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This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

The purpose of this systematic review is to critically analyze pharmaceutical industry case studies on the implementation of Lean 4.0 methodologies to synthesize key lessons, benefits and best practices. The goal is to inform decisions and guide investments in related technologies for enhancing quality, compliance, efficiency and responsiveness across production and supply chain processes.

The article utilized a systematic literature review (SLR) methodology following five phases: formulating research questions, locating relevant articles, selecting and evaluating articles, analyzing and synthesizing findings and reporting results. The SLR aimed to critically analyze pharmaceutical industry case studies on Lean 4.0 implementation to synthesize key lessons, benefits and best practices.

Key findings reveal recurrent efficiency gains, obstacles around legacy system integration and data governance as well as necessary operator training investments alongside technological upgrades. On average, quality assurance reliability improved by over 50%, while inventory waste declined by 57% based on quantified metrics across documented initiatives synthesizing robotics, sensors and analytics.

As a comprehensive literature review, findings depend on available documented implementations within the search period rather than direct case evaluations. Reporting bias may also skew toward more successful accounts.

Synthesized implementation patterns, performance outcomes and concealed pitfalls provide pharmaceutical leaders with an evidence-based reference guide aiding adoption strategy development, resource planning and workforce transitioning crucial for Lean 4.0 assimilation.

This systematic assessment of pharmaceutical Lean 4.0 adoption offers an unprecedented perspective into the real-world issues, dependencies and modifications necessary for successful integration, absent from conceptual projections or isolated case studies alone until now.

The information systems (IS) literature has indicated the importance of data analytics capabilities (DAC) in improving business performance in organizations. The literature has also highlighted the roles of organizations’ data-related resources in developing their DAC and enhancing their business performance. However, little research has taken resource quality into account when studying DAC for business performance enhancement. Therefore, the purpose of this paper is to understand the impact of resource quality on DAC development for business performance enhancement.

We studied DAC development using the resource-based view and the IS success model based on empirical data collected via 19 semi-structured interviews.

Our findings show that data-related resource (including data, data systems, and data services) quality is vital to the development of DAC and the enhancement of organizations’ business performance. The study uncovers the factors that make up each quality dimension, which is required for developing DAC for business performance enhancement.

Using the resource quality view, this study contributes to the literature by exploring the role of data-related resource quality in DAC development and business performance enhancement.

Improving productivity and efficiency has always been crucial for industrial companies to remain competitive. In recent years, the topic of environmental impact has become increasingly important. Published research indicates that environmental and economic goals can enforce or rival each other. However, few papers have been published that address the interaction and integration of these two goals.

In this paper, we identify both, synergies and trade-offs based on a systematic review incorporating 66 publications issued between 1992 and 2021. We analyze, quantify and cluster examples of conjunctions of ecological and economic measures and thereby develop a framework for the combined improvement of performance and environmental compatibility.

Our findings indicate an increased significance of a combined consideration of these two dimensions of sustainability. We found that cases where enforcing synergies between economic and ecological effects were identified are by far more frequent than reports on trade-offs. For the individual categories, cost savings are uniformly considered as the most important economic aspect while, energy savings appear to be marginally more relevant than waste reduction in terms of environmental aspects.

No previous literature review provides a comparable graphical treatment of synergies and trade-offs between cost savings and ecological effects. For the first time, identified measures were classified in a 3 × 3 table considering type and principle.

Aerospace is a demanding technological and industrial sector. Several regulations and policies via innovative digital transformation have been integrated to impact production systems and supply chains, including safety measures. Studies demonstrated that the Fourth Industrial Revolution (4IR) technologies could enhance productivity growth and safety measures. The 4IR role in influencing airlines’ growth is yet to receive in-depth studies in South Africa. Thus, this study aims to investigate the role of 4IR technologies in influencing airlines’ growth in South Africa.

This research used a qualitative research method. Primary data were compiled via 56 face-to-face semi-structured interviews with major stakeholders. The study achieved saturation. A thematic method was used to analyse the collected data.

Findings reveal the nine major factors influencing South African airlines’ growth in the 4IR era. This includes investment in ergonomics applications and research, governance is driven by 4IR, collaboration and incorporation of 4IR concepts, partnership with drone technology and high precision and efficiency with 4IR. Others are reskilling and upskilling, investment in 4IR software, policies to promote 4IR usage in the industry and policies to reduce human interface.

Understanding the relative significance of 4IR technologies’ role in airlines’ growth can assist critical stakeholders in promoting innovative policies and regulations tailored towards digitalised aerospace. Thus, the study contributes to strategies to improve digital innovation, airline growth and safety as components of the air travel demands in South Africa.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

This paper investigates the moderating-mediation roles of synchronous and asynchronous learning, as well as virtual self-efficacy between digital learning space experience and continuous use among learners in Ghanaian institutions of higher learning.

A convenience sampling technique was used in the selection of 604 students who answered questions on digital learning space experience, synchronous and asynchronous learning, virtual self-efficacy and learner continuous use within the Greater Accra Region of Ghana. The study employed regression analysis to measure the hypothesized paths.

The findings show that asynchronous learning partially mediates between digital learning space experience and learner continuous use, but the mediating effect of synchronous learning between digital learning space experience and learner continuous use was not significant. Further, virtual self-efficacy significantly moderates the mediated relationship between asynchronous learning and learner continuous use, but the moderated mediated role of synchronous learning was not established in the study.

Generalization of the study findings is limited due to the sampling scope, which was restricted to students of IHL in the Greater Accra Region of Ghana.

In this research, the academic scope of digital transformation was expanded from both digital structure elements and psychological perspectives within the domain of higher education literature.

The paper aims to enhance the understanding of robust food supply chains (FSC) by exploring the capabilities of various digital technologies and examining their interactions.

This study finding shows that digital technology enhances the resilience of the FSC by improving visibility, traceability and adaptability. This resilience provides a competitive advantage, ultimately enhancing the overall business performance.

In developing countries, inadequate infrastructure, poor Internet connectivity and diverse stakeholder systems pose challenges to implementing advanced digital solutions in the FSC.

This paper is among the first to investigate the impact of digital technology on FSC resilience, exploring visibility, flexibility and collaboration.

This paper aims to use Bayesian network (BN) methodology complemented by machine learning (ML) and what-if analysis to investigate the impact of digital technologies (DT) on logistics service quality (LSQ), employing the service quality (SERVQUAL) framework.

Using a sample of 244 Italian firms, this study estimates the probability distributions associated with both DT and SERVQUAL logistics, as well as their interrelationships. Additionally, BN technique enables the application of ML techniques to uncover hidden relationships, as well as a series of what-if analyses to extract more knowledge.

The results show that the average probability of firms investing in DT for analytics (DTA) is higher than that of investing inDT for immersive experiences (DTIE). Furthermore, adopting both offers only a moderate likelihood of successfully implementing SERVQUAL logistics. Additionally, certain technologies may not directly influence some SERVQUAL dimensions. The application of ML reveals hidden relationships among technologies, enhancing the predictions of SERVQUAL logistics. Finally, what-if analyses provide further insights to guide decision-making processes aimed at enhancing SERVQUAL logistics dimensions through DTA and DTIE.

This research delves into the influence of DTIE and DTA on SERVQUAL logistics, thereby filling a gap in the existing literature in which no study has explored the intricate relationships between these technologies and SERVQUAL dimensions. Methodologically, we pioneer the integration of BN with ML techniques and what-if analysis, thus exploring innovative techniques to be used in logistics and supply-chain studies.