CONTAINER HANDLING OPERATOR SAFETY AND TRAINING: ANALYSIS OF DATA FROM CRANE OPERATOR TRAINING SIMULATOR

Paolo Fadda, Gianfranco Fancello, Simone Loi
CRIMM Centro Ricerche Modelli di Mobilità
Dipartimento Ingegneria del Territorio, sez. Trasporti
Università di Cagliari  -  Piazza d’Armi 16 - 09123 Cagliari (ITALY)
Tel. +39 070 6755250         Fax: +39 070 6755261
e-mail: fadda@unica.it   gianfra@unica.it   simonloi@unica.it

Abstract

The ever-expanding demand for containerized freight shipping, the competition between container terminals to secure an increasing share of the market, also often by maximizing worker productivity and performance, is generating work loads that often reduce safety margins. The demand for highly specialized tasks aimed at enhancing operator performance in terms of number of containers moved creates conditions where human error frequently poses a threat. The simulator is a valuable tool for operator training as it allows to achieve and control over time the required level of performance of portainer operators without affecting container terminal productivity, as it is not necessary to temporarily remove equipment from service. Here we have analyzed, from the human factors perspective, the results achieved during crane operator training courses, carried out with the aid of a simulator. The objective of the study was to determine the principal correlations between the variables identified as being of the greatest significance in describing the tests and trainee performance and the parameters influencing crane operator activities within his work cycle: These parameters can be represented by means of the simulated activities and by the parameters considered in the design of training programmes. Furthermore, multiple correspondence analysis enabled to identify the main risks to which the operator is susceptible and which conditions increase the possibility of hazardous circumstances arising.
The results of an analysis of this kind lay the groundwork for simulator design, pinpointing those human factors aimed at determining the role and importance of each performance parameter in triggering operator fatigue and human error.
 


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