Search results

The spatiotemporal compression effect of China–Europe Railway Express (CR-Express) can reduce the flow costs of resources between China’s node cities. Additionally, it can break through the limitations of low-added-value marine products, significantly impacting the logistics industry efficiency. However, there are few literature verifying and analyzing its heterogeneity. This study explores the impact of CR-Express on the efficiency of logistics industry in node cities and analyzes the heterogeneity.

First, this study uses panel data to measure the efficiency of node city logistics industry. Secondly, this study discusses the impact of the opening of CR-Express on the efficiency of logistics industry in node cities based on the multi-period differential model. Finally, according to the node city difference, the sample city experimental group is grouped for heterogeneity analysis.

The results show that CR-Express can promote the urban logistics industry efficiency, with an average effect of 4.55%. According to the urban characteristics classification, the heterogeneity analysis shows that the efficiency improvement effect of logistics industry in inland cities is more obvious. The improvement effect of node cities and central cities in central and western China is stronger, especially in the sample of megacities and type I big cities. Compared with non-value chain industrial products, the CR-Express has significant promotion effects on the logistics efficiency of the cities where main goods are value chain products.

Under the background of double cycle development, this paper can provide a scientific basis for the investment benefit evaluation of CR-Express construction and the follow-up route planning.

This research delves into a comprehensive examination of Amazon’s Vendor Flex (VF) model, seeking to illuminate the intricacies of supply chain innovation through alliances between Amazon and its suppliers. Employing a multiple case study methodology, the study investigates the reduction of transaction costs, the establishment of strategic alliances for supply chain innovation and governance issues within these alliances.

A multiple case study methodology, incorporating personal interviews and triangulation with primary sources, was employed to unravel the dynamics of the VF model.

Results indicate that the VF model aligns with the reduction of transaction costs by leveraging Amazon’s specialized knowledge, although not necessarily through direct knowledge sharing. Amazon suppliers highlight competitive advantages gained through VF, showcasing efficient navigation of peak seasons and a focus on core activities with online retailing integration. The VF alliance represents a collaborative model where Amazon’s technological prowess enables a streamlined and innovative supply chain for online retailing, which resembles a vertical integration process.

This research underscores the potential of strategic alliances to drive innovation by incorporating industry-leading practices. The governance issues within the VF alliance reveal power imbalances, emphasizing the need for managers to govern dynamics, disclose information and build trust in large-scale alliances.

The primary goal of this review is to identify emerging themes in the cold supply chain (CSC) and their future research directions, methodologies, and theories.

The review looks at CSC related articles from Scopus database published in the years 2000–2020. Thereafter, bibliometric and co-citation analyses have been conducted to identify emerging themes, methodologies, and theoretical perspectives related to CSC management.

This study revealed a clear research gap in CSC literature with emerging themes relevant to diverse aspects. Primarily, the most prominent authors, methodologies, and theories were identified from bibliometric analysis. Next, we generated clusters to identify the insights of each cluster using co-citation analysis. Consequently, the significance of clusters concerning the number of articles, theoretical frameworks, methodologies, and themes was recognized. Finally, a few future research questions regarding emerging themes have been identified.

The importance of co-citation and bibliometric analyses in studying the evolution of research over a definite time is emphasized in this work. As per emerging themes, implementing digital technologies has increased the efficiency of traditional CSC and transformed it into digital CSC.

As per the authors' knowledge, this work is the first in literature to explore the significance of identifying emerging areas and future research directions in managing CSC through literature review based on bibliometric and co-citation analysis.

Additive Manufacturing technology (AMT) is swiftly gaining prominence to induce automation and innovation in manufacturing systems. It holds immense potential to change supply chain dynamics by providing the possibility of printing objects on demand. This study thus formulates and analyzes the framework to incorporate AMT to handle the spare parts supply chain management (SPSCM) in capital-intensive industries by identifying and assessing the critical success factors (CSFs).

Assessment of the CSFs is performed using the novel Grey Causal Modeling method (GCM) with the objective of making SPSCM resilient and efficient. GCM conducts causal analysis by taking into consideration cause, effects, the objectives, and the situations.

Findings indicate that; Logistics Lead Time (SD4), Time to manufacture (SD3), Management Support (SD11), and Risk Management (SD20) are the most prominent causal factor having a maximum impact when incorporating AMT in SPSCM. The results also reveal that the performance of manufacturing organizations that adopt AMT is substantially influenced by internal and external factors such as Management Support (SD11) and Government Regulations (SD16).

This research provides valuable information for getting the global spare parts supply chain equipped for the post-COVID age, where digital technologies such as AMT will be fundamental for bolstering supply chain resilience and efficiency.

This research proposes a framework for performance assessment when incorporating AMT in SPSCM. Study also demonstrates methodological application of novel Grey Causal Modelling technique using a real case in a spare parts manufacturing industry in India.

The present study aims to identify significant barriers to adopting prefabricated construction (PFC) in developing economies using a study in Sri Lanka and develop an integrated strategy framework to mitigate and overcome the obstacles.

The research process included a comprehensive literature review, a pilot study, a questionnaire survey for data collection, statistical analysis and a qualitative content analysis.

Ranking method revealed that all 23 barriers were significant. Top significant barriers include challenges in prefabricated component transportation, high capital investment costs and lack of awareness of the benefits of PFC among owners/developers. Factor analysis clustered six barrier categories (BCs) that fit the barrier factors, explaining 71.22% of the cumulative variance. Fuzzy synthetic evaluation revealed that all BCs significantly influence PFC adoption in Sri Lanka. Finally, the proposed mitigation strategies were mapped with barriers to complete the integrated framework.

The study outcomes are relevant to construction industry stakeholders of Sri Lanka, who are keen to enhance construction efficiencies. The implications can also benefit construction industry stakeholders and policymakers to formulate policies and regulations and identify mitigation solutions.

The study provides deeper insights into the challenges to adopting prefabrication in South Asian countries such as Sri Lanka. Furthermore, the integrated framework is a novel contribution that can be used to derive actions to mitigate barriers in developing economies.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

This paper aims to explore the impact of fourth-party logistics (4PL) adoption on the agility, adaptability and alignment (AAA) capabilities within humanitarian supply chains (HSCs).

Semi-structured interviews with individuals from a large non-government organisation were combined with secondary data to assess the influence of 4PL adoption on AAA capabilities in HSCs.

The results indicate that HSCs exhibit some of the AAA antecedents but not all are fully realised. While 4PL positively affects the AAA capabilities of HSCs, its adoption faces challenges such as the funding environment, data security/confidentiality and alignment with humanitarian principles. The study suggests an AAA antecedent realignment, positioning alignment as a precursor to agility and adaptability. It also identifies three core antecedents in HSCs: flexibility, speed and environmental uncertainty.

The study shows the positive impact 4PL adoption can have on the AAA capabilities of HSCs. The findings have practical relevance for those wishing to optimise HSC performance through 4PL adoption, by identifying the inhibiting factors to its adoption as well as strategies to address them.

This research empirically explores 4PL’s impact on AAA capabilities in HSCs, highlighting the facilitating and hindering factors of 4PL adoption in this environment as well as endorsing a realignment of AAA antecedents. It also contributes to the growing research on SC operations in volatile settings.

This paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement.

Literature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions.

The identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories.

This study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving.

The model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

This study aims to identify the challenges, promote respective solutions and construct strategies for the implementation of the halal supply chain in Indonesia’s small and medium-sized enterprises (SMEs).

This research used the analytic network process in nine respondents categorised into three groups of relevant stakeholders: government, academia and industry.

Seven essential criteria for the implementation of a halal supply chain in SMEs were identified, namely, the process, packaging, storage, transportation, fundamental aspects, supporting policy and technology. Notably, the high cost of adopting blockchain technology was the most crucial issue, particularly in SMEs.

Through the findings, several critical factors to consider in the implementation of halal supply chains for SMEs were identified. The investment in halal supply chain technology, which incurs a high cost carried by entrepreneurs, is a burden for SMEs. For this reason, further studies are required to formulate and develop cost-effective halal supply chain technology for SMEs.

To the best of the authors’ knowledge, this research is the first attempt of comprehensively mapping the barriers and developing strategic solutions to implement the halal supply chain by Indonesia’s SMEs.

The home page design of a digital news website is a key factor in determining its attractiveness to readers. This study aims to propose an approach to manage the frequent adjustment of the dynamic layout of the news content on the website home page in a real-time environment to increase its attractiveness to readers.

This paper shows that this news website layout design problem can be modeled as an optimization problem based on the information of news contents that change within a multiple-period planning horizon similar to the dynamic facility layout problem. A hybrid genetic algorithm-based approach integrated with local search heuristic methods is also proposed to improve the solution.

This paper finds that the DPLP model is effective in modeling the changing layout of a digital news website. The problem can solved in a timely manner using the proposed hybrid genetic algorithm.

This paper was based on hypothetical data and on the assumption of equal section size. Actual data would help fine-tune the application of the dynamic facility layout model. As well the algorithm could be enhanced for unequal size sections.

The model should help online newspapers apply sophisticated algorithms to optimize the layout of news websites dynamically in a timely manner.

News websites are increasingly the desired medium to consume news. So it has an important role in educating society. Thus optimizing and improving the process will help in this regard.

To the best of the authors’ knowledge, this paper is the first one to apply the DPLP model to the digital newspaper website dynamic design problem.