Search Results (380)

Centered on lean production, this study integrates operational technologies (OT), communication technologies (CT), and information technologies (IT) within an open-system software architecture. Under stochastic customer demand, reliance on static data and experience-based decision-making constrains firms’ responsiveness to market. The integration of lean management with a data-driven database enhances operational flexibility and decision quality, enabling small and medium-sized enterprises (SMEs) in the bicycle industry to develop responsive digital factory environments with real-time monitoring and improved operational transparency. The proposed platform is applicable to both manufacturing processes and operational management, improving overall equipment effectiveness (OEE), production efficiency, process optimization, and reducing quality losses, inventory levels, and workforce misallocation. This study investigates the application of the Analytic Hierarchy Process (AHP) and multi-criteria decision-making (MCDM) within a performance framework integrating ESG indicators and a balanced scorecard to identify key success factors for digital lean improvement in the bicycle industry. A case study of a bicycle manufacturer was conducted using questionnaire surveys and expert interviews with exporters. The results indicate that the five most critical success factors are: enhancing return on invested capital, strengthening digital capabilities, improving product quality, minimizing inventory waste, and reducing lead time. These findings provide practical guidance for decision-makers in designing more effective lean management strategies in highly competitive digital markets. Furthermore, by facilitating the adoption of appropriate digital technologies under a reasonable return on investment, this approach supports the systematic implementation of Industry 4.0 initiatives and transforms traditional lean practices into more efficient and sustainable digital lean operations. Full article

Break-bulk cargo operations are characterized by high variability and complex resource synchronization, yet they have received limited research attention compared to containerized logistics. This paper proposes an integrated lean-informed simulation framework for evaluating vessel service time (VST) in multipurpose terminals handling break-bulk cargo. The framework sequences three analytical stages: Value Stream Mapping paired with Ohno’s waste taxonomy to diagnose non-value-adding activities, a discrete-event simulation model built in Simio to quantify their impact on VST, and Sobol sensitivity analysis to decompose the remaining variability across operational factors. Demonstrated at DP World Lirquén, a multipurpose terminal in Chile, the lean diagnostic identified 101 min of waste per cycle across waiting, motion, and overproduction categories. Scenario evaluation showed that eliminating shift-transition delays and standardizing load composition reduced VST by 14.3% and 10.6%, respectively, without capital investment. The sensitivity decomposition revealed that warehouse machinery composition, particularly the interaction between equipment types, dominates VST variability, while truck fleet size operates as an independent factor. These findings demonstrate that coordination-related policy interventions outperform incremental resource additions. More specifically, machinery allocation must be optimized jointly rather than by equipment type in isolation. Full article

The cutting-edge era emphasizes developing key solutions to improve productivity and promote economic growth within limitations. To achieve this, the production management team employs various process improvement approaches to empower operations management. The aim of this article is to examine the recent trends in operations management scenarios in which industry professionals seek an ingenious path for selecting process improvement approaches through a systematic literature review. The study employed the PRISMA framework for a systematic literature review of 176 papers published from 2000 to 2026. The key finding shows the methodologies used for operations management scenarios, considering Industry 3.0–6.0. The methodologies include traditional approaches, concurrent approaches, data-driven assessment, real-life assessment for competent approaches, and sustainable approaches. The study focused on identifying obstacles to selecting and implementing a suitable decision-making process improvement approach to mitigate operations management issues in the Industry 3.0–6.0 work environment. These obstacles are recognized as several challenges and problems that arise on the shop floor and reduce the organization’s sustainability. This study identifies an emerging research area: the development of innovative, AI-driven operations management platforms for flexible, emerging work settings. Full article

Although Industry 4.0 has successfully advanced lean manufacturing through digitalization and automation, its primary focus on operational efficiency leaves emerging strategic priorities—human-centricity, sustainability, and resilience—outside its original scope. The Industry 5.0 agenda explicitly elevates these three pillars, creating new potential to drive lean transformation. However, how Industry 5.0 can systematically drive lean manufacturing transformation remains unclear. To address this knowledge gap, this study develops a strategic roadmap. First, a content-centric literature review identifies 12 key enablers for Industry 5.0-driven lean manufacturing. Second, Fuzzy Interpretive Structural Modeling (FISM) and expert opinions determine hierarchical relationships among the enablers and construct a multi-level structural model. Third, Matrices d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis evaluates the driving power and dependence of each enabler. Finally, a strategic roadmap is developed based on expert synthesis. The findings reveal that “government regulation and incentives” and “employee skill training” are the most critical enablers, while “value chain design and improvement” and “resource input and return” are the most complex and difficult to develop. The roadmap highlights the mediating role of “stakeholder participation and collaboration.” Importantly, the roadmap addresses potential tensions in lean implementation—such as the carbon footprint trade-off of frequent small-batch transport—by embedding sustainability assessment into value chain design and technology governance. This study offers a practical guide for manufacturers to prioritize investments and sequence actions toward lean transformation in the Industry 5.0 era. The main contribution of this study is a strategic roadmap that explains how Industry 5.0 can enable lean manufacturing transformation through prioritized actions and hierarchical enablers, while reconciling efficiency with sustainability and resilience goals. This roadmap offers a practical guide for manufacturers and policymakers to sequence investments and actions toward lean transformation in the Industry 5.0 era. Full article

Industrial systems such as lean practices, quality systems, workplace safety, and organisational culture are often managed as separate systems; however, in practice, they are interdependent. This study presents a preliminary survey instrument (CiE II) to assess organisational conditions commonly associated with effectiveness in manufacturing systems. A multi-stage refinement process was applied to an initial 107-item survey using pilot data (n = 127) collected from engineering students with work-integrated industry experience. The methodology combined exploratory factor analysis, item response theory, and thematic analysis to improve both statistical and conceptual coherence. The resulting instrument comprised 28 items, making it more suitable for industrial deployment. Analysis of responses (N = 127) identified three common facets that support lean, quality, safety, and culture. These are (i) Integrated Quality and Workflow Management (α = 0.960), referring to workers perceptions that quality standards exist and that they are resourced to meet them; (ii) Safe and Collaborative Work Culture (α = 0.901), referring to perceptions of behavioural norms and that workers will be treated fairly within the team; (iii) Supportive Leadership and Professional Growth (α = 0.852), referring to perceptions that management supports workers’ ongoing professional development. The potential benefit is the provision of a candidate survey that economically covers four key domains of relevance for manufacturing organisations. This has the potential to allow cross-domain correlations and larger-span regression models that integrate the four domains. Full article

This paper introduces the Normative Lean Performance Score (NLPS) model designed to evaluate lean operational performance using publicly available financial and accounting metrics, without requiring advanced analytics for practical implementation. The study applies an empirical research design based on a longitudinal dataset, where firms are first classified into lean-oriented groups, followed by logistic regression to identify significant indicators and Random Forest models to estimate their relative importance. The resulting index provides an objective, interpretable, and easily implementable performance measure suitable for cross-firm benchmarking and managerial decision support. Empirical testing using automotive manufacturers demonstrates strong alignment with lean classification and efficiency outcomes, providing evidence for the model’s relevance as an accounting-based benchmarking tool. In addition to its practical applicability, the framework contributes to lean performance measurement by translating machine learning insights into a reproducible index that can be applied in data-constrained environments. This approach ensures that the resulting index remains both empirically grounded and practically interpretable, while avoiding reliance on arbitrary or expert-assigned weighting schemes and qualitative assessment-based approaches. The model therefore offers a scalable and transparent alternative for practitioners, analysts, and researchers seeking robust lean performance evaluation when advanced modelling resources are unavailable. The study contributes a transparent, accounting-based normative index that reframes lean performance as a financial configuration rather than an operational maturity construct. The empirical analysis uses quarterly financial data from 17 publicly listed automotive manufacturers over the period 1994Q1–2024Q4. Full article

This study addresses the growing need to integrate operational excellence with sustainability objectives in manufacturing systems. Despite extensive research on Lean Management and Quality Management Systems (QMSs), their combined impact on process performance and resource efficiency remains insufficiently explored, particularly in real industrial contexts. The aim of this study is to develop and apply an integrated Lean–QMS–SPC analytical framework linking process performance improvement with sustainability-related outcomes. A case study was conducted in a high-volume manufacturing environment. The study combined process analysis, system-level assessment, and root cause identification to support targeted improvement actions. The results indicate that the implementation of Lean-oriented practices and supporting methods was associated with improved process stability, reduced variability, and decreased occurrence of nonconformities. These improvements translate into enhanced operational performance and reduced resource consumption associated with rework and defects. A scenario-based estimation model, based on observed defect reduction, is used to assess the potential impact on energy consumption and CO₂ emissions. The study contributes to the literature by operationally integrating SPC analysis, QMS assessment, root cause analysis, and Lean-oriented improvement activities within an industrial manufacturing context. The findings highlight that quality-driven process improvements may support operational efficiency while contributing to resource-efficiency performance. Full article

This study aims to enhance manufacturing efficiency in noodle production by integrating Lean Six Sigma and the Theory of Inventive Problem Solving (TRIZ). The DMAI structure of Lean Six Sigma is employed, with TRIZ tools incorporated into the improvement stage to generate innovative, technically feasible solutions. Results indicate that the noodle production process operates at a three-sigma performance level, with seven major categories of waste identified as the primary contributors to inefficiency. Implementation of the proposed improvement framework is projected to reduce total production time to 10,345 s and increase the proportion of value-added activities to 95.55%. Full article

Insulation-material manufacturers face increasing pressure to improve productivity, cost efficiency, energy performance and worker safety while maintaining stable quality in highly constrained production environments. Existing lean and smart-manufacturing studies often examine isolated tools, individual monitoring technologies or material-level sustainability, but fewer studies provide conservative plant-level validation of an integrated intervention in insulation-material production. This study therefore examines the optimization of insulation-material production in a human-supervised cyber–physical manufacturing system through an industrial before–after intervention. The framework combines bottleneck identification, value stream mapping, SMED, selective automation, preventive maintenance and KPI-based digital monitoring. The baseline system was constrained by manual crusher loading, long changeovers, inefficient pallet transport, repeated breakdowns, scrap and limited real-time visibility. After implementation, productivity increased from 7864 to 9000 kg/day (+14.5%), monthly production costs decreased from EUR 200,000 to EUR 180,000 (−10%), breakdown frequency fell from 5 to 3 events/month (−40%), scrap decreased from 5% to 3% (−40%), crusher loading time fell from 30 to 10 min/pallet (−66%), annual energy use dropped from 500 to 450 MWh (−10%) and reported safety incidents decreased to zero during the 12-month post-implementation observation period. An OEE-based surrogate model yielded pre- and post-state theoretical capacity estimates differing by less than 1%, supporting internal consistency. The results are interpreted as descriptive and practically meaningful before–after differences because the full raw monthly dataset is commercially sensitive and classical inferential testing was not performed. The study contributes by presenting a reproducible, conservative and human-supervised CPS-oriented plant-intervention protocol rather than by claiming a fully autonomous closed-loop CPS. Full article

The Lean Manufacturing concept is one of the most widely applied approaches to improving production processes, enabling greater efficiency, higher quality, and the reduction of various forms of waste. One of its key tools, Value Stream Mapping (VSM), supports a detailed analysis of material and information flows, helping to identify areas for improvement. In recent years, growing attention has been given to evaluating production processes from the perspective of energy efficiency, which has led to the development of an extended version of this tool—Energy Value Stream Mapping (EVSM). This study presents a practical verification of the applicability of the EVSM method for analyzing a manufacturing process in terms of electricity consumption and establishing its energy balance. Based on computer simulation, alternative future-state scenarios were developed and subsequently evaluated within a simulation environment. As a result, the most effective variant was selected, and its implementation demonstrated that for the same production batch, it is possible to reduce total electricity consumption from 485.13 kWh to 418.53 kWh. At the same time, total production lead time was reduced from 26,880 s to 17,833 s (by approximately 33.7%). Electricity consumption during machine operation decreased slightly from 398.86 kWh to 387.34 kWh, while idle energy consumption was significantly reduced from 86.27 kWh to 31.82 kWh. Additionally, the reorganization of production resources led to a reduction—though not a complete elimination—of existing bottlenecks, while the average work-in-process inventory decreased from 27 to 10 units. The results indicate that integrating EVSM with simulation modeling provides an effective approach to improving both energy efficiency and the operational performance of production systems. Full article

Attaining uniform weld quality in the resistance spot welding (RSW) of AISI 201 stainless steel remains challenging due to the complex interdependence of process parameters and the limited understanding of shielding atmosphere effects on this lean austenitic grade. This study integrates Taguchi optimization, analysis of variance (ANOVA), and complementary trend surface visualization to evaluate the effects of welding time, current, electrode pressure, and shielding atmosphere. An L27 orthogonal array was employed, with welding current identified as the dominant parameter for both tensile strength and hardness while nitrogen shielding exhibited a significantly greater influence on hardness than on tensile force, attributable to interstitial solid solution strengthening. The optimal conditions yielded a maximum tensile force of 12.2 kN and a hardness of 353 HV, with prediction errors below 1.5% for tensile force and below 0.5% for hardness. Trend surface visualization further revealed significant current–pressure interactions governing weld quality. These findings provide a validated optimization framework for the industrial RSW of AISI 201, with direct implications for automotive and structural manufacturing. Full article

Leveraging artificial intelligence (AI)―a cutting-edge technological tool―to drive the new quality transformation of Chinese manufacturing is a crucial foundation for China’s steady advancement of the new real economy, as well as an inevitable requirement for China to align with contemporary economic and technological trends. This study constructs a multi-sectoral equilibrium model to theoretically analyze the focal points of the new quality transformation in Chinese manufacturing and the impact AI has on it, followed by corresponding empirical tests. The results indicate that (1) AI has a positive impact on the qualitative transformation of China’s manufacturing sector; a one-unit increase in a firm’s AI level leads to a 0.171-unit increase in the sector’s qualitative transformation level. (2) This impact exhibits heterogeneity at the firm, industry, and regional levels. At the firm level, the impact varies depending on firm size, digitalization level, operational performance, internal control strength, and governance quality. At the industry level, the impact varies depending on technology intensity, industrial structure, strategic importance, and green development level. At the regional level, heterogeneity is reflected in geographical location, natural resource endowments, and the degree of urban agglomeration. (3) Artificial intelligence promotes the new quality transformation of Chinese manufacturing through the following mechanisms: reducing time lag costs and transaction costs in market penetration mechanisms; enhancing the quality of cutting-edge factor combinations and key core technologies in advanced innovation mechanisms; and improving resource utilization and operational management efficiency in lean production mechanisms. Full article

Machining Nomex honeycomb structures presents a major challenge due to their thin-walled architecture, orthotropic behavior, and sensitivity to adhesion and delamination. This study develops a three-dimensional numerical model using Abaqus/Explicit to analyze ultrasonic vibration-assisted milling in longitudinal and longitudinal-torsional modes. The model incorporates orthotropic behavior with progressive damage based on Tsai-Wu and experimental friction calibration to accurately reproduce tribological conditions. A parametric analysis examines the effect of vibration mode, amplitude (5–25 µm), frequency (21–22.5 kHz), cutting width, and tool geometry on stresses, bond wear, and material buildup. An optimal coefficient of friction ensures excellent simulation–experiment agreement. Compared to conventional milling, the longitudinal-torsional configuration reduces cutting forces by up to 50%, while frequency optimization allows for gains of 40 to 60%. Hybrid vibration coupling establishes intermittent contact and oscillatory micro-shearing, limiting adhesion and build-up. Thus, longitudinal-torsional assistance improves tribological stability, tool life and wall integrity, offering a validated digital strategy to optimize ultrasonic milling of composite honeycomb structures. Full article

This study examines how lean manufacturing practices are adopted in Singapore’s SME precision and electronics manufacturing industry. Its main goal is to assess the extent of lean manufacturing method adoption and the challenges involved. The study analyzed 36 responses from 150 surveys distributed online. The results show that about 50% of manufacturers find it difficult to implement lean manufacturing practices. Our research reveals that most SMEs face significant challenges when applying lean manufacturing techniques. The findings identify barriers such as a lack of experience, skills, and knowledge, which significantly slow progress. Additionally, the study emphasizes that management support is vital for successful lean implementation. Key factors include employee training, goal alignment, and the creation of a supportive environment. While tools and external expertise are helpful, internal resources and organizational culture are considered more critical. Full article

Lean production philosophy and sustainability approach have become a critical framework for efficiency improvement, waste reduction, and promoting sustainable manufacturing practices. In the age of artificial intelligence (AI), there is a synergy, which has now found new dimensions, data-driven decision-making, predictive analytics, and operational agility. AI technologies promise to transform industrial processes by converging lean production and sustainability principles, a synergy explored in this paper. AI APIs enable the use of AI to improve resource utilization, reduce environmental pressure, and maintain economic growth inherent to all business sectors while also fostering social accountability. In this study, a robust regression model is employed to study the role of AI in moderating the lean practices and sustainability outcomes relationship, using a sample of 528 manufacturing firms. The results show that the contribution of AI technologies to economic, ecological, and social sustainability is effectively multiplied by that of lean production. This research offers a framework to help practitioners and policymakers optimize production systems in line with Sustainable Development Goals. Finally, the study delivers actionable recommendations for navigating skill gaps and cybersecurity risks that were identified. In sum, this paper contributes to the rapidly emerging conversation by providing empirical evidence on AI’s moderating role in the lean–sustainability relationship and offering a strategic framework for practitioners. Full article