Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors

Subject Areas : Industrial Management

Rouhollah Khakpour ¹ , Ahmad Ebrahimi ² , Seyed Mohammad Seyed Hosseini ³

1 - . Ph.D. Candidate, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Assistant Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, University of Science and Technology, Tehran, Iran

Received: 2024-09-19 Accepted : 2025-01-05 Published : 2025-06-22

Keywords: Zero defect manufacturing, Sustainability, Machine learning, Quality improvement,

Abstract :

Abstract

his paper presents a stepwise approach to achieving zero-defect production. In addition to eliminating waste in manufacturing resources, it also evaluates the impact of these improvements on sustainability. The proposed method is grounded in a literature review on zero-defect manufacturing (ZDM), examining the effective dynamic factors that influence product quality during production. It integrates ZDM strategies with Industry 4.0 technologies, including the Internet of Things (IoT) and machine learning, to enhance manufacturing efficiency and precision. It goes beyond the conventional approaches including process oriented, product oriented, and emerging human oriented ZDM, and identifies the effective dynamic factors on product quality. It then, implements predict-prevent strategies to anticipate the timing of defect occurrence during production and prevents its occurrence accordingly. This research identifies, explains, and measures criteria for environmental, social, and economic pillars of sustainability affected by defects. The proposed approach is implemented in a real life industrial case, where, the outcomes prove its remarkable applicability and capability in avoiding defective products, increasing productivity of production resources, and improving the sustainability of manufacturing processes.

 

Key Words: zero defect manufacturing, sustainability, machine learning, quality improvement

 

Introduction

Achieving sustainability in manufacturing requires an extended view on its' environmental, economic, and social pillars, coined as triple bottom line (TBL) (Elkington, 1998). One of the important factors that affects TBL sustainability in manufacturing firms is defect (Goyal, Agrawal, & Saha, 2019), where the issues such as scrap generation (Lindström et al., 2019), costs of consumption/waste of resources (Grobler-Dębska, Kucharska, & Baranowski, 2022), and customer satisfaction level (Mourtzis, Angelopoulos, & Panopoulos, 2021) are examples of sustainability criteria which are affected by defects. Hence, quality management is employed to reduce defects using quality improvement (QI) methods for achieving sustainability in manufacturing companies. Psarommatis, Sousa, Mendonça, and Kiritsis (2022) define zero-defect manufacturing (ZDM) as "a holistic approach for ensuring both process and product quality by reducing defects through corrective, preventive, and predictive techniques, using mainly data-driven technologies and guaranteeing that no defective products leave the production site and reach the customer, aiming at higher manufacturing sustainability".ZDM employs four strategies consisting of detect, predict, repair, and prevent. When a defect is detected, it can be repaired (detect-repair). Moreover, the gathered data from defect detection can be used in two ways: to prevent defect occurrence in the future (detect-prevent) and to design algorithms for predicting when a defect may occur in the future, hence, to prevent defects before they arise (predict-prevent). The current work aims to present the development of ZDM by focusing on dynamic factors affecting product quality in order to improve sustainability in manufacturing processes in the era of industry 4.0.

Utilizing ZDM philosophy along with technological advances in cyber physical systems (CPS) such as IoT, big data, and advanced data analytics creates remarkable challenges and opportunities to develop new methodologies in continuous improvement of process efficiency and product quality (Leitão, Barbosa, Geraldes, & Coelho, 2018). Martinez, Al-Hussein, and Ahmad (2022) propose a framework integrating cyber physical production system (CPPS) and ZDM for quality prediction and operation control where the CPPS is used to facilitate data management and to extend data analysis towards ZDM goals. Leitão et al. (2018) apply multi-agent system (MAS) infrastructure, which combines with data analysis, provides early and real time detection of deviations, prevents defects occurrence and their propagation to downstream processes, and finally enables the system to be predictive by early detection of defects and to be proactive through self-adaptation with different situations.

Magnanini, Colledani, and Caputo (2020) propose applying the manufacturing execution system (MES) for real time data gathering and data analysis to be exploited in ZDM strategies. In the era of industry 4.0, one of the applicable and developing tools to achieve ZDM is digital twin (DT) that incorporates the IoT, big data, artificial intelligence (AI) and ML (Psarommatis & May, 2022). Mourtzis et al. (2021) focus on ZDM by equipment design optimization and propose an approach relying on the integration of DT for simulation of new design based on historical data gathered from already installed similar machines and production environment.

Overall, the outcomes of this paper's literature review reveal that:

1. Developing an approach to identify and analyze the effective root causes of product quality that enhances the insights and efficiency of ZDM to achieve zero defect products is still an overlooked area.
2. TheZDM literature lacks papers that demonstrate the ZDM impacts on all three pillars of sustainability, that is, environmental, economic, and social.

 

Methodology

This research presents a method to assess the sustainability of manufacturing processes throughout the value stream. The approach is grounded in effective dynamic factors of product quality and ZDM strategies. The methodology follows these steps:

Step 1: Analysing effective dynamic factors of product quality

Step2: Evaluating Triple Bottom Line (TBL) criteria

Step 3: Measuring current sustainability state

Step 4: Implementing ZDM strategies

Step 5: Measuring improvements in sustainability

 

Results

 Effects of Single Unit Defective Product on TBL Sustainability State in Value Stream

 

 

Summary of current sustainability state

Product model	Daily schedule (set)	Defective product rate (%)	Number of defective products (set)	Environmental sustainability State	Social sustainability state	Economic sustainability state
Refrigerator	480 set	3%	15	Wasted material: 15 set   Wasted energy: 239.25 kwh	Waste of manpower: 1650 pmin	Wasted costs: 3265.65 $

 

 

Future TBL sustainability state

Product model	Daily schedule (set)	Defective product rate (%)		Number of defective products (set)	Environmental sustainability state	Social sustainability state	Economic sustainability state
Refrigerator	480 set	0.2%		1	Wasted material: 1 set   Wasted energy: 15.95 kwh	Waste of manpower: 110 pmin	Wasted costs: 217.71 $

 

 

 

 

 

 

 

 

 

 

Discussion and conclusion

        Implementing the proposed approach aimed at achieving zero-defect products and enhancing TBL sustainability as its ultimate goal has provided valuable insights for practitioners and tangible improvements in the case study of this research. In this section, the proposed approach is discussed in light of existing approaches and the relevant challenges. Finally, the conclution and directions for future reseach are presented. Several papers have focused on the ZDM approach to demonstrate its capability to achieve zero defect product. However, the applied approaches are mainly product-oriented and process-oriented which concentrate on machine health to ensure product quality. Regarding the other effective parameters of product quality, focusing on machine health is not enough to guarantee the quality of the output product. Hence, the proposed method in this research builds upon the conventional ZDM approaches since it provides an extended view on the effective root causes that influence product quality. It further explains how these factors can affect the product quality.

Going through the papers that consider sustainability aspects in ZDM reveals that the ZDM literature lacks papers that address the ZDM impacts on all three pillars of sustainability, that is, environmental, economic, and social. To fill this gap, this paper identifies an extended range of TBL criteria that are affected by defects and improved through ZDM implementation, which presents a quantitative assessment of TBL criteria relevant to the case study. The recommended method is implemented in a real-life manufacturing case study in this paper. The results prove the practicality of the method. The case study reports remarkable improvement in reducing defect occurrence as well as enhancing TBL sustainability state. The empirical insights, drawn from the real-life case study of this research, indicate the challenges and complexities that arise in the path of achieving zero defect product and sustainability improvement. The extended view on effective root causes of product quality and the focus on improving TBL sustainability criteria in this research as well as its analytical approach offer practitioners valuable insights for improving their ZDM approaches in a more comprehensive way. Future research could explore the application of this method across additional levels of supply chain management.

 

Conflict of interest: none

 

 

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