The widespread adoption of Industry 4.0 technologies is resulting in a wide availability of real-time data gathered on the shop floor. This data, once properly elaborated, can be used to support dynamic decision-making, improving manufacturing companies’ capability to deal with uncertainty and thus leading to potential benefits in their performance. This paper presents a simulation model to assess the changes in manufacturing systems performance resulting from the use of real-time data in the dynamic scheduling of in-plant logistics activities. The model was developed considering a general factory layout and implemented in Python, a widely used open-source programming language. Therefore, the model can be used and extended by a wide community of researchers, serving as a base for future studies, and adapted to be applied to a large number of factories, thus favoring a more widespread adoption of dynamic scheduling systems in practice. In this study, the model was applied to the setting of a factory in the food industry in which a fleet of mobile robots supply materials to production stations and retrieve finished goods, carrying them to the factory warehouse. Results show that a dynamic scheduling system, in which in-plant logistics activities are scheduled considering real-time data on the status of shop floor resources, leads to better performance, in terms of production stations uptime, compared with the static system currently adopted by the company.
Assessing the value of real-time data for the dynamic scheduling of in-plant logistics activities
Moretti E.;Tappia E.;Agazzi A.;Melacini M.
2024-01-01
Abstract
The widespread adoption of Industry 4.0 technologies is resulting in a wide availability of real-time data gathered on the shop floor. This data, once properly elaborated, can be used to support dynamic decision-making, improving manufacturing companies’ capability to deal with uncertainty and thus leading to potential benefits in their performance. This paper presents a simulation model to assess the changes in manufacturing systems performance resulting from the use of real-time data in the dynamic scheduling of in-plant logistics activities. The model was developed considering a general factory layout and implemented in Python, a widely used open-source programming language. Therefore, the model can be used and extended by a wide community of researchers, serving as a base for future studies, and adapted to be applied to a large number of factories, thus favoring a more widespread adoption of dynamic scheduling systems in practice. In this study, the model was applied to the setting of a factory in the food industry in which a fleet of mobile robots supply materials to production stations and retrieve finished goods, carrying them to the factory warehouse. Results show that a dynamic scheduling system, in which in-plant logistics activities are scheduled considering real-time data on the status of shop floor resources, leads to better performance, in terms of production stations uptime, compared with the static system currently adopted by the company.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
