Development of a Quality Deterioration Index for Sustainable Quality Management in High-Tech Electronics Manufacturing

Abstract

In high-tech electronics manufacturing, non-quality costs significantly impact organizational profitability and competitiveness. This case study introduces a novel Quality Deterioration Index (QDI) to systematically identify and prioritize root causes of non-quality costs within a leading electronics manufacturer. The primary objective is to integrate sustainable quality management practices that align with green sustainability objectives, such as reducing electronic waste, improving energy efficiency, and minimizing hazardous materials usage. Our comprehensive methodology encompasses a literature review, interviews, document analysis, and statistical analysis of survey data to uncover the influence of procedural, cultural, and environmental factors on quality deviations. The key findings reveal critical areas for improvement, particularly in supply chain inefficiencies, workforce challenges, and procedural gaps. By employing the QDI, we provide a structured framework that enhances both operational efficiency and environmental performance. The novelty of this research lies in its dual approach to simultaneously address economic and environmental performance, offering actionable insights for manufacturers aiming to integrate robust quality management systems with sustainability objectives. This study contributes to the ongoing dialogue on sustainable manufacturing strategies, underscoring the pivotal role of quality management in achieving both economic viability and environmental stewardship. Future research should expand this approach across various industries and global contexts to validate and refine the integration of quality management and sustainability.

1. Introduction

The integration of sustainable practices with quality management in high-tech electronics manufacturing is crucial for operational efficiency and environmental responsibility [1]. Zero-Defect Manufacturing (ZDM) is a promising approach in this regard, offering superior performance to traditional quality improvement methods [1]. Quality management systems can facilitate environmental innovation and sustainability goals [2], and Industry 4.0 technologies can support sustainable manufacturing [3].

Total Quality Management (TQM) practices, including green innovation, can significantly influence corporate sustainable development [4]. The integration of environmental social governance (ESG) and TQM can further enhance sustainability [5]. However, challenges such as the need for customization to specific contexts must be addressed [6].

This case study examines the primary root causes of various non-quality cost factors within a high-tech electronics manufacturer, emphasizing the necessity of integrating sustainability into quality management systems. It focuses on how innovative quality management techniques can significantly reduce these costs while promoting environmental and social sustainability.

A Quality Deterioration Index (QDI) approach is introduced, which is tailored to identify and prioritize the most impactful factors. This approach addresses the immediate challenges of non-quality costs and aligns with broader sustainability goals, making a compelling case for synthesizing quality management and sustainable practices in the manufacturing sector.

By analyzing the root causes through a sustainability lens, this research significantly advances the ongoing dialogue in the field, highlighting the critical role of quality management as an essential component of sustainable manufacturing strategies. This integration promises to revolutionize operational efficiency, drastically reduce waste, and ensure that manufacturing processes are economically viable, environmentally responsible, and socially beneficial. This research underscores the imperative of synthesizing quality management with sustainability, positioning it as a cornerstone for achieving unparalleled success in the high-tech manufacturing sector.

The remainder of this paper is structured as follows: In Section 2, we present a comprehensive literature review that covers the strategic management of quality and non-quality costs, the integration of TQM and Six Sigma methodologies, and the implications of Environmental, Social, and Governance (ESG) activities. Section 3 details the materials and methods used in this study, including the development and application of the QDI and the research sample. Section 4 discusses the results of our analysis, focusing on the prioritized root causes of various non-quality cost factors and their implications for sustainable quality management. In Section 5, we provide an in-depth discussion of the findings, contextualizing them within the broader themes of sustainable manufacturing and quality management. Finally, Section 6 concludes the paper with a summary of key insights, limitations of the study, and suggestions for future research directions.

2. Literature Review

2.1. Strategic Management of Quality and Non-Quality Costs: Multifaceted Approaches for Organizational Efficiency and Economic Growth

The integration of quality and non-quality cost management is crucial for organizational profitability and competitiveness [7]. The use of ISO 9001 [8] can enhance the strategic relevance of quality management [9], and the incorporation of customer profitability analysis can further improve quality management systems [10].

The concept of quality costs can be applied to improve planning and control processes in manufacturing industries [11], and the integration of blockchain with ISO 9001 can enhance the effectiveness of TQM [12]. However, challenges exist in integrating quality and innovation management systems [13], and a sustainable quality cost model is needed to contribute to sustainable development [14]. The potential of Industry 4.0 technologies to improve quality management practices is also highlighted [15].

The effective control, treatment, and monitoring of these costs have been explored by [16,17,18,19], whose studies underscore the complexity of managing these aspects in varying organizational contexts. The categorization of quality costs into prevention, appraisal, internal failure, and external failure costs, as outlined by ref. [20], is vital for the strategic management and financial balancing of quality within organizations. Implementing ISO 9001, an internationally recognized quality management system, is also instrumental in this endeavor. In particular, ref. [16] provided a detailed examination of how companies awarded ISO 9000 [21] define and manage these quality costs, emphasizing the importance of monitoring and reporting on the progress of quality-improvement programs in high-tech electronics manufacturing. Additionally, ref [17] analyzed the economic impact of implementing ISO 9001:2008 [22] on overall organizational efficiency. It focuses on defining, classifying, and evaluating non-quality costs, as well as proposing relevant methods to highlight and quantify these costs, ultimately improving the control of non-quality costs within organizations. Moreover, ref. [18] highlighted the implementation of ISO 9001 in a traditional industry firm, noting improvements in firm performance, including reductions in customer complaints and rework, and enhancements in product quality and process efficiency, which significantly enhance the firm’s competitive advantages. Furthermore, ref. [19] reviewed the literature on the relationship between the ISO 9001 quality approach and organizational performance, discussing quality practices and performance and proposing conceptual models. They emphasized that ISO 9001 serves as a powerful performance lever for certified organizations, countering the perception of it being merely a procedural system.

As ref [23] suggests, ISO 9001 positively influences organizational performance by enhancing product quality, reducing customer complaints and the need to rework. Furthermore, optimizing these quality costs through algorithms and decision-making models, as proposed by ref [24], can significantly improve product quality while simultaneously reducing costs. However, the inadequacy of many cost-accounting systems poses a challenge because they fail to fully address the financial implications of quality costs, a gap highlighted by ref [25].

Addressing non-quality costs, which constitute up to 30% of a nation’s gross national product [26], requires a multifaceted approach. Implementing strategies such as statistical controls in production [27] and establishing national quality and productivity initiatives [28] enhance individual organizational efficiency and contribute significantly to national economic health.

A balanced approach towards quality-related expenses, which includes avoiding over-investment in areas that may not provide equivalent returns, is necessary. Specifically, strategic quality investments yield substantial returns in sectors like construction [29], with an optimal range of 2-4% of total revenue, resulting in at least a 2:1 return. The challenge of quality cost management extends even further to supply chain management. Ref. [30] developed a model to balance non-quality costs and inspection costs for economic efficiency while meeting customer quality expectations. Ref. [31] adds that effective quality programs can sustain reductions in nonconformance costs without increasing conformance expenditures.

In recent years, the incorporation of Quality 4.0 and Industry 4.0 technologies has further revolutionized the management of quality and non-quality costs. Ref. [32] illustrated how Industry 4.0 technologies enable collaboration in circular supply chains, enhancing the efficiency and sustainability of manufacturing processes. Similarly, ref. [33] provided a comprehensive review of Quality 4.0, highlighting its definitions, features, technologies, applications, and challenges in the modern manufacturing landscape.

Digitalization and real-time data have also played a significant role in improving quality management. Ref. [34] presented a framework for digital manufacturing systems that improve social sustainability at production sites. Ref. [35] discussed the implementation of Six Sigma frameworks in automotive manufacturing, emphasizing current scenarios and future directions.

Lean manufacturing and continuous improvement strategies continue to be pivotal in quality management. Ref. [36] proposed a Quality 4.0 transition framework for Tanzanian manufacturing industries, focusing on lean principles and continuous improvement for sustainable manufacturing. Ref. [37] discussed how Quality Control 4.0 can enhance quality performance and engage shop floor operators.

Sustainable manufacturing and cost management strategies are essential for addressing the environmental impacts of quality management. Ref. [38] discussed sustainable solutions for machine tools, emphasizing the importance of sustainability in manufacturing. Ref. [39] highlighted the significance of sustainability assessments in manufacturing operations.

In summary, managing non-quality costs is a complex, multifaceted challenge, necessitating the use of varied strategies ranging from statistical controls in production to incorporating advanced manufacturing paradigms in product development. Insights from researchers offer a holistic view of these challenges and their solutions, emphasizing the importance of quality management in enhancing both economic and operational efficiencies across industries.

2.2. Comprehensive Analysis of Non-Quality Costs: Integrating Perspectives from TQM, Six Sigma, and Advanced Cost Accounting

Non-Quality Cost Metrics is a multifaceted area that encompasses a variety of models and methodologies, each offering distinct insights and implications for the measurement and management of quality-related costs. Ref. [40] highlighted the significant impact of the Cost of Poor Quality (COPQ) on a company’s revenue, with potential losses ranging from a substantial 16.91% to a staggering 26.90%. This metric helps identify costs that could have been avoided by having optimal quality from the outset. Analyzing this metric leads to better decisions, strategies, and increased profitability, and is critical for identifying areas that require improvement and ensuring that unnecessary expenses are not incurred in the future. Traditional accounting systems fail to quantify and record these costs. Therefore, they remain concealed, which hinders proactive management responses. Ref. [40] further stressed the urgency of standardized, evolved quality cost measurement systems, particularly in the construction industry, to facilitate relevant, in-depth research and development. The absence of standardization in current systems hampers comprehensive understanding and mitigation of these concealed quality costs.

A range of studies have underscored the significant impact of non-quality costs on industries, particularly in construction and manufacturing. These costs, which can range from 16.91% to 26.90% of a company’s revenue, are often concealed and require advanced cost accounting models for accurate measurement and management [41,42]. The application of the Taguchi loss function and Statistical Process Control (SPC) has been found to be crucial in reducing these costs [7,43]. The role of Cost–Benefit Analysis (CBA) in assessing the financial feasibility of projects, particularly in the context of Six Sigma, has also been emphasized [11]. TQM has been identified as a holistic approach to minimizing non-quality costs, with a focus on customer satisfaction, brand reputation, and employee morale [44]. Additionally, ref. [41] performed a literature review revealing that existing accounting models are insufficient to detail quality costs in production processes, leading to the development of a new methodology to refine and extend activity-based costing for better quality cost identification. Furthermore, ref. [42] emphasized the lack of comprehensive quality cost-capturing systems in the construction industry, highlighting the Prevention–Appraisal–Failure (PAF) model’s utility in estimating these costs. Moreover, ref. [7] presented the integration of quality cost concepts within the Strategic Cost Management (SCM) framework, focusing on the interaction of PAF activities in a company’s value chain to enhance SCM implementation and performance. Additionally, ref. [43] explored the complexities of achieving high-quality standards in construction projects and the challenges in implementing Cost of Quality (COQ) systems. The study developed a comprehensive framework for quantifying both visible and hidden quality costs, revealing that hidden failure costs significantly exceed visible ones, often leading to higher overall project costs. The findings underscore the importance of adopting COQ systems to improve quality performance and reduce failure costs in construction projects. The positive impact of TQM practices on nonfinancial performance in the automotive engineering industry has been highlighted [45]. Practical implications of the COQ have been grouped into themes, including the benefits of measurement and the role of government [46].

Ref. [25] critiqued the shortcomings of conventional cost-accounting systems and their inability to capture the true essence of quality costs, particularly within the PAF framework. The study enhances this model by incorporating new categories, including extra resultant cost and estimated hidden cost, which offer a more nuanced classification of quality costs. This advanced approach also facilitates the calculation of total costs using a COQ account matrix that provides a more accurate financial representation of quality-related expenditures. Ref. [47] also emphasized the need for enhanced cost-accounting models that can better capture the comprehensive costs associated with poor quality.

Ref. [48] introduced an innovative customer and process-centric model that employs the Taguchi loss function. With its focus on customer requirements and the costs of poor quality, this approach leverages quality function deployment to effectively translate customer feedback into crucial process parameters, thereby estimating intangible costs. The ultimate goal of this model is to forge a strong connection between quality improvement initiatives and enhanced customer satisfaction and loyalty.

Ref. [49] emphasized the significant influence of tools such as SPC and quality management techniques on COPQ. Their study of heavy manufacturing in Pakistan reveals that disregarding these techniques can lead to a marked increase in COPQ, underscoring the need for a robust quality cost model and a comprehensive methodology for assessing internal and external failure costs.

Ref. [50] proposed applying opportunity costs when determining COPQ. Their study illustrates that in a continuous-process industry, COPQ is predominantly driven by opportunity losses. Factors included in these losses include the underutilization of installed capacity and subpar delivery services.

In the Six Sigma framework, CBA plays a paramount role in assessing the financial feasibility of various projects. A range of scholarly works reinforces this crucial aspect. Ref [51] notably emphasized the significance of CBA in a market-driven economy, where the interplay of demand and supply shapes resource allocation. He argued that prices are pivotal indicators, which synchronize consumer preferences with supply costs and underline the imperative to use sophisticated, customer-focused models and establish uniform measurement systems in quality management.

Ref. [52] explored the deployment of CBA, particularly its pervasive role in public policy formulation. They offer a balanced view of CBA, scrutinizing its theoretical foundations and practical implementations while addressing its strengths and weaknesses. Their analysis suggests methodologies to enhance CBA’s accountability, stressing the critical need for a detailed juxtaposition of costs and benefits, encompassing aspects such as implementation, operational, and initial expenses.

Ref [53] delved into the evolution of CBA in the setting of environmental projects. They observe that environmental CBA has progressed to overcome specific challenges, notably in appraising environmental changes and discounting future costs and benefits. This progress is marked by advancements in valuation techniques, a renewed focus on addressing efficiency and equity considerations and innovative approaches for discounting costs and benefits over extended periods.

Ref. [54] investigated advanced economic tools and methodologies applicable to CBA, including evaluating unpriced commodities. Their comprehensive guide caters to academics and practitioners, offering an in-depth exploration of contemporary economic instruments that are vital for formulating general equilibrium cost–benefit rules. These rules are adaptable to various projects, from minor undertakings to large-scale ventures. The authors cover temporal considerations and methods for valuing unpriced commodities thoroughly and elaborate on interconnected economic principles.

CBA is indispensable in the context of Six Sigma, especially during the improvement phase of the Define, Measure, Analyze, Improve, Control (DMAIC) process. It is instrumental for meticulously balancing the tangible costs of potential solutions against their anticipated benefits, factoring in diverse expenses, and quantifying the benefits post-implementation. The collective insights from these studies and methodologies accentuate the pivotal role of CBA in the precise measurement and effective management of non-quality costs. They highlight the essential need for more refined, customer-oriented models, standardized measurement systems, and the incorporation of advanced quality management strategies. These elements are crucial for minimizing non-quality costs, augmenting overall business efficiency, and driving profitability.

TQM is a holistic approach focusing on immediate financial outcomes and the long-term effects of quality on an organization’s overall success. It highlights the wide range of non-quality costs associated with poor quality, including both tangible and intangible factors. These low-quality costs can adversely impact various organizational aspects, such as financial performance, customer loyalty, brand reputation, and market position. Essential non-quality costs in TQM include the impact of poor quality on customer satisfaction and loyalty, potentially leading to customer loss and difficulties in attracting new customers due to negative perceptions [55]. TQM also stresses the importance of measuring the loss in market share due to quality issues, recognizing its significant impact on long-term organizational health.

TQM considers the effects of quality issues on an organization’s reputation and brand value, assessing how quality failures can damage a company’s image through market surveys, social media sentiment analysis, and brand valuation studies. Internally, TQM focuses on the implications of quality issues for employee morale and turnover, acknowledging that quality problems can reduce employee satisfaction and productivity, leading to increased recruitment and training costs. This approach examines extended costs associated with warranties, service contracts, and customer support, which can increase significantly if there are ongoing quality problems. Additionally, it considers the costs of regulatory non-compliance, including fines and legal fees. TQM addresses resource inefficiency and waste resulting from poor-quality processes and products and emphasizes the opportunity costs related to lost business and innovation due to quality issues.

Ref. [56] further illuminated how factors such as quality risk, product development cycles, inspections, and unit interdependence influence quality costs in TQM. Comprehensive research, including contributions by [57,58,59,60], affirmed the positive impact of TQM on organizational performance and competitive advantage. Their studies indicated that TQM enhanced production, customer-related performance, market share, and total factor productivity.

Specifically, ref. [60] examined the importance of incorporating TQM in the Malaysian manufacturing industry, highlighting significant correlations between TQM, production performance, and customer-related performance. The findings suggest that TQM enhances performance in Malaysian manufacturing companies, emphasizing greater attention to quality measurement aspects of TQM and increased management support for TQM initiatives to ensure strategic sustainable competitive advantage.

Additionally, ref. [57] found that TQM practices improve work performance, company competitiveness, and profitability. They categorized TQM practices into core and production-oriented practices, noting that service organizations benefit primarily from core practices while manufacturing organizations benefit from production-oriented practices. Moreover, ref. [58] explored the relationship between the degree of TQM adoption and competitive advantages achieved, finding strong support for this relationship. Their data also showed that organizational structure, specifically “control” and “exploration”, moderated the effectiveness of TQM implementation.

Additionally, ref. [59] highlighted the mixed results in TQM research due to different research designs and measurement constructs, underscoring the complexity of accurately assessing TQM’s impact on performance. In conclusion, TQM represents an all-encompassing approach to quality, scrutinizing each aspect of an organization’s operations for improvement opportunities. It aims to foster a culture of continuous improvement, focusing on enhancing overall performance, customer satisfaction, and competitive advantage while also paying close attention to the diverse costs associated with non-quality costs. In conclusion, TQM represents an all-encompassing approach to quality, scrutinizing each aspect of an organization’s operations for improvement opportunities. It aims to foster a culture of continuous improvement, focusing on enhancing overall performance, customer satisfaction, and competitive advantage while also paying close attention to the diverse costs associated with non-quality costs.

In summary, while existing literature provides substantial insights into the financial impacts of poor quality through non-quality cost metrics, several research gaps persist. Current studies emphasize the need for advanced accounting models and comprehensive quality management strategies to enhance organizational efficiency and profitability. However, there is a lack of integrated approaches that simultaneously address the economic, environmental, and social dimensions of non-quality costs. This study aims to bridge these gaps by introducing a novel quality management framework that not only mitigates non-quality costs but also aligns with sustainable manufacturing principles. By incorporating these broader sustainability objectives, the framework offers a holistic pathway to improve both economic and environmental performance. Future research should further explore this integration across diverse industries and global contexts to validate and refine these strategies, ultimately contributing to the global goals of sustainable and efficient manufacturing operations.

2.3. Factors Influencing Non-Quality Costs during Development and Transition to Production

A range of factors influence non-quality costs during development and production transfer in various industries. Delays in meeting delivery schedules, deviations in development hours, and repairs of products during warranty periods are significant contributors [61,62]. Other key factors include the rejection of parts and assemblies in manufacturing, low yields in production, and the quality of purchased materials [63,64,65]. The implementation of TQM is suggested as a valuable strategy to address these issues [66]. However, the existing accounting models are insufficient to identify quality costs in detail in production processes, and a new approach is needed [41]. An empirical study on non-physical waste factors in the construction industry by [61,67] identified significant Waste Factors (WFs) affecting project performance and delivery, emphasizing the need for integrated and holistic strategies to address these issues effectively.

Furthermore, modern manufacturing techniques, particularly 3D printing, present both opportunities and challenges in the industry [62]. This technology revolutionizes production by enhancing quality and efficiency while reducing costs, though it requires substantial investments and professional expertise. In the construction sector, quality issues related to the design and construction stages are critical. Effective management strategies and stakeholder integration are essential to address these issues and ensure project success.

According to [67], the contribution of stakeholders, particularly architects, is crucial during the design and construction stages of projects. Their study emphasizes that issues such as project budget, hiring contractors on the lowest bid, and preparation of checklists significantly impact quality outcomes in the Indian construction industry [67]. Additionally, ref. [63] identified top management leadership, customer focus, employee training, supplier management, information analysis, and process management as critical success factors for TQM implementation. These factors contribute to improved financial performance, underscoring the need for comprehensive approaches to quality management in various industries.

Quality 4.0 leverages advanced digital technologies to enhance manufacturing and services. This revolution digitizes quality systems, improving existing approaches and enabling real-time analysis to resolve issues promptly. Key technologies include automated root cause analysis, machine-to-machine connectivity, and real-time process simulation. These innovations, combined with sophisticated methods and sensors, boost productivity, ensure high-quality outputs, and enhance operational efficiency [64].

Ref. [65] emphasized the dynamic conditions of construction sites and underscores the significance of AI-based predictive models in reducing quality failures. According to a study, a total of 2527 Nonconformance Reports (NCRs) collected from 59 construction projects were analyzed using the Delphi method and logistic regression analysis. The study identified 25 critical cost impact factors, categorized into five main groups: Materials, Design, Installation, Operation, and Process. It highlighted that while some attributes have a significant individual impact on quality costs, others become critical when interacting with different attributes. The study advocates for a holistic quality control system that considers the domino effects of causal factors from planning to operation, aiming to reduce quality failures and prevent cost and time overruns [65].

The discussion of non-quality costs during development and production transfer in various industries is complex and multifaceted, encompassing a range of issues:

Delays in Meeting Delivery Schedules: Ref. [68] addressed the significant impact of delays in delivery schedules on non-quality costs, often resulting from project management challenges, workforce shortages, and technical issues. These delays can disrupt supply chains and affect customer relations, highlighting the need for efficient project management and resource allocation.

Deviation in Development Hours from Planned Budgets: Ref. [69] focused on the impact of deviations in development hours from planned budgets. Misunderstandings of client requirements and suboptimal project management are primary contributors to these deviations, leading to increased costs and project inefficiencies.

Repairs of Products During Warranty Periods: Ref. [70] discussed the necessity and cost implications of product repairs during warranty periods, which often stem from gaps in design definitions and unfinished production transfer processes. Indeed, these are the specific areas that require improvement during the initial phases of development.

Rejection of Parts and Assemblies in Manufacturing: Ref. [71] highlighted the rejection of parts and assemblies in manufacturing due to unclear engineering definitions and worker errors, which escalate production costs and lead to waste.

Low Yields in Production: Ref. [27] identified infrastructure malfunctions and human errors as key contributors to low production yields that lead to inefficiencies and increased costs.

Quality of Purchased Materials: Ref. [72] emphasized the quality nonconformances arising from defective materials as a significant issue, underscoring the importance of stringent quality control in the procurement process.

Mistakes in Product Development: Ref. [73] noted that errors in the product development phase can lead to significant non-quality costs, and highlight the need for meticulous planning and execution.

Inefficiency in Service Delivery: Ref. [74] suggested that project managers’ performance can significantly affect service delivery efficiency, while poor management can lead to delays and increased costs.

Costs of Poor Quality: Ref. [75] discussed the substantial costs of poor quality, including extra production costs, reworks, and inefficient planning.

Challenges in Developing Economies: Ref. [76] focused on the pronounced problems in developing economies, where quality management constraints and monopolies in some industries exacerbate non-quality issues.

Implementation of TQM: Ref. [77] suggested that TQM can be a valuable strategy to address these widespread issues.

In summary, various factors influence non-quality costs during the development and transition to production in different industries. Key issues discussed include delays in delivery schedules, deviations in development hours, and product repairs during warranty periods. Also addressed are the rejection of parts and assemblies in manufacturing, low production yields, and the quality of purchased materials. Furthermore, the significant impact of mistakes in product development, inefficiency in service delivery, and the overall costs associated with poor quality are highlighted. Special attention is given to the challenges faced by developing economies and the potential benefits of implementing TQM as a strategic approach to mitigate these non-quality costs.

2.4. ESG Activities

ESG activities are increasingly recognized as critical components of sustainable business practices. ESG encompasses a range of activities and guidelines designed to ensure that companies operate in an environmentally responsible, socially conscious, and ethically governed manner. Implementing ESG guidelines provides a framework for companies to achieve short-term goals while investing in long-term business opportunities and competitive strategies. These guidelines help stakeholders align their operations with sustainability objectives, ensuring that every aspect of the business, from raw material sourcing to end-product delivery, adheres to high standards of environmental and social responsibility. By integrating ESG principles, businesses can enhance their efficiency and effectiveness, making strategic decisions that support sustainable growth and resilience in the global market.

However, the concept of greenwashing, which refers to the practice of misleading stakeholders into believing that a company’s products or policies are more environmentally friendly than they actually are, poses a significant challenge. Genuinely environmentally and socially responsible practices involve substantive efforts to reduce environmental impact, improve social equity, and maintain transparent and accountable governance structures. A growing body of research underscores the importance of genuine ESG practices in sustainable business, highlighting the need for reliable ESG data [78].

The voluntary nature of Corporate Environmental Social Responsibility (CESR) can lead to greenwashing, necessitating a combination of voluntary and mandatory approaches [79]. Stakeholder legitimacy is crucial in firm greening and financial performance, with low ESG performance increasing the likelihood of greenwashing [80]. The complexity of ESG measurement and the need for more reliable ESG ratings and reports are also emphasized [81]. Financial constraints can motivate greenwashing, with intermediation potentially alleviating these constraints [82].

Despite increased stakeholder vigilance, the practice of greenwashing, which involves selective disclosure of positive information, remains prevalent [83]. This deceptive practice is common across various sectors, even among companies with high overall CSR scores [84]. Therefore, a robust and transparent ESG framework is essential to mitigate greenwashing and ensure that companies’ sustainability efforts are genuine and impactful.

In summary, while the adoption of ESG principles is vital for sustainable business practices, the challenge of greenwashing underscores the need for a balanced approach combining voluntary and mandatory ESG measures. This will ensure that ESG initiatives are genuinely contributing to environmental sustainability, social equity, and ethical governance, thereby enhancing the long-term competitiveness and resilience of businesses in the global market.

2.5. Existing Methodologies for Establishing a Hierarchy of Root Causes for Non-Quality Cost Factors

Root Cause Analysis (RCA) is a critical tool for identifying and addressing quality-related problems in manufacturing processes. It involves a systematic process for identifying underlying causes of problems or events across various industries, including healthcare, manufacturing, and more. RCA aims to prevent future occurrences by understanding why events happen [85,86]. However, its effectiveness can be limited due to inconsistent definitions and applications across organizations [87]. As [85] explained, RCA is designed to investigate and categorize the root causes of events with impacts on safety, health, the environment, quality, reliability, and production. They emphasize that understanding why an event occurred is key to developing effective recommendations that prevent recurrence. Additionally, ref. [86] showed that RCA detects system-level vulnerabilities and prevents them from recurring in the future. For example, in Korea, adverse events occur in 7.2–8.3 percent of inpatients, highlighting the need for comprehensive RCA guidelines to improve the quality of medical care and patient safety [86]. Despite these limitations, RCA continues to be widely used for investigating serious incidents in healthcare systems worldwide.

In the context of Industry 4.0, RCA tools and methods need to be adapted to handle the large amounts of data generated. Challenges in RCA include the need for expertise, employee bias, and poor data quality. These challenges can be addressed through visualization tools, collaborative platforms, and machine learning techniques [88]. Data-driven RCA systems, such as those using machine learning and causal knowledge graphs, have been proposed to improve the accuracy and efficiency of RCA [89,90]. A big data-driven framework of root cause analysis (RCA) for quality problems considers the multiple types of quality data from various sources. The framework includes the development of feature libraries to describe quality problems by data mining and provides machine learning algorithms to automatically predict the root causes of quality problems. This system and its algorithms have been successfully tested using real product quality data, illustrating that quality problems can be solved more efficiently and accurately using big data and machine learning approaches [89].

In addition, a data-driven framework to mine large-scale causalities between quality problems and production factors from Quality Problem-Solving (QPS) data has been proposed. This framework exploits a causal knowledge graph for quality problems (QPCKG) to express these causalities. By classifying QPS data to identify causality, employing causal linguistic patterns to extract cause and effect slots, and using BiLSTM-CRF to extract core problem content, the approach integrates discrete causalities into QPCKG. This method, validated in a real-world application at a leading automotive company, demonstrates three potential applications for quality diagnosis and prediction, revealing the core interaction mechanism of product quality and production factors, and providing decision-making support for RCA [90]. Root cause analysis for quality problem-solving is critical to improving product quality performance and reducing quality risk for manufacturers. Conventional methods, although frequently applied, face challenges due to increasingly complex product and supply chain structures, diverse working conditions, and massive component numbers. Therefore, data-driven RCA methods have attracted attention lately. Leveraging big operations data and the rapid development of data science, a big data-driven RCA system can significantly enhance the performance of RCA [89].

In the healthcare sector, RCA has been used to improve patient safety, but its effectiveness in preventing the recurrence of adverse events has been questioned [91,92]. Improving patient safety within hospitals has become a major focal point for administrative and clinical action. RCA is an analytical tool used by hospitals in quality improvement and patient safety efforts. While hospitals have widely embraced RCA, the effectiveness of the RCA process has been questioned in recent years. Based on a literature review and feedback from practicing administrators, ref. [92] identified current barriers to the effectiveness of the RCA process and suggested actions to overcome them. Additionally, a systematic review by ref. [91] further explored the effectiveness of RCA in healthcare. The review consolidated studies to determine whether RCA was an adequate method to decrease the recurrence of Avoidable Adverse Events (AAEs). The results indicated that despite the widespread implementation of RCA in the past decades, only two studies could establish an improvement in patient safety due to RCAs. The review highlighted that RCA was useful for identifying the remote and immediate causes of safety incidents but not for implementing effective measures to prevent their recurrence. The study emphasized the need for subsequent control and follow-up of RCA recommendations to translate findings into practice and effectively reduce AAEs [91]. The application of data analytics in Quality 4.0 was identified as a key area for future research [93].

Several methodologies are available to aid in Root Cause Analysis. Structured RCA techniques, such as extended 5W2H questioning and the 5 Whys method, have shown great promise in resolving non-conformities in the automotive industry. These tools help define the scope of the problem accurately, pinpoint root causes, and determine preventive actions [94]. RCA involves iterative inquiry into cause-effect chains to diagnose root causes, similar to medical diagnoses. The goal is to identify system-wide changes that prevent recurrence [95].

The PROACT methodology offers a structured approach to RCA implementation, aiming to systematically identify and address the underlying causes of problems [96]. PROACT stands for Problem, Root Causes, Optimum Solutions, Action Plans, Control, and Team. This methodology ensures a comprehensive analysis and effective implementation of solutions. Another technique, the ‘5W + 1H’ approach, involves asking who, what, where, when, why, and how to uncover root causes, though it may lack the depth needed for comprehensive analysis [97].

Cause mapping is another effective tool used in RCA, particularly in refining processes. Cause mapping increases the effectiveness of RCA by visually communicating interconnected failures [98]. With the rise of Industry 4.0, harnessing innovative systems and real-time data could make RCA more proactive. In collaborative design reviews, RCA has been shown to be highly beneficial for rapidly testing solutions through simulation [99].

In addition to RCA, various methodologies exist for establishing a hierarchy of root causes for non-quality cost factors. The Pareto principle, which suggests that 80% of effects come from 20% of causes, is widely used to identify critical factors in problem-solving and quality management [100]. Several studies have applied Pareto analysis to determine Critical Success Factors (CSFs) in TQM implementation [101,102,103].

The literature review by ref. [101] analyzed the CSFs of TQM using Pareto analysis. The review aimed to identify and propose a few vital CSFs for researchers and industries. Despite numerous TQM articles, few document CSFs using statistical methods. The main objective was to list CSFs by frequency of occurrence, reviewing studies from 1989 to 2003 [101]. An examination of 37 TQM empirical studies identified 56 CSFs. Implementing such a large number of CSFs in organizations is challenging. The study used Pareto analysis to sort CSFs by frequency, highlighting a few vital ones to facilitate TQM program implementation [101]. Future research may develop models to measure and sustain TQM implementation. The identified CSFs guide researchers in selecting reliable CSFs for studies, and industries can use these results for effective TQM implementation [101].

Ref. [102] conducted a detailed study using Pareto analysis to identify CSFs critical to the success of TQM in service industries. The study aimed to identify and propose a list of vital CSFs to benefit researchers and industry practitioners. By sorting and arranging these factors according to their criticality, the study highlighted a few essential CSFs, facilitating the smoother implementation of TQM programs in organizations. The study emphasizes the managerial implications, research recommendations, and scope for future research [102]. Additionally, ref. [103] conducted a critical literature review using Pareto analysis to assess TQM practices and their impact on business performance, identifying gaps and suggesting areas for future research.

The method has also been used in digital library project management and agile manufacturing [104,105]. For instance, ref. [104] defined and described Pugh matrix analysis as a method for decision making when multiple criteria must be factored into a decision. The use of Pugh matrix analysis in digital library projects aids in determining a course of action and gaining consensus within a project team. It is based on multiple criteria decision analysis techniques, helping teams understand the relationship of multiple issues within a project and the perspectives of team members and the user community [104]. The method has proven to be a valuable tool in prioritizing issues, factors, and courses of action in projects with multiple decision points, as evidenced by its application in both digital library project management and agile manufacturing contexts [104,105].

While Pareto analysis is praised for its simplicity and effectiveness in highlighting major issues, researchers have identified potential limitations, such as incorrect selection of vital factors and interrelationships among errors [106,107]. Ref. [106] discussed issues in Pareto analysis, such as the incorrect selection of the ‘vital few’ errors, the interrelationship among errors, and the merging of errors from different processes. They demonstrate these issues with a real-life case study in a service scenario and suggest appropriate remedial measures to effectively separate the ‘vital few’ causes from the ‘trivial or useful many’ causes, thereby enhancing the discriminating power of the Pareto graph. Additionally, ref. [107] explored the application of the Pareto principle in quality improvement efforts, emphasizing the need for economic evaluation and consideration of opportunities for problem occurrence. They propose a modified Pareto analysis based on defect rates rather than frequencies, using statistical methods for more accurate problem prioritization. This approach provides a clearer understanding of problem severity and helps in prioritizing quality improvement efforts more effectively.

Failure Mode and Effects Analysis (FMEA) is another well-established methodology. It is a systematic approach to identifying potential failure modes, their causes, and their effects on system performance [108]. It involves scoring each potential failure mode based on its severity, occurrence, and detectability. This method provides a detailed and structured analysis and helps prioritize risks based on quantitative scores [109]. However, it can be time-consuming and requires extensive data collection and analysis.

Several studies highlight the limitations of traditional FMEA, including inadequate definitions, high uncertainty, and decision-making failures [110,111]. Ref. [111] examined the reliability and validity of FMEA outputs, revealing that while the multidisciplinary mapping process is valuable, the technique is time-consuming and lacks quantitative support for prioritizing process failures. Additionally, ref. [110] discussed issues in the conventional FMEA approach, such as the inadequacy of the detection index and the inconsistency of the Risk Priority Number (RPN). To address these shortcomings, the authors recommend organizing FMEA around failure scenarios and evaluating risk using probability and cost to facilitate more meaningful and consistent risk evaluation.

To address these issues, researchers propose modifications such as scenario-based FMEA and fuzzy FMEA using multi-criteria decision-making methods [112,113]. For example, the integration of fuzzy TOPSIS with fuzzy AHP allows for the use of linguistic variables, improving the accuracy of risk evaluations and prioritizing failure modes based on the knowledge and experience of experts [112]. Additionally, hybrid approaches such as the combination of the Analytical Hierarchy Process (AHP) with the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) have been proposed to overcome limitations associated with the traditional RPN method [113]. These hybrid methods enhance the precision of prioritizing failure modes by addressing issues such as the relative importance of risk factors and the sensitivity of RPN values to small changes [113].

In establishing a hierarchy of root causes for non-quality cost factors, various methodologies can be applied to prioritize these causes effectively. The Analytic Hierarchy Process (AHP) is a widely-used multi-criteria decision-making tool that structures complex problems into hierarchies and uses pairwise comparisons to derive priority scales [114,115]. AHP relies on the judgments of experts to measure intangibles in relative terms, using a scale of absolute judgments to compare how much more one element dominates another with respect to a given attribute. This method addresses inconsistencies in judgments and provides a synthesized priority scale by multiplying these scales by the priority of their parent nodes and summing them [114]. Ref. [115] elaborates on the use of AHP in various domains, highlighting its versatility in solving problems across economic, social, and technological areas. It discusses the necessity of the eigenvector, its applications in contemporary societal issues, and the integration of AHP with other decision-making methodologies.

In the context of sustainable urban development, methodologies for environmental assessment play a crucial role [116]. Leading authorities provide insight into how well these methods evaluate the ecological integrity and equity of resource distribution in urban development. These evaluations examine the instruments of environmental assessment, approaches based on systems thinking, and methods for environmental, economic, and social assessments to evaluate urban sustainability. Notably, the Analytic Hierarchy Process (AHP) is highlighted as a key tool within these methodologies, offering a structured approach to prioritize and evaluate complex urban sustainability issues. The Sustainable Urban Development Series, funded by the European Commission’s BEQUEST (Building, Environmental Quality Evaluation for Sustainability) network, provides a comprehensive framework, set of protocols, and toolkit for policymakers, academics, professionals, and advanced students in urban planning and related fields [116].

AHP has been applied in various fields, including water resource management and medicine [117,118]. For instance, in the context of water resource management in Thailand, AHP has been utilized to address complex decision-making problems by integrating criteria concerning social, economic, and environmental factors [117]. This approach involves reviewing the literature, analyzing strengths and limitations using SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis, and enhancing efficacy by combining AHP with other techniques, such as the Delphi method, to mitigate bias in expert judgments [117]. Additionally, AHP is extensively applied in environmental and civil engineering, as detailed in a comprehensive study [118]. This study explains the steps required to perform AHP, including hierarchy design, prioritization, criteria weights, and consistency checks. It also discusses the method’s applications in various fields, such as business decision-making and decision theory in medicine, while addressing its limitations [118]. While AHP is praised for its structured approach to complex problems, it has limitations, including potential bias from expert judgments and time-consuming processes [117,118].

In summary, combining RCA with methodologies such as the Pareto Principle, FMEA, and AHP can provide a comprehensive framework for identifying and prioritizing root causes of non-quality cost factors. These tools help organizations focus their improvement efforts on the most critical issues, thereby enhancing overall quality and efficiency [100,114,115]. Specifically, ref. [114] explains how AHP measures and prioritizes both intangible and tangible elements through expert judgments and pairwise comparisons to derive priority scales. Furthermore, ref. [115] provides a comprehensive overview of AHP, detailing its applications across economic, social, political, and technological areas, and discusses its methodologies, including hierarchy design, prioritization, and consistency checks. Additionally, ref. [100] explores the origin, application, and misapplication of the Pareto Principle, noting that about 80 percent of wealth is held by roughly 20 percent of the population.

By leveraging these methodologies, organizations can systematically identify and address the underlying causes of quality issues, ensuring continuous improvement and sustainable business practices. This integrated approach not only helps in pinpointing the root causes of non-quality cost factors but also in establishing a clear hierarchy of these causes, enabling targeted interventions and effective resource allocation.

3. Materials and Methods

3.1. Methodology

This study integrated qualitative and quantitative research methods, adopting a structured approach with three phases. Central to this research is its design as a case study, which facilitates a comprehensive contextual exploration of non-quality cost factors and their root causes in a real-world setting, fostering a deeper understanding of complex industry-specific challenges [119,120,121].

Ref. [120] provided a thorough examination of case study methodology, covering design, implementation, and exemplary case studies from various academic and applied fields. Similarly, ref. [121] offered a comprehensive overview of case study research, emphasizing its design, implementation, and integration across different fields, and highlighting the importance of ethnographic case studies in understanding complex phenomena in real-life contexts. Complementing these, ref. [119] outlined a detailed process for inductively developing theories through case studies, emphasizing a highly iterative and data-driven approach and underscoring its suitability for exploring new research areas and generating empirically valid and testable theories.

The initial phase of the study involved collecting non-quality cost data to investigate various factors and their root causes, spanning from the development phase to production. Insights about various factors and their potential root causes of non-quality costs were meticulously gathered from multiple sources, including a literature review (Section 2.3), interviews with key organizational personnel, and an examination of the organization’s quality reports. These sources identified critical issues such as delays in meeting delivery schedules, deviations in development hours, and repairs during warranty periods, which are significant contributors to non-quality costs.

The interviews included the following questions:

• What are the most common reasons for delays in meeting delivery schedules?
• How often do deviations in development hours occur, and what are their main causes?
• Can you describe typical scenarios that lead to repairs during warranty periods?
• How do workforce shortages impact project timelines and quality outcomes?
• What role does project management play in addressing non-quality costs?
• How effective are current quality control measures in preventing non-quality costs?
• What improvements do you suggest to reduce non-quality costs?
• How do external factors, such as supplier reliability, affect non-quality costs?

At the end of the initial phase of the study, the following non-quality cost factors and their root causes were identified:

• Customer Delivery Schedule Delays

  ○

  Delays in receiving supplies from suppliers, manufacturers, or contractors;

  ○

  Poor quality of created or purchased raw materials;

  ○

  Project development problems due to lack of professionalism;

  ○

  Project management issues;

  ○

  Shortage of workforce in development;

  ○

  Shortage of workforce in operations;

  ○

  Technical problems in the manufacturing, assembly, or testing of the product.

• Exceeding Budgeted R&D Hours

  ○

  Delays in supplies from suppliers, manufacturers, or contractors;

  ○

  Incorrect estimation of working hours;

  ○

  Ineffective integration of engineering and operational factors;

  ○

  Late design changes due to failure in testing and validation;

  ○

  Workforce shortage;

  ○

  Misunderstanding of part of the client’s requirements;

  ○

  Suboptimal project management.

• Post-Delivery Warranty Repairs

  ○

  Gaps in design definitions;

  ○

  Incomplete transition process to production;

  ○

  Low yields in production;

  ○

  Products deviating from specifications;

  ○

  The product did not undergo adequate testing, validation, and reliability checks during development;

  ○

  The product did not meet customer requirements properly and stably.

• Rejection of Parts or Assemblies in the Manufacturing Process

  ○

  Engineering changes during production;

  ○

  Engineering definitions are unclear and unambiguous for production;

  ○

  Procurement before product approval;

  ○

  Incomplete or missing documentation;

  ○

  Lack of worker training in manufacturing;

  ○

  Manufacturer or supplier errors;

  ○

  Worker errors.

• Low Yields in Production

  ○

  Design issues;

  ○

  Faults in testing equipment;

  ○

  Human errors;

  ○

  Infrastructure malfunctions;

  ○

  Manufacturing issues: difficulty in producing, assembling, testing, or disassembling;

  ○

  Missing or incomplete documentation;

  ○

  Partial and incomplete test coverage;

  ○

  The quality of purchased items does not meet the requirements.

In the second phase, a comprehensive questionnaire was developed based on the non-quality cost factors and their root causes identified in the exploratory phase. The questionnaire was tailored to the company’s development projects, underwent rigorous content validation, and was tested on a pilot sample. Using insights from the pilot, the questionnaire was refined and then distributed to personnel at various organizational levels, ensuring broad data collection representative of diverse perspectives. The survey presented participants with a list of root causes associated with each non-quality factor and asked them to rank these causes in order of importance, with the most critical cause at the top. This ranking system helped identify key areas needing improvement and provided valuable insights into the underlying reasons behind the occurrence of non-quality factors.

In the third and final phase, the survey data was analyzed to unearth insights into the causal factors contributing to quality erosion. Using the above hierarchical classification schema and frequency analysis, the relative prevalence and severity of all identifiable root causes were accurately gauged. Building on these findings, the QDI assigns detailed ordinal scores to each causal factor, considering nuances such as time-to-detection and error cascade effects. This comprehensive index encompasses the interplay of procedural, cultural, and environmental factors, analyzing their collective impact on accelerating or mitigating deviation incidents. This structured approach, grounded in the factors influencing non-quality costs discussed in Section 2.3, enabled the identification of critical areas for targeted mitigation efforts and improvement strategies.

By connecting the identified factors in Section 2.3 with the methodology applied in Section 3, this study provides a comprehensive framework for addressing non-quality costs, ensuring continuous improvement, and sustainable business practices. The consolidation of information regarding the prevalence, intensity, and implications of root causes responsible for quality degradation sheds new light on the sources of unnecessary expenses. This analytical approach reinterprets interconnected challenges, transforming them into actionable opportunities for improvement. It is a technically comprehensive yet nuanced methodology, providing optimal inputs for developing scalable preventive strategies and predictive interventions across various projects and facilities. The following flowchart (Figure 1) presents the methodology presented in the manuscript.

The diagram shows that the steps are as follows:

• Data Collection Phase
  • Literature Review

    ○

    Identify potential root causes of non-quality costs.

  • Interviews

    ○

    Conduct interviews with key organizational personnel.

  • Quality Reports

    ○

    Examine the organization’s quality reports to gather insights on non-quality cost factors.

• Questionnaire Development Phase
  • Design Questionnaire

    ○

    Develop a comprehensive questionnaire based on the identified root causes.

  • Content Validation

    ○

    Validate the content of the questionnaire.

  • Pilot Testing

    ○

    Test the questionnaire on a pilot sample.

  • Refinement

    ○

    Refine the questionnaire based on pilot test feedback.

  • Distribution

    ○

    Distribute the questionnaire to personnel at various organizational levels.

• Data Analysis Phase
  • Survey Data Analysis

    ○

    Analyze the survey data to identify causal factors contributing to quality erosion.

  • Hierarchical Classification Schema

    ○

    Apply hierarchical classification schema and frequency analysis.

  • QDI

    ○

    Assign detailed ordinal scores to each causal factor using the QDI;

    ○

    Consider nuances such as time-to-detection and error cascade effects.

  • Identify Key Clusters

    ○

    Identify critical areas for targeted mitigation efforts and improvement strategies.

• Implementation and Continuous Improvement
  • Targeted Mitigation Efforts

    ○

    Implement strategies to address identified root causes.

  • Monitor and Review

    ○

    Continuously monitor the effectiveness of mitigation efforts.

  • Refinement and Adjustment

    ○

    Adjust strategies based on ongoing feedback and data analysis.

In summary, this case study provides a computational analysis of survey data, offering insights into the mechanisms behind quality non-conformities in high-tech electronics manufacturing companies. The tailored indexing approach facilitates targeted mitigations by clarifying the decision chain of triggering events. This method emphasizes the importance of proactive solutions in quality management, contributing to the field by enabling more effective interventions in high-tech industries.

3.2. Structure and Components of the Questionnaire: Analyzing Factors Contributing to Non-Quality Costs

The questionnaire was composed of two parts. In the first part, the focus was on several aspects related to the professional background of the respondents and details of the project they are involved in. Participants were asked to provide information about their role, academic qualifications, area of expertise, tenure in their current position, the number of projects undertaken in their current role, total years of experience in their field, and the name of the relevant project. They were also requested to provide details on the total planned supply quantities and the current status of their project from the development stage to the completion of serial production.

The second part of the questionnaire detailed factors leading to non-quality costs and their potential causes. Participants were asked, based on their personal experiences and expertise, to rank the potential reasons for the occurrence of each non-quality cost factor in descending order, starting with the most significant reason to the least significant.

One factor leading to non-quality costs is delays in meeting the supply schedule with the client. This issue can arise due to problems in project management, workforce shortages in development or operations, lack of expertise in project development, poor quality of raw materials, technical problems in manufacturing, assembly, or testing, and delays in deliveries from suppliers, manufacturers, or contractors. These reasons were revealed in interviews with key personnel in the organization and documented in quality reports. Based on a synthesis of information from three sources (literature, interviews, and quality reports), the second part of the questionnaire included the factor “Delays in Meeting the Supply Schedule with the Client” with the following potential causes: problems in project management, workforce shortages in development or operations, project development issues due to lack of expertise, poor quality of raw materials, technical issues in manufacturing, assembly or testing, and delays in deliveries from suppliers, manufacturers, or contractors.

A second factor contributing to non-quality costs is exceeding the budgeted development hours (zero-based budget). This can be caused by misunderstandings of client requirements, suboptimal project management, late design changes due to failure in conducting tests and validations during development stages, inadequate integration of engineering and operational factors, incorrect estimation of working hours, workforce shortages, and delays in supplies from suppliers, manufacturers, or contractors. These reasons were identified in interviews with key personnel in the organization and documented in quality reports. Based on the synthesis of information from three sources (literature, interviews, and quality reports), the second part of the questionnaire included the second factor, “Exceeding Budgeted Development Hours” with the following potential causes: misunderstanding some client requirements, suboptimal project management, late design changes due to failure in tests and validation during development, inadequate integration of engineering and operational factors, incorrect estimation of working hours, workforce shortages, and delays in supplies from suppliers, manufacturers, or contractors.

A third factor leading to non-quality costs is the repair of products during the warranty period that were supplied to the customer. This can be due to gaps in design definitions, incomplete transition processes to production, products not meeting customer requirements adequately, low yields in production, insufficient testing, validation, and reliability checks during development stages, and products deviating from specifications. These reasons were uncovered in interviews with key personnel in the organization and documented in quality reports. Based on the synthesis of information from three sources (literature, interviews, and quality reports), the second part of the questionnaire included a third factor, “Repair of Products During Warranty Period Supplied to the Customer”, with the following potential causes: gaps in design definitions, incomplete transition to production, products not adequately meeting customer requirements, low yields in production, insufficient testing, validation, and reliability in development stages, and products deviating from specifications.

A fourth factor contributing to non-quality costs is the rejection of parts and assemblies in manufacturing. This issue can arise from unclear and ambiguous engineering definitions for production, engineering changes during production, initiating full-scale procurement before product approval, lack of training for production workers, incomplete or missing documentation, worker errors, manufacturer errors, and supplier errors. Based on the synthesis of information from three sources (literature, interviews, and quality reports), the second part of the questionnaire included the factor “Rejection of Parts, Assemblies in the Manufacturing Process” with the following potential causes: unclear and ambiguous engineering definitions for production, engineering changes during production, initiating full-scale procurement before product approval, lack of training for production workers, incomplete or missing documentation, worker errors, manufacturer errors, and supplier errors.

A fifth factor leading to non-quality costs is low yields in production. This can be due to infrastructure and testing equipment malfunctions, design issues, incomplete or missing documentation, partial and insufficient testing coverage, production issues—difficulties in manufacturing, assembling, testing, and disassembling, human errors, and the quality of purchased items not meeting requirements. Based on the synthesis of information from three sources (literature, interviews, and quality reports), the second part of the questionnaire included a fifth factor, “Low Yields in Production”, with the following potential causes: infrastructure malfunctions, testing equipment malfunctions, design issues, incomplete or missing documentation, partial and insufficient testing coverage, production issues—difficulties in manufacturing, assembling, testing, disassembling, human errors, and the quality of purchased items not meeting requirements.

The questionnaire was personally administered to each research participant included in the sample described in Section 3.3. Each participant was scheduled in advance to find a convenient time and setting for completing the questionnaire. Before administering the questionnaire, a conversation was held between the researcher and the respondent to explain the purpose and significance of the research. Participants were encouraged to contribute to the advancement of science by diligently fulfilling the research objectives. The questionnaire was completed at times convenient for the participants without compromising their comfort. Completing the questionnaire took approximately 30 min, allowing participants ample time to carefully consider their responses.

3.3. Research Sample

The research sample was created using a purposive sampling method. The sample consisted of 34 participants in diverse roles: program managers, product engineers, quality assurance managers, project managers, and lead engineers. Each participant held a unique position in the company. Their educational qualifications varied, with 55.9% holding a bachelor’s degree, 32.4% a master’s degree, and the remaining 11.8% having a technician’s degree or no academic degree. The distribution of participants based on education shows that most of those with bachelor’s degrees graduated in electrical and electronic engineering (52.9%), followed by industrial engineering and management (20.6%). Most master’s degree holders were business administration graduates (53.3%), followed by software and computer science graduates (20%).

The participants’ current positions varied from less experienced roles to senior positions and their experience tenure ranged from a minimum of two years to a maximum of 40 years. The average tenure was 15.84 years, with a standard deviation of 8.73. The average years of experience was 18.25, with a standard deviation of 11.55. They were involved in various projects, ranging from just one project to as many as 500 projects. The average number of projects per participant was 81.91, with a standard deviation of 118.64. The supply quantities for the products covered by the questionnaire ranged from 100 to 10,000 units. Some products were in the prototype testing phase, while others were in serial production with ongoing supply. Of the 34 participants, 14.7% were in their first production run, 17.6% were in development, 20.6% were in prototype testing, and 47.1% were in ongoing serial production.

3.4. QDI

The QDI, introduced in this study, is a novel approach that combines the frequency and perceived importance of root causes into a single, quantifiable metric. This method involves participants ranking potential causes, which are then assigned weighted values to calculate the QDI.

This study established a ranking method to identify the root causes of non-quality cost factors, labeled Y, with potential causes named x₁ through x_n. The research participant received a template featuring n horizontal boxes, each corresponding to a root cause. The leftmost box was allocated to the most critical root cause of Y, with the subsequent boxes arranged in descending importance for the second, third, and so forth, culminating with the n-th box for the least important cause. Participants filled out the template according to their perception of the importance of the hierarchy of Y’s root causes. If a participant deemed a particular root cause irrelevant to factor Y, that cause could be excluded. If multiple root causes were perceived as equally important, they were each assigned a box reflecting their importance.

The ranking process involved tallying the frequency of each root cause’s appearance in the template’s boxes. To illustrate, consider a simplified scenario with only five root causes: x₁, x₂, x₃, x₄, and x₅. If root cause x_i is ranked first (n₁ times), second (n₂ times), and so on, down to the fifth position (n₅ times), each occurrence is counted accordingly. If a root cause x_i is absent in a specific ranking, it is counted as zero for that rank. Subsequently, each root cause x_i is assigned a QDI value, calculated using the following formula:

$${Value}_{{Quality Deterioration Index}_{x_i}}=n_1\times 10000+n_2\times 1000+n_3\times 100+n_4\times 10+n_5$$

Thus, each of the causes x₁ through x₅ is assigned a QDI value, with higher values indicating a more substantial impact on quality deterioration and lower values indicating a lesser effect. The root cause with the highest QDI substantially impacts Y’s quality deterioration.

The QDI was calculated for each of the following non-quality cost factors, and their root causes were identified:

• Customer Delivery Schedule Delays
  • Delays in receiving supplies from suppliers, manufacturers, or contractors;
  • Poor quality of created or purchased raw materials;
  • Project development problems due to lack of professionalism;
  • Project management issues;
  • Shortage of workforce in development;
  • Shortage of workforce in operations;
  • Technical problems in the manufacturing, assembly, or testing of the product.
• Exceeding Budgeted R&D Hours
  • Delays in supplies from suppliers, manufacturers, or contractors;
  • Incorrect estimation of working hours;
  • Ineffective integration of engineering and operational factors;
  • Late design changes due to failure in testing and validation;
  • Workforce shortage;
  • Misunderstanding of part of the client’s requirements;
  • Suboptimal project management.
• Post-Delivery Warranty Repairs
  • Gaps in design definitions;
  • Incomplete transition process to production;
  • Low yields in production;
  • Products deviating from specifications;
  • The product did not undergo adequate testing, validation, and reliability checks during development;
  • The product did not meet customer requirements properly and stably.
• Rejection of Parts or Assemblies in the Manufacturing Process
  • Engineering changes during production;
  • Engineering definitions are unclear and unambiguous for production;
  • Procurement before product approval;
  • Incomplete or missing documentation;
  • Lack of worker training in manufacturing;
  • Manufacturer or supplier errors;
  • Worker errors.
• Low Yields in Production
  • Design issues;
  • Faults in testing equipment;
  • Human errors;
  • Infrastructure malfunctions;
  • Manufacturing issues: difficulty in producing, assembling, testing, or disassembling;
  • Missing or incomplete documentation;
  • Partial and incomplete test coverage;
  • The quality of purchased items does not meet the requirements.

The QDI was developed to address the need for a precise, quantifiable method of identifying and prioritizing the root causes of quality issues in a structured manner. Traditional methods often rely on qualitative assessments or simple frequency counts, which may not adequately capture the relative importance of different factors. The QDI’s structured approach offers several advantages:

○

Prioritization: By assigning weighted values to the ranks, the QDI helps in identifying the most critical factors affecting quality. This prioritization is essential for efficient resource allocation in quality improvement initiatives;

○

Comprehensive Analysis: The QDI considers both the frequency and the perceived importance of root causes, providing a more nuanced view of quality deterioration compared to simple occurrence counts;

○

Actionable Insights: The QDI facilitates targeted interventions by highlighting the most impactful root causes, allowing for more effective and focused quality improvement strategies.

Advantages of the QDI:

○

Weighted Importance: Unlike simple frequency counts, the QDI incorporates the relative importance of each root cause, offering a more detailed and actionable analysis;

○

Structured Data Collection: The use of a standardized template ensures consistency in data collection, improving the reliability and validity of the findings;

○

Enhanced Decision-Making: By providing a clear hierarchy of root causes, the QDI aids in making informed decisions regarding quality improvement efforts.

Disadvantages of the QDI:

○

Complexity: The calculation and interpretation of the QDI can be more complex than simpler metrics, potentially requiring additional training for stakeholders;

○

Subjectivity: The QDI relies on participant perceptions, which can introduce bias. Ensuring a representative and knowledgeable sample of participants is crucial to mitigate this issue;

○

Data Collection Effort: Gathering and analyzing the data required for QDI calculation can be time-consuming and resource-intensive compared to more straightforward methods.

In summary, the QDI offers a robust and detailed method for identifying and prioritizing the root causes of quality issues. While it introduces some complexity and subjectivity, its benefits in providing a weighted, structured, and comprehensive analysis make it a valuable tool in quality management, particularly in high-tech industries where precision and targeted interventions are critical.

4. Results

On the basis of the aforementioned frequency analysis and QDI, we prioritized the root causes of five non-quality cost factors: Customer Delivery Schedule Delays, Exceeding Budgeted R&D Hours, Post-Delivery Warranty Repairs, Rejection of Parts or Assemblies in the Manufacturing Process, and Low Yields in Production.

4.1. Customer Delivery Schedule Delays

The exploratory study identified seven underlying root causes for the non-quality cost factor of Customer Delivery Schedule Delays. Listed alphabetically, these root causes are as follows:

• Delays in receiving supplies from suppliers, manufacturers, or contractors;
• Poor quality of created or purchased raw materials;
• Project development problems due to lack of professionalism;
• Project management issues;
• Shortage of workforce in development;
• Shortage of workforce in operations;
• Technical problems in the manufacturing, assembly, or testing of the product.

Participants ranked these root causes on five levels of significance levels, with “1” being the most significant and “5” the least significant, as shown in

Table 1. Rank, frequency, and QDI for the Root Causes of Customer Delivery Schedule Delays.

Root Cause	Ranking					QDI
	1	2	3	4	5
	Frequency
Delays in supplies from suppliers, manufacturers, or contractors	7	5	3	2	0	75,320
Shortage of workforce in development	7	5	2	0	0	75,200
Project management issues	6	5	1	1	0	65,110
Project development problems due to lack of professionalism	6	4	0	1	0	64,010
Technical problems in the manufacturing, assembly, or testing of the product	3	3	4	2	1	33,421
Poor quality of created or purchased raw materials	1	1	6	1	1	11,611
Shortage of workforce in operations	0	6	3	1	1	6311

Note: QDI = Quality Deterioration Index.

. The table displays the frequency of each root cause within each ranking level and the QDI for each root cause, arranged in descending order of QDI.

The QDI values displayed in Table 1 clearly show a definitive hierarchy of root causes for the non-quality cost factor Customer Delivery Schedule Delays. Specifically, delays in supplies from suppliers, manufacturers, or contractors top this hierarchy with a staggering QDI of 75,320, underscoring the paramount influence of the supply chain. This critical issue can disrupt many downstream activities, from production to assembly, and therefore demands focused analysis in order to reduce its impact on delivery schedules.

Shortage of workforce in development is close behind, with a QDI of 75,200, indicating that gaps in workforce capabilities are a recurring obstacle, potentially due to insufficient capability analysis or flawed business processes. Thus, it is essential to rectify this situation to improve development efficiency.

Project management issues are also prominent, with a QDI of 65,110 that reflects the profound effects of management practices on delivery timelines, because resource management and coordination improvements are imperative. Next, project development problems due to lack of professionalism have a QDI of 64,010, pointing towards the repercussions of insufficient expertise on project quality, signaling a need for enhancement in knowledge and training.

Further, technical problems in manufacturing, assembly, or testing of the product, with a QDI of 33,421, draw attention to the technical challenges that can mar the production stages, emphasizing the need for technical vigilance and quality assurance.

Poor quality of created or purchased raw materials, with a QDI of 11,611, highlights concerns about the quality of materials, suggesting widespread issues of procurement and manufacturing that necessitate thorough investigation to improve material standards. Lastly, the shortage of workforce in operations, with a QDI of 6311, shows that a scarcity of workers is an operational challenge and stresses the importance of strategic planning to bolster workforce and operational productivity.

This structured hierarchy, defined by the severity of the QDIs, is a strategic tool for effectively prioritizing and addressing these root causes, so as to mitigate their impact on customer delivery schedules. The analysis shown in Table 1 leverages QDI to rank the root causes of the non-quality cost factor of Customer Delivery Schedule Delays. Hierarchical clustering illuminates the underlying issues and organizes them into two distinct clusters, each encapsulating root causes with proximate QDI values. The meticulous application of classification techniques, including hierarchical clustering, classification trees, two-step clustering, and K-Means, lends credence to the clusters’ validity.

Cluster 1 includes a quartet of pivotal root causes: delays in supplies from suppliers, manufacturers, or contractors (QDI = 75,320), shortage of workforce in development (QDI = 75,200), project management issues (QDI = 65,110), and project development problems due to lack of professionalism (QDI of 64,010). This cluster symbolizes the core challenges spanning supply chain intricacies, workforce dynamics, managerial excellence, and professional insight. Concurrently, cluster 2 consists of three root causes: technical problems in manufacturing, assembly, or testing of the product (QDI = 33,421), poor quality of created or purchased raw materials (QDI = 11,611), and shortage of workforce in operations (QDI = 6311). This cluster reflects the tangible issues within the technical realms, material quality, and active workforce, each contributing to the complex tapestry of delivery schedule adherence. Together, these clusters offer a strategic framework, enabling a deeper comprehension of the root causes that influence delivery schedules, guiding targeted improvements for the non-quality cost factor Customer Delivery Schedule Delays with precision and insight.

4.2. Exceeding Budgeted R&D Hours

The exploratory study pinpointed seven root causes for the non-quality cost factor Exceeding Budgeted R&D Hours. The root causes, organized alphabetically, are as follows:

• Delays in supplies from suppliers, manufacturers, or contractors;
• Incorrect estimation of working hours;
• Ineffective integration of engineering and operational factors;
• Late design changes due to failure in testing and validation;
• Workforce shortage;
• Misunderstanding of part of the client’s requirements;
• Suboptimal project management.

Participants ranked the non-quality cost factor Exceeding Budgeted R&D Hours on four levels of importance, with “1” being the most significant, and “4”, the least so. It is worth noting that the evaluation process was limited to four levels because a fifth level of significance was not included in the assessment.

Table 2. Rank, frequency, and QDI of Root Causes for Non-Quality Cost Factor Exceeding Budgeted R&D Hours.

Root Cause	Ranking				QDI
	1	2	3	4
	Frequency
Misunderstanding of part of the client’s requirements	10	2	1	0	10,210
Incorrect estimation of working hours	6	6	3	2	6632
Suboptimal project management	6	2	5	1	6251
Late design changes due to failure in testing and validation	5	7	2	0	5720
Workforce shortage	4	2	4	0	4240
Ineffective integration of engineering and operational factors	1	7	4	4	1744
Delays in supplies from suppliers, manufacturers, or contractors	0	2	1	0	210

Note: QDI = Quality Deterioration Index.

categorizes the root causes associated with Exceeding Budgeted R&D Hours, according to how frequently they occur at each ranking level, accompanied by their respective QDI, arranged by descending QDI, to emphasize those with the most significant perceived effect on the quality deterioration.

Table 2 displays a comprehensive analysis of the non-quality cost factor Exceeding Budgeted R&D Hours by assessing the frequency and QDI of each root cause, which is pivotal for understanding their impact on project quality and budget. Misunderstanding the client’s requirements stands out with the highest QDI of 10,210, indicating that critical challenges in grasping the client could significantly drive up development hours. Next, incorrect estimation of working hours and suboptimal project management, with QDIs of 6632 and 6251, respectively, underscore issues with project planning and execution that may result in budget overruns. Late design changes due to failure in testing and validation and workforce shortage are also prominent factors. Their QDIs of 5720 and 4240, respectively, highlight the repercussions of technical failures and staffing deficits on project timelines. Further down the hierarchy, ineffective integration of engineering and operational factors, with a QDI of 1744, and delays in supplies from suppliers, manufacturers, or contractors with the lowest QDI of 210, are less critical but still represent notable contributors to project delays. This hierarchical view encapsulates the varying degrees of influence the root causes have on extending development hours beyond the planned budget.

In addition to the QDI-based hierarchy of root causes, shown in Table 2, the root causes of Exceeding Budgeted R&D Hours can be organized into three clusters based on QDI proximity. Hierarchical clustering, decision trees, two-step clustering, and K-means clustering were employed to validate and confirm these clusters. Cluster 1 contains only one root cause, a misunderstanding of part of the client’s requirements, which stands alone at the peak due to its significantly higher QDI (10,210), far above the next root cause. Cluster 2 consists of four root causes, incorrect estimation of working hours (QDI = 6632), suboptimal project management’ (QDI = 6251), late design changes due to failure in testing and validation (QDI = 5720), and workforce shortage (QDI = 4240). Cluster 3 consists of two root causes, ineffective integration of engineering and operational factors (QDI = 1744) and delays in supplies from suppliers, manufacturers, or contractors (QDI = 210).

4.3. Post-Delivery Warranty Repairs

The exploratory study identified six essential root causes for the non-quality cost factor of Post-Delivery Warranty Repairs. These root causes, in alphabetical order, are as follows:

• Gaps in design definitions;
• Incomplete transition process to production;
• Low yields in production;
• Products deviating from specifications;
• The product did not undergo adequate testing, validation, and reliability checks during development;
• The product did not meet customer requirements properly and stably.

Participants ranked the root causes of Post-Delivery Warranty Repairs, based on their significance, categorizing them into four distinct levels of importance, with “1” being the most significant, and “4” being the least significant. The evaluation deliberately excluded a fifth level of significance from the assessment framework. There was a conspicuous absence of any reference to the root cause of gaps in design definitions. This omission stands out in the assessment process.

Table 3. Rank, frequency, and QDI of Root Causes for Non-Quality Cost Factor Post-Delivery Warranty Repairs.

Root Cause	Ranking				QDI
	1	2	3	4
	Frequency
The product did not meet customer requirements properly and stably	6	5	1	0	6510
The product did not undergo adequate testing, validation, and reliability checks during development	6	1	1	0	6110
Incomplete transition process to production	4	5	1	0	4510
Products deviating from specifications	1	3	4	1	1341
Low yields in production	1	2	2	0	1220

Note: QDI = Quality Deterioration Index.

presents a structured overview of root causes associated with Post-Delivery Warranty Repairs, by frequency of occurrence, and decreasing QDI, emphasizing those that are most influential in degrading quality.

Utilizing data from Table 3, a detailed hierarchy of root causes impacting the non-quality cost factor of Post-Delivery Warranty Repairs was established based on their QDI. The foremost root cause, the product did not meet customer requirements properly and stably (QDI = 6510), signals significant development verification and quality issues. Next, the product did not undergo adequate testing, validation, and reliability checks during development (QDI = 6110) and incomplete transition to production (QDI = 4510), which points to related concerns. Moreover, products deviating from specifications (QDI = 1341) and low production yields (QDI = 1220) indicate overall operational and overall product quality challenges.

In addition to this hierarchy, these root causes are organized into distinct clusters by proximity in QDI values. Hierarchical clustering, decision trees, two-step clustering, and K-Means clustering confirm the organization of these causes into specific clusters. The QDI-based clusters provide a comprehensive, structured perspective on the main factors that affect product repairs during the warranty period. Cluster 1 includes the three most critical causes related to development verification and quality, while cluster 2 includes those associated with operational and quality issues. Thus, the clusters expose the various dimensions of quality deterioration related to product repair during the warranty period.

4.4. Rejection of Parts or Assemblies in the Manufacturing Process

The exploratory study pinpointed seven vital root causes for the non-quality cost factor of Rejection of Parts or Assemblies in the Manufacturing Process. These root causes are presented in alphabetical order:

• Engineering changes during production;
• Engineering definitions are unclear and unambiguous for production;
• Procurement before product approval;
• Incomplete or missing documentation;
• Lack of worker training in manufacturing;
• Manufacturer or supplier errors;
• Worker errors.

Participants ranked these root causes based on their level of importance, according to five levels of significance, with “1” being the highest level of importance and “5” the lowest level. The evaluation scale was used to determine the criticality of each root cause and its impact on the manufacturing process.

Table 4. Rank, frequency, and QDI of Root Causes for Non-Quality Cost Factor Rejection of Parts or Assemblies in the Manufacturing Process.

Root Cause	Ranking					QDI
	1	2	3	4	5
	Frequency
Engineering changes during production	11	5	0	4	0	115,040
Manufacturer or supplier errors	8	5	6	1	3	85,613
Procurement before product approval	5	5	3	1	0	55,310
Worker errors	2	1	2	3	0	21,230
Engineering definitions are unclear and unambiguous for production	1	5	0	1	0	15,010
Lack of worker training in manufacturing	1	0	0	0	1	10,001
Incomplete or missing documentation	0	2	7	3	0	2730

Note: QDI = Quality Deterioration Index.

provides a comprehensive overview of these root causes, organizing them according to the frequency of their occurrence at each level of significance and their corresponding QDI. This table arranges the causes by descending QDI, thereby highlighting those that have the most significant impact on quality degradation. Table 4 comprehensively analyzes the root causes for the non-quality cost factor Rejection of Parts or Assemblies in the Manufacturing Process, evaluated by QDI. Leading the list is engineering changes during production, with a QDI of 115,040, which signifies critical issues in simultaneous development and manufacturing processes that contribute to the rejection of parts or assemblies. Next, manufacturer or supplier errors (QDI = 85,613) suggest difficulties adhering to company specifications, potentially leading to production and assembly errors. Procurement before product approval is another significant factor (QDI = 55,310), which indicates the risks associated with early procurement decisions.

Other key causes include worker errors (QDI = 21,230), hinting at problems arising from insufficient training or non-compliance with instructions; engineering definitions are unclear and unambiguous for production (QDI = 15,010), reinforcing the need for clear engineering specifications; lack of worker training in manufacturing (QDI = 10,001), underscoring the importance of adequate training; and incomplete or missing documentation (QDI = 2730), reflecting issues with documentation processes in manufacturing.

Further analysis organizes the root causes into distinct clusters based on their QDI proximity. Hierarchical clustering, decision trees, two-step clustering, and K-Means clustering confirm the clustering of these causes. The hierarchy and clustering of root causes for the Rejection of Parts or Assemblies in the Manufacturing Process provide an in-depth understanding of their interrelations based on quality deterioration indicators. Cluster 1 encompasses the three most significant causes, focusing on development, engineering, and quality issues, while cluster 2 includes causes related to worker errors, engineering definitions, and documentation. This clustering method effectively highlights distinct aspects of quality deterioration in manufacturing processes.

4.5. Low Yields in Production

The exploratory study identified eight primary root causes for the non-quality cost factor associated with Low Yields in Production. These root causes, listed in alphabetical order, are as follows:

• Design issues;
• Faults in testing equipment;
• Human errors;
• Infrastructure malfunctions;
• Manufacturing issues: difficulty in producing, assembling, testing, or disassembling.
• Missing or incomplete documentation;
• Partial and incomplete test coverage;
• The quality of purchased items does not meet the requirements.

The root causes of the non-quality cost factor “Low Yields in Production” were evaluated based on their impact and then categorized into five levels of importance, from “1”, representing the most critical cause, to “5”, the least significant.

Table 5. Rank, frequency, and QDI of Root Causes for Non-Quality Cost Factor Low Yields in Production.

Root Cause	Ranking					QDI
	1	2	3	4	5
	Frequency
Design issues	10	3	6	0	1	103,601
Manufacturing issues: difficulty in producing, assembling, testing, or disassembling	8	7	4	0	0	87,400
The quality of purchased items does not meet the requirements	3	5	1	3	1	35,131
Partial and incomplete test coverage	3	2	2	4	0	32,240
Faults in testing equipment	1	5	0	3	1	15,031
Missing or incomplete documentation	1	2	2	0	0	12,200
Human errors	1	0	6	0	0	10,600

Note: QDI = Quality Deterioration Index.

presents a detailed summary of these causes, organized by frequency at each importance level and QDI. By listing them by decreasing QDI, the table highlights those that most significantly contribute to quality reduction. reduction.

Table 5 shows that design issues, with a QDI of 103,601, have the most significant impact on the non-quality cost factor Low Yields in Production. This is a major factor contributing to production delays and reduced yields, underscoring the critical role of design in manufacturing efficiency. Next, difficulty in producing, assembling, testing, or disassembling has a significant QDI of 87,400, which reflects the complexities faced during the transition from development to manufacturing, impacting production schedules and the supply chain. Therefore, it is crucial to identify and address factors that lead to decreased production efficiency.

The quality of purchased items does not meet the requirements, with a QDI of 35,131, draws attention to the substantial impact of non-compliant materials on production quality and costs, emphasizing the need for effective vendor management and stringent quality checks. Partial and incomplete test coverage has a QDI of 32,240, indicating potential quality issues and failure to meet customer expectations, highlighting the importance of comprehensive testing for product integrity.

Faults in testing equipment, with a QDI = 15,031, point to technical problems in production, assembly, and testing that can lead to broad quality deficits. Missing or incomplete documentation has a QDI of 12,200, emphasizing the need for complete and precise documentation to prevent the rejection of parts and assemblies in production. Finally, human errors, with a QDI of 10,600, underline the consequences of worker mistakes in manufacturing and stress the importance of proper training and adherence to procedures.

In addition to identifying a hierarchy of these root causes, hierarchical clustering, decision trees, two-step, and K-means clustering were used to analyze the results. These processes yielded three distinct clusters of causes with similar QDI values. Cluster 1 includes design issues and manufacturing issues: difficulty in producing, assembling, testing, or disassembling, which focuses on root causes related to development, engineering, and quality control. Cluster 2 is comprised of the quality of purchased items not meeting requirements and partial and incomplete test coverage, focused on quality issues in procurement and testing. Cluster 3 encompasses faults in testing equipment, missing or incomplete documentation, and human errors, dealing with quality challenges related to equipment, documentation, and human factors. This structured, QDI-based approach offers a comprehensive understanding of the primary factors influencing Low Yields in Production, highlighting their interplay and interconnected nature.

5. Discussion

This study introduced a QDI approach to understand the root causes of various non-quality cost factors within the high-tech electronics manufacturing context, emphasizing the integration of sustainable quality management practices. practices.

For the non-quality cost factor Customer Delivery Schedule Delays, the analysis identified a prominent cluster comprised of delays in supplies from suppliers, manufacturers, or contractors, shortage of workforce in development, project management issues, and project development problems due to lack of professionalism. This suggests that supply chain coordination, workforce planning, project oversight, and professional competency development must be prioritized to mitigate delays. This result echoes the findings of [68,69], who discuss the effects of supply chain delays and project management issues on schedule adherence. It also relates to the observation of [70], suggesting that there is a need for improved transfer processes.

The frequency analysis and QDI evaluation of Exceeding Budgeted R&D Hours identified a significant cluster, comprised of frequent engineering design changes, unclear product definition, and unrealistic project timelines. This highlights the necessity for enhanced engineering standards, comprehensive requirement documentation, and realistic scheduling. These findings align with the principles discussed by [25,48], who emphasized customer-focused modeling as a key approach to controlling costs.

Analysis of the Post-Delivery Warranty Repairs revealed a notable cluster that includes incomplete design documentation and untested design changes, underscoring the critical need for thorough design validation and effective change management. The issues of incomplete documentation and untested changes are in line with [71] who found that unclear definitions have a significant impact on quality.

For Rejection of Parts or Assemblies in the Manufacturing Process analysis identified key clusters around inspection process errors and poor quality of purchased materials. This underscores the necessity for enhanced inspection rigor and improved supplier quality protocols. The issues related to inspection and supplier quality resonate with [72]’s findings on procurement quality control, which emphasize the importance of these aspects in manufacturing processes.

Analysis of Low Yields in Production revealed significant clusters related to process malfunctions and worker errors. This highlights the critical need for robust production processes and comprehensive workforce training. These issues concerning process and worker errors are consistent with the research conducted by [27], which emphasizes the impact of infrastructure and human factors on production yields.

The case study presented in this paper demonstrates a method for establishing a hierarchy of root causes for non-quality cost factors, underlining significant findings related to supply chain inefficiencies, workforce challenges, and procedural gaps that directly affect quality and sustainability outcomes. By designing a survey to rank the relevant factors and determine their frequency, it is possible to organize these root causes into clusters that highlight critical areas for improvement. This approach aligns with the broader themes discussed in the special issue, particularly emphasizing quality management as a pivotal element in achieving sustainable manufacturing processes. For instance, delays in supplies and shortages in the development workforce not only impact production schedules but also lead to increased waste and energy consumption, which are critical sustainability concerns. Additionally, the QDI encourages a structured approach to identifying and addressing these environmental impacts by prioritizing root causes based on their severity and frequency. Social impacts, such as workforce shortages, are also considered, highlighting the importance of addressing both environmental and social factors to enhance overall sustainability. The integration of these findings into a structured framework makes it possible to address these inefficiencies effectively, enhancing both operational and environmental performance.

Moreover, the study’s focus on using the QDI to prioritize root causes encourages a more structured approach to identifying and addressing quality-related issues in a manner that also considers environmental impacts. This methodological approach is particularly relevant to the themes of the special issue, as it demonstrates how targeted interventions can simultaneously enhance quality and reduce environmental footprints. The frequency analysis and QDI evaluations underscore the necessity of integrating advanced technologies such as Industry 4.0 tools, which can facilitate more precise monitoring and management of quality and sustainability metrics. This integration is pivotal in driving the shift from reactive to proactive quality management practices, a transition that is essential for fostering sustainable manufacturing landscapes.

A significant limitation of this study, however, is its focus on a single organization, which may limit the generalizability of the results to other firms, even within the electronics sector. Furthermore, the QDI is built on subjective rankings, which could introduce biases. To address these limitations, future research could replicate this methodology across multiple organizations to more robustly validate the identification of clusters. Additionally, integrating alternative modeling techniques could enhance the statistical analysis.

Ultimately, this study presents a quality management framework aimed at addressing non-quality costs while aligning with sustainable manufacturing principles. The approach suggests potential pathways to enhance both economic and environmental performance. By implementing innovative techniques, we identified and prioritized key clusters, which may guide targeted mitigation efforts and improvement strategies to reduce quality deterioration. Although the insights provided can support manufacturers in integrating quality management systems with sustainability objectives, further research is necessary to validate and refine this approach across different industries and contexts to better understand its broader applicability and impact.

6. Conclusions

In this study, we explored the integration of sustainable quality management practices within high-tech electronics manufacturing through a detailed case study. Our findings underscore the significance of aligning quality management with green sustainability objectives to enhance operational efficiency, reduce waste, and ensure environmentally responsible manufacturing processes. The QDI and the sustainable practices discussed in this paper are not confined to the specific context of the case study. These methodologies can be adapted and applied across various manufacturing sectors globally. The principles of QDI provide a structured approach to identifying and prioritizing non-quality costs, which is a common challenge in many industries.

The relevance of our findings extends to both developing and developed countries. In developing countries, where resources may be more constrained, the application of QDI can lead to significant improvements in efficiency and cost savings, thereby contributing to economic growth and sustainability. In developed countries, where advanced manufacturing processes and sustainability initiatives are often more established, integrating QDI with existing quality management systems can further enhance environmental stewardship and operational excellence. By demonstrating the adaptability and broad applicability of the QDI and sustainable quality management practices, this study provides a valuable framework for manufacturing organizations worldwide. Future research should focus on validating these approaches in different industrial and geographical contexts to further establish their efficacy and generalizability.

This study introduced the QDI as a novel framework for identifying and prioritizing the root causes of non-quality costs, providing a structured and quantifiable method for quality management. By aligning quality management practices with green sustainability objectives, this research contributes to the growing body of literature on sustainable manufacturing, emphasizing the dual focus on economic and environmental performance. The study offers a comprehensive analysis that includes procedural, cultural, and environmental factors, thereby advancing the understanding of the complex interplay between these elements in quality deterioration.

The implementation of QDI provides actionable insights for manufacturers, enabling them to target critical areas for improvement and enhance operational efficiency. By addressing root causes that lead to waste and increased energy consumption, the QDI framework supports more environmentally responsible manufacturing practices. The study highlights the importance of addressing workforce challenges and project management issues, providing practical recommendations for improving professional competency and project oversight.

Implementing innovative techniques facilitated the identification and prioritization of key clusters, which can guide targeted mitigation efforts and improvement strategies to reduce quality deterioration, all underpinned by rigorous statistical analysis. The insights provided herein are instrumental for manufacturers aiming to integrate robust quality management systems with sustainability objectives, ultimately leading to more sustainable, efficient, and competitive manufacturing operations. Future research should expand this approach across different industries and global contexts to validate and refine the integration of quality management and sustainability, thereby contributing to the global goals of green manufacturing.