Most recent issue published online in the International Journal of Six Sigma and Competitive Advantage.
International Journal of Six Sigma and Competitive Advantage
http://www.inderscience.com/browse/index.php?journalID=101&year=2023&vol=14&issue=4
Inderscience Publishers Ltd
en-uk
support@inderscience.com
International Journal of Six Sigma and Competitive Advantage
1479-2494
1479-2753
© 2023 Inderscience Enterprises Ltd.
© 2023 Inderscience Publishers Ltd
editor@inderscience.com
International Journal of Six Sigma and Competitive Advantage
https://www.inderscience.com/images/files/coverImgs/ijssca_scoverijssca.jpg
http://www.inderscience.com/browse/index.php?journalID=101&year=2023&vol=14&issue=4
-
A bibliometric analysis on green Lean Six Sigma based on Scopus data using Biblioshiny
http://www.inderscience.com/link.php?id=134460
This study aims to explore the research landscape of green Lean Six Sigma from a bibliometric perspective. The analysis was conducted over the dataset extracted from Scopus database using Biblioshiny app, a web-interface for bibliometrix. Bibliometrix is an open-source tool for executing science mapping analysis of literature. This study is mainly focused on the application of bibliometric approach to analyse the research progress in the field of green Lean Six Sigma through the identification of research trends, authors' productivity, collaboration, co-occurrence, topmost contributions and trending themes. The results indicate that publications evolved initially in 2013 and the major contributors in this research area are from India. Enablers, barriers and application of green Lean Six Sigma in manufacturing sector are identified as the main driving theme for the future research. This study contributes to the field by presenting research status, thematic focus, and research hotspots along with future direction.
A bibliometric analysis on green Lean Six Sigma based on Scopus data using Biblioshiny
Arish Ibrahim; Gulshan Kumar
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 371 - 383
This study aims to explore the research landscape of green Lean Six Sigma from a bibliometric perspective. The analysis was conducted over the dataset extracted from Scopus database using Biblioshiny app, a web-interface for bibliometrix. Bibliometrix is an open-source tool for executing science mapping analysis of literature. This study is mainly focused on the application of bibliometric approach to analyse the research progress in the field of green Lean Six Sigma through the identification of research trends, authors' productivity, collaboration, co-occurrence, topmost contributions and trending themes. The results indicate that publications evolved initially in 2013 and the major contributors in this research area are from India. Enablers, barriers and application of green Lean Six Sigma in manufacturing sector are identified as the main driving theme for the future research. This study contributes to the field by presenting research status, thematic focus, and research hotspots along with future direction.]]>
10.1504/IJSSCA.2023.134460
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 371 - 383
Arish Ibrahim
Gulshan Kumar
Department of Mechanical Engineering, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates ' Department of Mechanical Engineering, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates
green Lean Six Sigma
bibliometric analysis
R programming
Biblioshiny
Scopus
lean manufacturing
Six Sigma
2023-10-23T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
14
4
371
383
2023-10-23T23:20:50-05:00
-
Impact of Lean Six Sigma on companies' performance during the COVID-19 pandemic
http://www.inderscience.com/link.php?id=134431
This study aims to assess how Lean Six Sigma (LSS) affected companies' performance during the Coronavirus pandemic. A total of 390 LSS specialists who live in the USA and work for a US company filled out a questionnaire. The research tool was validated, and reliability was assessed. Statistical analysis methods, such as multiple regression and correlation, were employed to test the hypothesis. The results show that LSS projects have a substantial influence on a company's overall performance and all of it is sub-dimensions: financial, operational, and innovative performance. However, the effects were minor, and the connections were strained. These findings may appear odd, but only when taken out of context, in that this study was conducted on LSS projects implemented during the COVID-19 pandemic, which includes a plethora of internal and external variables that may influence a company's performance.
Impact of Lean Six Sigma on companies' performance during the COVID-19 pandemic
Osama Abdelhadi; Ihssan Samara
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 384 - 407
This study aims to assess how Lean Six Sigma (LSS) affected companies' performance during the Coronavirus pandemic. A total of 390 LSS specialists who live in the USA and work for a US company filled out a questionnaire. The research tool was validated, and reliability was assessed. Statistical analysis methods, such as multiple regression and correlation, were employed to test the hypothesis. The results show that LSS projects have a substantial influence on a company's overall performance and all of it is sub-dimensions: financial, operational, and innovative performance. However, the effects were minor, and the connections were strained. These findings may appear odd, but only when taken out of context, in that this study was conducted on LSS projects implemented during the COVID-19 pandemic, which includes a plethora of internal and external variables that may influence a company's performance.]]>
10.1504/IJSSCA.2023.134431
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 384 - 407
Osama Abdelhadi
Ihssan Samara
Petra University, Airport Rd. 317, Amman, 1196, Jordan ' Petra University, Airport Rd. 317, Amman, 1196, Jordan
Lean Six Sigma
LSS
financial performance
operational performance
company performance
innovation
COVID-19 pandemic
coronavirus
USA
United States
2023-10-23T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
14
4
384
407
2023-10-23T23:20:50-05:00
-
'DMAICS 2 CRISP DM' approach for improving and optimising the performance of an industrial mining production process
http://www.inderscience.com/link.php?id=134444
In order to meet the challenges of the economic world, mining companies are always trying to improve the performance of their production chains by optimising production to the maximum possible extent. Until now, some of the most powerful and effective tools to achieve positive and sustainable operational results in organisations around the world to improve the performance of a production chain are Lean Six Sigma (LSS) and knowledge discovery in database (KDD). Therefore, using a combination of these two proven process improvement approaches (Lean Six Sigma and KDD) for the development of a mining chain efficiency prediction system will help mine managers to rapidly, and continuously improve their production chain. Indeed, the system will be an effective and efficient tool allowing each interested mining company to project itself over time, to predict the performance of its activity, to identify management alerts in advance, and to control its overall production system.
'DMAICS 2 CRISP DM' approach for improving and optimising the performance of an industrial mining production process
Ilham Battas; Hicham Behja; Laurent Deshayes
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 408 - 436
In order to meet the challenges of the economic world, mining companies are always trying to improve the performance of their production chains by optimising production to the maximum possible extent. Until now, some of the most powerful and effective tools to achieve positive and sustainable operational results in organisations around the world to improve the performance of a production chain are Lean Six Sigma (LSS) and knowledge discovery in database (KDD). Therefore, using a combination of these two proven process improvement approaches (Lean Six Sigma and KDD) for the development of a mining chain efficiency prediction system will help mine managers to rapidly, and continuously improve their production chain. Indeed, the system will be an effective and efficient tool allowing each interested mining company to project itself over time, to predict the performance of its activity, to identify management alerts in advance, and to control its overall production system.]]>
10.1504/IJSSCA.2023.134444
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 408 - 436
Ilham Battas
Hicham Behja
Laurent Deshayes
Engineering Research Laboratory (LRI), Modeling System Architecture and Modeling Team (EASM), National and High School of Electricity and Mechanic (ENSEM), Hassan II University, Casablanca, 8118, Morocco; Research Foundation for Development and Innovation in Science and Engineering, Casablanca, 16 469, Morocco; Innovation Lab for Operations, Mohammed VI Polytechnic University, Benguerir, 43150, Morocco ' Engineering Research Laboratory (LRI), Modeling System Architecture and Modeling Team (EASM), National and High School of Electricity and Mechanic (ENSEM), Hassan II University, Casablanca, 8118, Morocco ' PLM-CCI Academy of Vichy-France, France
Six Sigma
lean
DMAICS
knowledge discovery in database
KDD
CRISP DM
data analysis
prediction system
decision-making support system
mining industrial efficiency
optimisation
2023-10-23T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
14
4
408
436
2023-10-23T23:20:50-05:00
-
Process and dimensional variation analysis of automobile assembly in development phase using Six Sigma DMAIC
http://www.inderscience.com/link.php?id=134442
The perceived quality of a part/assembly depends on aesthetic and ergonomic aspects and if not given importance in the early phase of manufacturing, then more time for rework is required after the assembling the product to achieve the optimum gap between adjacent trims during the pilot phase of the product. This delays the launch of the product. Thus, present study analyses relative gap between the neighbouring or adjacent trims section to find the causes for defects like increased gap between adjacent trims and process variation causing it and provide an understanding of the assembly variations to operators and engineers. Using a Six Sigma: define, measure, analyse, improve and control (DMAIC) methodology, gap between the trims is analysed, and significant error contributors were identified. After that Monte Carlo simulation is performed. Based on simulation results, major contributors affecting the variations were identified. Further influence of each error contributor was reduced to increase the process capability (C<SUB align="right">p). Improvement of about 46.5% is observed in critical trim sections during the development phase of a new vehicle. This eliminated the need for further processing and reworking, which is the reason for the delay in the launch date and provides an understanding about the sections more prone to dimensional variations.
Process and dimensional variation analysis of automobile assembly in development phase using Six Sigma DMAIC
Vikas Sisodia; Sachin Salunkhe; Prakash Pantawane; B. Rajiv; Rahul Diggi; Sakshi Raut
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 437 - 467
The perceived quality of a part/assembly depends on aesthetic and ergonomic aspects and if not given importance in the early phase of manufacturing, then more time for rework is required after the assembling the product to achieve the optimum gap between adjacent trims during the pilot phase of the product. This delays the launch of the product. Thus, present study analyses relative gap between the neighbouring or adjacent trims section to find the causes for defects like increased gap between adjacent trims and process variation causing it and provide an understanding of the assembly variations to operators and engineers. Using a Six Sigma: define, measure, analyse, improve and control (DMAIC) methodology, gap between the trims is analysed, and significant error contributors were identified. After that Monte Carlo simulation is performed. Based on simulation results, major contributors affecting the variations were identified. Further influence of each error contributor was reduced to increase the process capability (C<SUB align="right">p). Improvement of about 46.5% is observed in critical trim sections during the development phase of a new vehicle. This eliminated the need for further processing and reworking, which is the reason for the delay in the launch date and provides an understanding about the sections more prone to dimensional variations.]]>
10.1504/IJSSCA.2023.134442
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 437 - 467
Vikas Sisodia
Sachin Salunkhe
Prakash Pantawane
B. Rajiv
Rahul Diggi
Sakshi Raut
Department of Manufacturing Engineering and Industrial Management, College of Engineering †Pune, Maharashtra †411005, India ' Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai †600062, Tamil Nadu, India ' Department of Manufacturing Engineering and Industrial Management, College of Engineering †Pune, Maharashtra †411005, India ' Department of Manufacturing Engineering and Industrial Management, College of Engineering †Pune, Maharashtra †411005, India ' Department of Manufacturing Engineering and Industrial Management, College of Engineering †Pune, Maharashtra †411005, India ' Department of Manufacturing Engineering and Industrial Management, College of Engineering †Pune, Maharashtra †411005, India
process capability
pilot batch
pre-production batch
trims
perceived quality
product quality
manufacturing quality
assembly
rework
Six Sigma
2023-10-23T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
14
4
437
467
2023-10-23T23:20:50-05:00
-
Comparative analysis of multivariate capacity indicators for serial and parallel systems
http://www.inderscience.com/link.php?id=134454
In this research, a comparative analysis of multivariate capacity indicators for serial and parallel production systems was developed, and supported by Six Sigma metrics. As theoretical foundation, the concepts of serial systems and parallel systems supported with l, six-sigma metrics and multivariable capacity indicators were used. As a methodology, a heuristic, quantitative, and factual analysis was carried out, which implied: 1) characterisation of the information associated with the serial and parallel production systems; 2) calculation of the Six Sigma metrics (DPMO, Z level and performance), periodic and punctual assessment of the performance of the four processes in the production systems; 3) As results it can be noted that the serial system shows a better performance at the sigma level. As future research, the scientific community is invited to implement the methodology indicated in different organisations of goods and services.
Comparative analysis of multivariate capacity indicators for serial and parallel systems
Tomás José Fontalvo Herrera; Ana Gabriela Banquez Maturana
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 468 - 489
In this research, a comparative analysis of multivariate capacity indicators for serial and parallel production systems was developed, and supported by Six Sigma metrics. As theoretical foundation, the concepts of serial systems and parallel systems supported with l, six-sigma metrics and multivariable capacity indicators were used. As a methodology, a heuristic, quantitative, and factual analysis was carried out, which implied: 1) characterisation of the information associated with the serial and parallel production systems; 2) calculation of the Six Sigma metrics (DPMO, Z level and performance), periodic and punctual assessment of the performance of the four processes in the production systems; 3) As results it can be noted that the serial system shows a better performance at the sigma level. As future research, the scientific community is invited to implement the methodology indicated in different organisations of goods and services.]]>
10.1504/IJSSCA.2023.134454
International Journal of Six Sigma and Competitive Advantage, Vol. 14, No. 4 (2023) pp. 468 - 489
Tomás José Fontalvo Herrera
Ana Gabriela Banquez Maturana
Industrial Management Program, Faculty of Economics, University of Cartagena, Cartagena, Colombia ' Industrial Management Program, Faculty of Economics, University of Cartagena, Cartagena, Colombia
Six Sigma
multivariable capacity indicator
linear production
parallel production
2023-10-23T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
14
4
468
489
2023-10-23T23:20:50-05:00