| Publikationstyp: | Buchbeitrag |
| Art der Begutachtung: | Editorial review |
| Titel: | Economic measures of forecast accuracy for demand planning : a case-based discussion |
| Autor/-in: | Ott, Thomas Glüge, Stefan Bödi, Richard Kauf, Peter |
| DOI: | 10.1007/978-3-030-11821-1_20 |
| Erschienen in: | Applied data science : lessons learned for the data-driven business |
| Herausgeber/-in des übergeordneten Werkes: | Braschler, Martin Stadelmann, Thilo Stockinger, Kurt |
| Seite(n): | 371 |
| Seiten bis: | 386 |
| Erscheinungsdatum: | 2019 |
| Verlag / Hrsg. Institution: | Springer |
| Verlag / Hrsg. Institution: | Cham |
| ISBN: | 978-3-030-11820-4 978-3-030-11821-1 |
| Sprache: | Englisch |
| Fachgebiet (DDC): | 338: Produktion |
| Zusammenfassung: | Successful demand planning relies on accurate demand forecasts. Existing demand planning software typically employs (univariate) time series models for this purpose. These methods work well if the demand of a product follows regular patterns. Their power and accuracy are, however, limited if the patterns are disturbed and the demand is driven by irregular external factors such as promotions, events, or weather conditions. Hence, modern machine-learning-based approaches take into account external drivers for improved forecasting and combine various forecasting approaches with situation-dependent strengths. Yet, to substantiate the strength and the impact of single or new methodologies, one is left with the question how to measure and compare the performance or accuracy of different forecasting methods. Standard measures such as root mean square error (RMSE) and mean absolute percentage error (MAPE) may allow for ranking the methods according to their accuracy, but in many cases these measures are difficult to interpret or the rankings are incoherent among different measures. Moreover, the impact of forecasting inaccuracies is usually not reflected by standard measures. In this chapter, we discuss this issue using the example of forecasting the demand of food products. Furthermore, we define alternative measures that provide intuitive guidance for decision makers and users of demand forecasting. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/17378 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | Life Sciences und Facility Management |
| Organisationseinheit: | Institut für Computational Life Sciences (ICLS) |
| Publiziert im Rahmen des ZHAW-Projekts: | Comprehensive Sales Forecasting for Supply Chain Optimization in Food Industry |
| Enthalten in den Sammlungen: | Publikationen Life Sciences und Facility Management |
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Ott, T., Glüge, S., Bödi, R., & Kauf, P. (2019). Economic measures of forecast accuracy for demand planning : a case-based discussion. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 371–386). Springer. https://doi.org/10.1007/978-3-030-11821-1_20
Ott, T. et al. (2019) ‘Economic measures of forecast accuracy for demand planning : a case-based discussion’, in M. Braschler, T. Stadelmann, and K. Stockinger (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 371–386. Available at: https://doi.org/10.1007/978-3-030-11821-1_20.
T. Ott, S. Glüge, R. Bödi, and P. Kauf, “Economic measures of forecast accuracy for demand planning : a case-based discussion,” in Applied data science : lessons learned for the data-driven business, M. Braschler, T. Stadelmann, and K. Stockinger, Eds. Cham: Springer, 2019, pp. 371–386. doi: 10.1007/978-3-030-11821-1_20.
OTT, Thomas, Stefan GLÜGE, Richard BÖDI und Peter KAUF, 2019. Economic measures of forecast accuracy for demand planning : a case-based discussion. In: Martin BRASCHLER, Thilo STADELMANN und Kurt STOCKINGER (Hrsg.), Applied data science : lessons learned for the data-driven business. Cham: Springer. S. 371–386. ISBN 978-3-030-11820-4
Ott, Thomas, Stefan Glüge, Richard Bödi, and Peter Kauf. 2019. “Economic Measures of Forecast Accuracy for Demand Planning : A Case-Based Discussion.” In Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler, Thilo Stadelmann, and Kurt Stockinger, 371–86. Cham: Springer. https://doi.org/10.1007/978-3-030-11821-1_20.
Ott, Thomas, et al. “Economic Measures of Forecast Accuracy for Demand Planning : A Case-Based Discussion.” Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler et al., Springer, 2019, pp. 371–86, https://doi.org/10.1007/978-3-030-11821-1_20.
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