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In the context of bottleneck detection, most data-driven approaches employ data from diverse production variables (machine processing times, machine state tags, input timestamps, etc.) for a detailed analysis of bottlenecks. However, for manufacturing companies initiating their digitalization process (i.e. requiring the smallest hardware investment), a bottom-top approach is still missing. In this work, a data-driven model based on minimal information (MI) retrieved from a manufacturing execution system is proposed for bottleneck detection. We consider MI timestamps when each product exits each station and show that this is the most elementary information from production-line operations, enough to autonomously generate an abstract manufacturing layout, and to detect and predict bottlenecks. A general abstract model of a production line is proposed, named queue directed graph (QDG). Incorporating the MI, the QDG model is able to represent a job-shop with a discrete production environment and to calculate production metrics. This work has been employed in the production system of a Bosch factory, in Portugal, using their manufacturing data sets for validation. Different variants of two well-known bottleneck detection methods were implemented and adapted to Bosch’s use case: the Active Period Method and the Average Active Period Method.

A data-driven model with minimal information for bottleneck detection - application at Bosch thermotechnology