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The purpose of this study is to examine how the implementation of edge computing can enhance the progress of the circular economy within supply chains and to address the challenges and best practices associated with this emerging technology.

This study utilized a streamlined evaluation technique that employed Latent Dirichlet Allocation modeling for thorough content analysis. Extensive searches were conducted among prominent publishers, including IEEE, Elsevier, Springer, Wiley, MDPI and Hindawi, utilizing pertinent keywords associated with edge computing, circular economy, sustainability and supply chain. The search process yielded a total of 103 articles, with the keywords being searched specifically within the titles or abstracts of these articles.

There has been a notable rise in the volume of scholarly articles dedicated to edge computing in the circular economy and supply chain management. After conducting a thorough examination of the published papers, three main research themes were identified, focused on technology, optimization and circular economy and sustainability. Edge computing adoption in supply chains results in a more responsive, efficient and agile supply chain, leading to enhanced decision-making capabilities and improved customer satisfaction. However, the adoption also poses challenges, such as data integration, security concerns, device management, connectivity and cost.

This paper offers valuable insights into the research trends of edge computing in the circular economy and supply chains, highlighting its significant role in optimizing supply chain operations and advancing the circular economy by processing and analyzing real time data generated by the internet of Things, sensors and other state-of-the-art tools and devices.

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

The purpose of this study is to examine the impact of sustainable supply chain management (SSCM) practices on both competitive advantage (CA) and organizational performance (OP) in the manufacturing sector in Ethiopia.

Data for the study were collected from a sample of 221 manufacturing companies operating in the four manufacturing groups/sectors in Ethiopia. In addition, data analysis was performed using the partial least squares method, which is a variance-based Structural Equation Modeling approach in the Smart-PLS software version (SmartPLS 4.0).

Based on the statistical analysis of the collected data, it demonstrates that SSCM has a significant and positive impact on both competitive advantage and organizational performance. Furthermore, statistical findings offer proof of the clear connection between competitive advantage and organizational performance. Moreover, competitive advantage indirectly mediates the relationship between SSCM and OP.

The primary limitation of this research is its reliance on a cross-sectional design. The generalizability of the findings obtained from the present study may be hindered. The variable under investigation in this research assessed organizational performance, a concept that is widely acknowledged to be extremely dynamic.

The study provides managers and researchers with valuable information on Sustainable Supply Chain Management strategies and how they influence competitive advantage and organizational performance in commercial and industrial environments.

This paper adds to the body of knowledge by providing new data and empirical insights into the relationship between SSCM practices and the performance of manufacturing companies in Ethiopia.

The study explores the extent to which business analytics can address business problems using the task-technology fit theory.

The qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes from the empirical data.

The study establishes the relationships between various forms of fit, arguing that the iterative application of business analytics improves problem understanding and solutions, and contends that both under-fit and over-fit can be acceptable due to the increasing costs of achieving ideal fit and potential unaffected outcomes, respectively. The study demonstrates that managers should appreciate that there may be a distinction between those who create business analytics solutions and those who apply business analytics solutions to solve problems.

Extant studies on business analytics have not focused on how the match between business analytics and tasks affects the level to which problems can be addressed that determines business value. This study enriches the literature on business analytics by linking business analytics and business value through problem resolution demonstrated by task-technology fit. To the authors’ knowledge, this study might be the first to apply pattern matching to study the fit between technology and tasks.

Warehouses are under pressure to operate as efficiently as possible. In pursuit of attaining high efficiency in the order picking process, the warehouse manager must take several planning decisions, typically supported by a central planning system. However, highly centralised work erodes the autonomy of warehouse workers, interfering with worker well-being and productivity. This study holistically explores the impact of a work system with more decision autonomy for order pickers.

We conduct a unique field experiment in a real-world warehouse and use a within-subjects design to compare two work systems, one with worker autonomy and one without. 18 permanent employees participate in our study, in which we measure both psychosocial and physical well-being as well as productivity. Post-experimental interviews are conducted to delve deeper into the observed effects.

Our study illustrates that involving order pickers in operational decisions can benefit their job satisfaction and motivation without compromising productivity. Although we fail to find significance at the conventional level (α = 0.05), we do find marginally significant effects of our treatment on physical well-being aspects. Furthermore, our intervention invoked a highly positive user experience.

We show that slightly loosening tight process control results in organisational and individual benefits without endangering smooth operational flows. The warehouse in this paper acknowledged this and decided to permanently work according to this philosophy.

This study is the first to holistically explore the effects of a participatory work setting in a real-world warehouse.

Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

The purpose of this study is to analyze the operations and performance dynamics of a supply chain (SC) subject to disruptions. The preparedness of Moroccan responders in handling emergencies could be enhanced significantly, by devising digital twin-based decision support systems (DSSs).

The authors create a discrete-event simulation model to investigate proactively risks and resilience of a Moroccan basic-items SC (BISC). In this study, the authors analyze the effects of catastrophe-related disruptions (CRDs) on the Moroccan BISC, by the use of a simulation-based decision-supporting quantitative method.

In the disruption-free simulation experiment, the outcome was a satisfactory 100% coverage. By implementing CRDs, inventory levels have dropped, service levels decreased, lead time raised and there was an increase in backlogged products and late orders numbers. The highest impact was observed for the shutdown of paths linking suppliers to warehouses, whereas the increase in demand had a comparatively minor effect. The risk analysis approach helps to identify critical products for which the time-to-recover is longer and requires more commitment to enhance their resilience.

The model serves to deduce quantitative resilience assessment from simulation, streamline the selection of recovery strategies and enable the best-informed reactive decision-making to minimize the impact.

The research brings organizing solutions to catastrophe-related emergencies in Morocco. It would contribute significantly by visualizing, examining and unveiling the effects of disruptions on a BISC and offering actionable recommendations for remedial measures.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.

This paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.

The final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

There are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.

This manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.