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. 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. 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. 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 aims to quantify the interaction effects between a Lean tool (Poka-Yoke) and an Industry 4.0 (I-4.0) technology (a machine vision camera) on operational performance, as measured by Overall Equipment Effectiveness (OEE). A controlled laboratory-based experiment was conducted using a within-subject design with four treatment conditions: Control (paper-based instructions), Lean (Poka-Yoke), I-4.0 (machine vision inspection) and Lean 4.0 (a combination of Lean and digital inspection). Participants completed standardized assembly tasks under each condition. Operational performance was evaluated using OEE, decomposed into performance and quality components. Statistical analyses, including repeated-measures ANOVA and post hoc tests, were applied to examine significant differences among treatments and to assess the individual and combined effects of Lean and I-4.0 interventions. The results demonstrate that the treatment, I-4.0, significantly improves OEE (p < 0.05), indicating an immediate operational benefit. In contrast, the Lean and Lean 4.0 treatments did not yield statistically significant improvements in OEE. These findings suggest that while digital technologies can independently enhance operational performance, the anticipated synergistic benefits of integrating Lean and I-4.0 may not materialize without careful alignment and contextual considerations. This study was conducted in a controlled academic laboratory environment, which may not fully represent the complexities and dynamic conditions of real-world manufacturing settings. Furthermore, the relatively small sample size (n = 48) may constrain the generalizability of the findings. This study provides empirical evidence on the interaction between a specific Lean tool and an I-4.0 technology, addressing a key gap in “Lean 4.0” research. It shows how one-to-one complementarity can be evaluated rather than assumed. The findings challenge the idea of automatic synergy and offer practical insights for researchers, practitioners and policymakers. The experimental model also supports teaching and training on integrating Lean and I-4.0 in smart manufacturing.
Purpose This study aims to identify, validate and structure strategies for integrating big data analytics (BDA) and lean six sigma (LSS) to support circular economy (CE) adoption in construction supply chains. It aims to provide a prioritised strategy framework for sequencing BDA–LSS adoption. Design/methodology/approach This study adopts a hybrid multi-criteria decision-making approach. Firstly, strategies for promoting BDA–LSS integration CSCs were identified through an extensive literature review and expert consultation. Secondly, the fuzzy delphi method was applied to screen and validate the strategies based on expert consensus, reducing the preliminary list to 24 retained strategies. Thirdly, interpretive structural modelling was used to unfold the direct and hidden interactions among the validated strategies. Finally, Matrice d’Impacts Croisés Multiplication Appliquée à un Classement analysis was applied to classify the strategies into “driving”, “linkage” and “dependent” clusters. The natural resource-based view and institutional theory were used to interpret the results. Findings The findings identify “Integrate BDA–LSS into national plans” and “Top management support” as the strongest system drivers, with each strategy recording the maximum driving power of 24 and the minimum dependence power of two. “Provide targeted financial incentives” and “Advocate for collaborative contract models” form the secondary drivers, while “Governance”, “Establish unified data standards”, “Cross-functional BDA–LSS improvement teams” and several capability-building strategies occupy the feedback-sensitive linkage cluster. “Predictive analytics” and the operational LSS and CE practices are largely dependent on these upstream strategies. Practical implications The findings are translated into a three-phase, maturity-informed implementation roadmap covering system activation, coordination and capability building and operational value realisation. The roadmap may help policymakers and construction managers diagnose capability gaps, prioritise investments and avoid introducing advanced analytics before suitable governance, data, infrastructure and workforce conditions are established. Originality/value This study develops a construction-specific and theory-informed framework for BDA–LSS adoption in CE-oriented construction supply chains. Its originality lies in examining the integration strategies together and distinguishing three complementary analytical functions: consensus-based screening, hierarchical implementation sequencing and driving–dependence classification. This moves beyond the identification or ranking of isolated factors in much of the prior research, which has examined these domains separately or predominantly within manufacturing contexts.
Purpose - Existing sustainable supply chain management (SSCM) maturity models remain fragmented and predominantly diagnostic, offering limited guidance for prioritizing improvements across interdependent sustainability dimensions. This study aims to develop an integrated diagnostic-prescriptive maturity model to support the sustainable and digital path (twin transition) in supply chains by combining sustainability assessment, Industry 5.0-oriented human-centric digitalization, operational excellence and the Sustainable Development Goals (SDGs). The model is particularly relevant to Ibero-American supply chains operating under resource constraints, institutional heterogeneity and increasing pressure to align digital transformation with sustainability priorities. Design/methodology/approach - Following design science research, a systematic literature review identified 26 maturity models, which were examined through qualitative content analysis to reveal structural gaps and derive the artifact's design requirements. The model integrates 187 variables across 4 dimensions and 13 subdimensions. It was refined and validated through a two-round Fuzzy Delphi process involving 15 experts from five Ibero-American countries. A group-based ordinal multicriteria method was embedded to aggregate stakeholders' linguistic judgments into a consensus ranking of improvement priorities. An illustrative application demonstrated its use. Findings - The model assesses environmental, social, economic and transversal maturity while translating identified gaps into ranked improvement priorities and a strategic roadmap. Digital technologies, including the Internet of Things, cyber-physical systems and big data analytics, are framed as sociotechnical enablers of transparency, traceability and responsiveness. The model therefore supports the assessment of Industry 5.0, Operational Excellence 5.0 and SDG-oriented improvement efforts. Originality/value - This study combines sustainability assessment, human-centric digitalization and multicriteria prioritization within a single maturity artifact. By moving beyond diagnosis toward collective, context-sensitive prioritization, it provides decision support for the twin transition, while large-scale applications remain necessary to confirm usability and performance benefits.
Purpose Nonprofit organizations (NPOs) often struggle to sustain social innovation (SI) beyond isolated initiatives and to translate strategic intent into consistent improvements in quality of life (QoL) outcomes. This study aims to examine how Hoshin Kanri (HK), conceptualized as a lean-based strategy deployment architecture, enables the institutionalization of SI in NPOs and how this process contributes to QoL improvements in intellectual disability services. Design/methodology/approach A longitudinal action research (AR) design was conducted in an NPO operating in the intellectual disability sector. Fourteen AR-HK cycles were analyzed using qualitative process data and survey-based QoL assessments to examine how strategy deployment routines shaped organizational practices and perceived outcomes over time. Findings HK operated as an organizational architecture embedding strategic priorities into routines of alignment, cross-level dialogue and iterative review. Through these mechanisms, SI evolved from fragmented initiatives into institutionalized and repeatable organizational practice. Organizational capability for SI emerged progressively through routinized coordination, participation and disciplined learning. The translation of institutionalized SI into QoL outcomes was differentiated across dimensions, with stronger effects in empowerment-and participation-related domains and more heterogeneous patterns in material and physical well-being. Practical implications The findings demonstrate how HK can be configured to stabilize QoL-oriented priorities and sustain socially driven transformation beyond episodic initiatives. Social implications By institutionalizing SI within governance routines, HK can support sustained social value creation in complex, person-centred service systems. Originality/value The study advances lean-as-learning-system theory by identifying HK-supported strategy deployment as a concrete institutionalization mechanism that enables and stabilizes SI within governance routines and builds organizational capability for sustained social value creation in NPOs.
