Error chain analysis—an effective method for tighter manufacturing process control

Abstract

One of aims of manufacturing quality control is to ensure that products are made free from defects according to specifications without unnecessarily increasing time and cost of production. Over-control of a process can be as detrimental to a manufacturer as under-control. It is common in industry that operators use their personal knowhow and intuition to decide where to implement process verification, and where to tighten it when processes are not meeting specifications. This is partially because there is little scientific guidance that can assist operators in making a decision on levels of quality control of a process at varying stages. To remedy this, a new method for manufacturing quality control, namely Error Chain Analysis (ECA), is introduced and its application is illustrated in this article. ECA is capable of statistically analysing the quality of a multi-stage manufacturing process based on existing control measures, and it enables to indicate where added or tighter control may need to be effectively implemented. For testing its applicability, ECA was built into a user-friendly tool that was subsequently used to analyse data gathered from a large manufacturing company in the UK.

Keywords:

• Quality control
• quality assurance
• FMEA
• manufacturing
• production

Acknowledgement

The authors wish to thank the key colleagues who assisted the first author in this project at the case study company, whose name must remain confidential, for supplying the technical support throughout this research-based project.

Disclosure statement

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

Notes

1 Available upon request.

2 Dockree (2019) provides a full breakdown in Tables 7.1–7.7.

Additional information

Notes on contributors

James Dockree

James Dockree holds a BEng (Hons) in Mechanical Engineering from the University of Portsmouth, UK. He works as an Engineering Business Analyst at a manufacturing company in the UK. His role is to analyse and improve processes and methods for continuous improvement of efficiency and quality, in projects across different departments from engineering and maintenance to manufacturing and production.

Qian Wang

Qian Wang is a Senior Lecturer in the School of Mechanical and Design Engineering, University of Portsmouth, UK. He has broad interests in manufacturing systems design, information systems for manufacturing, logistics and supply chain integration, sustainable manufacturing and lean production, and digital manufacturing. He had experience of completing many research projects; most research projects were carried out in close collaboration with industry.

Regina Frei

Regina Frei is an Associate Professor in Operations and Supply Chain Management at Southampton Business School, University of Southampton, UK. From 2013 to 2019, she was a Senior Lecturer in Manufacturing Engineering and Supply Chain Management at the University of Portsmouth, UK. Previously, she was a Postdoc at Cranfield University and Imperial College London. She holds a PhD in Distributed Robotics from Universidade Nova, Lisbon, Portugal, and an MSc in Micro-Engineering from the Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland. Her research interests include sustainable business, reverse supply chains, product returns in retail, and the circular economy.