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The role of the forecasting process in improving forecast accuracy and operational performance

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  • Danese, Pamela
  • Kalchschmidt, Matteo

Abstract

Several operations decisions are based on proper forecast of future demand. For this reason, manufacturing companies consider forecasting a crucial process for effectively guiding several activities and research has devoted particular attention to this issue. This paper investigates the impact of how forecasting is conducted on forecast accuracy and operational performances (i.e. cost and delivery performances). Attention is here paid on three factors that characterize the forecasting process: whether structured techniques are adopted, whether information from different sources is collected to elaborate forecasts, and the extent to which forecasting is used to support decision-making processes. Analyses are conducted by means of data provided by the fourth edition of the Global Manufacturing Research Group survey. Data was collected from 343 companies belonging to several manufacturing industries from six different countries. Results show that companies adopting a structured forecasting process can improve their operational performances not simply because forecast accuracy increases. This paper highlights the importance of a proper forecasting-process design, that should be coherent with how users intend to exploit forecast results and with the aim that should be achieved, that is not necessarily improving forecast accuracy.

Suggested Citation

  • Danese, Pamela & Kalchschmidt, Matteo, 2011. "The role of the forecasting process in improving forecast accuracy and operational performance," International Journal of Production Economics, Elsevier, vol. 131(1), pages 204-214, May.
  • Handle: RePEc:eee:proeco:v:131:y:2011:i:1:p:204-214
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    References listed on IDEAS

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    Cited by:

    1. Manikas, Andrew S. & Patel, Pankaj C., 2016. "Managing sales surprise: The role of operational slack and volume flexibility," International Journal of Production Economics, Elsevier, vol. 179(C), pages 101-116.
    2. Bortolotti, Thomas & Danese, Pamela & Flynn, Barbara B. & Romano, Pietro, 2015. "Leveraging fitness and lean bundles to build the cumulative performance sand cone model," International Journal of Production Economics, Elsevier, vol. 162(C), pages 227-241.
    3. repec:spr:grdene:v:24:y:2015:i:2:d:10.1007_s10726-014-9392-8 is not listed on IDEAS
    4. Kalchschmidt, Matteo, 2012. "Best practices in demand forecasting: Tests of universalistic, contingency and configurational theories," International Journal of Production Economics, Elsevier, vol. 140(2), pages 782-793.
    5. Bortolotti, Thomas & Boscari, Stefania & Danese, Pamela, 2015. "Successful lean implementation: Organizational culture and soft lean practices," International Journal of Production Economics, Elsevier, vol. 160(C), pages 182-201.
    6. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael, 2014. "Collaborative forecasting in the food supply chain: A conceptual framework," International Journal of Production Economics, Elsevier, vol. 158(C), pages 120-135.
    7. Sa-ngasoongsong, Akkarapol & Bukkapatnam, Satish T.S. & Kim, Jaebeom & Iyer, Parameshwaran S. & Suresh, R.P., 2012. "Multi-step sales forecasting in automotive industry based on structural relationship identification," International Journal of Production Economics, Elsevier, vol. 140(2), pages 875-887.
    8. Danese, Pamela & Romano, Pietro & Formentini, Marco, 2013. "The impact of supply chain integration on responsiveness: The moderating effect of using an international supplier network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 125-140.
    9. Bocca, Felipe Ferreira & Rodrigues, Luiz Henrique Antunes & Arraes, Nilson Antonio Modesto, 2015. "When do I want to know and why? Different demands on sugarcane yield predictions," Agricultural Systems, Elsevier, vol. 135(C), pages 48-56.
    10. Scarpel, Rodrigo Arnaldo, 2015. "An integrated mixture of local experts model for demand forecasting," International Journal of Production Economics, Elsevier, vol. 164(C), pages 35-42.

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