Enhancing operations management through smart sensors: measuring and improving well-being, interaction and performance of logistics workers

Abstract

Purpose

The purpose of the research is to conduct an exploratory investigation of the material handling activities of an Italian logistics hub. Wearable sensors and other smart tools were used for collecting human and environmental features during working activities. These factors were correlated with workers' performance and well-being.

Design/methodology/approach

Human and environmental factors play an important role in operations management activities since they significantly influence employees' performance, well-being and safety. Surprisingly, empirical studies about the impact of such aspects on logistics operations are still very limited. Trying to fill this gap, the research empirically explores human and environmental factors affecting the performance of logistics workers exploiting smart tools.

Findings

Results suggest that human attitudes, interactions, emotions and environmental conditions remarkably influence workers' performance and well-being, however, showing different relationships depending on individual characteristics of each worker.

Practical implications

The authors' research opens up new avenues for profiling employees and adopting an individualized human resource management, providing managers with an operational system capable to potentially check and improve workers' well-being and performance.

Originality/value

The originality of the study comes from the in-depth exploration of human and environmental factors using body-worn sensors during work activities, by recording individual, collaborative and environmental data in real-time. To the best of the authors' knowledge, the current paper is the first time that such a detailed analysis has been carried out in real-world logistics operations.

Citation

Aloini, D., Fronzetti Colladon, A., Gloor, P., Guerrazzi, E. and Stefanini, A. (2022), "Enhancing operations management through smart sensors: measuring and improving well-being, interaction and performance of logistics workers", The TQM Journal, Vol. 34 No. 2, pp. 303-329. https://doi.org/10.1108/TQM-06-2021-0195