Announcements

Special Issue on BJO&PM

2023-05-05

Call for articles for a special issue on: " New developments in statistical process monitoring and control"

Guest editors:

Subhabrata Chakraborti (Chair of the special issue editorial board), University of Alabama, USA

Felipe S. Jardim, Universidade Federal Fluminense, Brazil

Martin Guillermo Cornejo Machado, Universidad de Lima, Peru

Marcela Aparecida Guerreiro Machado, Universidade Estadual Paulista, Brazil

Statistical monitoring of the quality of products and services has always been of fundamental importance for companies to remain healthy in an increasingly competitive market. Research and applications in the field of statistical process monitoring and control (SPMC) are undergoing several changes that accompany significant technological innovations in real applications, such as artificial intelligence, 3D printers, the Internet of Things, Big Data and machine-to-machine communication. For example, these new technologies enable faster and more reliable data collection that can be used to improve the effectiveness of quality control and monitoring methods, such as control charts.

Neste novo cenário, há um aumento do interesse e da demanda tanto por acadêmicos quanto por profissionais na área de monitoramento e controle estatístico de processos e pesquisadores vêm estudando novas estratégias para aumentar a eficiência de diversos gráficos de controle, como gráficos por variáveis ou atributos, com parâmetros conhecidos e desconhecidos, para monitoramento da média e da variabilidade de processos univariados ou multivariados, com ou sem correlação.

The aim of this special issue is to capture some of the new trends and studies in this field. The aim is to identify recent research and new issues in the field, as well as future areas of interest. Original methodological contributions, along with applications and case studies are of interest. The special issue is aimed at Brazilian and international researchers working on these topics, to build possible collaborations.

Topics
Suitable topics related to Statistical Process Monitoring and Control include, but are not limited to, the following:

Monitoring and non-parametric statistical process control;
Statistical control of multivariate processes;
Economic design of control charts;
Capability indices, acceptance sampling and acceptance control charts;
Profile monitoring;
Effects of parameter estimation on control charts;
Control charts in health applications;
Control charts for autocorrelated data;
Control charts for modern environments (such as Big Data) and non-standard applications;
Network monitoring;
Other topics not listed above.

Important dates
Manuscript submission deadline: November 2024
Notification of First Decision: February 2025
Revised version sent: March 2025

Guidelines for future authors can be found at

https://bjopm.org.br/bjopm/about/submissions

Call for Papers

2022-08-19

When titans meet – Industry 4.0, Lean and Circular Economy opportunities towards operational excellence and sustainability

Guest editor(s): Daniel Nascimento¹, Guilherme Tortorella², Rodrigo Goyannes Gusmão Caiado³. Juan Manuel Maqueira¹

¹ University of Jaén, Spain

² The University of Melbourne, Australia

³ Pontifical Catholic University of Rio de Janeiro, Brazil

The effects of climate change are perceived by society as increasingly severe and intense, causing frequent catastrophes and expectations of a threatening future. According to the United State Environmental Protection Agency report, the industry comprises 22.9% of the total emissions, with iron and steel and metallurgical coke production the main factor with 41.3% of the total emissions in the industry in 2019. In fact, the main opportunities for reducing emissions in the industry are Energy Efficiency, Fuel Switching, Recycling, Training, and Awareness. One of these opportunities is recycling iron and steel to manufacture new raw materials from scrap, generating new products, and reducing waste, CO2 emissions, and mineral exploration.

Agenda 2030 is a blueprint for ending poverty, protecting the planet, and improving the prospects of people around the world by transforming society toward sustainable development. Considering these facts, the integrated practices of Circular Economy (CE) and Industry 4.0 (I4.0) technologies are critical success factors to increase the capacity and efficiency of this transformation toward sustainability. Although several studies related to I4.0 and CE or numerous articles focused on Lean Management (LM) and I4.0 exist, no work has been identified in the literature to date that considers the integration of I4.0, LM, and CE to achieve sustainable development. LM is a management system oriented toward efficiency that could complement the interrelation between I4.0 and CE. In this context, integrating LM, I4.0, and CE practices is a crucial success requirement for circular production systems to contribute to the continuous and incremental improvement of Sustainable Supply Chains.

To solve real-world operations and supply chain problems in sustainable, circular, and digitalization contexts surrounded by complex, social, environmental, technical, political, and economic interactions with natural systems, the need for practical, robust, and intelligent models is still urgent.

In recent decades, several multi-criteria decision-making (MCDM) methods, and multivariate and machine learning models have been proposed to solve operational excellence and sustainability problems, attracting much attention from academics and practitioners. In this context, there is a need to explore I4.0, Lean and Circular opportunities, and capabilities, considering sustainable development dimensions jointly and individually, with the help of multi-methods and interdisciplinary approaches (such as statistics, mathematics, decision sciences, and social sciences).

Topics of interest in this Special Issue:

Business Models that use LM and I4.0 to Improve CE practices;
Systematic Literature Review (SLR) or Scoping Reviews that integrate LM, I4.0, and CE;
Case Studies applying LM, I4.0, and CE in Real World Industry;
Theoretical Frameworks that use Lean 4.0 to Improve CE practices;
Empirical Studies using statistical techniques, such as Structural Equation Modelling (SEM) to Analyse the Casual Relationship between LM, I4.0, and CE;
Data mining or Machine learning techniques for discovering hidden patterns and relationships in operations and supply chain management (OSCM);
MCDM methods and hybrid decision-making models in sustainable-OSCM or OSCM4.0 problems;
Data-driven MCDM models for evaluating circular, lean and digital OSCM.

Important dates

Manuscript Submission Deadline: February 2023

Notification of First Decision: April 2023

Revised Version Submission: May 2023

Final Decision: July 2023

Note: The actor must describe in the body article that it belongs to the special issue.