Sentence examples for industrial forecasting from inspiring English sources

Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions.

For some years now, employing industrial forecasting models in accident forecasting relying on multiple factors has been justified by the fact that causal factors of accidents are attributed to human, equipment and managerial deficiencies (Cooke and Rohleder, 2006; Mohaghegh et al. 2009; Rathnayaka et al. 2011).

The results obtained from these model applications in industrial accident forecasting have also been very encouraging.

Industrial accident forecasting has not been tackled in grey fuzzy Markov literature.

Thus, for the aforementioned issues, we consider both UPMs and multivariate prediction models inappropriate for industrial accident forecasting.

UPMs are classical predictive models such as auto-regression and integrated moving average (ARIMA), exponential smoothing (ESM) and moving average (MA) adapted and applied in industrial accident forecasting (Kim et al. 2011; Kang et al. 2012; Aidoo and Eshun 2012).

It is believed that this novel approach to industrial accident forecasting will aid proper anticipation, planning, control and management of future accident occurrences in industrial organisations on the one hand, and also provide a promising alternative tool to forecasting under uncertain conditions on the other.

Industrial accidents forecasting, as argued in this paper, is central to the attainment of industry's stability and a guarantee to survive in the long run since litigation fees resulting from accidents could be reduced to the barest minimum through the adoption of a merit-driven forecasting technique.

None of this seems to have unsettled the markets unduly, with the Dow Jones Industrial Average forecast to open 58 points higher.

The aim of the work is to create a standalone GFM model capable of making accurate industrial accidents forecasts.

Future investigations could be directed at improving the industrial accidents forecast by studying how combined or hybrid models can be developed using GFMAPR for the purpose of producing more improved forecast performance.