Towards Zero Defects in the Aerospace Industry Through Statistical Process Control a Case Study at GKN Aerospace Engine Systems
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Towards Zero Defects in the Aerospace Industry through Statistical Process Control A Case Study at GKN Aerospace Engine Systems Hugo Andrén Industrial and Management Engineering, master's level 2020 Luleå University of Technology Department of Business Administration, Technology and Social Sciences Acknowledgements Among those who have helped me along the way, making it possible for me to conduct this master’s thesis and complete my degree in engineering, several people have my gratitude. I owe special thanks and appraisal to Soren¨ Knuts, my supervisor and representative at GKN Aerospace Trollhattan.¨ Without Soren’s¨ tireless interest and efforts in pointing me in the right direction, this would not have been possible. Second, I would like to thank Professor Erik Vanhatalo at Lulea˚ University of Technology for supervising my work and providing invaluable feedback through an academic lens. I would also like to thank my friend and opponent Albert Stenback¨ Juhrich for the feedback throughout the duration of the work. Together, Soren,¨ Erik, and Albert have helped me enhance both relevance and quality of my thesis. Lastly, I would like to thank all the people who participated in the interviews, enabling me to collect the qualitative data, which I see as a cornerstone for the thesis, and of course GKN Aerospace Trollhattan¨ for letting me conduct my thesis despite the outbreak of the COVID-19 pandemic. Abstract With the ongoing transformation of modern manufacturing systems in an industry 4.0 environment, industrial actors may see great improvements with respect to quality towards a state of near zero defects. For the aerospace industry, where increased quality and reduced risk is strongly related, new technologies may be used in manufacturing to see to the increasing demands on products. The safety, as well as the manufacturing complexity of products and processes, make the collected measurement data an integral asset for enterprises within the aerospace industry. Collected data may be analysed using statistical tools and methods to improve process capability and, in extension, product quality. Communicating the need for zero defects, original equipment manufacturers demand increased capability from product and component manufacturers. Hence, zero defects are typically operationalised through exhibiting a process capability of Cpk= 2.0. In response to the challenge, GKN Aerospace need to raise the traditional process capability targets of Cpk=1.33. By employing an exploratory research strategy with a deductive approach, the thesis combines theoretical knowledge from the literature with empirical findings in a thematic analysis. The thematic analysis was conducted by employing six phases as suggested by Braun and Clarke (2006) and resulted in the identification of 50 codes from a total of 459 data extracts. Based on the empirical interview data, a framework for how zero defects is interpreted at GKN Aerospace was developed, which describes zero defects as a cycle. Taking into account that zero defects is operationalised through Cpk= 2.0, the cycle consists of six phases that start with a vision and is completed by delivering a true and reliable Cpk of 2.0. In addition, the codes from the thematic analysis were collated into a thematic mind map, focusing on key aspects of working with statistical process control (SPC) to support zero defects. Two main themes are presented in the mind map, statistical approach to improvement work; highlighting necessary aspects of statistical process control and measurability, and removing barriers for improvement; highlighting fundamental organisational barriers that impede proactive quality improvement. To support the findings and give a practical example of how process data may be presented and analysed using tools and methods within statistical process control, an SPC study was conducted on a set of data. In the SPC study, the construction and analysis of individuals Shewhart control charts and moving range charts were described in detail. These procedures provide better insights about process behaviour through statistical thinking and thus better knowledge on how to approach more proactive process improvements. KEYWORDS: Zero Defects, Statistical Process Control, Quality Management, Industry 4.0, Aerospace Industry. Abbreviations Abbreviation Meaning AI Artificial Intelligence CPS Cyber Physical Systems GAS GKN Aerospace Sweden Trollhattan¨ IoT Internet of Things KC Key Characteristic KPI Key Performance Index SPC Statistical Process Control ZD Zero Defects ZDM Zero Defects Manufacturing QSYS Internal database of process data at GAS Q3 Suspected or confirmed internal non-conformance Contents 1 Introduction ............................................ 1 1.1 Background . 1 1.2 GKN Aerospace Engine Systems . 3 1.3 Problem Discussion . 3 1.4 Aim............................................... 5 1.5 Delimitations . 5 2 Literature Overview ........................................ 6 2.1 Industry 4.0 . 6 2.2 A Review on Quality within Industry . 6 2.3 Statistical Process Control . 8 2.4 Zero Defects . 11 2.5 Organisational Implications . 14 3 Methodology ............................................ 16 3.1 Research Approach . 16 3.2 Literature Overview . 17 3.3 Interviews . 18 3.4 Thematic Analysis . 19 3.5 SPC Study . 22 3.6 Research Quality . 23 4 Results and Analysis ........................................ 25 4.1 Zero Defects at GKN Aerospace . 25 4.2 Thematic Analysis . 33 5 SPC Study ............................................. 42 5.1 Current Process Control . 42 5.2 Distribution Fitting . 43 5.3 Control Charts and Analysis . 45 5.4 Capability Study . 48 6 Findings and Recommendations ................................. 49 7 Discussion .............................................. 51 8 References ............................................. 52 Appendix A Interview Guide ..................................... i Appendix B Complete List of Codes ................................. iv 1 Introduction The introduction provides a background on zero defects and quality management as well as how Industry 4.0 is transforming modern manufacturing systems and affecting quality management practices. What follows is a short introduction of GKN Aerospace Engine Systems, a problem discussion, and finally the aim of the thesis, research questions, and delimitations. 1.1 Background Quality is of critical concern for organisations in order to meet customer expectations and the threat of competitors’ products (Dogan & Gurcan, 2018). Foidl and Felderer (2015) argue that increasing customer requirements and competitiveness implies quality management being an essential prerequisite and key to sustained economic performance. For original equipment manufacturers (OEMs) supplying products that are subject to meticulous safety, the concern for product quality is amplified. The aerospace industry is an example where the safety of products is of great concern. Within the aerospace industry, the quality of a product is measured by data of multiple geometric specifications and the quality of the manufacturing processes are characterised by process data sets (Wang, 2013). Quality can be referred to as the ability to reduce variation to the point where customer expectations are met or even exceeded. Within industrial manufacturing however, defects are a fairly tangible way to measure quality. Defects are described by Montgomery (2012) as ”nonconformities that are serious enough to significantly affect the safe and effective use of the product” (p. 9). Being affected by facilities, equipment, and manufacturing processes, the quality of a product is subject to several sources that have the potential to cause errors that generate defects (Wang, 2013). To supply an organisation with the right prerequisites for enhancing quality monitoring and optimisation, Wang (2013) suggests shifting the focus from product data to process data. Zero defects (ZD) is a concept practised within manufacturing for the purpose of minimising defects in a process by doing things right from the very beginning, ultimately aiming for zero defective products (Wang, 2013). It is, according to Tatipala et al. (2018), the increasing demands on produced parts that has led to the escalating importance of zero defects in manufacturing. Although it can be traced back to the 1960s (Montgomery, 2012), it was during the 1990s that ZD saw wide efforts of implementation when automotive companies wanted to cut costs by reducing quality inspections while simultaneously increasing demands on quality from suppliers. Wang (2013) address the necessity of a system for zero defect manufacturing (ZDM) to prevent failures and increase the reliability and safety for manufacturing industries. Tatipala et al. (2018) suggest that part of such a system is the ability to control product and process parameters with the use of connected manufacturing technologies and control systems that handle machine and other process data. It is however implied that such systems require the ability to collect and handle large amounts of data supported by advanced and reliable internet, IT and other technologies. These technologies have just recently become advanced and reliable enough to support the required scalability within industrial manufacturing systems. In 2014, Lasi, Fettke, Kemper, Feld, and Hoffmann (2014) anticipated that the industrial community was soon to experience a new paradigm. It was expected that a way for ”smart” machinery and products were to emerge as a result of recent advancement