Quotation Kummer, Sebastian. 2021. A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry. Supply Chain Forum: An International Journal.


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Abstract

In the era of modern technology, the competitive paradigm among organisations is changing at an unprecedented rate. New success measures are applied to the organisation’s supply chain performance to outperform the competition. However, this lead can only be obtained and sustained if the organisation has an effective and efficient supply chain and an appropriate forecasting technique. Thus, this study presents the demand-forecasting model, i.e., a good fit for the pharmaceutical sector, and shows promising results. Through this study, it is observed that combining forecasting algorithms can result in greater forecasting accuracies. Therefore, a combined forecasting technique ARIMA-HW hybrid1 i.e. (ARHOW) combines the Autoregressive Integrated Moving Average and Holt’ s-Winter model. The empirical findings confirm that ARHOW performs better than widely used forecasting techniques ARIMA, Holts Winter, ETS and Theta. The results of the study indicate that pharmaceutical companies can adopt this model for improved demand forecasting.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Supply Chain Forum: An International Journal
Language English
Title A hybrid demand forecasting model for greater forecasting accuracy: the case of the pharmaceutical industry
Year 2021
Reviewed? Y
DOI https://doi.org/10.1080/16258312.2021.1967081
Open Access Y
Open Access Link https://www.tandfonline.com/doi/full/10.1080/16258312.2021.1967081

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People
Kummer, Sebastian (Details)
Organization
Institute for Transport and Logistics Management (Details)
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