Monitoring multinomial processes based on a weighted chi-square control chart

Ali, Achouri; Khedhiri, Emira; Talmoudi, Ramzi; Taleb, Hassen

doi:10.1590/1806-9649-2021v28e43

Cited by 2 publications

(2 citation statements)

References 11 publications

(15 reference statements)

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“…Statistical Process Control (SPC) is a collection of tools that, when applied, identifies the variability, thus enhancing the reach of a stable process whose capacity can be improved (Ali et al, 2021). Capability is the producing concept according to specification, or generically, doing with quality (Oakland;Oakland, 2018).…”

Section: Statistical Control Processmentioning

confidence: 99%

Using DMAIC for in-plant logistic activities improvement

Bonetti,

Bueno,

Silva

et al. 2023

BJO&PM

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Goal: In-plant logistics activities are important for increasing the performance of the supply chains, so our research aims to study the application of Six Sigma tools for in-plant logistics activities in the cement industry. Our research contributes to the literature by developing a real case study that provides insights into the practical implementation of continuous improvement programs. Design / Methodology / Approach: This study uses the Industrial Case Study in a large cement plant, a branch of a multinational business group, in Brazil’s Middle-west region. This research applies Define, Measure, Analyze, Improve, Control (DMAIC) guidelines with Statistical Process Control tools for solving a real problem for out-control processes. From this, we propose an improvement plan to correct flaws in the in-plant cement loading and unloading process (in-plant logistics). Results: The results suggest that based on the control chart, the studied in-plant logistics activities were out of control. These processes exhibit a high variability, between 3σ and 5σ, presenting 26 problems with causes related to machine, measure, and human resources. An out-control action plan was proposed aiming for improvements to solve these problems. Limitations of the investigation: On the out-control action plan, this study presents an improvement proposal. The action plan does not fully develop the control step for DMAIC. Practical implications: Managers in the cement industry can use our case for insights and learning about improvement programs, especially for the in-plant logistics activities addressing processing-based manufacturing environments. Originality / Value: Our research contributes a real case study that applies the DMAIC methodology, with a specific focus on in-plant logistics activities. By developing the application of improvement programs within the cement industry, our study offers practical insights into how processing industries can effectively implement such programs.

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Section: Statistical Control Processmentioning

confidence: 99%

Using DMAIC for in-plant logistic activities improvement

Bonetti,

Bueno,

Silva

et al. 2023

BJO&PM

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“…Previous studies showed that scale above 0.7, presents good consistency between the relationships (Chan, et al, 2002). Through Pearson's chi-square test (χ2), also found in Ali, et al, (2021), it was decided to eliminate the variables contained in the governance construct and their evidence as they did not demonstrate the necessary reliability for statistical purposes, and the others were submitted to "exploratory factor analysis of main components for the evaluation of their correlations", as suggested by Schreiber (2021, p.2) and similar in previous literature (MacCallum et al, 1999& Marsh et al, 1998.…”

Section: Exploratory Factor Analysis -Main Componentsmentioning

confidence: 99%

Innovations in courts: Validation of a scale of technological innovation

Barbosa¹,

Sousa²,

Nacife³

et al. 2022

R. G. Secr.

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The development of studies on technological innovations in the public sector, specifically in justice system, is still little explored in the literature. This article aimed to develop and validate a scale of technological innovation in the justice system during the period of the COVID-19 pandemic. Data collection procedures were carried out by means of questionnaires sent to 20.727 e-mails of civil servants and judges of the state courts of justice in Brazil. The relationships among the innovation variables that make up the technological innovation construct in the Brazilian judiciary were studied. The factor analyses resulted in the main factors listed by the respondents, as the innovative trend factor (IT); technological resources factor (TR); governance factor and its evidence (G); and innovation and technology factor (IT). For responses to the studies, descriptive statistical analysis was performed, and the innovative sensitivity and technological integration variables presented greater commonalities, and the two factors extracted explain 74% and 67% of the variance. After the descriptive statistical treatment, the confidence level was 99% and the error margin was 4.87%, resulting in a sample of 679 respondents.

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Monitoring multinomial processes based on a weighted chi-square control chart

Cited by 2 publications

References 11 publications

Using DMAIC for in-plant logistic activities improvement

Using DMAIC for in-plant logistic activities improvement

Innovations in courts: Validation of a scale of technological innovation

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