Search Results (63)

Strategic logistics planning is used by management to define action plans that will enable organizations to always make decisions that are in the organization’s best interests. They are based on a knowledge repository of business experiences, which is usually represented by a centralized, organized, and searchable digital system where organizations store and manage critical institutional knowledge. Thus, an institutional knowledge base provides sustainability, making the experiences readily available while keeping them well organized. In this research, the experiences of logistics experts from selected scholarly designs for six-sigma business improvement projects have been collected, classified, and organized to form a logistics knowledge management system. Although originally meant to facilitate current and future decisions in strategic logistics planning of the cooperating companies, it is also used in logistics education to introduce knowledge-based engineering principles to enterprise strategic planning, based on continuous improvement of quality-related product or process performance indicators. The main goal of this article is to highlight the benefits of knowledge-based engineering over the established ontological logistics knowledge base in smart production, based on the predisposition that ontological institutional knowledge base management is more efficient, adaptable, and sustainable. Full article

Purpose: The aim of this in vitro study was to evaluate the influence of different models of commercially available portable dental radiometers on the measurement of light irradiance emitted by light-emitting diode (LED) photocuring units. Materials and Methods: Eight LED photocuring units, all emitting light in a single-wavelength spectrum, were tested. Light irradiance (mW/cm²) was measured using six portable dental radiometers: four digital models (D1–D4) and two analog models (A1, A2). Digital model D1 was used as the reference (control). All measurements were conducted under standardized conditions, and each LED–radiometer combination was tested in triplicate. Data were analyzed using Sigma Plot 12.0 (Palo Alto, CA, USA) to verify the assumptions of normality and homogeneity of variances. A one-way analysis of variance (ANOVA) was used to assess the effect of the radiometer model on irradiance values, followed by Tukey’s post hoc test for multiple comparisons. The significance level was set at α < 0.05. Results: No statistically significant difference in irradiance was found between D1 (control) and D2. However, significantly lower values were recorded with A2, while D3, D4, and A1 produced significantly higher irradiance values compared to the control (p < 0.05). Conclusion: Irradiance measurements can vary significantly depending on the radiometer model used. Clinicians should be aware of this variability and are encouraged to regularly check the irradiance of the light-curing units used in daily practice, ensure their proper maintenance, and implement periodic monitoring to maintain effective clinical performance. Full article

Ensuring high levels of quality and efficiency is essential for compliance with ISO standards in chemical manufacturing. Traditional methods, such as Statistical Process Control (SPC) and Six Sigma, often lack adaptability and fail to offer interpretable insights. This study proposes a hybrid quality control model based on Explainable Artificial Intelligence (XAI), integrating fuzzy C-means clustering (FCM), machine learning (ML), and Fuzzy Inference Systems (FISs) to enhance defect prediction and interpretability in industrial environments. The approach uses fuzzy clusters to segment production batches, improving the understanding of process variability. A supervised ML model (XGBoost) is trained on historical data to predict defect probabilities, while an explainable FIS refines the final assessment using expert-defined rules. XAI techniques (SHAP and LIME) offer transparency and insight into the decision-making process. Experimental validation using a real-world white wine dataset, evaluated in terms of accuracy and interpretability, shows that the proposed model outperforms traditional approaches in both predictive performance and transparency. The results demonstrate the effectiveness of combining unsupervised clustering, predictive analytics, and fuzzy reasoning in an Industry 4.0 framework. This study provides a scalable and adaptable solution for real-time quality control in chemical manufacturing, improving decision support systems and enabling automated and explainable quality assessments. Full article

The article presents the results of research on the resilience of companies using management concepts such as Lean Management, Agile, and Six Sigma to the crises that companies have had to face in recent years: the COVID-19 pandemic, rising energy prices, and the war in Ukraine. The implementation of these management concepts should lead to process improvements and a reduction in the consumption of production resources, including energy. The aim of the study was to determine how these crises have affected the efficiency of companies and to determine whether the solutions used so far are sufficient or require modification. The authors used three research methods. Firstly, they analyzed the literature—scientific publications, studies, and expert reports. Secondly, they analyzed the financial results (net profits and share of Costs of Goods Sold in the value of Revenues) in the period before (2016–2019) and after the outbreak of the COVID-19 pandemic (2020–2023) of companies using Lean Management, Agile, and Six Sigma strategies and their combinations. To compare the effectiveness of these management methods, they also analyzed the financial results of international corporations and Polish companies. Third, they conducted a survey among Polish companies applying the Lean Management concept. The results of this research show that the crises of recent years, even if they caused a deterioration in financial performance, were short-lived as companies were able to adapt to the new conditions. Japanese companies using Lean Management increased their profits by an average of 55.56% between 2020 and 2023, and “Lean” American organizations even more (71.64%). Polish companies have been steadily increasing their profits for years (134.14% before the pandemic and 143.27% after the outbreak). The share of COGS will remain at a similar (high) level for many years to come. There are no significant increases in these costs due to crises in the companies’ environment (e.g., increase in energy prices), and, on the other hand, there is no tendency for them to decrease in a large proportion of companies. In the years 2020–2023, the largest decreases in the share of these costs occurred in companies combining Lean and Six Sigma (−11.85%). In companies that use the Agile strategy, there was an increase of 8.05%. However, these are average data, and the analysis of the results of companies from individual groups leads to the conclusion that it is not only the management concept that is important, but also how it is implemented in a given company. In addition, streamlining processes only by eliminating waste is not enough these days. It is necessary to use modern technologies (digital technologies, Industry 4.0). Increasing the efficiency of production or logistics processes leads to a reduction in energy consumption and external costs. However, new, specialized solutions are needed. The issue of energy efficiency is indeed gaining more and more importance in companies and is included in management concepts, e.g., in Lean Management. Full article

Background/Objectives: Clinical laboratories are fundamental to healthcare systems, contributing to over 70% of clinical decisions while accounting for only 2–3% of hospital budgets. Among them, microbiology laboratories provide critical information that directly influences patient outcomes and satisfaction. This study presents a structured review of the current state of Lean Six Sigma (LSS) implementation in microbiology and comparable laboratory environments. The objective is to identify relevant contributions within the state of the art to highlight potential benefits applicable to microbiology laboratories and to detect persistent gaps and unresolved needs. Methods: A systematic literature review was performed across six databases (Web of Science, ScienceDirect, Scopus, ProQuest, PubMed, and Google Scholar) to identify studies published between 2012 and September 2024. After screening, 33 studies were selected for full-text analysis. Results: The selected literature was analyzed to assess the extent to which LSS methodologies have been applied in microbiology laboratories. Particular attention was given to the definition and use of key performance indicators (KPIs). While industry-adapted metrics such as cost reduction and turnaround time are commonly employed, clinical indicators, such as patient impact, satisfaction, and diagnostic accuracy, are underutilized. Additionally, the analysis revealed a frequent omission of the control phase in LSS projects, limiting long-term process monitoring. The review also identifies the most suitable LSS tools and evaluates how laboratories manage interruptions in routine workflows. Conclusions: Future research should prioritize the integration of clinical KPIs into LSS frameworks, establish robust control phases for sustained monitoring, and systematically address the impact of process interruptions on optimization efforts. Full article

Winding deformities in distribution transformers pose significant risks to operational reliability and system safety. Frequency response analysis (FRA) is a well-established technique for identifying mechanical faults; however, its diagnostic reliability is hindered by subjectivity in interpreting response signatures. This study proposes a novel diagnostic technique, termed FRA6σ, which integrates Six Sigma (6σ) statistical tools with FRA to enable objective fault detection. The methodology employs control charts ($\overline{X}$ chart, $\overline{R}$-chart) to monitor deviations from baseline signatures and utilizes process capability indices ($C_p$ and $C_{pk}$) to quantify the severity of deviations. Three transformer cases were evaluated across five defined frequency regions (10 Hz to 2 MHz), each associated with distinct physical fault types. The FRA6σ approach successfully identified early-stage faults across all cases. In one instance, axial and radial winding deformation was detected with a $C_p$ of 1.0 and corresponding range chart violations, preceding any visible damage. Another case revealed inter-turn insulation degradation in the 100 kHz–1 MHz band with $C_{pk}$ values below 0.9, prompting immediate intervention. Compared to traditional FRA interpretation, the proposed method improved diagnostic sensitivity by 31.25% and enabled fault detection earlier based on retrospective physical inspection benchmarks. The integration of Six Sigma with FRA provides a structured, quantifiable, and repeatable approach to transformer fault diagnostics. FRA6σ enhances early detection of winding deformities and dielectric issues, offering a robust alternative to subjective analysis and supporting predictive maintenance strategies in power systems. Full article

Avian reovirus (ARV) is one of the main causes of viral arthritis, tenosynovitis, malabsorption syndrome (MAS), runting-stunting syndrome, and immunodepression. In recent years, due to the emergence of new ARV strains, outbreaks of the disease have brought significant economic losses to chicken flocks. To determine the prevalence of ARV in China from 2010 to 2024, a total of 409 tissue samples from different breeding farms were collected from chickens presenting clinical signs of lameness and swollen joints in various flocks located in 18 provinces. As performed on these tissue samples, the ARV-specific reverse transcription-polymerase chain reaction (RT-PCR) assay indicated 111 ARV-positive samples with a positive rate of 27.14%. After viral isolation from the necropsied chicken samples, 69 ARV strains were isolated, and specific sigma C (σC) genes were amplified and sequenced. The sequence analysis of σC genes showed that these 69 isolates were grouped into six clusters, including 14 ARV isolates from cluster I (20.29%), 12 ARV isolates from cluster II (17.39%), 3 ARV isolates from cluster III (4.35%), 8 ARV isolates from cluster IV (11.59%), 3 ARV isolates from cluster V (4.35%), and 29 ARV isolates from cluster VI (42.03%). Except for cluster V, each of the other five clusters could be divided into two subclusters. Homology analysis showed that ARV isolates in clusters II–VI had only 50.3 to 60.8% homology with the commercial S1133 vaccine strain which is derived from cluster I. The ARVs in subcluster Ia had high homology with the S1133 vaccine strain (93.5–98.0%), while the ARVs in subcluster Ib had a low homology with the S1133 strain (73.4–76.4%). Further, the cluster VI viruses, the main epidemic genotype in China, had only 50.3–55.7% homology with the S1133 strain. The results of the pathogenicity test showed that the representative strains of the six different clusters all caused swelling of the footpads in SPF chickens, and the incidence rate was not significantly different. The present study will be helpful in the understanding the prevalence of ARV strains in China and revealed the genetic differences between the ARV isolates and the commercial vaccine strain. Full article

Flux-switching wound field machines (FSWFMs) offer high torque density and independence from rare-earth materials, making them promising candidates for sustainable electric vehicles and industrial applications. However, their adoption is limited by challenges such as high torque ripple, efficiency variations, and sensitivity to manufacturing tolerances. This study presents a Design for Six Sigma (DFSS) optimization framework that integrates sensitivity analysis, response surface modeling (RSM), and multi-objective genetic algorithms to address these challenges. The optimized solution reduces torque ripple by 7.69%, improves torque output, and enhances energy efficiency. By incorporating Six Sigma principles, the framework ensures robust performance under manufacturing variations, bridging the gap between theoretical optimization and practical implementation. This scalable and efficient methodology establishes FSWFMs as viable solutions for industrial applications, revolutionizing electric machine design. Full article

In the pursuit of operational excellence and enhanced competitiveness, a wide range of industries have turned to methodologies such as Lean and Six Sigma; however, in the chemical sector, their application is very limited. This paper presents a Lean Six Sigma framework to identify and reduce sources of variability occurring in the final product composition of a Spanish SME fertilizer manufacturer. The company faced important challenges related to product variability, adversely affecting overall productivity. A real-life case of the Lean Six Sigma implementation was conducted over two years, and its applicability and ability to improve productivity performance were thoroughly assessed. The proposed framework successfully integrated Lean and Six Sigma methodologies, i.e., process mapping (value stream mapping), root cause analysis (Ishikawa cause–effect diagram), project management (SIPOC and DMAIC), and statistical process control, and demonstrated practical benefits for the case company by identifying the key variables affecting product variability and determining their optimal levels. A substantial 50% reduction in the variability of several products and a 42% reduction in material preparation time were achieved. These reductions resulted in a 40% reduction in costs associated with product losses and a 54% reduction in costs from raw material losses. Full article

This study proposes a methodology that combines Six Sigma and Data Envelopment Analysis (DEA) to measure the quality of banking services. The proposed framework emphasizes seven essential quality dimensions: prompt response, efficient channels, fraudulence, processes, dependable service, credibility, customer satisfaction, and risk management. Integrating both techniques enables a holistic approach to quality evaluation and provides valuable information for the banking industry’s continual improvement. To validate the properties of the methodology, we developed a case study involving 25 Colombian banks. Using Six Sigma metrics, DEA models, and slacks analysis, the results provide a comprehensive study of the quality performance, identifying each bank’s relative strengths and weaknesses in several quality dimensions. The data indicate that some banks perform better on quality characteristics such as customer happiness, dependable service, and procedures. However, this study also reveals a promising finding: banks still have the potential for development, particularly in their response time, channel efficiency, fraud, and credibility, offering hope for the future of banking services. Full article

This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs’ competitiveness and sustainability in the global market. Full article

Laparoscopic cholecystectomy (LC) is the gold standard technique for gallbladder diseases in both emergency and elective surgery. The incidence of the disease related to an increasingly elderly population coupled with the efficacy and safety of LC treatment resulted in an increase in the frequency of interventions without an increase in surgical mortality. For these reasons, managers implement strategies by which to standardize the process of patients undergoing LC. Specifically, the goal is to ensure, in accordance with the guidelines of the Italian Ministry of Health, a reduction in post-operative length of stay (LOS). In this study, a Lean Six Sigma (LSS) methodological approach was implemented to identify and subsequently investigate, through statistical analysis, the effect that corrective actions have had on the post-operative hospitalization for LC interventions performed in a University Hospital. The analysis of the process, which involved a sample of 478 patients, with an approach guided by the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle, made it possible to reduce the post-operative LOS from an average of 6.67 to 4.44 days. The most significant reduction was obtained for the 60–69 age group, for whom the probability of using LC is higher than for younger people. The LSS offers a methodological rigor that has allowed us, as already known, to make significant improvements to the process, standardizing the result by limiting the variability and obtaining a total reduction of post-operative LOS of 67%. Full article

The purpose of this study is to develop a nested theoretical model (LSS-DC) by critically examining two distinct theoretical concepts, including Lean Six Sigma (LSS) and Dynamic Capabilities (DC), for achieving organizational sustainable performance optimizations (PO). The robust integration of this dynamic concept is achieved using a systematic literature review, synthesis, and empirical evidence derived from 2005 to 2022. The vital benefits of LSS-DC are identified. This study utilizes a systematic literature review method adapted. It reveals the cross-sectional literature search strategy deploying selective keywords DCs, LSS, DCs and LSS, DCs and LSS and PO. In this niche domain employing descriptive and thematic analysis, key insights are extracted from the literature, encompassing a total of 21 peer-reviewed journals. The selection criteria revolve around three aspects: ‘Purpose’, ‘Authorship’, and ‘Credibility and Accuracy’. The authors gathered the secondary data from credible databases such as Scopus, Web of Science, PubMed, ERIC, and IEEE using the keyword search. The study reveals the robust integration of theoretical concepts of LSS and DCs and their impact on organisational performance. The findings suggest that integrating the micro-foundations of DCs (sensing, seizing, and transforming) with LSS allows organisations to not only identify improvement opportunities but also efficiently and effectively act upon them, ultimately leading to sustainable performance optimisation across various aspects of the business. The specific type of DC integration with LSS depends on the organisation’s goals and priorities. The findings of this study are subjective to some extent due to the applied research methodology. Further empirical research is needed to gain a deeper understanding of the phenomenon. This study considers LSS as DC providing an empirical (LSS-DCs) model for sustainable performance optimisation. This is achieved by robustly integrating two distinct theoretical concepts derived from an extensive literature review and the analysis of the data-driven implementation. Finally, the study offers a deeper understanding in terms of how contextual organisational characteristics enhance the outcome of LSS-DC. Full article

The objective of Six Sigma Analysis (SSA) is to determine the robustness level of a current design, process or system considering the expected range of an input parameter. This strategy has been successfully applied to several fields, including healthcare management. This work presents a novel study of SSA to assess the mini-plate fixation employed for mandible fracture. The objective is to reflect the number of concerns in a surgical operation case by performing a statistical measurement of operation capability. A three-dimensional finite element model of a clinical case is elaborated. Some muscles may be severed or damaged during surgery and unable to function to their full potential. To obtain reliable designs, these muscle forces are considered as random variables. The Six Sigma analysis is used to determine if the output parameters satisfy the Six Sigma quality criteria or not. The remarked potential failure modes in this study are found to be similar to those found in a previous reliability study that was applied to the same clinical case. According to the results of SSA, the assessment level (2.462 << 6) means that much of the data are outside of the demand, and require several improvements to ensure patient satisfaction. Full article

Quality construction contributes to the overall sustainability of the built environment, especially for infrastructure projects. High-quality housing infrastructure projects benefit individuals, communities, and the economy. Most studies are concerned with identifying the reasons for the quality of a construction project. However, only a few of them have been concerned with housing infrastructure. In addition, no studies have considered the interdependencies among the factors affecting the quality of housing infrastructure projects, leading to these causes not being evaluated effectively. This paper aims to specify and organize the significant factors affecting the quality of housing infrastructure projects. These projects suffer from the availability of all infrastructure services simultaneously before their execution. A comprehensive literature review was implemented to collect all the factors affecting their quality. Construction sector experts designed and filled out a questionnaire based on the collected data. The survey data were then statistically analyzed using a partial least squares structural equation model (PLS-SEM) to organize the causes and examine the interdependencies among the quality of each cause. Our main finding revealed that based on the PLS-SEM, the top three factors affecting the construction quality were the skill and experience of supervisory staff, errors and omissions in design documents, and the lack of communication between supervisors and laborers. Based on the PLS-SEM ranking, labor, equipment, and site staff were responsible for more than half of the top 10 causes. The PLS-SEM results showed that the contractor material (CM) and project design (PD) percentages were 20% and 30%.. In addition, there is an interaction influence between the labor/equipment/site staff (LES) causes and PD causes. This study assists stakeholders in understanding how to use Six Sigma construction concepts to enhance performance in the nation’s construction industry, which helps contractors make improvements in variability reduction and save costs in construction projects. Full article