Assessing the Value of Real-Time Data for the Dynamic Scheduling of In-Plant Logistics Activities

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.