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An Investigation on Engine Mass Airflow Sensor Production via TQM, TPM, and Six Sigma Practices

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Abstract

In the Industry 4.0 era, companies need to be highly efficient in utilising their resources to remain competitive. This paper aims to investigate, appraise, and suggest possible improvements for engine mass airflow sensors (MAFS) production at a UK automotive parts manufacturer via combining total quality management (TQM), Six Sigma practices, and total productive maintenance (TPM)—with a focus on overall equipment effectiveness (OEE). The aim is achieved through a systems approach, root cause analyses, and investigating three variables: availability, performance, and quality. Data are extracted both autonomously and manually to determine diverse root causes. The key findings showed that the majority of availability losses stem from maintenance waiting times (16%), performance losses from packaging cycles (31%), and improper housing selection leading to quality rejects (1–3%). Critical appraisal suggested introducing universality amongst staff, continuous improvement via technology, TPM activities, and employing a newly developed optimisation matrix. Also, changes in data infrastructure, reliance on data-driven decision-making instead of traditional methods, and adopting pay-per-use Cloud solutions are advised. These recommendations can effectively monitor processes; reduce underlying losses; boost OEE, total quality, and profits; and support future manufacturing processes. This investigation contributes towards enhancing automotive engine parts production research, presents a novel case study highlighting the effectiveness of combining management techniques to resolve problems in a UK enterprise, and indicates scope for future developments.

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Data availability

The author declares that data supporting the findings of this study are available within the article (and its 15 files).

Abbreviations

4M:

Man, Machine, Method, Material.

5S:

Sort, Set in Order, Shine, Standardise, Sustain

DMAIC:

Define, Measure, Analyze, Improve, Control

DV:

Dependent variable

EV:

Electric vehicle

FTS:

Final testing

FTT:

First-time through

IV:

Independent variable

MAFS:

Mass airflow sensor

MLR:

Multiple linear regression

OEE:

Overall equipment efficiency

PM:

Preventive maintenance

PTM:

Production team member

QV:

Quality-Volume

TPM:

Total productive maintenance

TQM:

Total quality management

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Acknowledgements

Heartiest thanks to Dr. Peter Farrell, Dr. Mark Busfield, Dr. Kondal Kandadi, and the National Centre of Motorsports Engineering (NCME) staff for their diverse contribution towards this paper.

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  1. University of Bolton, Bolton, Greater Manchester, UK

    Mohammad Harris

  2. University of Hertfordshire, Hatfield, Hertfordshire, UK

    Mohammad Harris

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Harris, M. An Investigation on Engine Mass Airflow Sensor Production via TQM, TPM, and Six Sigma Practices. Oper. Res. Forum 2, 61 (2021). https://doi.org/10.1007/s43069-021-00102-y

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