<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Forecasting is important in many branches of logistics, including the logistics related to Tourism supply chains. With an increasing inflow of American tourists, planning and forecasting the US tourists’ inflow to Slovenia have gained far more importance attention amongst scholars and practitioners. This study, therefore, was conducted to forecast the American tourists’ inflow to Slovenia using one of the predictive models based on the exponential smoothing approach, namely Holt-Winters damped additive (HWDA) exponential smoothing method. The model was modified by several improvements, while the obtained results were generalized to other supply chain components. The results show that the forecasting system can predict well the observed inflow, while the methodology used to derive the model might have enriched the plethora of existing practical forecasting approaches in the tourism domain. Benchmarking demonstrates that the proposed model outperforms a competitive ARIMA model and official forecasts. The practical implications are also discussed in this paper.</p></abstract>

Forecasting is important in many branches of logistics, including the logistics related to Tourism supply chains. With an increasing inflow of American tourists, planning and forecasting the US tourists’ inflow to Slovenia have gained far more importance attention amongst scholars and practitioners. This study, therefore, was conducted to forecast the American tourists’ inflow to Slovenia using one of the predictive models based on the exponential smoothing approach, namely Holt-Winters damped additive (HWDA) exponential smoothing method. The model was modified by several improvements, while the obtained results were generalized to other supply chain components. The results show that the forecasting system can predict well the observed inflow, while the methodology used to derive the model might have enriched the plethora of existing practical forecasting approaches in the tourism domain. Benchmarking demonstrates that the proposed model outperforms a competitive ARIMA model and official forecasts. The practical implications are also discussed in this paper.

Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains

University of Malaya (UM) and International University of Malaya-Wales (IUMW)

Logistics, Supply Chain, Sustainability and Global Challenges

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Forecasting is important in many branches of logistics, including the logistics related to Tourism supply chains. With an increasing inflow of American...

Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains

Pubblicato online: 28 giu 2019

Pagine: 11 - 30

Ricevuto: 12 apr 2019

DOI: https://doi.org/10.2478/jlst-2019-0002

Parole chiave
Tourism industry logistics and supply chains, Tourism forecasting, US tourists and Slovenian Tourism, Damped Holt-Winters additive model, Time series analysis and prediction

© 2019 Dejan Dragan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains

Pubblicato online: 28 giu 2019

Pagine: 11 - 30

Ricevuto: 12 apr 2019

DOI: https://doi.org/10.2478/jlst-2019-0002

Parole chiaveTourism industry logistics and supply chains, Tourism forecasting, US tourists and Slovenian Tourism, Damped Holt-Winters additive model, Time series analysis and prediction

© 2019 Dejan Dragan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Tourism industry logistics and supply chains, Tourism forecasting, US tourists and Slovenian Tourism, Damped Holt-Winters additive model, Time series analysis and prediction