Pedro Carlos Oprime

Purpose: Statistical process monitoring has been a relevant practice incorporated into quality management systems. In a controlled process, the variabilities of statistical parameter estimates are expected to fluctuate within a pattern over time. Whenever this pattern is not identified, the root causes must be identified, and the necessary actions should be taken. However, there are situations where special causes are present but not practically significant. Recent studies on profile monitoring have tailored solutions for efficiently detecting trends and seasonality in high-capability processes. This paper proposes using profile curves and statistical modelling based on the location control chart approach. Design/methodology/approach: Our research was carried out using decision-prescriptive models and the Design Science Research approach to identify solutions for real and complex problems. Additionally, the modeling and simulation method was utilized to assist in developing, analyzing, and testing the model, which was classified as an artifact. Findings: The modeling results clearly demonstrate that utilizing location control charts and modified graphs is essential in defining equipment adjustments. This approach guarantees the minimum acceptable capacity and maximizes the use of productive resources. Originality: This study provides novel opportunities for developing and implementing control charts in systems that do not conform to the principles of randomness but rather display intricate temporal patterns. This is particularly relevant in the context of Quality 4.0, where real-time data collection is pervasive.

Industry 4.0 is based on cyber-physical systems (CPSs); therefore, the risks of cyberattacks tend to increase. A cyberattack is any malicious act that could jeopardize the security of production, accounting and financial systems. This review hypothesizes quality management is in a position to play an important role in preventing and detecting hacker attacks. To verify this hypothesis, Lakato’s research program was utilized in a bibliometric review. This review was separated into two stages: the first was a descriptive analysis and the second was a scientific mapping, done using a technique known as text mining. This technique was guided by factorial and cluster analysis. The analysis revealed that this subject is emerging and relevant. It was observed that understanding the vulnerabilities of connected systems is crucial for combating cyberattacks. Furthermore, the study highlighted the potential of multivariate control charts, such as Hotelling’s T-squared charts and Exponentially Weighted Moving Average (EWMA) control charts, in efficiently detecting anomalies in connected systems. This article introduces an approach integrating quality management, statistical analysis and cybersecurity to safeguard CPSs. By leveraging advanced statistical techniques, such as Hotelling T² charts and EWMA charts, in conjunction with the foundational principles of quality management systems (QMS), such as continuous improvement and process standardization, this could be a potentially robust defense against cyberattacks. The integration of these tools allows for the early detection of anomalies and potential cyber threats, ensuring the prevention of integrity and security of interconnected industrial processes.

Offshore wind energy has achieved significant reductions in its levelized cost of energy (LCoE) in the past decade, but still needs efficiency improvements. Approximately 18% of the LCoE is related to logistical costs, underscoring the need for optimization in this area. Despite its importance, logistical decisions during offshore wind farm installations remain underexplored in the literature. This article aims to identify and structure the relationships of logistic decisions to optimize total installation costs. A conceptual framework is proposed, detailing logistical decisions and their influencing factors. The results are based on a literature review and survey research for validation with specialists in logistics and offshore wind farms. The findings include the key decisions: port installation selection; vessel fleet selection; installation strategy selection; turbine pre-assembly method selection; aggregate planning approach; installation schedule coverage; storage strategy of components; and the degree of sharing information. The framework reveals the importance of coordinating the value chain in the installation process, mainly due to the influence of weather factors; the logistic decisions, when considered in a systemic view, can contribute to a global efficiency gain in the installation process.

Purpose: This paper aims to provide empirical evidence regarding Lean Six Sigma (LSS) practices supported by Industry 4.0 (I4.0) technologies in heavy vehicle manufacturing processes.Design/methodology/approach: A two-case study was performed involving LSS specialists, leaders and managers of two heavy vehicle manufacturers in Brazil. The data analysis procedure combined content analysis techniques, conceptual maps and network analysis.Findings: The results provide consistent evidence of synergies between LSS and I4.0, including digital mistake-proofing, digital andon, e-kanban, statistical monitoring as well as process mapping aided by cyber-physical systems (CPS) and big data analytics (BDA). To enable such interactions, companies need to invest in automation architectures, system integration, human–machine interfaces and analytical skills.Research limitations/implications: This study relies on data from a two-case study carried out in two companies from a single manufacturing sector in Brazil. For this reason, the findings cannot be generalized to the entire automotive industry.Originality/value: There is still a lack of comprehensive research on the application of digital technologies in LSS practices. This is the first study which provides empirical evidence regarding the LSS practices supported by I4.0 technologies used by heavy vehicle manufacturers.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification. The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach. Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied. The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

Recently, the topic of data protection has been on the rise in Brazil due to the entry into force of Law No. 13,709/2018, denominate General Personal Data Protection Law (LGPD). The objective of this article, characterized in terms of the research method as a case study, is to present a proposal for a methodology to adapt to the LGPD based on the method developed by the Digital Government Secretariat (SGD) of the Federal Government and describe the adapted adoption at the Federal University of Mato Grosso (UFMT), based on the analysis of the actions planned and carried out in the compliance project. The results provide insights into innovations in the actions developed, aiming to contribute to other public institutions that seek to implement the LGPD. Recentemente, o tema proteção de dados está em alta no Brasil devido a entrada em vigor da Lei nº 13.709/2018, denominada Lei Geral de Proteção de Dados Pessoais (LGPD). O objetivo deste artigo, caracterizado quanto ao método de pesquisa como estudo de caso, é apresentar uma proposta de metodologia de adequação à LGPD baseada no método desenvolvido pela Secretaria de Governo Digital (SGD) do Governo Federal e descrever a adoção adaptada na Universidade Federal de Mato Grosso (UFMT), a partir da análise das ações planejadas e realizadas no projeto de conformidade. Os resultados fornecem percepções de inovações nas ações desenvolvidas, visando contribuir com outras instituições públicas que buscam implementar a LGPD.

Purpose: Competitive pressures force companies to seek solutions to eliminate wastes while improving product quality. Lean Six Sigma (LSS) has been considered one of the most effective approaches for business transformation. This article aims to present an empirical case study where LSS and Define, Measure–Analyze–Improve–Control (DMAIC) methodologies are applied to reduce defects in a car parts manufacturer.Design/methodology/approach: The study follows the DMAIC methodology. Design of experiments and hypothesis testing were applied in a single case study.Findings: The main defects and the main factors that cause defective parts were indicated for die-casting and machining processes. Solutions implemented reduced the defect incidence from a chronically high level to an acceptable one. The sigma level rose from 3.4 s to 4 s sustainably.Research limitations/implications: The study is limited to a single case study, without intention of generalizing the results to other types of industries.Practical implications: This paper can be a useful guide of how to use DMAIC Six Sigma approach to defect reduction and can be applied in many sectors.Social implications: This study offers the knowledge on how to apply the Six Sigma DMAIC methodology, reducing the dependence on specialization courses.Originality/value: This study describes in detail the process used in a structured improvement exercise including sigma-level calculation, factorial experiments and hypothesis tests – a set of techniques still poorly combined in the literature.

Purpose: This paper aims to characterize a sample of Lean Six Sigma (LSS) projects in healthcare settings and discuss some specificities of operational excellence (OPEX) initiatives in hospitals and healthcare organizations in the USA.Design/methodology/approach: A content analysis involving a sample of 23 documents shared by US hospitals was performed in order to achieve the research objectives. Such analysis was based on a conceptual framework developed from the literature review. It was also applied to a quantitative approach, including descriptive statistics, hypothesis testing and correspondence analysis that supported the research.Findings: Most LSSH projects were focused on business transformation and strategic improvements. Simple techniques and tools were predominant such as descriptive statistics, process mapping, 5S and spaghetti charts, usually implemented by Green Belts and Black Belts through the define, measure, analyze, improve and control (DMAIC) method. In addition to the expressive findings reported, these projects' results have been aligned with lead time and operational cost reduction, quality improvement and capacity increase.Research limitations/implications: The study adds knowledge to the OPEX literature by analyzing the Lean Six Sigma healthcare (LSSH) in hospitals and healthcare institutions in the USA. It also demonstrates that different approaches, such as the kaizen event and DMAIC project show different results according to some techniques and tools applied in the hospital environment.Originality/value: The empirical evidence presented in this study provides scenery of the LSS practices in the healthcare settings, highlighting the implementation areas, outcomes, tools and techniques mostly used in the North American healthcare institutions.