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Nova scientia

On-line version ISSN 2007-0705


BERRONES SANZ, Luis David; CANO OLIVOS, Patricia; SANCHEZ PARTIDA, Diana  and  MARTINEZ FLORES, José Luis. Late or inaccurate road freight deliveries and their relationship to working conditions of drivers: A model of logistic regression. Nova scientia [online]. 2018, vol.10, n.20, pp.413-441. ISSN 2007-0705.


Cargo drivers are essential to maintain the efficiency and competitiveness of the supply chain, which subjects them to demands and strong pressures derived from their working conditions. However, it is not clear how their working conditions affect the efficiency of their activities. The objective of this work is to identify how the working conditions of cargo drivers influence on-time delivery of shipments and thus impact the competitiveness of the supply chain.


26,312 shipments over a period of two years were analyzed. These shipments were managed by a manufacturing company with an 80% market share in light construction systems in Mexico. The shipments were identified as correct or incorrect delivery caused by the driver. Due to the binary nature of this variable, a logistic regression was applied to the data, in order to analyze how working conditions were related to the non-compliance of on-time delivery events.


The model generated through the logistic regression provided a 96.3 percent global precision of the sample in predicting the failed delivery of the shipment. Eleven independent variables were obtained from the analyzed shipments, and five of those variables -dissatisfaction, level of stress, time of transit, type of vehicle, and medical coverage - were strongly associated with the inability of drivers to deliver the cargo on time.

Discussion or Conclusions:

Results show a promising tool to provide meaningful interpretations that may be used for future improvements in the development of cargo and freight companies. Results also show the importance of working conditions and the way they influence the compliance of on-time delivery.

Keywords : driver trucking; supply chain; working conditions; logistic regression binary; on-time delivery.

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