Quantifying the integration impact of Lean and Industry 4.0 on operational efficiency: insights from an experimental case study
Md Monir Hossain, Victoria Ballard, Danny J. O’Leary, Gregory T. Purdy, Richard Sesek, Gregory A. HarrisPurpose
This study aims to quantify the interaction effects between a Lean tool (Poka-Yoke) and an Industry 4.0 (I-4.0) technology (a machine vision camera) on operational performance, as measured by Overall Equipment Effectiveness (OEE).
Design/methodology/approach
A controlled laboratory-based experiment was conducted using a within-subject design with four treatment conditions: Control (paper-based instructions), Lean (Poka-Yoke), I-4.0 (machine vision inspection) and Lean 4.0 (a combination of Lean and digital inspection). Participants completed standardized assembly tasks under each condition. Operational performance was evaluated using OEE, decomposed into performance and quality components. Statistical analyses, including repeated-measures ANOVA and post hoc tests, were applied to examine significant differences among treatments and to assess the individual and combined effects of Lean and I-4.0 interventions.
Findings
The results demonstrate that the treatment, I-4.0, significantly improves OEE (p < 0.05), indicating an immediate operational benefit. In contrast, the Lean and Lean 4.0 treatments did not yield statistically significant improvements in OEE. These findings suggest that while digital technologies can independently enhance operational performance, the anticipated synergistic benefits of integrating Lean and I-4.0 may not materialize without careful alignment and contextual considerations.
Research limitations/implications
This study was conducted in a controlled academic laboratory environment, which may not fully represent the complexities and dynamic conditions of real-world manufacturing settings. Furthermore, the relatively small sample size (n = 48) may constrain the generalizability of the findings.
Originality/value
This study provides empirical evidence on the interaction between a specific Lean tool and an I-4.0 technology, addressing a key gap in “Lean 4.0” research. It shows how one-to-one complementarity can be evaluated rather than assumed. The findings challenge the idea of automatic synergy and offer practical insights for researchers, practitioners and policymakers. The experimental model also supports teaching and training on integrating Lean and I-4.0 in smart manufacturing.