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In the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.

A novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.

Throughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.

A novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

The purpose of this paper is to propose an adjusted approximate regenerative model (ARM) for a constant‐work‐in‐process (CONWIP)‐based production system that solves the problem of deciding the number of intermediate bulk container in a pharmaceutical company to hold the work in process.

The problem was modeled as a CONWIP system and the ARM was adjusted to estimate the throughput.

By comparing the results from the original ARM and adjusted ARM, a clear superiority of the proposed method is shown for the real case. In addition, the robustness of the adjusted ARM is demonstrated in terms of the violation of rigid assumption and the impacts brought by the limitation of buffer space before the bottleneck station.

The novelty of the proposed model is to take the long transportation time between machines into consideration.

This paper proposes an infrastructure of a responsive supply chain network, focusing on the deployment of the m‐commerce technology which transforms a traditional supply chain network to be more effective in coping with market changes.

The proposed supply chain infrastructure embraces the concepts of distributed object technology, wireless markup language (WML), and extensible markup language (XML) schema to enable efficient data exchange among various data objects which reside in distributed platforms over geographically‐isolated regions, thereby leveraging the responsiveness of the entire supply chain network. A case study is conducted to evaluate the feasibility of the proposed model.

Recent studies have found that wireless technology, mobile computing and internet programming techniques drive the development of mobile solution in various industries. Apart from location tracking of goods as well as relevant services, m‐commerce is able to play an important role to enhance the performance of a supply chain network, which is concerned with the proper monitoring of suppliers and production circles, encompassing a wide spectrum of value chain activities ranging from product design to after‐sales services.

The significance of this research is the demonstration of the synergy of using a combination of emerging technologies to form an integrated system that helps achieve flexibility and agility in supply chain network.

The purpose of this paper is to examine the potential impacts of various variables on product return activities after online shopping. Previous studies on customer behaviour have been predominantly concerned with return on used products and other product-quality-related constructs in the model. This study aims to specially examine the logistics service-related and customer intention–related variables for general products under the e-commerce circumstance.

Structured questionnaire data for this study were collected in the two southeast cities of China (162 useable responses). Structural equation modelling was used to examine the latent variables.

The results confirmed that product return intention has the greatest impact on online shopping returns with a direct effect of 0.63, followed by the flexibility in return (logistics service) with a direct effect of 0.49.

Such a model not only enriches the theoretical understanding of customer behaviour studies but also offers online shopping stores and platforms a quantitative benchmark and new perspective on the design of online shopping supply chains by considering product returns so as to improve the customer satisfaction.

There is evidence that quality improvement projects developed with the participation of patients and family members are more likely to result in a sustainable change. To identify the intervening factors is an important step in promoting and supporting patient and family members’ engagement.

A survey was carried out with 90 hospitals. A total of 35 intervening factors were evaluated by the healthcare professionals from the quality area using a Likert scale. Factor analysis was applied to identify the relationship among the factors and cluster analysis and the standardized scores for each new latent variable were obtained to observe the association between them and hospitals profile. Cluster analysis allowed to group the hospitals with similar responses and to analyze whether there was any association with the profile of the institutions.

A total of ten intervening factors are identified: two in the financial dimension, five in the structural and three in the personal and cultural. The standardized scores of latent variables suggest that the financial factors could be affected by the hospital capacity. The structural factors could be impacted by the accreditation status, location (region) and administrative control (ownership). And the personal and cultural factors could be by the location and dominant organizational culture. All of factors are influenced by the performed quality management activities. The cluster analysis allowed the identification of three groups in the financial dimension, and four in the other two dimensions. Except for the accreditation status in the personal and cultural dimension, no evidence of association between the groups and the variables raised to characterize the profile of the hospitals was found.

The study contributed to identify the relationship among the intervening factors turning possible to simplify and reduce them more comprehensively than those originally identified in the literature and at the same time maintaining the representativeness of the original variables.

This paper gives a bibliographical review of the finite element methods (FEMs) applied to the analysis of ceramics and glass materials. The bibliography at the end of the paper contains references to papers, conference proceedings and theses/dissertations on the subject that were published between 1977‐1998. The following topics are included: ceramics – material and mechanical properties in general, ceramic coatings and joining problems, ceramic composites, ferrites, piezoceramics, ceramic tools and machining, material processing simulations, fracture mechanics and damage, applications of ceramic/composites in engineering; glass – material and mechanical properties in general, glass fiber composites, material processing simulations, fracture mechanics and damage, and applications of glasses in engineering.

Under the Patient Protection and Affordable Care Act (ACA), many low-income consumers have become eligible for government support to buy health insurance. Whether these consumers are able to take advantage of the support and to make sound decisions about purchasing health insurance likely depends on their knowledge and skills in navigating complex financial products. This ability is frequently referred to as “financial literacy.” We examined the level and distribution of consumers' financial literacy across income groups, using 2012 data collected in the RAND American Life Panel, an internet panel representative of the US population. Low financial literacy was particularly prevalent among individuals with incomes between 100% and 400% of the federal poverty level, many of whom will be eligible for health insurance subsidies. In this group, people who are young, less educated, female, and have less income were more likely to have low financial literacy. Our findings suggest the need for targeted policies to support vulnerable consumers in making good choices for themselves, possibly above and beyond the support measures already part of the ACA.