Most recent issue published online in the International Journal of Logistics Systems and Management.
International Journal of Logistics Systems and Management
http://www.inderscience.com/browse/index.php?journalID=134&year=2024&vol=47&issue=3
Inderscience Publishers Ltd
en-uk
support@inderscience.com
International Journal of Logistics Systems and Management
1742-7967
1742-7975
© 2024 Inderscience Enterprises Ltd.
© 2024 Inderscience Publishers Ltd
editor@inderscience.com
International Journal of Logistics Systems and Management
https://www.inderscience.com/images/files/coverImgs/ijlsm_scoverijlsm.jpg
http://www.inderscience.com/browse/index.php?journalID=134&year=2024&vol=47&issue=3
-
The impact of green and sustainable supply chain management practices on sustainable performance: a comparative analysis
http://www.inderscience.com/link.php?id=136855
The objective of this paper is to provide a comparative analysis of the impact of green supply chain management (GSCM) and sustainable supply chain management (SSCM) practices on sustainable performance (SP) (economic, environmental and social). This analysis will help companies to choose between GSCM and SSCM practices according to their needs. This study has provided a classification of the 16 supply chain (SC) practices influences on the three performances. Another categorisation of GSCM and SSCM practices among 16 SC practices was established based on literature articles. Finally, a comparison was made between the effects of GSCM and SSCM practices on each sustainable performance. The results found a positive relationship between SSCM practices and the three dimensions performance: economic (50%), environmental (34%), and social performance (33%). Moreover, the GSCM practices have a significant impact on the environmental performance (58%) and economic performance (29%), but no influence on social performance was discovered.
The impact of green and sustainable supply chain management practices on sustainable performance: a comparative analysis
Imane Tronnebati; Fouad Jawab
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 267 - 294
The objective of this paper is to provide a comparative analysis of the impact of green supply chain management (GSCM) and sustainable supply chain management (SSCM) practices on sustainable performance (SP) (economic, environmental and social). This analysis will help companies to choose between GSCM and SSCM practices according to their needs. This study has provided a classification of the 16 supply chain (SC) practices influences on the three performances. Another categorisation of GSCM and SSCM practices among 16 SC practices was established based on literature articles. Finally, a comparison was made between the effects of GSCM and SSCM practices on each sustainable performance. The results found a positive relationship between SSCM practices and the three dimensions performance: economic (50%), environmental (34%), and social performance (33%). Moreover, the GSCM practices have a significant impact on the environmental performance (58%) and economic performance (29%), but no influence on social performance was discovered.]]>
10.1504/IJLSM.2024.136855
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 267 - 294
Imane Tronnebati
Fouad Jawab
Technologies and Industrial Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco ' Technologies and Industrial Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco
green
sustainable
supply chain management
SCM
supply chain
SC
GSCM
SSCM
practices
social
economic
environmental
performance
2024-02-23T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
47
3
267
294
2024-02-23T23:20:50-05:00
-
Dynamic planning design of three level distribution network with horizontal and vertical exchange
http://www.inderscience.com/link.php?id=136859
Inventory management in distribution networks remains a challenging task due to the demand nature and the limited storage capacity. In this work, we study a three-level, a multi-product and a multi-period distribution network consisting of a central warehouse, three distribution centres and six wholesalers. Each of them faces a random demand. In order to optimise the inventory management in the distribution network, we first propose to make a horizontal cooperation between actors of the same level in the form of product exchange; then we propose a second approach based on vertical-horizontal cooperation. Both approaches are modelled as a MIP model and solved using the CPLEX solver. The objective of this study is to analyse the performance in terms of costs, quantities in stock and customer satisfaction.
Dynamic planning design of three level distribution network with horizontal and vertical exchange
Abdennour Ilyas Benfriha; Lamia Triqui-Sari; Aimade Eddine Bougloula; Mohammed Bennekrouf
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 295 - 326
Inventory management in distribution networks remains a challenging task due to the demand nature and the limited storage capacity. In this work, we study a three-level, a multi-product and a multi-period distribution network consisting of a central warehouse, three distribution centres and six wholesalers. Each of them faces a random demand. In order to optimise the inventory management in the distribution network, we first propose to make a horizontal cooperation between actors of the same level in the form of product exchange; then we propose a second approach based on vertical-horizontal cooperation. Both approaches are modelled as a MIP model and solved using the CPLEX solver. The objective of this study is to analyse the performance in terms of costs, quantities in stock and customer satisfaction.]]>
10.1504/IJLSM.2024.136859
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 295 - 326
Abdennour Ilyas Benfriha
Lamia Triqui-Sari
Aimade Eddine Bougloula
Mohammed Bennekrouf
Laboratory of Automation and Manufacturing Engineering, Industrial Engineering Department, Batna 2 University, Batna 05000, Algeria ' Manufacturing Engineering Laboratory of Tlemcen, Engineering Sciences Department, Aboubekr Belkaïd University, BP 230, Tlemcen 13000, Algeria ' Laboratory of Automation and Manufacturing Engineering, Industrial Engineering Department, Batna 2 University, Batna 05000, Algeria ' Manufacturing Engineering Laboratory of Tlemcen, Engineering Sciences Department, Aboubekr Belkaïd University, BP 230, Tlemcen 13000, ESSA Tlemcen, Algeria
inventory management
distribution network
supply chain
product exchange
cooperation
2024-02-23T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
47
3
295
326
2024-02-23T23:20:50-05:00
-
Analysis of machine learning integration into supply chain management
http://www.inderscience.com/link.php?id=136856
The application of machine learning (ML) techniques in supply chain (SC) processes has been gaining popularity over the last years, because ML significantly helps making the SC faster and more efficient, automatising its processes, improving decision making, and mitigating risks, among other benefits that results in cost savings or more profits. The goal of this work was to analyse the existing studies about the integration of ML into supply chain management (SCM), exploring gaps and trends, from a bibliometric analysis of the articles published. The analysis consisted of assessing the total number of published documents between 2000 and 2020. The main contribution of this research was the identification of significant details about the studies conducted involving the integration of ML and SCM, which will help in the development of new studies in this important area.
Analysis of machine learning integration into supply chain management
Elen Yanina Aguirre RodrÃguez; Elias Carlos Aguirre RodrÃguez; Aneirson Francisco da Silva; Paloma Maria Silva Rocha Rizol; Rafael de Carvalho Miranda; Fernando Augusto Silva Marins
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 327 - 355
The application of machine learning (ML) techniques in supply chain (SC) processes has been gaining popularity over the last years, because ML significantly helps making the SC faster and more efficient, automatising its processes, improving decision making, and mitigating risks, among other benefits that results in cost savings or more profits. The goal of this work was to analyse the existing studies about the integration of ML into supply chain management (SCM), exploring gaps and trends, from a bibliometric analysis of the articles published. The analysis consisted of assessing the total number of published documents between 2000 and 2020. The main contribution of this research was the identification of significant details about the studies conducted involving the integration of ML and SCM, which will help in the development of new studies in this important area.]]>
10.1504/IJLSM.2024.136856
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 327 - 355
Elen Yanina Aguirre RodrÃguez
Elias Carlos Aguirre RodrÃguez
Aneirson Francisco da Silva
Paloma Maria Silva Rocha Rizol
Rafael de Carvalho Miranda
Fernando Augusto Silva Marins
Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 †Guaratinguetá, SP, Brazil ' Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 †Guaratinguetá, SP, Brazil ' Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 †Guaratinguetá, SP, Brazil ' Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 †Guaratinguetá, SP, Brazil ' Federal University of Itajubá (UNIFEI), Av. BPS, 1303 †Itajubá, MG, Brazil ' Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 †Guaratinguetá, SP, Brazil
machine learning
supply chain management
SCM
classification
trends
gaps
2024-02-23T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
47
3
327
355
2024-02-23T23:20:50-05:00
-
An optimisation model to increase the cross-dockings operational utilisation
http://www.inderscience.com/link.php?id=136857
Introducing a new version of a cross-docking assignment problem when forklifts carry bulk materials, we address an operational solution to reduce the impact of forklifts congestion in centre of terminals. We develop a mixed-integer bilinear programming model, a Tabu-search algorithm, where the main objective of the model is still to minimize the total transshipment costs, and additionally trying to minimize the weighted forklifts travelled distance (WTD) and the weighted unloading/loading labour costs at inbound and outbound doors (WLC). We hypothesize that longer WTD could shift the location of weighted forklift congestion concentration from the centre of the terminal toward the sides. We assuming four cost pattern's distributions, all are supposed to lead high flows to doors with low labour costs, which ultimately impact the total WTD, WLC, and TTC. The proposed Tabu-search outperform CPLEX by finding a set of a diverse and high-quality solution within a reasonable computing time.
An optimisation model to increase the cross-dockings operational utilisation
Vahid Ghomi; Farnaz Ghazi Nezami; Sina Shokoohyar; Mina Ghofrani Esfahani
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 356 - 387
Introducing a new version of a cross-docking assignment problem when forklifts carry bulk materials, we address an operational solution to reduce the impact of forklifts congestion in centre of terminals. We develop a mixed-integer bilinear programming model, a Tabu-search algorithm, where the main objective of the model is still to minimize the total transshipment costs, and additionally trying to minimize the weighted forklifts travelled distance (WTD) and the weighted unloading/loading labour costs at inbound and outbound doors (WLC). We hypothesize that longer WTD could shift the location of weighted forklift congestion concentration from the centre of the terminal toward the sides. We assuming four cost pattern's distributions, all are supposed to lead high flows to doors with low labour costs, which ultimately impact the total WTD, WLC, and TTC. The proposed Tabu-search outperform CPLEX by finding a set of a diverse and high-quality solution within a reasonable computing time.]]>
10.1504/IJLSM.2024.136857
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 356 - 387
Vahid Ghomi
Farnaz Ghazi Nezami
Sina Shokoohyar
Mina Ghofrani Esfahani
School of Business, Pennsylvania State University, Mont Alto, PA, USA ' Department of Industrial and Manufacturing Engineering, Kettering University, 1700 University Ave, Flint, MI 48504, USA ' Department of Computing and Decision Sciences, Stillman School of Business, Seton Hall University, South Orange, New Jersey, 07079, USA ' School of Business Administration, Penn State Harrisburg, 777 West Harrisburg Pike, Middletown, PA 17057, USA
cross-docking assignment problem
CDAP
Tabu-search algorithm
optimisations
2024-02-23T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
47
3
356
387
2024-02-23T23:20:50-05:00
-
GSCM barriers to sustainable development in Indian manufacturing organisations: a mixed-method approach
http://www.inderscience.com/link.php?id=136858
The objective of this study is to identify the major roadblocks to green supply chain management (GSCM) techniques in order to achieve India's industrial sectors' long-term development goals. MICMAC analysis and an interpretive structural modelling (ISM)-based methodology were utilised to categorise the reciprocal interaction of these identified barriers into four groups. Fifteen significant hurdles have been identified based on previous research and expert comments from industry professionals in order to achieve sustainable development goals in GSCM processes. The findings of this study reveal that, of the 15 barriers identified, the absence of government support systems (AGSS) is the most dependent and influential barrier, while the lack of innovative green practices is the least important barrier. This research also aids in establishing a key foundation for developing a greener manufacturing platform. The findings of the study will aid practitioners, policymakers, and managers in making informed decisions about how to improve the long-term sustainability of Indian manufacturing sectors using GSCM techniques. These GSCM barriers will be implemented with an emphasis on GSCM techniques for a better knowledge of how to prevent significant bottlenecks in today's manufacturing sectors. The uniqueness of this study stems from the necessity of GSCM since most manufacturing companies set up their facilities to be sensitive to economic, social, and environmental changes in order to improve organisational performance.
GSCM barriers to sustainable development in Indian manufacturing organisations: a mixed-method approach
Deepak Kumar Pathak; Ajay Verma; Vimal Kumar
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 388 - 410
The objective of this study is to identify the major roadblocks to green supply chain management (GSCM) techniques in order to achieve India's industrial sectors' long-term development goals. MICMAC analysis and an interpretive structural modelling (ISM)-based methodology were utilised to categorise the reciprocal interaction of these identified barriers into four groups. Fifteen significant hurdles have been identified based on previous research and expert comments from industry professionals in order to achieve sustainable development goals in GSCM processes. The findings of this study reveal that, of the 15 barriers identified, the absence of government support systems (AGSS) is the most dependent and influential barrier, while the lack of innovative green practices is the least important barrier. This research also aids in establishing a key foundation for developing a greener manufacturing platform. The findings of the study will aid practitioners, policymakers, and managers in making informed decisions about how to improve the long-term sustainability of Indian manufacturing sectors using GSCM techniques. These GSCM barriers will be implemented with an emphasis on GSCM techniques for a better knowledge of how to prevent significant bottlenecks in today's manufacturing sectors. The uniqueness of this study stems from the necessity of GSCM since most manufacturing companies set up their facilities to be sensitive to economic, social, and environmental changes in order to improve organisational performance.]]>
10.1504/IJLSM.2024.136858
International Journal of Logistics Systems and Management, Vol. 47, No. 3 (2024) pp. 388 - 410
Deepak Kumar Pathak
Ajay Verma
Vimal Kumar
Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal-462007, India ' Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal-462007, India ' Department of Information Management, Chaoyang University of Technology, Wufeng, Taichung-41349, Taiwan
green supply chain management
GSCM
MICMAC
analysis
driver dependence power analysis
interpretive structural modelling
Indian manufacturing industry
2024-02-23T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
47
3
388
410
2024-02-23T23:20:50-05:00