Overall equipment effectiveness of a manufacturing line (OEEML)

Abstract

Purpose - Overall equipment effectiveness (OEE) is the key metric to measure the performance of individual equipment. However, when machines operate jointly in a manufacturing line, OEE alone is not sufficient to improve the performance of the system as a whole. The purpose of this paper is to show how to overcome this limitation, by presenting a new metric (overall equipment effectiveness of a manufacturing line OEEML) and an integrated approach to assess the performance of a line. Design/methodology/approach An alternative losses classification structure is developed to divide the losses that can be directly ascribed to equipment, from the ones that are spread in the line. Starting from this losses classification structure, an approach based on OEE is developed to evaluate the criticalities and the effectiveness of the line. Findings This method has been applied to an automated line for engine basements production. Results show that OEEML successfully highlights the progressive degradation of the ideal cycle time, explaining it in terms of: bottleneck inefficiency, quality rate, and synchronisation-transportation problems. Research limitations/implications OEEML alone fails to explain to which extent effectiveness is supported by in process-inventories and should be integrated with additional metrics to estimate the inventories-related costs. Practical implications OEEML provides practitioners with an operative tool useful to highlight the points where the major inefficiencies take place and to foresee the potential benefits of corrective actions. Originality/value In relation to other methodologies, OEEML presents two main advantages: it detects and quantifies the line's critical points and it can be applied even in presence of buffers, without underestimating the efficiency of the system.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 115 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.