Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215022
P. Rathnayake, C. Kavirathna
Currently, in Sri Lanka, the road (truck) has always been the dominant transport mode for moving import/export containerized cargo to/from Colombo port. According to the past literature, there are a lot of road-based containerized cargo transportation issues in Sri Lanka. This study introduces a dry port-based containerized import/export cargo transportation method using a railway network to connect the port of Colombo and the Board of Investment (BOI) Export Processing Zones (EPZ). A dry port is an inland intermodal terminal, directly connected by a railway line to the Colombo port. The study mainly focuses on two dry port-based networks under several alternative network configurations. The locations of the proposed dry ports are Orugodawatta’s current customs clearing yard and cargo inspection center in Kerawalapitiya proposed by the Asian Development Bank (ADB). In this study, mathematical models were developed to analyze and compare the advantages of the proposed network considering current freight demand under several scenarios from economic and environmental, and travel time perspectives. Then the study estimates the import/export BOI freight demand for 2050 and analyzes the potential of the proposed railway-based cargo transport system for 2050. These figures can be further reduced by optimizing the dry port location. Therefore, a simulation-based approach was considered to optimize the dry port location by Greenfield analysis method with “Supply Chain Guru” software. Through simulation results, the study shows the new dry-port location compatibility for the proposed system. The findings of the study have demonstrated a systematic approach to decision-making by optimizing the local cargo handling process. By adopting this system, Sri Lankan inland logistic operations will become more efficient and the total transportation costs, environmental pollution, and transportation time will decrease significantly.
{"title":"Impacts of Integrated Railway-Based Containerized Cargo Transport Network to Connect the Port of Colombo and Free Trade Zones in Sri Lanka","authors":"P. Rathnayake, C. Kavirathna","doi":"10.1109/SCSE59836.2023.10215022","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215022","url":null,"abstract":"Currently, in Sri Lanka, the road (truck) has always been the dominant transport mode for moving import/export containerized cargo to/from Colombo port. According to the past literature, there are a lot of road-based containerized cargo transportation issues in Sri Lanka. This study introduces a dry port-based containerized import/export cargo transportation method using a railway network to connect the port of Colombo and the Board of Investment (BOI) Export Processing Zones (EPZ). A dry port is an inland intermodal terminal, directly connected by a railway line to the Colombo port. The study mainly focuses on two dry port-based networks under several alternative network configurations. The locations of the proposed dry ports are Orugodawatta’s current customs clearing yard and cargo inspection center in Kerawalapitiya proposed by the Asian Development Bank (ADB). In this study, mathematical models were developed to analyze and compare the advantages of the proposed network considering current freight demand under several scenarios from economic and environmental, and travel time perspectives. Then the study estimates the import/export BOI freight demand for 2050 and analyzes the potential of the proposed railway-based cargo transport system for 2050. These figures can be further reduced by optimizing the dry port location. Therefore, a simulation-based approach was considered to optimize the dry port location by Greenfield analysis method with “Supply Chain Guru” software. Through simulation results, the study shows the new dry-port location compatibility for the proposed system. The findings of the study have demonstrated a systematic approach to decision-making by optimizing the local cargo handling process. By adopting this system, Sri Lankan inland logistic operations will become more efficient and the total transportation costs, environmental pollution, and transportation time will decrease significantly.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114529672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215005
Shalitha Alahakon, Tharindu Siriwardana, Deshan Udupihilla, T. Wickramasinghe, S. Rajapaksha
Retailers are crucial in supply chains, acting as the bridge between consumers and resources. However, there is limited analytic-based literature on block design in grocery stores. This paper employs an algorithmic approach with optimization techniques to efficiently design the interior space of a provided supermarket. The objective is to create an analytical method for handling design issues without relying on human-centered approaches. Using data from supermarket store arrangements, the paper showcases efficient space utilization by aligning item measurements with customer needs. Decision variables offer decision makers a precise collection of non-dominated designs. Previous studies demonstrate the effectiveness of this approach in analytically designing a data-driven structure for supermarket block layouts. The model identifies layouts that maximize space utilization while meeting industry standards. Although primarily focused on Asian retailers, the approach is generally applicable due to the similarity of grocery store layouts worldwide. The method and results are easily translatable for other retailers.
{"title":"Developing and Training a Mathematical Model for Optimizing a Given Interior Space of a Supermarket","authors":"Shalitha Alahakon, Tharindu Siriwardana, Deshan Udupihilla, T. Wickramasinghe, S. Rajapaksha","doi":"10.1109/SCSE59836.2023.10215005","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215005","url":null,"abstract":"Retailers are crucial in supply chains, acting as the bridge between consumers and resources. However, there is limited analytic-based literature on block design in grocery stores. This paper employs an algorithmic approach with optimization techniques to efficiently design the interior space of a provided supermarket. The objective is to create an analytical method for handling design issues without relying on human-centered approaches. Using data from supermarket store arrangements, the paper showcases efficient space utilization by aligning item measurements with customer needs. Decision variables offer decision makers a precise collection of non-dominated designs. Previous studies demonstrate the effectiveness of this approach in analytically designing a data-driven structure for supermarket block layouts. The model identifies layouts that maximize space utilization while meeting industry standards. Although primarily focused on Asian retailers, the approach is generally applicable due to the similarity of grocery store layouts worldwide. The method and results are easily translatable for other retailers.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130049703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215040
Hmkt Gunawardhana, B. Kumara, Kapila T. Rathnayake, P. Jayaweera
For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study’s findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces.
{"title":"Effectiveness of Machine Learning Algorithms on Battling Counterfeit Items in E-commerce Marketplaces","authors":"Hmkt Gunawardhana, B. Kumara, Kapila T. Rathnayake, P. Jayaweera","doi":"10.1109/SCSE59836.2023.10215040","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215040","url":null,"abstract":"For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study’s findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117236417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215013
Rahul Adihetti, S. Jayalal
The spread of fake news in the social media has grown significantly over the past few years. According to the New York Times, fake news is defined as “made-up articles meant to deceive.” Additionally, the way they are released is almost identical to that of conventional news organizations. The issue is that a significant number of news outlets outside the major and reliable ones are disseminating unreliable information. This problem is exacerbated by the ease with which anything can be published from anywhere on well-known social networking and social media platforms. People can use this to their advantage by disseminating any type of message on various social networking sites to accomplish their objectives. In the Sri Lankan context, content posted in Sinhala greatly impacts fake news in Sri Lanka. Because utilizing the Sinhala language to describe emotions and feelings makes it easier to connect with Sinhala-speaking people than using content that has been published in other languages, like English. The use of Sinhala on social media has grown over the past few years. Additionally, as the use of the Sinhala language expanded, so did the number of occurrences of fake news. Based on the literature, approaches to identifying fake news depend on the features of the news content. Therefore, this research proposed an autoencoder-based method for Sinhala fake news detection, which is an unsupervised method. The method uses Text, User, Propagation, and Image features from the news content. And also, this research found the best feature combination to detect Sinhala language fake news content, which is a combination of Text, User, and Image features. The method gained an accuracy of 98% and 88% in Precision, Recall, and F1 Score by outperforming other existing anomaly detection methods. The main stakeholder of this study was fact-checking organizations in Sri Lanka.
{"title":"Sinhala Language Fake News Detection In Social Media Using Autoencoder-Based Method","authors":"Rahul Adihetti, S. Jayalal","doi":"10.1109/SCSE59836.2023.10215013","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215013","url":null,"abstract":"The spread of fake news in the social media has grown significantly over the past few years. According to the New York Times, fake news is defined as “made-up articles meant to deceive.” Additionally, the way they are released is almost identical to that of conventional news organizations. The issue is that a significant number of news outlets outside the major and reliable ones are disseminating unreliable information. This problem is exacerbated by the ease with which anything can be published from anywhere on well-known social networking and social media platforms. People can use this to their advantage by disseminating any type of message on various social networking sites to accomplish their objectives. In the Sri Lankan context, content posted in Sinhala greatly impacts fake news in Sri Lanka. Because utilizing the Sinhala language to describe emotions and feelings makes it easier to connect with Sinhala-speaking people than using content that has been published in other languages, like English. The use of Sinhala on social media has grown over the past few years. Additionally, as the use of the Sinhala language expanded, so did the number of occurrences of fake news. Based on the literature, approaches to identifying fake news depend on the features of the news content. Therefore, this research proposed an autoencoder-based method for Sinhala fake news detection, which is an unsupervised method. The method uses Text, User, Propagation, and Image features from the news content. And also, this research found the best feature combination to detect Sinhala language fake news content, which is a combination of Text, User, and Image features. The method gained an accuracy of 98% and 88% in Precision, Recall, and F1 Score by outperforming other existing anomaly detection methods. The main stakeholder of this study was fact-checking organizations in Sri Lanka.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124389666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1109/scse59836.2023.10215033
{"title":"Keynote 1: Innovation in the Age of AI Unpacking 2023’s AI Innovations and Their Sweeping Global Implications","authors":"","doi":"10.1109/scse59836.2023.10215033","DOIUrl":"https://doi.org/10.1109/scse59836.2023.10215033","url":null,"abstract":"","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134353198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1109/scse59836.2023.10215002
{"title":"SCSE 2023 Cover Page","authors":"","doi":"10.1109/scse59836.2023.10215002","DOIUrl":"https://doi.org/10.1109/scse59836.2023.10215002","url":null,"abstract":"","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124523905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215028
T. A. Gamage, E. Sandamali, Pradeep Kalansooriya
Electroencephalogram (EEG) based emotion recognition approaches have proven to be successful with the latest technologies, and therefore, driver emotion recognition is also being widely discussed for enhancing road safety. This paper reveals a unique approach to driver emotion recognition for the calm, fear, sad, and anger emotional states where calm is the desired state of mind while driving. Emotiv EPOC X 14 channel EEG headset is utilised for the EEG collection, and ten subjects are involved in the experiment. EEG preprocessing of the collected EEG data is done using the EEGLAB toolbox in Matlab. EEG feature extraction is performed using Matlab, and feature selection and classification model training is done using the Classification Learner app in Matlab. ANOVA and ReliefF are employed as the feature selection algorithms, and Support Vector Machine (SVM) and Naïve Bayes classifiers are utilised for the emotion classification. The outcomes reveal that the highest mean accuracy of 95% is achieved from the Coarse Gaussian SVM classifier, while the lowest mean accuracy of 85% is obtained from the Fine Gaussian SVM classifier detecting the calm, fear, sad, and anger emotional states. In addition, all the other trained classifier models have an accuracy between 85% and 95%. Therefore, the findings suggest that the proposed EEG-based implementation approach of an emotion classification model for drivers is highly successful and can be employed in future research in the paradigm of driver emotion recognition as well. Besides, this research presents a critical literature review concerning critical aspects of EEG-based emotion recognition research.
基于脑电图(EEG)的情绪识别方法已被最新技术证明是成功的，因此驾驶员情绪识别也被广泛讨论以提高道路安全。本文揭示了一种独特的方法来识别司机的情绪平静，恐惧，悲伤和愤怒的情绪状态，而冷静是驾驶时的理想心态。EEG采集采用Emotiv EPOC X 14通道脑电耳机，实验共涉及10名受试者。利用Matlab中的EEGLAB工具箱对采集到的脑电信号进行预处理。利用Matlab进行脑电特征提取，利用Matlab中的classification Learner app进行特征选择和分类模型训练。使用ANOVA和ReliefF作为特征选择算法，使用支持向量机(SVM)和Naïve贝叶斯分类器进行情感分类。结果表明，粗高斯SVM分类器检测平静、恐惧、悲伤和愤怒情绪状态的平均准确率最高，达到95%，而细高斯SVM分类器检测平静、恐惧、悲伤和愤怒情绪状态的平均准确率最低，为85%。此外，所有其他训练的分类器模型的准确率在85%到95%之间。因此，研究结果表明，基于脑电图的驾驶员情绪分类模型的实现方法是非常成功的，也可以用于未来驾驶员情绪识别范式的研究。此外，本研究对基于脑电图的情绪识别研究的关键方面进行了批判性的文献综述。
{"title":"DrivEmo: A Novel Approach for EEG-Based Emotion Classification for Drivers","authors":"T. A. Gamage, E. Sandamali, Pradeep Kalansooriya","doi":"10.1109/SCSE59836.2023.10215028","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215028","url":null,"abstract":"Electroencephalogram (EEG) based emotion recognition approaches have proven to be successful with the latest technologies, and therefore, driver emotion recognition is also being widely discussed for enhancing road safety. This paper reveals a unique approach to driver emotion recognition for the calm, fear, sad, and anger emotional states where calm is the desired state of mind while driving. Emotiv EPOC X 14 channel EEG headset is utilised for the EEG collection, and ten subjects are involved in the experiment. EEG preprocessing of the collected EEG data is done using the EEGLAB toolbox in Matlab. EEG feature extraction is performed using Matlab, and feature selection and classification model training is done using the Classification Learner app in Matlab. ANOVA and ReliefF are employed as the feature selection algorithms, and Support Vector Machine (SVM) and Naïve Bayes classifiers are utilised for the emotion classification. The outcomes reveal that the highest mean accuracy of 95% is achieved from the Coarse Gaussian SVM classifier, while the lowest mean accuracy of 85% is obtained from the Fine Gaussian SVM classifier detecting the calm, fear, sad, and anger emotional states. In addition, all the other trained classifier models have an accuracy between 85% and 95%. Therefore, the findings suggest that the proposed EEG-based implementation approach of an emotion classification model for drivers is highly successful and can be employed in future research in the paradigm of driver emotion recognition as well. Besides, this research presents a critical literature review concerning critical aspects of EEG-based emotion recognition research.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122730296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1109/scse59836.2023.10215018
{"title":"Advisory Committee","authors":"","doi":"10.1109/scse59836.2023.10215018","DOIUrl":"https://doi.org/10.1109/scse59836.2023.10215018","url":null,"abstract":"","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121206309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10215026
Charuka Kothalawala, Chaminda Rathnayeka
Today, social media usage is an essential tool for communication among individuals and organizations. However, evidence suggests that some industry sectors are striving to understand the relationship between social media usage during office hours and job performance. In the Sri Lankan context, the apparel sector is struggling to understand this relationship. Thus, this study investigated the impact of social media usage on employee performance with special reference to a leading apparel manufacturing company in Sri Lanka. A deductive approach was adopted to conduct the research. Individual social media usage (ISM) and work-related social media usage (WSM) are considered as independent variables and employee job performance is the dependent variable. Findings suggest that ISM and WSM enhance the job performance of apparel industry workers in Sri Lanka. Furthermore, findings indicate that the apparel industry must not discourage social media usage during office hours, instead, must find methods of utilizing social media usage for the betterment of the firm. Practical and theoretical implications, limitations, and suggestions for future research are mentioned in the Discussion. Concluding remarks are discussed in the Conclusion.
{"title":"The impact of social media usage during office hours on employee performance: Evidence from a Sri Lankan apparel manufacturing firm","authors":"Charuka Kothalawala, Chaminda Rathnayeka","doi":"10.1109/SCSE59836.2023.10215026","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10215026","url":null,"abstract":"Today, social media usage is an essential tool for communication among individuals and organizations. However, evidence suggests that some industry sectors are striving to understand the relationship between social media usage during office hours and job performance. In the Sri Lankan context, the apparel sector is struggling to understand this relationship. Thus, this study investigated the impact of social media usage on employee performance with special reference to a leading apparel manufacturing company in Sri Lanka. A deductive approach was adopted to conduct the research. Individual social media usage (ISM) and work-related social media usage (WSM) are considered as independent variables and employee job performance is the dependent variable. Findings suggest that ISM and WSM enhance the job performance of apparel industry workers in Sri Lanka. Furthermore, findings indicate that the apparel industry must not discourage social media usage during office hours, instead, must find methods of utilizing social media usage for the betterment of the firm. Practical and theoretical implications, limitations, and suggestions for future research are mentioned in the Discussion. Concluding remarks are discussed in the Conclusion.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115485145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1109/SCSE59836.2023.10214988
K. Nawurunnage, A. Prasadika, A. Wijayanayake
The growing need to address the threat of global warming and greenhouse gas emissions has placed immense pressure on logistics companies to adopt sustainable practices. With logistics operations being a significant source of greenhouse gas emissions, incorporating green supply chain management practices (GSCM) has become crucial to achieving environmental sustainability within the third-party logistics (3PL) industry. Exploring the existing literature under the concepts of Total Quality Management and Green Supply Chain Management reveals the need for future investigations into how those practices might potentially improve the logistics firm’s performance to achieve sustainability. Therefore, the main objective of this study is to identify the interrelationships of TQM practices and supply chain performance third-party logistics industry in terms of overall performance and identify the suitable TQM practices that can be applied to enhance the overall performance of Sri Lankan 3PLs and assess moderating effect of GSCM practices on that TQM-performance relationships. An online survey instrument was used to collect the data from executives, senior executives, and managers of 3PL firms in Sri Lanka. The statistical data analysis was done using PLS-SEM. The results found that top management support, customer focus, statistical process control, and continuous improvements are the significant total quality management practice for overall performance in the Sri Lankan 3PL industry. The study’s findings are useful for the top management of 3PLs, policymakers, and academia to identify the level of GSCM implementation within the industry, and results provide insights into further considerations regarding the implementation of GSCM practices and TQM practices to achieve the supply chain performance of the 3PLs while achieving sustainability.
{"title":"TQM Practices on Supply Chain Performance of Third-Party Logistics Services in Sri Lanka: The Moderating Role of Green Supply Chain Practices","authors":"K. Nawurunnage, A. Prasadika, A. Wijayanayake","doi":"10.1109/SCSE59836.2023.10214988","DOIUrl":"https://doi.org/10.1109/SCSE59836.2023.10214988","url":null,"abstract":"The growing need to address the threat of global warming and greenhouse gas emissions has placed immense pressure on logistics companies to adopt sustainable practices. With logistics operations being a significant source of greenhouse gas emissions, incorporating green supply chain management practices (GSCM) has become crucial to achieving environmental sustainability within the third-party logistics (3PL) industry. Exploring the existing literature under the concepts of Total Quality Management and Green Supply Chain Management reveals the need for future investigations into how those practices might potentially improve the logistics firm’s performance to achieve sustainability. Therefore, the main objective of this study is to identify the interrelationships of TQM practices and supply chain performance third-party logistics industry in terms of overall performance and identify the suitable TQM practices that can be applied to enhance the overall performance of Sri Lankan 3PLs and assess moderating effect of GSCM practices on that TQM-performance relationships. An online survey instrument was used to collect the data from executives, senior executives, and managers of 3PL firms in Sri Lanka. The statistical data analysis was done using PLS-SEM. The results found that top management support, customer focus, statistical process control, and continuous improvements are the significant total quality management practice for overall performance in the Sri Lankan 3PL industry. The study’s findings are useful for the top management of 3PLs, policymakers, and academia to identify the level of GSCM implementation within the industry, and results provide insights into further considerations regarding the implementation of GSCM practices and TQM practices to achieve the supply chain performance of the 3PLs while achieving sustainability.","PeriodicalId":429228,"journal":{"name":"2023 International Research Conference on Smart Computing and Systems Engineering (SCSE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116662574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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