Search Results (80)

The fourth industrial revolution has been discussed generously in literature, as it centers around offering high value and customized products or services to the consumer by harnessing the potential of cutting-edge technologies. It comes as no surprise that it has brought about a paradigm shift in the manufacturing and services sector; however, it is imperative to analyze the variables which influence its adoption. Although there has been an increasing number of studies helping us to understand the adoption of Industry 4.0, there is no structured and process-oriented implementation roadmap that brings together contextual factors for the adoption, nor a step-by-step methodology regarding improvements. Therefore, the authors have conducted a review in which the barriers to Industry 4.0 adoption have been analyzed in a manufacturing context and their corresponding drivers have been discussed. The study reveals that top management commitment, clear strategy, and a skilled workforce play a significant role in the adoption of Industry 4.0 technologies. Afterwards, the authors have developed a conceptual framework for Industry 4.0 adoption by combining DMAIC with a Technology–Organization–Environment (TOE) framework. The recommended framework is designed to facilitate sustainable digital transformation, helping organizations navigate through a structured ability-building process, upskill their workforce, and embrace technologies that align with sustainability objectives. From an academic perspective, the research makes key contributions to technology management literature by utilizing the TOE approach in a proper manner through DMAIC principles. For practitioners, the research work provides an easy four-step process that can assist them in adopting Industry 4.0 technologies in a proper manner. Full article

In recent years, the touch panel industry has experienced rapid growth. With technological maturation and progressive cost reduction, touch technology has been widely adopted in human–machine interfaces. Currently, touch panels are predominantly employed in smartphones and tablet devices, and the industry is increasingly pursuing thinner, lighter designs, driving the development of diverse touch technologies, including one-glass solution (OGS), on-cell, and in-cell architectures. To enhance competitive advantage within the touch panel industry, it is essential to improve production efficiency and elevate product quality; consequently, yield has become a critical metric for evaluating industrial competitiveness. This study adopts the electrical test yield of Touch-on-Lens (TOL) touch-sensing glass as the primary performance indicator. A Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) framework is applied to systematically address impedance-related quality defects occurring during manufacturing. First, key quality characteristics (KQCs) of the TOL touch-sensing glass process are rigorously defined. Subsequently, measurement system analysis (MSA) and process capability assessment are conducted. Next, the Taguchi method is employed to identify the most influential process factors affecting electrical test yield. Finally, response surface methodology (RSM) is utilized to determine the optimal combination of process parameter settings that maximize electrical test yield. Results from the empirical case study demonstrate that the electrical test yield improved significantly—from 90.2% to 93.6%. This outcome validates that the integrated application of the Six Sigma DMAIC methodology, combined with the Taguchi method and RSM, effectively enhances the electrical test yield of TOL sensing glass. The proposed approach offers a robust, data-driven improvement framework applicable to touch panel manufacturers seeking to optimize sensing-glass fabrication processes—thereby supporting broader industry efforts to improve product quality and reduce manufacturing costs. Full article

Background: Hospital appointment systems suffer from extended patient waits, manual interventions, and suboptimal resource allocation, reducing satisfaction and efficiency. Methods: This study develops IPAS using Business Process Analysis (BPA), Bizagi modeling for As-Is/To-Be workflows, SWOT analysis, TQM, and Six Sigma DMAIC. It integrates ML/NLP with BioBERT-BiLSTM triage (AUC 0.92, F1 0.87) for symptom analysis, specialist matching, and automated booking, validated via Bizagi simulations. Results: Simulations show booking time was reduced 96.3% (155 to 5.73 min) and human intervention was cut 70%, with enhanced patient satisfaction and process capability. Conclusions: IPAS demonstrates simulation-based gains in scheduling efficiency, pending real-world validation. Full article

This study provides a practical and replicable improvement model for productivity and inspection reliability improvement in resource-constrained food logistics environments. This study presents an engineering-based optimization of productivity and process reliability in an agro-industrial post-harvest processing system for canned young green jackfruit using an integrated Define–Measure–Analyze–Improve–Control (DMAIC) and Failure Mode and Effects Analysis (FMEA) framework. The case-study production system experienced high raw-material loss, prolonged blanching cycles, and low inter-operator inspection agreement, which reduced process yield and logistics throughput. Root causes were identified through process mapping and fishbone analysis and prioritized using FMEA Risk Priority Number (RPN) scoring. Key improvement actions included optimizing blanching time, standardizing supplier grading to reduce material variability, and strengthening inspection decisions through Attribute Gage Repeatability and Reproducibility (Gage R&R)-based training and criteria alignment. After implementation, productivity increased by 2.31%, raw-material loss decreased by 1.90%, and inter-operator inspection agreement improved by 16%, exceeding the benchmark. Blanching time was reduced from 3 to 1 min at ≥90 °C, shortening cycle time by 67% and generating an estimated annual cost saving of USD 7200 without major capital investment. The results demonstrate that structured, risk-based improvement combined with validated measurement systems can enhance workforce consistency, process stability, and logistics flow efficiency in agro-industrial food processing environments, providing a replicable improvement model for agro-industrial processing small and medium-sized enterprises (SMEs). Full article

Urban freight transport systems are increasingly recognised as a critical factor in metropolitan sustainability. In the case of Sfax, Tunisia’s second-largest city, persistent congestion, logistical inefficiencies and environmental pressures have severely constrained urban mobility and competitiveness. This paper therefore proposes a strategic framework for optimising urban freight transport by integrating the Six Sigma methodology within the urban logistics system. A combination of quantitative and qualitative methods is used to identify freight flows, evaluate performance indicators and examine the interactions between local and regional logistics networks. The Six Sigma DMAIC framework is used to identify process inefficiencies, minimise variability and establish data-driven improvement strategies. The results show that performance outcomes are strongly influenced by spatial organisation, stakeholder coordination and the adaptation of industrial systems to dynamic urban environments. Theory and practice benefit from the study’s findings, which demonstrate how Six Sigma principles can support decision-making in urban logistics management. This, in turn, enables continuous performance enhancement and ensures that freight mobility is in line with sustainability goals. The proposed framework can be transferred to other medium-sized cities facing similar logistical and environmental constraints. In future, this framework is going to be expanded by incorporating digital transformation tools and AI-based predictive analytics. This will advance the development of smart and sustainable urban freight ecosystems. Full article

In recent years, the food processing industry in the Gulf Cooperation Council (GCC) has faced increasing pressures to improve operational efficiency while improving its environmental performance. This research examines whether Lean Six Sigma (LSS) methodologies can be used as tools to incorporate sustainability into current operational processes at a date processing facility in Saudi Arabia. In addition to illustrating the ways in which production was improved, this research developed and preliminarily validated a Sustainability Integration Index (SII) framework to measure the contributions of improvement projects to sustainable practices in terms of their impact on the environment, society, and economy. Furthermore, this research examined the role of organizational culture as a moderator of the effectiveness of integrated LSS–sustainability approaches using a Cultural Readiness Assessment Model (CRAM). This research addressed production bottlenecks and aligned production with selected United Nation Sustainable Development Goals (SDGs) using the Define–Measure–Analyze–Improve–Control (DMAIC) methodology. Production bottlenecked in packaging operations resulted in schedule overruns and excessive overtime; therefore, the intervention focused on improving the production process in these areas. There were three distinct improvement streams: demand-based resource leveling, advanced production planning to allow for pull-based flow, and targeted maintenance to raise Overall Equipment Effectiveness (OEE) from 48.2% to 74.6%. Results indicated a 23% increase in daily processing capacity, a 38 min decrease in the average length of time of production closures, and estimated annual cost savings of 940,000 SAR (approximately USD 250,000). The SII framework showed a 21.2% improvement in sustainability scores, with a total composite score improvement from 0.66 to 0.80. Social sustainability had the greatest relative increase (+24.2%). Exploratory correlation analysis found that improvements in cultural maturity and cross-functional collaboration are possible predictors of successful sustainability integration; however, the limitations of the single case study limit the ability to draw causal inferences. The results provide both empirical evidence and possible measurement tools to an under-explored area: the use of LSS in Middle Eastern food processing industries with specific sustainability goals. Validation of the frameworks across different industries will be necessary to establish generalizability. Full article

Occupational exposure to sharps waste represents a critical challenge for public health systems, directly affecting healthcare workers’ safety, institutional costs, and environmental sustainability. This study aimed to analyze sharps waste management practices and to structure improvement actions for biosafety governance in Brazilian Emergency Care Units (ECUs) through the application of the Lean Six Sigma (LSS) and DMAIC method (Define, Measure, Analyze, Improve, and Control). A single multiple-case study was conducted across three public units in different regions of Brazil, combining direct observation, regulatory checklists based on ANVISA Resolution No. 222/2018 (RDC), and cause–and–effect (5M) analysis. The diagnostic phase identified recurrent nonconformities in labeling, documentation, and internal transport routes, primarily due to managerial and behavioral gaps. Based on these findings, the DMAIC framework supported the development of a low-cost, evidence-based action plan that outlined proposed interventions, including visual checklists, standardized internal routes, and key performance indicators (KPIs), intended to strengthen biosafety traceability and occupational safety. The se proposed actions are expected to support continuous learning, staff engagement, and a culture of shared responsibility for safe practices. Overall, the study provides a structured basis for future implementation and empirical validation of continuous improvement initiatives, aimed at enhancing public health governance and occupational safety in resource-constrained healthcare environments. Full article

Background: This paper presents an analysis and a structured framework for improving inventory accuracy in an automotive factory, considering the current context of global disruptions. In 2023, the company recorded 20,340 inventory adjustments (1695 per month) and a 0.24% monthly net value discrepancy (EUR 256,594 YTD), with a baseline absolute discrepancy of 2.21% of sales. The project aimed to reduce adjustments to below 700 per month and the net value discrepancy to 0.1%. Methods: The research followed the Six Sigma methodology’s Define, Measure, Analyze, Improve and Control (DMAIC) phases, integrating Root Cause Analysis (RCA) and Failure Mode and Effects Analysis (FMEA) to enhance inventory accuracy in manufacturing operations. Results: Implementation significantly improved inventory accuracy: monthly adjustments decreased from 1695 to 971, the highest RPN was reduced from 576 to 144, and the absolute discrepancy-to-sales ratio stabilized at 0.98% (a 56% improvement). Financial variance was reduced to EUR 1948.10 in Q4 2024, while organizational discipline, role clarity and process control also increased. Conclusions: The integrated DMAIC–RCA–FMEA framework proved effective and replicable, enabling systematic identification of root causes, targeted corrective actions and sustainable KPI-driven improvements. The results demonstrate a scalable approach to inventory optimization that supports operational resilience and supply chain performance. Full article

The importance of safety culture in high-risk sectors such as railway transport has gained increasing prominence, particularly within the evolving European regulatory landscape. Commission Delegated Regulation (EU) 2018/762 requires railway organisations to establish strategies for the continuous improvement of safety culture, emphasizing both behavioural and systemic dimensions of safety. This paper presents a structured literature review and proposes a conceptual model of performance indicators designed to support the implementation of these strategies in railway enterprises. Drawing on established continuous improvement methodologies—Kaizen, Six Sigma, and the DMAIC (Define–Measure–Analyse–Improve–Control) framework—the model aligns with Safety Management System (SMS) and Maintenance Management System (MMS) processes. The proposed indicators encompass domains such as risk assessment, change management, employee competence, incident reporting, and system monitoring. The model aims to transform railway organisations into learning systems capable of proactively adapting to emerging risks, including those related to cybersecurity as addressed by the NIS2 Directive. Through a structured literature review and conceptual synthesis, this study provides a theoretical foundation for the integration of continuous improvement and sustainability in safety management. The findings offer practical guidance for policymakers and railway operators seeking to strengthen data-driven, resilient, and sustainable transport safety governance in the European context. Full article

The increasing adoption of Digital Construction Management (DCM) has introduced new ergonomic risks for construction professionals who now spend extended hours on computers in dynamic and often suboptimal work environments. While existing ergonomic research in construction has documented musculoskeletal disorders among both manual workers and office-based personnel, these studies have significant limitations: they primarily rely on subjective assessment methods (questionnaires and surveys) without validated ergonomic tools, and lack biomechanical validation of observational findings. This study addresses this critical gap by integrating Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and Digital Human Modeling (DHM) within a Six Sigma Define, Measure, Analyze, Improve, Control (DMAIC) framework to evaluate and mitigate musculoskeletal risks among construction professionals. A sample of 160 participants across 5 construction firms was observed and assessed through ergonomic scoring, biomechanical stress modeling using HumanCAD^®, and follow-up interventions. The results revealed that 87.5% of participants reported musculoskeletal symptoms, with neck and back being the most affected regions. Post-intervention evaluations showed significant reductions in ergonomic risk scores (RULA: 34%, REBA: 33.3%) and symptom prevalence (up to 46% reduction in neck discomfort). This study provides a validated, scalable framework for ergonomic risk management in digital construction roles and offers actionable design and policy recommendations to enhance occupational health and productivity. Full article

Aircraft maintenance delays (AMD) remain a significant challenge in commercial aviation, adversely affecting operational efficiency, flight punctuality, and passenger satisfaction. Despite advancements in maintenance strategies, recurring disruptions continue to generate financial losses and reputational risks. This study proposes an integrated five-step framework that combines failure mode and effects analysis (FMEA) with the Define–Measure–Analysis–Improve–Control (DMAIC) methodology to systematically address and reduce AMD. The framework involves the definition of problems, the identification of contributing factors and failure modes, the assessment of risk and root cause analysis, the mitigation of risk, and continuous monitoring. The main contribution of this study lies in the integration of FMEA and DMAIC into a unified data-driven system that proactively reduces maintenance delays, offering a novel approach to continuous process improvement in aviation operations. Its practical applicability is demonstrated through a case study of the AFRIQIYAH Airways Airbus A320 fleet, which represents the majority of the airline’s operations. High-risk landing gear failure modes were identified, evaluated and addressed through targeted improvement projects, including predictive maintenance, supplier diversification, inventory optimization, and improved quality assurance for critical spare parts. Implementing these initiatives is expected to reduce the overall Risk Priority Number (RPN) by approximately 59%, highlighting the effectiveness and potential to minimize AMD. Full article

Background: This study deploys the Lean Six Sigma DMAIC framework to achieve systemic optimization of the school subscription process in Tunisia’s public transport service, a critical administrative operation affecting efficiency and customer satisfaction across the urban mobility network. Methods: Beyond conventional applications, the research integrates advanced analytical and process engineering tools, including capability indices, measurement system analysis (MSA), variance decomposition, and root-cause prioritization through Pareto–ANOVA integration, supported by a structured control plan aligned with ISO 9001:2015 and ISO 31000:2018 risk-management standards. Results: Quantitative diagnosis revealed severe process instability and nonconformities in information flow, workload balancing, and suboptimal resource allocation that constrained effective capacity utilization. Corrective interventions were modeled and validated through statistical control and real-time performance dashboards to institutionalize improvements and sustain process stability. The implemented actions led to a 37.5% reduction in cycle time, an 80% decrease in process errors, a 38.5% increase in customer satisfaction, and a 38.9% improvement in throughput. Conclusions: This study contributes theoretically by positioning Lean Six Sigma as a data-centric governance framework for stochastic capacity optimization and process redesign in public service systems, and practically by providing a replicable, evidence-based roadmap for operational excellence in governmental organizations within developing economies. Full article

This study implemented a Lean Six Sigma (LSS) methodology to enhance the productivity of the mechanical and industrial engineering technology workshops of an international higher education institution. The efficiency and effectiveness of the engineering workshops were often compromised by poor housekeeping and operational practices, which resulted in incomplete tasks, long operational and activity times, disorganized tools, cluttered workspaces, and a lack of systematic processes for managing materials. These issues led to waste in the form of lost time, unnecessary movement, and safety risks. This eventually affected the overall productivity of the workshops. Following the combination of the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology of Six Sigma with Lean manufacturing, the investigation was conducted in two parts. The first part of this research mainly consisted of measuring the existing state of the three workshops to map the process and frame issues and origins of variations. During the second part of this study, the focus shifted towards Lean thinking while applying the chosen Lean Six Sigma (LSS) tools. Implementation revealed several benefits in the workshops during each phase of DMAIC. A Plan–Do–Check–Act (PDCA) continuous improvement board was installed in the main workshop to promote continuous improvement and sustainability. The process capability increased for the main workshop and welding laboratory, which shows an increase in service and performance standards after LSS implementation. For the main workshop, the process capability ‘Cp’ increased from 0.33 to 1.24 and the process capability index (Cpk) increased from 0.26 to 0.99. The process capability index (Cpk) for the main workshop increased; however, it did not reach the value of 1.33 due to the computer workstation installation not being completed during the study. The welding laboratory showed an increased ‘Cp’ from 0.67 to 2.13, and the process capability index (Cpk) increased from 0.18 to 1.34. The layout of the workshop office was improved to support efficient workflow by providing easy access to frequently used resources while keeping movement paths clear, thereby minimizing interruptions and promoting productivity. As a result, machines and tools were used more productively and operation times decreased. The mechanical workshops can continue increasing their process capability by following the outcomes and findings of the current study, leading to sustainable quality, efficiency, and operational reliability improvements. Full article

The present paper aims to develop a systematic, prescriptive, and exclusively statistical problem-solving methodology that integrates scientific experimental design methods with the Six Sigma philosophy. This methodology was used for the study and continuous improvement of a direct dyeing process for textile materials. In the first stages of the methodology, the process was systematically analyzed; color difference was identified, using rank correlation as the main quality requirement of the customer; and the influence of the electrolyte concentration in the dye bath on this quality characteristic was tested, using analysis of variance. In the subsequent stages, a full factorial experiment was carried out to obtain a mathematical model describing the action of the main selected influence factors on the color difference; response surfaces and constant level curves were plotted to find the optimal settings of these influence factors. It was concluded that cotton fabric provides a more uniform chromatic reproduction, i.e., a lower color difference, compared to linen, and the electrolyte concentration of 20 g/L yielded the most stable chromatic performance for both fiber types. Full article

Welding-based manufacturing and joining processes are extensively used in various areas of industrial production. While welding has been used as a primary method of joining in many applications, its capability to fabricate metal components such as the Wire Arc Additive Manufacturing (WAAM) method should not be undermined. WAAM is a promising method for producing large metal parts, but it is still prone to defects such as porosity that can reduce structural reliability. To ensure these defects are found and measured in a consistent way, inspection methods must be tied directly to code-based acceptance limits. In this work, a three-pass WAAM joint specimen was made in a welded-joint configuration using robotic GMAW-based deposition. This setup provided a stable surface for Phased Array Ultrasonic Testing (PAUT) while still preserving WAAM process conditions. The specimen, which was intentionally seeded with porosity, was divided into five zones and inspected using the 6 dB drop method for defect length and amplitude-based classification, with AWS D1.5 serving as the reference code. The results showed that porosity was not uniform across the bead. Zones 1 and 3 contained the longest clusters (15 mm and 16.5 mm in length) and exceeded AWS length thresholds, while amplitude-based classification suggested they were less critical than other regions. This difference shows the risk of relying on only one criterion. By embedding these results in a DMAIC (Define–Measure–Analyze–Improve–Control) workflow, the inspection outcomes were linked to likely causes such as unstable shielding and cooling effects. Overall, the study demonstrates a code-referenced, dual-criteria approach that can strengthen quality control for WAAM. Full article