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This study aims to investigate the impact of I4.0 technologies and their interoperability on supply chains (SCs) performance and how the integration of such technologies and their interoperability can create pathways for SCs resilience post-COVID-19. This is of paramount importance in the context of COVID-19 as the investigation around I4.0 technologies may provide relevant insights on how SCs may better respond to unexpected situations like the current pandemic with the use of digital technologies.

A survey research method was designed based on some constructs extracted from the literature regarding the main disruptive technologies, interoperability, elements of supply chains processes (SCPs) performance such as integration, collaboration, transparency, efficiency, responsiveness and profitability. The data were collected from March to July 2020 from different regions of the world when the peak of the first wave of the pandemic had occurred. The survey resulted in 115 valid responses. The study used a combination of descriptive, correlation and multiple regression methods to analyse the data.

The study indicates that disruptive technologies significantly impact SCPs performance (integration, collaboration, responsiveness and transparency) and their resilience. The findings did not support the notion that these technologies improve the efficiency of SCs, a significant contrast to the existing literature. Our findings also refute the existing understanding that interoperability moderates the impact of disruptive technologies on SCPs performance and enhancing the resilience of SCs. However, the findings show that the integration of I4.0 technologies and their interoperability has a positive impact on SCPs profitability.

The findings strongly advocate that this integration plays an important role in improving SC performance, and a future pathway of SC resiliency post-COVID-19. Considering that the I4.0 trend will impact SCs in the coming years, this study brings a relevant contribution to researchers and practitioners.

This study makes a unique contribution by investigating a novel causal relationship between the main elements (I4.0 technologies, interoperability, processes performance and strategic outcomes) related to the SC in this new context.

This study aims to explore the mediating role of digital technologies-based supply chain integrating (SCI) strategies on the agri-supply chain performance (SCP) and firm performance (FP). This research has introduced recently emerged digital technologies such as Internet of Things (IoT). Further, based on theoretical support and an extensive literature review, this research has proposed some hypotheses, which have been quantitatively validated for their significance.

A conceptual model was formulated based on an extensive literature review. Data for this research were gathered from a survey completed by 119 respondents from different departments of agri-firms. Further, partial least square (PLS)-based structured equation modelling (SEM) was used to test the proposed hypothetical model.

The results confirm that IoT-based digital technologies and supply chain processes (organization integration [OI], information sharing and customer integration [CI]) have a significant positive correlation. Furthermore, supply chain practices are positively associated with SCP. Finally, it has been found that FP is positively impacted by SCP.

This research is used to analyse the mediating impacts of digital supply chain processes as a linking strategy for SCP and FP. For practical purposes, this research provides investment decisions for implementing digital technologies in SC strategies. The findings have proposed implications for managers and practitioners in agri-firms based on existing theories: contingency theory (CT) and relational view theory. Also, this study suggests the deployment of smarter electronically based tags and readers, which improve the data analytics capabilities based on auto-captured data. Thus, the availability of quality information improves the data-driven decisional capabilities of managers at company level.

This is a unique and original study exploring the relationship between digitalization, resilient agri-food supply chain (AFSC) management practices and firm performance. This research may be extended to other industries in view of the results from SCP and impact of digitalization.

The purpose of the article is to contribute to the body of research on digital transformation among members of the supply chain operating in an emerging economy. This paper researches the digital transformation trends happening across Vietnamese supply chains, by investigating the current adoption rates, predicted impact levels and financial investments being made in key Industry 4.0 technologies.

By using a semi-structured online survey, the experiences of 281 supply chain professionals in Vietnam were captured. Subsequently, statistical techniques examining variances in means, regression analysis and Monte Carlo simulation were applied.

The findings of this study offer a comprehensive understanding of Industry 4.0 technology in Vietnam, highlighting the prevalent technologies being prioritized. Big data analytics and the Internet of things are expected to have the most substantial impact on businesses over the next 5–10 years and have received the most financial investment. Conversely, Blockchain is perceived as having less potential for future investment. The study further identifies several technological synergies, such as combining advanced robotics, artificial intelligence and the Internet of things to build effective and flexible factories, that can lead to more comprehensive solutions. It also extends diffusion of innovation theory, encompassing investment and impact considerations.

This study offers valuable insights into the impact and financial investment in Industry 4.0 technologies by Vietnamese supply chain firms. It provides a theoretical contribution via an extension of the diffusion of innovation theory and contributes toward a better understanding of the current Industry 4.0 landscape in developing economies. The findings have significant implications for future managerial decision-making, on the impact, viability and resourcing needs when undertaking digital transformation.

The purpose of the study is to propose a framework for fleet management and make suitable distribution solution choices in the food industry.

This study reviews the literature to examine food distribution criteria. These criteria are used in the analytic hierarchy process (AHP) assessment and combined with discrete events simulation in a structured framework, which is validated through an empirical study.

The empirical case results demonstrate that both the AHP and discrete events simulation converge toward the same solution in most cases.

This study contributes to the literature on distribution management and develops a framework that can both guide future research and aid logistics practitioners in analysing distribution decision-making systems in dynamic environments.

This study investigates the impact of green supply chain management (GSCM) practices on environmental performance in firms operating in the discretionary sector in the G20 countries. The sample covers 749 firms for the period 2010–2020.

This study combines qualitative and quantitative data to examine the impact of the implementation of GSCM on accounting performance measured by the operating margin (OM) and return on assets (ROA). The authors also moderate the effects of Six Sigma and quality management (QM) and ISO 9000 and control for firm variables and COVID 19.

Using a panel data regression and structural equation modeling (SEM), results indicate that discretionary firms with internal solid GSCM practices combined with external environmental monitoring of suppliers are likely to outperform their peers in environmental issues. Using hierarchical regression, results indicate that both ISO 9000 and S&QM have moderating effects at some level of performance. Furthermore, environmental performance is positively correlated with accounting performance. This study contributes to the literature by addressing the impact of GSCM and the importance of reinforcing green and social regulations to protect the planet.

The paper is one of the first to measure GSCM triple components and account for COVID-19 in the context of discretionary companies and G20 countries. It highlights the impact of green initiatives to cope with major disruptions and decrease pollution and environmental disasters.

This paper aims to contribute to the discussion about the misuse of legal persons for money laundering and terrorism financing (ML/TF) through the study of methodologies for countries’ legal persons and legal arrangements risk assessments in accordance with the transparency and beneficial ownership of legal persons and arrangements requirements of the Financial Action Task Force.

To do that, the authors will review some of the results of preview risk assessments by selected countries, and based on that, the authors will discuss the Brazilian methodology and the achieved results.

The authors argue that this methodology can lead to better results than the most common ones and deliver outputs that are important to the development of countries’ anti-ML/TF.

To the best of the authors’ knowledge, this paper is the first to discuss the results and limitations of the current most widely used methodology for legal persons and arrangements money laundering risk assessment and to propose the use of a more efficient one.

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

This paper aims at identifying the main contributions of Industry 4.0 (I4.0) technologies to the enhancement of knowledge management (KM).

A scoping review based on two stages was carried out. In the first stage, a numerical and descriptive analysis was conducted. In the second stage, the latent content of those papers was explored through a qualitative analysis, in which papers were assessed regarding the outcome of the implementation of ten I4.0 technologies on 14 KM abilities and 6 KM processes.

Six technologies (e.g. Internet of Things (IoT), big data, cloud computing, simulation, visualisation and industrial robot) were claimed to have a significant contribution on KM, especially for creation, organisation, dissemination and application processes. Overall, results indicated that technologies can significantly contribute to KM, although the extent of such contribution varies across technologies, learning levels, KM processes and abilities.

Existing studies in I4.0 fall short in exploring how embodied knowledge is articulated in practice, especially regarding opportunities to use new technological alternatives in favour of knowledge and learning development. This study complements the existing literature on I4.0 by identifying which technologies can contribute to KM. Also, a framework was proposed to examine the contribution of I4.0 technologies to KM at individual, team, and organisational levels.

The connection between digital manufacturing technologies (Industry 4.0) and the environment has sparked discussions on firms' disclosure of negative information on pollutant emissions and the pursuit of positive environmental outcomes. However, very few studies explore how it relates to a firm's robot usage and its mechanism. The purpose of this paper is to investigate the impacts of robot penetration on firms' environmental governance in China.

The ordered probit model (and probit model) are employed and empirically tested with a sample of 1,579 Chinese listed firms from 2010 to 2019.

The study reveals a negative relationship between robot usage and the disclosure of negative indicators and a U-shaped relationship between robot usage and positive environmental outcomes. Among the sample, nonstate-owned enterprises (SOEs) display unsatisfactory performance, while heavily polluting industries disclose more information on pollutant emissions. The robot–environmental governance nexus is conditional on firm size, capital intensity and local economic development.

The study proposes a fresh view of corporate environmental governance to assess the environmental implications of robot adoption. It also contributes to identifying the curvilinear, moderating and heterogenous effects in the robot–environment nexus. The results provide rich policy implications for the development of industrial intelligence and corporate environmental governance in the circular economy (CE) context.