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Regular version of the site
Of all publications in the section: 2
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Article
Fantazzini D., Toktamysova Z. International Journal of Production Economics. 2015. Vol. Volume 170, Part A. No. December . P. 97-135.

Long-term forecasts are of key importance for the car industry due to the lengthy period of time required for the development and production processes. With this in mind, this paper proposes new multivariate models to forecast monthly car sales data using economic variables and Google online search data. An out-of-sample forecasting comparison with forecast horizons up to 2 years ahead was implemented using the monthly sales of ten car brands in Germany for the period from 2001M1 to 2014M6. Models including Google search data statistically outperformed the competing models for most of the car brands and forecast horizons. These results also hold after several robustness checks which consider nonlinear models, different out-of-sample forecasts, directional accuracy, the variability of Google data and additional car brands.

 
Added: Jun 2, 2016
Article
Sokolov Boris, Ivanov D., Pavlov A. et al. International Journal of Production Economics. 2017. No. 183. P. 503-513.

Recent research on closed-loop supply chains (SC) and reverse logistics extensively emphasizes the crucial role of reducing negative return flows such as emissions, waste, etc. In this study, we consider the return flows in the SC in light disruptive events in the SC. The objective of this study is to compare the performance impact of different recovery policies on return flows subject to the simultaneously optimized reconfiguration plans for material flows. We formulate a multi-objective problem with return flow reduction function for a multi-period, multi-stage, multi-product SC. We consider a recovery problem with ripple effect, performance impact assessment and re-planning decisions. The developed multi-objective hybrid linear programming-system dynamics model allows simultaneously re-computing the material flows in a multi-stage SC after a disruption and comparing the performance impact of different recovery policies subject to return flows, gradual capacity recovery, variable recovery costs and time. The results suggest that the consideration of gradual capacity recovery leads to minimization of disruption-related return flows in both upstream and down stream SC parts. Fast and expensive recovery strategy provides the lowest return costs in the upstream SC part as compared to normal and slow recovery policies. Similar, the profits and service levels are increased. In the fast and expensive recovery policy, the performance in the upstream and downstream does not change with the introduction of the gradual recovery considerations. The effects of gradual capacity recovery introduction become evident if smaller time sub-periods are considered with in the recovery period.

Added: Feb 5, 2019