IDEAS home Printed from https://ideas.repec.org/a/jns/jbstat/v224y2004i1-2p166-184.html
   My bibliography  Save this article

Ein Prognose- und Simulationswerkzeug zur Unterstützung der kurzfristigen Personalbedarfsplanung in einem Call Center / A Forecasting and Simulation Tool for Personnel Requirement in a Call Center

Author

Listed:
  • Schuhr Roland

    (Schuhr, Wirtschaftswissenschaftliche Fakultät der Universität Leipzig, D-04109 Leipzig, Germany)

Abstract

Since call center services are labour-intensive, human resource planning is a critical management task in terms of service quality and operating costs. This paper introduces a planning software tool to support short-term human resource planning. It is designed to forecast the stream of inbound telephone calls and to simulate transaction processes in order to estimate the minimum number of call center agents, required to achieve service objectives at a future working day.

Suggested Citation

  • Schuhr Roland, 2004. "Ein Prognose- und Simulationswerkzeug zur Unterstützung der kurzfristigen Personalbedarfsplanung in einem Call Center / A Forecasting and Simulation Tool for Personnel Requirement in a Call Center," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(1-2), pages 166-184, February.
    • Handle: RePEc:jns:jbstat:v:224:y:2004:i:1-2:p:166-184
    DOI: 10.1515/jbnst-2004-1-213
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jbnst-2004-1-213
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jbnst-2004-1-213?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Tych, Wlodek & Pedregal, Diego J. & Young, Peter C. & Davies, John, 2002. "An unobserved component model for multi-rate forecasting of telephone call demand: the design of a forecasting support system," International Journal of Forecasting, Elsevier, vol. 18(4), pages 673-695.
    2. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Atanas Christev, 2006. "Learning Hyperinflations," Computing in Economics and Finance 2006 475, Society for Computational Economics.
    2. Nabil Channouf & Pierre L’Ecuyer & Armann Ingolfsson & Athanassios Avramidis, 2007. "The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta," Health Care Management Science, Springer, vol. 10(1), pages 25-45, February.
    3. F. Gonzalez Miranda & N. Burgess, 1997. "Modelling market volatilities: the neural network perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 3(2), pages 137-157.
    4. Constantinos VORLOW & Antonios ANTONIOU & Catherine KYRTSOU, 2004. "Surrogate Data Analysis and Stochastic Chaotic Modelling: Application to Stock Exchange Returns Series," Computing in Economics and Finance 2004 27, Society for Computational Economics.
    5. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    6. Barr, Jason & Saraceno, Francesco, 2009. "Organization, learning and cooperation," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 39-53, May.
    7. Catherine Kyrtsou & Michel Terraza, 2003. "Is it Possible to Study Chaotic and ARCH Behaviour Jointly? Application of a Noisy Mackey–Glass Equation with Heteroskedastic Errors to the Paris Stock Exchange Returns Series," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 257-276, June.
    8. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2019. "Statistical and economic evaluation of time series models for forecasting arrivals at call centers," Empirical Economics, Springer, vol. 57(3), pages 923-955, September.
    9. Jozef Baruník, 2008. "How Do Neural Networks Enhance the Predictability of Central European Stock Returns?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 58(07-08), pages 358-376, Oktober.
    10. Rajae Azrak & Guy Melard & Hassane Njimi, 2004. "Forecasting in the analysis of mobile telecommunication data: correction for outliers and replacement of missing observations," ULB Institutional Repository 2013/13748, ULB -- Universite Libre de Bruxelles.
    11. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
    12. Anderson, Richard G. & Binner, Jane M. & Schmidt, Vincent A., 2012. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Economics Letters, Elsevier, vol. 117(1), pages 174-177.
    13. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    14. Raimundo Soto, "undated". "Nonlinearities in the Demand for money: A Neural Network Approach," ILADES-UAH Working Papers inv107, Universidad Alberto Hurtado/School of Economics and Business.
    15. Rómulo Chumacero E., 2004. "Forecasting Chilean Industrial Production and Sales With Automated Procedures," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(3), pages 47-56, December.
    16. Theresa Maria Rausch & Tobias Albrecht & Daniel Baier, 2022. "Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables," Journal of Business Economics, Springer, vol. 92(4), pages 675-706, May.
    17. Jonathan B. Hill, 2004. "LM-Tests for Linearity Against Smooth Transition Alternatives: A Bootstrap Simulation Study," Econometrics 0401004, University Library of Munich, Germany, revised 05 Jul 2004.
    18. Tung-Shan Liao, 2017. "Interaction Model of Superior Performance Based on Technological Resources and Competitive Actions in the Nascent Cycle of the Tablet Industry," Business, Management and Economics Research, Academic Research Publishing Group, vol. 3(11), pages 218-231, 11-2017.
    19. H. Peter Boswijk & Philip Hans Franses & Dick van Dijk, 2000. "Asymmetric and Common Absorption of Shocks in Nonlinear Autoregressive Models," Econometric Society World Congress 2000 Contributed Papers 0765, Econometric Society.
    20. Heinemann, Maik, 1997. "Convergence of Adaptive Learning and the Concept of Expectational Stability in Linear Rational Expectations Models with Multiple Equilibria," Hannover Economic Papers (HEP) dp-207, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jns:jbstat:v:224:y:2004:i:1-2:p:166-184. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.