Search results

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

As internet dividends are gradually disappearing, loyalty programs have become the panacea for monetizing traffic, attracting new customers and retaining existing customers. Improving their effectiveness has thus become key to enterprises’ market competitiveness. However, member customers’ hedonic adaptation to this relationship strategy undermines its effectiveness. Based on the hedonic adaptation theory, this study aims to analyze the process of member customers' hedonic adaptation to preferential treatment in loyalty programs and explore the boundary conditions of alleviating this effect.

This study surveyed 271 member customers in China and tested the hypothesized relationships using structural equation modeling and multigroup analysis.

Preferential treatment suffers from hedonic adaptation to member customer engagement and customer gratitude, and customer tenure is a key condition for these effects. Customer gratitude is an intermediary mechanism that explains the hedonic adaptation effect of preferential treatment to member customers engagement. In addition, the structural characteristics of loyalty programs form the boundary condition that alleviates hedonic adaptation. The authors found that high-tier and -payment strategies are more likely to mitigate hedonic adaptation of preferential treatment to customer gratitude.

This study elucidates the factors that influence the effectiveness of preferential treatment and provides constructive insights into customer relationship management and for improving enterprise performance.

The literature that is presently available on sustainable supply chain management (SSCM) combining optimization and industry 4.0 techniques falls short in its depictions of the recent developments, budding pertinent areas and the importance of SSCM in the growth of industrial economies around the world. This article's main objective is to analyze current trends, highlight the latest initiatives and perform a meta-analysis of the literature that is currently accessible in the SSCM area with a special focus on optimization and industry 4.0 techniques. The paper also proposes a conceptual framework that will assist in illuminating how the ideas of optimization and industry 4.0 may contribute to realizing sustainability in supply chains.

The proposed study systematically reviews 85 research publications published between 2010 and 2022 in referenced peer-reviewed journals in diverse fields, including engineering, business and management, services and healthcare. Numerous categories are considered throughout the examination of the literature, including year-wise publications, prominent journals, type of research design, concerned industry and research technique used.

The study demonstrates a deeper comprehension of the literature in the field and its evolution throughout numerous industry sectors, which is helpful for both practitioners and academics. The results from the content analysis highlight various future research opportunities in the domain.

This is one of the first research articles that have attempted to establish, analyze and highlight the current trends and initiatives in the SSCM domain from an optimization and industry 4.0 techniques viewpoint. The cluster-based future research propositions also enhance the novelty of the study.

As the onset of the Russia–Ukraine military conflict on February 24, 2022, individuals from Ukraine have been relocating in search of safety and refuge. This study aims to investigate how the influx of Ukrainian refugees has impacted the stock markets and exchange rates of Ukraine's neighboring states.

The authors focused on the neighboring countries that share a western border with Ukraine and have received the highest number of refugees: Hungary, Poland, Romania and Slovakia. The analysis covered the period from April 24 to December 31, 2022. After this period, the influence of the refugees is small, insignificant. Wavelet coherence, wavelet power spectrum and the time-varying parameter vector autoregressions method were used for data processing.

The key finding are as follows: a link exists between the dynamics of refugees from Ukraine and volatility of the stock indices and exchange rate of the host countries; volatility was significant in the first weeks after the start of the conflict in all the analyzed states; and the highest volatility was recorded in Hungary and Poland; the effect of refugees was stronger on stock indices than that on exchange rates.

To the best of the authors’ knowledge, it is the first research that presents the impact of refugees from Ukraine on stock markets and exchange rates volatility in the countries analyzed.

Three-dimensional (3D) casting means using additive manufacturing (AM) techniques to print the mould for casting the cast tool. The printed mould, however, should be checked for its dimensional accuracy. 3D scanning can be used for the same. The purpose of this study is to combine the different AM techniques for 3D casting with 3D scanning to produce parts with close tolerance for preparing electrical discharge machining (EDM) electrodes.

The four processes, namely, stereolithography, selective laser sintering, fused deposition modelling and vacuum casting, are used to print the casting mould. The mould is designed in two halves, assembled to form a complete mould. The mould is 3D scanned in two stages: before and after using it as a casting mould. The mould's average and maximum dimensional deviations are calculated using 3D-scanned results. The eutectic Sn-Bi alloy is cast in the mould. The surface roughness of the mould and the cast tool are measured.

The cast tool is selected from the four processes in terms of dimensional accuracy and surface finish. The same is electroplated with copper. The microstructure of the cast tool (low-melting-point alloy) and deposited copper is analysed using a scanning electron microscope. Energy dispersive spectroscopy and X-ray diffraction techniques are used to verify the composition of the cast and coated alloy. The electroplated tool is finally tested on the EDM setup. The material removal rate and tool wear are measured. The performance is compared with a solid copper tool. The free-form customised EDM mould is also prepared, and the profile is cast out. The same is tested on the EDM. Thus, the developed path can be successfully used for rapid tooling applications.

The eutectic composition of Sn-Bi is cast in the 3D-printed mould using different AM techniques combined with 3D scanning quality to check its feasibility as an EDM electrode, which is a novel work and has not been done previously.