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Purpose -The purpose of this paper is to develop a computer simulation model to evaluate the bowl phenomenon and the allocation at the end of the line of stations with either greater mean operation times or higher variability of operation times. Design/methodology/approach -The model was developed on the basis of a realistic case problem and applied to a six-station assembly line. The evaluation criteria were the: minimization of the total elapsed time; maximization of the average percentage of working time; and minimization of the average time in the system. Findings -The performance of an assembly line with independently normally distributed operation times could be improved by applying the bowl phenomenon. The allocation of large operation mean times to stations located near the end of the line did not produce improved results. Instead a more balanced allocation proved to be more significantly effective. On the other hand, the assignment of larger variability of operation times to the stations near the end of the line improved the performance of the assembly line. Originality/value -The investigation contributed to the computer simulation approach to solving assembly line problems that dealt with the impact of normally distributed operation times on the bowl phenomenon and assembly lines with increasing mean operation times and higher variability of operation times at the end of the line of stations.

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Being more competitive is routine in the aeronautical sector. Airline competitiveness is affected by such factors as time, price, reliability, availability, safety, technology, quality, and information management. To remain competitive, airlines must promptly identify and correct failures found in their fleet. This study aims at reducing the time spent on identifying and correcting such failures logged. Utilizing Text Mining techniques during the pre-processing phase, our study processes an extensive database of events from commercial regional jets. The result is a unique list of keywords that describes each reported failure. Later, an Artificial Neural Network (ANN) identifies and classifies failure patterns, yielding a respective disposition for 742 J Intell Inf Syst (2012) 38:741-766 a given failure pattern. Approximately five years of historical data was used to build and validate the present model. Results obtained were promising.

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