Background: Exploring the impact of combining Industry 4.0 technologies and Lean Manufacturing tools on organizational performance has been a popular topic in recent years. Design/Methodology/Approach: We propose a novel Smart and Sustainable Manufacturing System (SSMS) to provide management insights related to social impact, economic performance, and environmental impact. Some tools called Dynamic Lean 4.0 tools, such as Sustainable Value Steam Mapping (VSM), Extended Single Minute Exchange of Die (SMED), and Digital Poka-Yoke, are presented as outputs of synergistic relationships that optimize production processes. Originality/Research gap: There are few studies on the application of SSMS. This work presents a case study, aiming to fill this gap. A case study of vacuum degassing equipment fabrication is presented to demonstrate the improvement of utilizing the Define-Measure-Analyze-Improve-Control (DMAIC) method with Digital Poka-Yoke. Key statistical results: The implementation of this project increased the process capability index, Cpk, from 1.278 to 2. Practical Implications: It was concluded that the company successfully implemented a smart and sustainable manufacturing system, and created a safer working environment and new job opportunities, while increasing production yield from 99.44% to 100%, improving worker utilization, and directly saving NT$68,000. Limitations of the investigation: This paper is the use of a single case study. More applications of Dynamic Lean 4.0 tools in SSMS should be explored.

Many industries have successfully implemented the define-measure-analyze-improve-control (DMAIC) method of Lean Six Sigma to improve their production activities. Value stream mapping (VSM) for Industry 4.0 allows us to understand the current state of operations in order to plan future improvements. In this study, we propose an improvement model based on DMAIC with VSM 4.0 for a truck cooler manufacturer to improve the picking workstation design with a human-centered approach. We use the DMAIC method to analyze the project step by step. After identifying the root cause, we identified countermeasures to improve the productivity. To reduce human error, the project team adopted a human-centered approach and applied lean tools, such as visual management, error prevention, and waste analysis. As a result of this case study, the yield rate was improved from 98% to 100%, and the direct savings of the project amounted to EUR 3180, mainly due to the freeing up of space. A human-centered LSS framework is also presented as a novel contribution of this study.

Sustainability in operations and supply chains is becoming more popular among academics and practitioners through Sustainable Supplier Selection (SSS). In addition to balancing economic, social, and environmental factors, the awareness of the United Nations Sustainable Development Goals 2030 has affected the selection of long-term suppliers, ensuring green operations and sustainable supply chains. The criteria for SSS have multiple dimensions and are interdependent; this mimics the real-world scenario rather than assuming independently from an analytic hierarchy process. We use the multi-criteria decision-making (MCDM) model, combining DEMATEL-based on ANP (called DANP) with VIKOR, to solve the SSS problem. The DANP method is used to model and assess the interdependent relationships between criteria. Then, ranking the available alternatives and selecting the best one can be accomplished using the VIKOR method. We consider the electronic manufacturing industry in Taiwan as an empirical case. This study, in addition to selecting the best sustainable supplier, demonstrates the use of influential network relationship maps to analyze and improve the gaps in each dimension and criterion.

Purpose: This study aims to propose a novel sustainable performance metric (SPM) to integrate environmental and operational dimensions into a performance evaluation framework. This metric aims to enhance sustainable process improvement within green lean six sigma (GLSS), particularly for automotive manufacturing. Design/methodology/approach: This study uses a case study conducted in an Indonesian manufacturing industry. The proposed SPM originates from the company’s environmental problems in energy, waste and resource consumption. By using the SPM, the company’s sustainability performance can be measured, leading to improvement in the production process. The improvement is embedded within the GLSS concept, and supported by lean six sigma (LSS) tools to enhance the production process. Findings: The company’s initial SPM value was approximately 10%, indicating poor sustainability performance. After analyzing the problems and implementing improvements using LSS tools, the SPM rose to 68%, with total monthly cost savings of approximately US$20,467.86. Originality/value: This paper introduces a novel metric, the SPM, which modifies the traditional measurement overall equipment effectiveness by incorporating environmental indicators: energy, waste and resource consumption. Unlike conventional productivity measures that focus solely on machine efficiency, the SPM provides a more comprehensive assessment of operational sustainability. Furthermore, this metric is systematically embedded within the GLSS concept to offer a measurement and decision-making mechanism. This integrated approach bridges gaps in existing performance metrics and supports the development of more sustainable, resilient manufacturing systems.

This study attempts to apply Failure Mode Effects and Criticality Analysis (FMECA) to improve the safety of production system, especially on the production process of an oil company in Indonesia. Since food processing is a worldwide issue and the self management of a food company is more important than relying on government regulations, so the purpose of this study is to identify and analyze the criticality of potential failure mode on the production process, then take corrective actions to minimize the probability of making the same failure mode and re-analyze its criticality. This corrective actions are compared with the before improvement condition by testing the significance of the difference between before and after improvement using two sample t-test. Final result that had been measured is Criticality Priority Number (CPN), which refers to severity category and probability of making the same failure mode. Recommended actions that proposed on the part of FMECA give less CPN significantly compare with before improvement, with increment by 48.33% on coconut cooking oil case study. This study attempts to apply Failure Mode Effects and Criticality Analysis (FMECA) to improve the safety of production system, especially on the production process of an oil company in Indonesia. Since food processing is a worldwide issue and the self management of a food company is more important than relying on government regulations, so the purpose of this study is to identify and analyze the criticality of potential failure mode on the production process, then take corrective actions to minimize the probability of making the same failure mode and re-analyze its criticality. This corrective actions are compared with the before improvement condition by testing the significance of the difference between before and after improvement using two sample t-test. Final result that had been measured is Criticality Priority Number (CPN), which refers to severity category and probability of making the same failure mode. Recommended actions that proposed on the part of FMECA give less CPN significantly compare with before improvement, with increment by 48.33% on coconut cooking oil case study.

The advancement of automated driving technologies offers potential safety and efficiency gains, yet safety remains the primary barrier to higher-level deployment. Failures in automated driving systems rarely result from a single technical malfunction. Instead, they emerge from coupled organizational, technical, human, and environmental factors, particularly in partial and conditional automation where human supervision and intervention remain critical. This study systematically identifies safety failures in automated driving systems and analyzes how they propagate across system layers and human–machine interactions. A qualitative case-based analytical approach is adopted by integrating the Swiss Cheese model and the SHELL model. The Swiss Cheese model is used to represent multilayer defensive structures, including governance and policy, perception, planning and decision-making, control and actuation, and human–machine interfaces. The SHELL model structures interaction failures between liveware and software, hardware, environment, and other liveware. The results reveal recurrent cross-layer failure pathways in which interface-level mismatches, such as low-salience alerts, sensor miscalibration, adverse environmental conditions, and inadequate handover communication, align with latent system weaknesses to produce unsafe outcomes. These findings demonstrate that autonomous driving safety failures are predominantly socio-technical in nature rather than purely technological. The proposed hybrid framework provides actionable insights for system designers, operators, and regulators by identifying critical intervention points for improving interface design, operational procedures, and policy-level safeguards in autonomous driving systems.