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.

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.

Purpose: This study presents a framework that links process-level sustainability performance and economic loss analysis in manufacturing by integrating the triple bottom stream method (TBSM), a value-stream-oriented process-flow assessment framework, into the Lean Six Sigma-define–measure–analyze–improve–control (LSS-DMAIC) cycle. The purpose of this study is to provide a structured method for identifying how operational, environmental and social inefficiencies relate to financial outcomes and to support sustainable process improvement. Design/methodology/approach: The proposed framework uses TBSM to evaluate operational, environmental and social sustainability performance. These dimensions are integrated into a Composite Performance Index (CPI) that represents overall manufacturing sustainability performance. A diagnostic sustainability-loss indicator is further used to assess sustainability-related deficiencies, while economic performance is evaluated through economic-loss analysis based on observed process and problem costs. The framework enables the identification of process-level inefficiencies across the operational, environmental and social dimensions while providing a separate assessment of economic consequences at the process level. It is subsequently operationalized through the LSS-DMAIC cycle to support structured process improvement. Findings: In an Indonesian manufacturing SME, the initial TBSM assessment yielded a CPI of 64.17%, indicating acceptable overall sustainability performance; however, detailed process-level assessment and economic-loss evaluation revealed that several manufacturing processes still exhibited substantial operational, environmental and social deficiencies. After implementing improvement actions, the CPI increased to 78.92%, reflecting improvement across operational, environmental and social dimensions. The improvements generated an estimated gross monthly saving of US$4,807.38 and a net monthly financial benefit of US$3,780.49 after deducting operational costs. Originality/value: Prior approaches often evaluate selected dimensions without providing an integrated assessment of operational, environmental and social sustainability performance alongside process-level economic consequences. To address this gap, this study introduces the TBSM, which integrates operational, environmental and social sustainability assessments into the CPI while examining economic loss through a separate economic-consequence analysis path. This framework enables a more structured assessment of sustainability performance and economic consequences at the process level. The proposed framework supports broader sustainability objectives, including Sustainable Development Goal (SDG) 9 and SDG 12 while advancing integrated and system-oriented sustainability practices in manufacturing.

This study develops a unified optimization framework to evaluate and compare the performance of electric, conventional, and hybrid vehicle routing strategies under consistent operational conditions. The proposed model integrates routing decisions, energy consumption, infrastructure constraints, and emission considerations within a single mathematical formulation, enabling a comprehensive assessment of both economic and environmental performance. Using a benchmark urban transportation network, the study analyzes route configurations, operational costs, financial feasibility, and carbon emissions across the three vehicle technologies. The results indicate that electric vehicles offer lower and more stable operational costs, with zero direct emissions. In contrast, conventional vehicles offer greater operational flexibility, albeit at the expense of higher costs and a greater environmental impact. The hybrid model, which allows endogenous technology selection at the route level, achieves the most balanced performance by assigning vehicle types based on route-specific characteristics. This leads to significant reductions in both operational costs and emissions compared to single-technology approaches. The findings underscore the importance of integrated and flexible routing strategies in supporting sustainable transportation planning, offering practical insights for policymakers and transport operators in designing efficient and environmentally responsible systems.