomposition of model results across sub-scenarios generated according to the high dimensional model representation theory. The new method allows analysts to quantify the effects of factors, their synergies and to identify the key drivers of scenario results. The method is applied to the scenario analysis of the workforce allocation model by Corominas et al. (Annals of Operations Research 128:217–233, 2004).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ata, B., & Shneorson, S. (2006). Dynamic control of an M/M/1 service system with adjustable arrival and service rates. Management Science, 52(11), 1778–1791.

    Article  Google Scholar 

  • Beraldi, P., & Bruni, M. E. (2009). A probabilistic model applied to emergency service vehicle location. European Journal of Operational Research, 196(1), 323–331.

    Article  Google Scholar 

  • Borgonovo, E., & Apostolakis, G. E. (2001). A new importance measure for risk-informed decision making. Reliability Engineering & System Safety, 72(2), 193–212.

    Article  Google Scholar 

  • Borgonovo, E. (2008). Differential importance and comparative statics: an application to inventory management. International Journal of Production Economics, 111(1), 170–179.

    Article  Google Scholar 

  • Borgonovo, E. (2009). Sensitivity analysis with finite changes: an application to modified-EOQ models, European Journal of Operational Research, doi:10.1016/j.ejor.2008.12.025.

  • Borgonovo, E., & Peccati, L. (2008a). Sensitivity analysis in decision making: a consistent approach. In M. Abdellaoui & J. D. Hey (Eds.), Theory and Decision Library Series : Vol. 42. Advances in decision making under risk and uncertainty (pp. 65–89). Berlin: Springer. 241 pages, ISBN:978-3-540-68436-7.

    Chapter  Google Scholar 

  • Borgonovo, E., & Peccati, L. (2008b) Finite change comparative statics for risk coherent inventories, submitted.

  • Castillo, I., Joro, T., & Li, Y. Y. (2009). Workforce scheduling with multiple objectives. European Journal of Operational Research, 196, 162–170.

    Article  Google Scholar 

  • Corominas, A., Lusa, A., & Pastor, R. (2004). Planning annualized hours with a finite set of weekly working hours and joint holidays. Annals of Operations Research, 128, 217–233.

    Article  Google Scholar 

  • Corominas, A., Lusa, A., & Pastor, R. (2007a). Planning annualized hours with a finite set of weekly working hours and cross-trained workers. European Journal of Operational Research, 176(1), 230–239.

    Article  Google Scholar 

  • Corominas, A., Lusa, A., & Pastor, R. (2007b). Using a MILP model to establish a framework for an annualized hours agreement. European Journal of Operational Research Volume, 177(3), 1495–1506.

    Article  Google Scholar 

  • Debo, L. G., Toktay, L. B., & Van Wassenhove, L. N. (2008). Queuing for expert services. Management Science, 54(8), 1497–1512.

    Article  Google Scholar 

  • Duder, J. C., & Rosenwein, M. B. (2001). Towards “zero abandonments” in call center performance. European Journal of Operational Research, 135, 50–60.

    Article  Google Scholar 

  • Efron, B., & Stein, C. (1981). The Jackknife estimate of variance. The Annals of Statistics, 9(3), 586–596.

    Article  Google Scholar 

  • Eschenbach, T. G. (1992). Spiderplots versus tornado diagrams for sensitivity analysis. Interfaces, 22, 40–46.

    Article  Google Scholar 

  • Grossman, T. A., & Brandeau, M. L. (2002). Optimal pricing for service facilities with self-optimizing customers. European Journal of Operational Research, 141, 39–57.

    Article  Google Scholar 

  • Higle, J. L., & Wallace, S. W. (2003). Sensitivity analysis and uncertainty in linear programming. Interfaces, 33(4), 53–60.

    Article  Google Scholar 

  • Hinojosa, M. A., Mármol, A. M., & Thomas, L. C. (2005). Core, least core and nucleolus for multiple scenario cooperative games. European Journal of Operational Research, 164(1), 225–238.

    Article  Google Scholar 

  • Hoeffding, W. (1948). A class of statistics with asymptotically normal distributions. Annals of Mathematical Statistics, 19, 293–325.

    Article  Google Scholar 

  • Hong, J. (2007). Location determinants and patterns of foreign logistics services in Shanghai, China. Service Industries Journal, 27(4), 339–354.

    Article  Google Scholar 

  • Hosanagar, K., Krishnan, R., Chuang, J., & Choudhary, V. (2005). Pricing and resource allocation in caching services with multiple levels of quality of service. Management Science, 51(12), 1844–1859.

    Article  Google Scholar 

  • Jungermann, H., & Thuring, M. (1988). The labyrinth of experts’ minds: some reasoning strategies and their pitfalls. Annals of Operations Research, 16, 117–130.

    Article  Google Scholar 

  • Kahn, H., & Wiener, A. J. (1967). The year 2000: a framework for speculation on the next thirty-three years. London: Macmillan & Co.

    Google Scholar 

  • Kiely, J., Beamish, N., & Armistead, C. (2004). Scenarios for future service encounters. The Service Industries Journal, 24(3), 131–149.

    Article  Google Scholar 

  • Koltai, T., & Terlaky, T. (2000). The difference between the managerial and mathematical interpretation of sensitivity analysis results in linear programming. International Journal of Production Economics, 65(3), 257–274.

    Article  Google Scholar 

  • Linneman, R. E., & Kennell, J. D. (1977). Shirt-sleeve approach to long-range plans. Harvard Business Review, 55(12), 141–150.

    Google Scholar 

  • Little, J. D. C. (1970). Models and managers: the concept of a decision calculus. Management Science, 16(8), B466–B485. Application series.

    Article  Google Scholar 

  • Mulvey, J. M., Rosenbaum, D. P., & Shetty, B. (1999). Parameter estimation in stochastic scenario generation systems. European Journal of Operational Research, 118(3), 563–577.

    Article  Google Scholar 

  • O’Brien, F. A. (2004). Scenario planning—lessons for practice from teaching and learning. European Journal of Operational Research, 152, 709–722.

    Article  Google Scholar 

  • Rabitz, H., & Alis, O. F. (1999). General foundations of high-dimensional model representations. Journal of Mathematical Chemistry, 25, 197–233.

    Article  Google Scholar 

  • Saltelli, A., Tarantola, S., & Campolongo, F. (2000). Sensitivity analysis as an ingredient of modeling. Statistical Science, 19(4), 377–395.

    Google Scholar 

  • Saltelli, A., & Tarantola, S. (2002). On the relative importance of input factors in mathematical models: safety assessment for nuclear waste disposal. Journal of the American Statistical Association, 97(459), 702–709.

    Article  Google Scholar 

  • Sobol’, I. M. (1993). Sensitivity estimates for nonlinear mathematical models. Matematicheskoe Modelirovanie, 2(1), 112–118 (1990) (in Russian). English Transl.: MMCE, 1(4) (1993) 407–414.

    Google Scholar 

  • Sobol’, I. M. (2001). Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation, 55(1), 271–280.

    Article  Google Scholar 

  • Sobol’, I. M. (2003). Theorems and examples on high dimensional model representation. Reliability Engineering and System Safety, 79, 187–193.

    Article  Google Scholar 

  • Sobol’, I. M., Tarantola, S., Gatelli, D., Kucherenko, S. S., & Mauntz, W. (2007). Estimating the approximation error when fixing unessential factors in global sensitivity analysis. Reliability Engineering and System Safety, 92, 957–960.

    Article  Google Scholar 

  • Tietje, O. (2005). Identification of a small reliable and efficient set of consistent scenarios. European Journal of Operational Research, 162, 418–432.

    Article  Google Scholar 

  • Wahab, M. I. M., Wu, D., & Chi-Guhn, L. (2008). A generic approach to measuring the machine flexibility. European Journal of Operational Research, 186, 137–149.

    Article  Google Scholar 

  • Wallace, S. W. (2000). Decision making under uncertainty: is sensitivity analysis of any use? Operations Research, 1, 20–25.

    Article  Google Scholar 

  • Wendell, R. E. (2004). Tolerance sensitivity and optimality bounds in linear programming. Management Science, 50(6), 797–803.

    Article  Google Scholar 

  • Wu, T.-H., & Lin, J.-N. (2003). Solving the competitive discretionary service facility location problem. European Journal of Operational Research, 144, 366–378.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. Peccati.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Borgonovo, E., Peccati, L. Managerial insights from service industry models: a new scenario decomposition method. Ann Oper Res 185, 161–179 (2011). https://doi.org/10.1007/s10479-009-0617-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-009-0617-1

Keywords

Navigation