Pub Date : 2023-12-12DOI: 10.3390/logistics7040099
Kim Häring, Carina Pimentel, Leonor C. Teixeira
Background: Industry 4.0 signifies a profound global transformation in production and service activities through its novel organizational principles, including digital connectivity, information transparency, technical assistance, and decentralized decision making. This transformation poses significant challenges for businesses, particularly small- and medium-sized enterprises (SMEs). In response, maturity models have been developed and adapted to facilitate a transparent and supportive entry into this transformative domain. Method/Aim: This study is dedicated to the comprehensive analysis of Industry 4.0 maturity models through a systematic literature review to identify and evaluate previously published recommendations for the adoption and utilization of Industry 4.0. The aim is to provide valuable insights in this context, with a particular focus on easing entry into this domain for SMEs. Results: Quantitative findings reveal a growing demand for fundamental support when entering this domain, with maturity models capable of meeting the demand for structured guidance. However, these models are currently under-validated, lacking transparency, and are often unsuitable for SMEs. Qualitative results categorize numerous insights and recommendations into ten distinct categories related to Industry 4.0. Conclusions: This paper provides a structured summary to support newcomers, research institutions, and businesses in effectively initiating and optimizing their Industrsy 4.0 activities.
{"title":"Industry 4.0 Implementation in Small- and Medium-Sized Enterprises: Recommendations Extracted from a Systematic Literature Review with a Focus on Maturity Models","authors":"Kim Häring, Carina Pimentel, Leonor C. Teixeira","doi":"10.3390/logistics7040099","DOIUrl":"https://doi.org/10.3390/logistics7040099","url":null,"abstract":"Background: Industry 4.0 signifies a profound global transformation in production and service activities through its novel organizational principles, including digital connectivity, information transparency, technical assistance, and decentralized decision making. This transformation poses significant challenges for businesses, particularly small- and medium-sized enterprises (SMEs). In response, maturity models have been developed and adapted to facilitate a transparent and supportive entry into this transformative domain. Method/Aim: This study is dedicated to the comprehensive analysis of Industry 4.0 maturity models through a systematic literature review to identify and evaluate previously published recommendations for the adoption and utilization of Industry 4.0. The aim is to provide valuable insights in this context, with a particular focus on easing entry into this domain for SMEs. Results: Quantitative findings reveal a growing demand for fundamental support when entering this domain, with maturity models capable of meeting the demand for structured guidance. However, these models are currently under-validated, lacking transparency, and are often unsuitable for SMEs. Qualitative results categorize numerous insights and recommendations into ten distinct categories related to Industry 4.0. Conclusions: This paper provides a structured summary to support newcomers, research institutions, and businesses in effectively initiating and optimizing their Industrsy 4.0 activities.","PeriodicalId":507203,"journal":{"name":"Logistics","volume":"20 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139182831","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 : 2023-12-11DOI: 10.3390/logistics7040097
Al-Amin Abba Dabo, A. Hosseinian-Far
Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability.
{"title":"An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0","authors":"Al-Amin Abba Dabo, A. Hosseinian-Far","doi":"10.3390/logistics7040097","DOIUrl":"https://doi.org/10.3390/logistics7040097","url":null,"abstract":"Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability.","PeriodicalId":507203,"journal":{"name":"Logistics","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139183408","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 : 2023-11-27DOI: 10.3390/logistics7040089
Mathew Azarian, Hao Yu, A. T. Shiferaw, T. K. Stevik
Background: Systematic literature review (SLR) is increasingly utilized to maximize the element of rigor and minimize the individual bias of research synthesis. An analysis of the Web of Science (WoS) database indicates that 90% of the literature review studies using SLR have been published between 2012 and 2022. However, this progressive agenda is impaired by the lack of methodological consistency and rigorousness. To fill this gap, this paper aims at mapping the theoretical comprehension and practices of SLR and providing a stepwise approach to employing such a framework. Methods: A comprehensive narrative review is used in this paper to analyze the studies concerning the literature review typology and the structural assessment of the SLR. Furthermore, the methodological approach of the literature review studies that adopted the SLR and were published in the Logistics journal is assessed across a set of vital criteria associated with conducting an SLR. Results: There is a concrete link between the purpose of a review, i.e., to describe, test, extend, or critique, and the literature review type. There are 17 distinct literature review types, e.g., a narrative review, a bibliometric analysis, etc., which must be justified meticulously regardless of the SLR. The ambiguity in conceiving the SLR either as a toolkit or a review type, the lack of justification regarding the review purpose and type, and vague conceptual distinguishment between the bibliometric analysis, as a distinct review type, and the SLR framework, are only a few of the shortcomings observed in the analyzed papers. Conclusions: Given the significant role of SLR in elevating the element of rigor within the literature review studies, it is deemed essential to employ this framework by paying attention to two holistic factors: (1) theoretical distinction between the literature review purpose, the literature review type, and the SLR; (2) strict adherence to the SLR procedure with a high degree of accuracy and explicitness.
{"title":"Do We Perform Systematic Literature Review Right? A Scientific Mapping and Methodological Assessment","authors":"Mathew Azarian, Hao Yu, A. T. Shiferaw, T. K. Stevik","doi":"10.3390/logistics7040089","DOIUrl":"https://doi.org/10.3390/logistics7040089","url":null,"abstract":"Background: Systematic literature review (SLR) is increasingly utilized to maximize the element of rigor and minimize the individual bias of research synthesis. An analysis of the Web of Science (WoS) database indicates that 90% of the literature review studies using SLR have been published between 2012 and 2022. However, this progressive agenda is impaired by the lack of methodological consistency and rigorousness. To fill this gap, this paper aims at mapping the theoretical comprehension and practices of SLR and providing a stepwise approach to employing such a framework. Methods: A comprehensive narrative review is used in this paper to analyze the studies concerning the literature review typology and the structural assessment of the SLR. Furthermore, the methodological approach of the literature review studies that adopted the SLR and were published in the Logistics journal is assessed across a set of vital criteria associated with conducting an SLR. Results: There is a concrete link between the purpose of a review, i.e., to describe, test, extend, or critique, and the literature review type. There are 17 distinct literature review types, e.g., a narrative review, a bibliometric analysis, etc., which must be justified meticulously regardless of the SLR. The ambiguity in conceiving the SLR either as a toolkit or a review type, the lack of justification regarding the review purpose and type, and vague conceptual distinguishment between the bibliometric analysis, as a distinct review type, and the SLR framework, are only a few of the shortcomings observed in the analyzed papers. Conclusions: Given the significant role of SLR in elevating the element of rigor within the literature review studies, it is deemed essential to employ this framework by paying attention to two holistic factors: (1) theoretical distinction between the literature review purpose, the literature review type, and the SLR; (2) strict adherence to the SLR procedure with a high degree of accuracy and explicitness.","PeriodicalId":507203,"journal":{"name":"Logistics","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139233076","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 : 2023-11-24DOI: 10.3390/logistics7040088
Emad Sadoon, U. Venkatadri, Alireza Ghasemi
Background: Logistics management in the clinical trials industry is a very challenging undertaking because it involves multiple stakeholders, complex processes, diverse software applications, intensive white-collar jobs, and onerous quality standards. Current business practices are inefficient and difficult to automate technologies. Methods: This paper reviews the theories and concepts of clinical trials logistics management. The inefficiencies in current logistics management industry are then addressed by building a conceptual framework based on contemporary software tools and architectures, such as web portals, software agents, business process management system, project cards, and resource cards, all interacting with specialized software applications such as accounting, inventory, and label design software. The framework supports data analysis at multiple levels of decision making. To this end, a project planning tool for facilitating and optimizing the operational planning in this industry is designed and presented. Results: The planning tool also contributes to the literature by contrasting several different resource scenarios such as the shared pool, dedicated resources for each project, and the creation of several work groups with dedicated resources. These are Pareto trade-offs. Conclusions: A framework employing a business process management is proposed for clinical trials logistics management. Different managerial scenarios with shared, dedicated, and work group resources are investigated using a case study.
{"title":"A Conceptual Framework for Logistics Management and Project Planning in the Clinical Trials Industry","authors":"Emad Sadoon, U. Venkatadri, Alireza Ghasemi","doi":"10.3390/logistics7040088","DOIUrl":"https://doi.org/10.3390/logistics7040088","url":null,"abstract":"Background: Logistics management in the clinical trials industry is a very challenging undertaking because it involves multiple stakeholders, complex processes, diverse software applications, intensive white-collar jobs, and onerous quality standards. Current business practices are inefficient and difficult to automate technologies. Methods: This paper reviews the theories and concepts of clinical trials logistics management. The inefficiencies in current logistics management industry are then addressed by building a conceptual framework based on contemporary software tools and architectures, such as web portals, software agents, business process management system, project cards, and resource cards, all interacting with specialized software applications such as accounting, inventory, and label design software. The framework supports data analysis at multiple levels of decision making. To this end, a project planning tool for facilitating and optimizing the operational planning in this industry is designed and presented. Results: The planning tool also contributes to the literature by contrasting several different resource scenarios such as the shared pool, dedicated resources for each project, and the creation of several work groups with dedicated resources. These are Pareto trade-offs. Conclusions: A framework employing a business process management is proposed for clinical trials logistics management. Different managerial scenarios with shared, dedicated, and work group resources are investigated using a case study.","PeriodicalId":507203,"journal":{"name":"Logistics","volume":"68 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139240512","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 : 2023-11-22DOI: 10.3390/logistics7040087
Ahmed Talaat, M. Gheith, Amr Eltawil
Background: In container terminals, optimizing the scheduling of external trucks and yard cranes is crucial as it directly impacts the truck turnaround time, which is one of the most critical performance measures. Furthermore, proper scheduling of external trucks contributes to reducing CO2 emissions. Methods: This paper proposes a new approach based on a mixed integer programming model to schedule external trucks and yard cranes with the objective of minimizing CO2 emissions and reducing truck turnaround time, the gap between trucking companies’ preferred arrival time and appointed time, and the energy consumption of yard cranes. The proposed approach combines data analysis and operations research techniques. Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. Additionally, a two-stage mathematical model is applied. The first stage employs a bi-objective mathematical model to plan the arrival of external trucks at the terminal gates. The second stage involves a mathematical model that schedules yard cranes’ movements between different yard blocks. Results: The results show that implementing this methodology in a hypothetical case study may lead to a substantial daily reduction of approximately 31% in CO2 emissions. Additionally, the results provide valuable insights into the trade-off between satisfying the trucking companies’ preferred arrival time and the total turnaround time. Conclusions: The integration of data clustering with mathematical modeling demonstrates a notable reduction in emissions, underscoring the viability of this strategy in promoting sustainability in port-related activities.
背景：在集装箱码头，优化外部卡车和堆场起重机的调度至关重要，因为它直接影响到卡车的周转时间，而周转时间是最关键的性能指标之一。此外，外部卡车的合理调度还有助于减少二氧化碳排放。方法：本文提出了一种基于混合整数编程模型的新方法，用于调度外部卡车和堆场起重机，目标是最大限度地减少二氧化碳排放，缩短卡车周转时间、卡车运输公司首选到达时间与指定时间之间的差距以及堆场起重机的能耗。所提出的方法结合了数据分析和运筹学技术。具体来说，它采用 K 均值聚类算法来减少集装箱装卸所需的卡车行程次数。此外，还采用了两阶段数学模型。第一阶段采用双目标数学模型来规划外部卡车到达码头闸口的时间。第二阶段采用数学模型，安排堆场起重机在不同堆场区块之间的移动。结果结果表明，在假定案例研究中实施该方法可使每天的二氧化碳排放量大幅减少约 31%。此外，结果还提供了关于满足卡车运输公司首选到达时间与总周转时间之间权衡的宝贵见解。结论：将数据集群与数学建模相结合，可显著减少排放量，这表明这一策略在促进港口相关活动的可持续发展方面是可行的。
{"title":"A Multi-Stage Approach for External Trucks and Yard Cranes Scheduling with CO2 Emissions Considerations in Container Terminals","authors":"Ahmed Talaat, M. Gheith, Amr Eltawil","doi":"10.3390/logistics7040087","DOIUrl":"https://doi.org/10.3390/logistics7040087","url":null,"abstract":"Background: In container terminals, optimizing the scheduling of external trucks and yard cranes is crucial as it directly impacts the truck turnaround time, which is one of the most critical performance measures. Furthermore, proper scheduling of external trucks contributes to reducing CO2 emissions. Methods: This paper proposes a new approach based on a mixed integer programming model to schedule external trucks and yard cranes with the objective of minimizing CO2 emissions and reducing truck turnaround time, the gap between trucking companies’ preferred arrival time and appointed time, and the energy consumption of yard cranes. The proposed approach combines data analysis and operations research techniques. Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. Additionally, a two-stage mathematical model is applied. The first stage employs a bi-objective mathematical model to plan the arrival of external trucks at the terminal gates. The second stage involves a mathematical model that schedules yard cranes’ movements between different yard blocks. Results: The results show that implementing this methodology in a hypothetical case study may lead to a substantial daily reduction of approximately 31% in CO2 emissions. Additionally, the results provide valuable insights into the trade-off between satisfying the trucking companies’ preferred arrival time and the total turnaround time. Conclusions: The integration of data clustering with mathematical modeling demonstrates a notable reduction in emissions, underscoring the viability of this strategy in promoting sustainability in port-related activities.","PeriodicalId":507203,"journal":{"name":"Logistics","volume":"26 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139247818","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 : 2023-11-22DOI: 10.3390/logistics7040086
Fábio Polola Mamede, R. F. da Silva, Irineu de Brito Junior, H. Yoshizaki, C. M. Hino, C. Cugnasca
Background: Transportation demand forecasting is an essential activity for logistics operators and carriers. It leverages business operation decisions, infrastructure, management, and resource planning activities. Since 2015, there has been an increase in the use of deep learning models in this domain. However, there is a gap in works comparing traditional statistics and deep learning models for transportation demand forecasts. This work aimed to perform a case study of aggregated transportation demand forecasts in 54 distribution centers of a Brazilian carrier. Methods: A computational simulation and case study methods were applied, exploring the characteristics of the datasets through autoregressive integrated moving average (ARIMA) and its variations, in addition to a deep neural network, long short-term memory, known as LSTM. Eight scenarios were explored while considering different data preprocessing methods and evaluating how outliers, training and testing dataset splits during cross-validation, and the relevant hyperparameters of each model can affect the demand forecast. Results: The long short-term memory networks were observed to outperform the statistical methods in ninety-four percent of the dispatching units over the evaluated scenarios, while the autoregressive integrated moving average modeled the remaining five percent. Conclusions: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management.
{"title":"Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers","authors":"Fábio Polola Mamede, R. F. da Silva, Irineu de Brito Junior, H. Yoshizaki, C. M. Hino, C. Cugnasca","doi":"10.3390/logistics7040086","DOIUrl":"https://doi.org/10.3390/logistics7040086","url":null,"abstract":"Background: Transportation demand forecasting is an essential activity for logistics operators and carriers. It leverages business operation decisions, infrastructure, management, and resource planning activities. Since 2015, there has been an increase in the use of deep learning models in this domain. However, there is a gap in works comparing traditional statistics and deep learning models for transportation demand forecasts. This work aimed to perform a case study of aggregated transportation demand forecasts in 54 distribution centers of a Brazilian carrier. Methods: A computational simulation and case study methods were applied, exploring the characteristics of the datasets through autoregressive integrated moving average (ARIMA) and its variations, in addition to a deep neural network, long short-term memory, known as LSTM. Eight scenarios were explored while considering different data preprocessing methods and evaluating how outliers, training and testing dataset splits during cross-validation, and the relevant hyperparameters of each model can affect the demand forecast. Results: The long short-term memory networks were observed to outperform the statistical methods in ninety-four percent of the dispatching units over the evaluated scenarios, while the autoregressive integrated moving average modeled the remaining five percent. Conclusions: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management.","PeriodicalId":507203,"journal":{"name":"Logistics","volume":"28 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139249486","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}
Background: A hospital’s operating theater service system is a large-scale, complicated system that must be carefully managed to offer the best possible results for its patients. Unlike other industries such as manufacturing and logistics, system dynamics (SD) methodologies are not extensively applied in hospital operating theaters. This study deals with the future development and possible future scenarios for hospital operating rooms in Bangladesh. Methods: Due to demographic dynamics and demographic processes, increased pressures on hospital care are expected in Bangladesh. The SD model anticipates possible future scenarios, reconciles service capacities and the variability of patient demand, and reduces patient congestion and waiting times in the hospital area. This study introduces a causal loop diagram to show a causal link between the hospital operating theater system variables. It also introduces a stock flow diagram to understand the dynamic behavior of the system. Results: The model validation testing reports that in extreme conditions, such as a 50% reduction in the patient arrival rate, the model is valid and runs as usual. Conclusions: This first work of SD modeling for hospital operating theater systems can help healthcare managers, decision makers, or researchers of any responsibility level make better predictions in order to reduce patient waiting times and backlogs and make appropriate decisions.
{"title":"Modeling Hospital Operating Theater Services: A System Dynamics Approach","authors":"Md Mahfuzur Rahman, Rubayet Karim, Md. Moniruzzaman, Md. Afjal Hossain, Hammad Younes","doi":"10.3390/logistics7040085","DOIUrl":"https://doi.org/10.3390/logistics7040085","url":null,"abstract":"Background: A hospital’s operating theater service system is a large-scale, complicated system that must be carefully managed to offer the best possible results for its patients. Unlike other industries such as manufacturing and logistics, system dynamics (SD) methodologies are not extensively applied in hospital operating theaters. This study deals with the future development and possible future scenarios for hospital operating rooms in Bangladesh. Methods: Due to demographic dynamics and demographic processes, increased pressures on hospital care are expected in Bangladesh. The SD model anticipates possible future scenarios, reconciles service capacities and the variability of patient demand, and reduces patient congestion and waiting times in the hospital area. This study introduces a causal loop diagram to show a causal link between the hospital operating theater system variables. It also introduces a stock flow diagram to understand the dynamic behavior of the system. Results: The model validation testing reports that in extreme conditions, such as a 50% reduction in the patient arrival rate, the model is valid and runs as usual. Conclusions: This first work of SD modeling for hospital operating theater systems can help healthcare managers, decision makers, or researchers of any responsibility level make better predictions in order to reduce patient waiting times and backlogs and make appropriate decisions.","PeriodicalId":507203,"journal":{"name":"Logistics","volume":"40 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139263469","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 : 2023-11-16DOI: 10.3390/logistics7040084
A. Adenigbo, Joash Mageto, Rose Luke
Background: The air cargo logistics industry has been deemed reluctant to adopt new technologies for their business operations. So, this study aims to examine the adoption of technological innovations in the air cargo logistics industry in South Africa. The specific objective is to emphasise the effects of technologies on air cargo logistics operations to address the reluctance to adopt technological innovations in the industry. Methods: A questionnaire survey was conducted randomly on three hundred and seventy-three (373) cargo agents at the OR Tambo International Airport. The data were subjected to a nonparametric test with Kruskal–Wallis, exploratory factor analysis and regression analysis to explore the effects of technologies for prompt adoption and implementation of emerging innovations that enhance quality service delivery. Results: The study found that promptly adopting emerging technological innovations in the air cargo logistics industry promotes efficient operations, improves warehousing, and enhances cargo delivery services for customer satisfaction. Also, the study established that technologically driven operations and warehousing are significant determinants of quality service delivery in the air cargo logistics industry. Conclusions: This study encourages the prompt adoption and implementation of technological innovations for improved quality service delivery, customer satisfaction, and loyalty in the air cargo logistics industry.
{"title":"Adopting Technological Innovations in the Air Cargo Logistics Industry in South Africa","authors":"A. Adenigbo, Joash Mageto, Rose Luke","doi":"10.3390/logistics7040084","DOIUrl":"https://doi.org/10.3390/logistics7040084","url":null,"abstract":"Background: The air cargo logistics industry has been deemed reluctant to adopt new technologies for their business operations. So, this study aims to examine the adoption of technological innovations in the air cargo logistics industry in South Africa. The specific objective is to emphasise the effects of technologies on air cargo logistics operations to address the reluctance to adopt technological innovations in the industry. Methods: A questionnaire survey was conducted randomly on three hundred and seventy-three (373) cargo agents at the OR Tambo International Airport. The data were subjected to a nonparametric test with Kruskal–Wallis, exploratory factor analysis and regression analysis to explore the effects of technologies for prompt adoption and implementation of emerging innovations that enhance quality service delivery. Results: The study found that promptly adopting emerging technological innovations in the air cargo logistics industry promotes efficient operations, improves warehousing, and enhances cargo delivery services for customer satisfaction. Also, the study established that technologically driven operations and warehousing are significant determinants of quality service delivery in the air cargo logistics industry. Conclusions: This study encourages the prompt adoption and implementation of technological innovations for improved quality service delivery, customer satisfaction, and loyalty in the air cargo logistics industry.","PeriodicalId":507203,"journal":{"name":"Logistics","volume":"17 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139270612","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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