Variability propagation in manufacturing systems: the impact of the processing time distribution

ABSTRACT

In manufacturing and service systems, the negative effects of variability propagation are usually addressed through approximation models and other tools, such as simulation and optimization algorithms. This paper investigates the conditions in which these approaches are ineffective, and considering only the mean and variance of the processing time distribution is misleading. A scenario analysis deepens the impacts of considering the entire processing time distribution (beyond its mean and variance) on the inter-departure times in balanced and unpaced lines modeled through Discrete Event Simulation. The results correlate the impacts on variability propagation of approximating the processing time distribution with system characteristics such as utilization levels, line sizes, and inter-arrival and processing time variability. Production planning approaches can benefit from these results to reduce variability propagation, particularly in flexible and reconfigurable manufacturing systems, largely adopted in Industry 4.0, that can highly influence processing time distributions by varying product mix and line configuration.

Graphical abstract

A balanced and unpaced line, with a single inter-arrival time distribution, and two processing time distributions, alpha and beta, rispectively, which have the same Squared Coefficient of Variation and different shape that lead to different variability propagation in the inter-departure time distribution at the end of the line.

KEYWORDS:

• System variability
• variability propagation
• production planning
• flow lines
• skewness
• discrete event simulation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/21681015.2024.2346080

Additional information

Notes on contributors

Arianna Alfieri

Arianna Alfieri is a full professor at Politecnico di Torino at Turin, Italy, where she currently teaches production planning and control and system simulation. Her research area includes scheduling, supply chain management and system simulation optimization.

Claudio Castiglione

Claudio Castiglione is an assistant professor at Politecnico di Torino in Turin, Italy. He graduated in Management Engineering at Politecnico di Torino and got his PhD in Management, Production and Design at Politecnico di Torino in 2021. His research areas include production planning and control, system simulation optimization, industrial symbiosis development, performance assessment of manufacturing systems.

Erica Pastore

Erica Pastore is an assistant professor at Politecnico di Torino, in Turin, Italy. She graduated in Mathematical Engineering at Politecnico di Torino and got her Ph.D. in Management, Production and Design at Politecnico di Torino in 2018. Her research areas include production planning and control, system simulation optimization, production scheduling, and supply chain management.