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  2. Life Sciences und Facility Management
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  4. Publikationen Life Sciences und Facility Management
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dc.contributor.authorOtt, Thomas-
dc.contributor.authorGlüge, Stefan-
dc.contributor.authorBödi, Richard-
dc.contributor.authorKauf, Peter-
dc.date.accessioned2019-06-28T14:39:45Z-
dc.date.available2019-06-28T14:39:45Z-
dc.date.issued2019-
dc.identifier.isbn978-3-030-11820-4de_CH
dc.identifier.isbn978-3-030-11821-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17378-
dc.description.abstractSuccessful 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.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofApplied data science : lessons learned for the data-driven businessde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc338: Produktionde_CH
dc.titleEconomic measures of forecast accuracy for demand planning : a case-based discussionde_CH
dc.typeBuchbeitragde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-11821-1_20de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end386de_CH
zhaw.pages.start371de_CH
zhaw.parentwork.editorBraschler, Martin-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.parentwork.editorStockinger, Kurt-
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewEditorial reviewde_CH
zhaw.webfeedBio-Inspired Methods & Neuromorphic Computingde_CH
zhaw.webfeedPredictive Analyticsde_CH
zhaw.webfeedData Management & Visualisationde_CH
zhaw.webfeedDatalabde_CH
zhaw.funding.zhawComprehensive Sales Forecasting for Supply Chain Optimization in Food Industryde_CH
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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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