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Increasing academic communities and practitioners begin to explore a novel method to reduce environmental pollution and realize green logistics delivery. Additionally, China's Statistical Yearbook shows that the number of private cars has reached 165 million in China. Under this background, this study proposes a green delivery method by the combination of sharing vehicle (private cars) and IoT (Internet of things) from the perspective of vehicle energy efficiency and aims to improve the energy efficiency of social vehicles and provides more convenient delivery services.

This study builds an IoT architecture consisting of customer data layer, information collection layer, cloud optimization layer and delivery task execution layer. Especially in the IoT architecture, a clustering analysis method is used to determine the critical value of customers' classification and shared delivery, a routing optimization method is used to solve the initial solution in could layer and shared technology is used in the implementation of shared delivery.

The results show that the delivery method considering shared vehicles has a positive effect on improving the energy utilization of vehicles. But if all of delivery tasks are performed by the shared vehicle, the application effect may be counterproductive, such as delivery cost increases and energy efficiency decreases. This study provides a good reference for the implementation of green intelligent delivery business, which has a positive effect on the improvement of logistics operation efficiency.

This study designs a novel method to solve the green and shared delivery issues under the IoT environment, which integrates the IoT architecture. The proposed methodology is applied in a real case in China.

This paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions.

This study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case.

The results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises’ conditions (e.g. customers’ locations and demand patterns) for better distribution routes planning.

There are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions.

Prior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.

Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement.

A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context.

The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability.

This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF.

With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.

This research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.

The results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.

The data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.

Prior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.

The transportation of fresh food requires cold chain logistics to maintain a low-temperature environment, which can reduce food waste and ensure product safety. Therefore, temperature control is a major challenge that cold chain logistics face.

This research proposes a prediction model of refrigerated truck temperature and air conditioner status (air speed and air temperature) based on hybrid mayfly algorithm (MA) and extreme learning machine (ELM). To prove the effectiveness of the proposed method, the mayfly algorithm–extreme learning machine (MA-ELM) is compared with the traditional ELM and the ELM optimized by classical biological-inspired algorithms, including the genetic algorithm (GA) and particle swarm optimization (PSO). The assessment is conducted through two experiments, including temperature prediction and air conditioner status prediction, based on a case study.

The prediction method is evaluated by five evaluation indicators, including the mean relative error (MRE), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R²). It can be concluded that the biological algorithm, especially the MA, can improve the prediction accuracy. This result clearly proves the effectiveness of the proposed hybrid prediction model in revealing the nonlinear patterns of the cold chain logistics temperature.

The case study illustrates the effectiveness of the proposed temperature prediction method, which helps to keep the product fresh. Even though the performance of MA is better than GA and PSO, the MA has the disadvantage of premature convergence. In the future, the modified MA can be designed to improve the performance of MA-ELM.

In prior studies, many scholars have conducted related research on the subject of temperature monitoring. However, this monitoring method can only identify temperature deviations that have occurred that harmed fresh food. As a countermeasure, research on the temperature prediction of cold chain logistics that can actively identify temperature changes has become the focus. Once a temperature deviation is predicted, temperature control measures can be taken in time to resolve the risk.

The purpose of this paper is to examine publication characteristics and dynamic evolution of the Industrial Management & Data Systems (IMDS) over the past 25 years from volume 94, issue 1, in 1994 through volume 118, issue 9, in 2018, using a bibliometric analysis, and identify the leading trends that have affected the journal during this time frame.

A bibliometric approach was used to provide a basic overview of the IMDS, including distribution of publication and citations, articles citing the IMDS, top-cited papers and publication patterns. Then, a complex network analysis was employed to present the most productive, influential and active authors, institutes and countries/regions. In addition, cluster analysis and alluvial diagram were used to analyze author keywords.

This study presents the basic bibliometric results for the IMDS and focuses on exploring its performance over the last 25 years. And it reveals the most productive, influential and active authors, institutes and countries/regions in IMDS. Moreover, this study detects the existence of at least five different keywords clusters and discovers how themes have evolved through the intricate citation relationships in IMDS.

The main contribution of this paper is the use of multiple analysis techniques from a complex network paradigm to emphasize the time evolving nature of the co-occurrence networks and to explore the variation of the collaboration networks in the IMDS. For the first time, the evolution of research themes is revealed with a purely data-driven approach.

With rapid industrialization and urbanization, municipal solid waste (MSW) management has become a serious challenge worldwide, especially in developing countries. The Beijing Municipality is a representative example of many local governments in China that are facing MSW management issues. Although there have been studies in the area of MSW management in the literature, less attention has been devoted to developing a structured framework that identifies and interprets the barriers to MSW management in megacities, especially in Beijing. Therefore, this study focuses on identifying a comprehensive list of barriers affecting the successful implementation of MSW management in Beijing.

Through an extensive review of related literature, 12 barriers are identified and classified into five categories: government, waste, knowledge dissemination, MSW management process and market. Using an integrated approach including the decision-making trial and evaluation laboratory (DEMATEL), maximum mean de-entropy algorithm (MMDE) and interpretive structural modeling (ISM), a conceptual structural model of MSW implementation barriers is constructed to provide insights for industrial decision-makers and policymakers.

The results show that a lack of economic support from the government, imperfect MSW-related laws and regulations, the low education of residents and the lack of publicity of waste recycling knowledge are the main barriers to MSW management in Beijing. Combined with expert opinions, the paper provides suggestions and guidance to municipal authorities and industry practitioners to guide the successful implementation of MSW management.

The findings of this study can provide a reference for MSW management in other metropolises in China and other developing countries.

This study proposes a hybrid DEMATEL-MMDE-ISM approach to resolve the subjectivity issues of the traditional ISM approach and it analyzes the barriers that hinder MSW management practices in Beijing.

In the last decade, cloud manufacturing (CMfg) has attracted considerable attention from academia and industry worldwide. It is widely accepted that the design and analysis of cloud manufacturing architecture (CMfg-A) are the basis for developing and applying CMfg systems. However, in existing studies, analysis of the status, development process and internal characteristics of CMfg-A is lacking, hindering an understanding of the research hotspots and development trends of CMfg-A. Meanwhile, effective guidance is lacking on the construction of superior CMfg-As. The purpose of this paper is to review the relevant research on CMfg-A via identification of the main layers, elements, relationships, structure and functions of CMfg-A to provide valuable information to scholars and practitioners for further research on key CMfg-A technologies and the construction of CMfg systems with superior performance.

This study systematically reviews the relevant research on CMfg-A across transformation process to internal characteristics by integrating quantitative and qualitative methods. First, the split and reorganization method is used to recognize the main layers of CMfg-A. Then, the transformation process of six main layers is analysed through retrospective analysis, and the similarities and differences in CMfg-A are obtained. Subsequently, based on systematic theory, the elements, relationships, structure and functions of CMfg-A are inductively studied. A 3D printing architecture design case is conducted to discuss the weakness of the previous architecture and demonstrate how to improve it. Finally, the primary current trends and future opportunities are presented.

By analyzing the transformation process of CMfg-A, this study finds that CMfg-A resources are developing from tangible resources into intangible resources and intelligent resources. CMfg-A technology is developing from traditional cloud computing-based technology towards advanced manufacturing technology, and CMfg-A application scope is gradually expanding from traditional manufacturing industry to emerging manufacturing industry. In addition, by analyzing the elements, relationships, structure and functions of CMfg-A, this study finds that CMfg-A is undergoing a new generation of transformation, with trends of integrated development, intelligent development, innovative development and green development. Case study shows that the analysis of the development trend and internal characteristics of the architecture facilitates the design of a more effective architecture.

This paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. The reason for considering Chinese articles is that CMfg was proposed by the Chinese and a lot of Chinese CMfg-A articles have been published in recent years. CMfg is suitable for the development of China’s manufacturing industry because of China’s intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg-A.

Prior studies ignore the identification and analysis of development process and internal characteristics for the current development of CMfg-A, including the main layers identification of different CMfg-As and the transformation process analysis of these main layers, and in-depth analysis of the inner essence of CMfg-A (such as its elements, relationships, structure and functions). This study addresses these limitations and provides a comprehensive literature review.

The purpose of this study is to identify and prioritize the factors that can positively influence the implementation of a blockchain-based supply chain via an integrated framework. To the best of the authors' knowledge, no previous study has focused on prioritizing these factors.

First, this study conducts a multivocal literature review, and a total of 48 success factors (SFs) are identified and mapped into 11 categories. Second, the identified success factors and their categories are further validated by industry practitioners using a questionnaire survey approach. Finally, this study applies an analytical hierarchy process to prioritize the identified SFs and their categories and to assess their importance for successful blockchain implementation in the supply chain management process.

The “Accessibility” category has the highest importance, and the “Overall efficiency” category has the second highest rank. As far as the success factors are concerned, “Trackability” and “Traceability” are considered to be the prime success factors of a blockchain-based supply chain. The taxonomy of the categories and their success factors provide an outline for supply chain organizations to establish a strategy to implement blockchain technology.

This technology can be practically applied in a sustainable supply chain. Another vital application of this blockchain technology is in banking and finance because of the blockchain's immutable data recording property.

To the best of the authors' knowledge, there is no previous study focused on building a taxonomic model that allows supply chain organizations to compare this paper's model with existing models and outline the necessary actions to improve supply chain activities. The questionnaire-based survey developed to validate the success factors in real-world practices and the factors' prioritization can help academic researchers and industrial practitioners to set their strategic goals accordingly.

This study aims to examine whether the cyber supply chain risk management (CSCRM) practices adopted by manufacturing firms contribute to achieving cyber supply chain (CSC) visibility. Studies have highlighted the necessity of having visibility across interconnected supply chains. Thus, this study examines the extent of CSCRM practices enabling CSC visibility to act as a mediator in achieving CSC performance.

A survey method was used to obtain data from the electrical and electronics manufacturing firms registered with the Federations of Malaysian Manufacturers directory. Data from 130 respondents were analysed using IBM SPSS and PLS-SEM.

This study empirically proves a dedicated governance team's integral role in setting the security tone within its CSC. The result also confirms the significant role that CSC visibility plays in achieving CSC performance. As theorised in the literature, there is also a strong direct relationship between CSC visibility and CSC performance, assuring manufacturing firms that investments and policies devised to improve CSC visibility are fruitful.

The significance of supply chain visibility in an integrated supply chain is recognised and studied using analytical models, behavioural techniques and case studies. Substantial empirical evidence on the CSCRM practices which contributes towards achieving supply chain visibility is still elusive. This study's major contribution lies in identifying CSCRM practices that can contribute towards achieving CSC visibility, and the mediating role CSC visibility plays in achieving CSC performance.