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The present study is aimed at developing a multi-level framework for assessing circularity in agri-food industries by providing the user with a step-by-step approach and selecting a customized set of indicators capable of accurately assessing the circular economy (CE) level.

The framework is composed of four stages. In the first stage, a CE theoretical model based on operations, product and services, culture, organization and ecosystem criteria has been implemented and adapted to the agri-food sector. In the second stage, users are required to collect a set of indicators capable of measuring each criterion. In the third stage, a weight is assigned to each indicator using analytical hierarchy process (AHP). Lastly, a geometric multi-criteria decision-making (MCDM) model, called axial distance-based aggregated measurement (ADAM) model, is used to normalize, assess and aggregate the results and produce final scores for the different alternatives to be ranked based on their final circularity scores.

The model can be a useful tool to support corporate decisions in the CE, making entrepreneurs aware of their starting level. It indicates the extent to which companies are implementing circular business models across different dimensions and, thus, where they are still lacking.

Beyond the attempts to measure the circularity of corporate performance from a purely environmental perspective, the study adopts a holistic view, considering the complexity and disruption of all the principles of the CE.

Study on the internal legalization process of strategic change for a large number of ultra-large enterprises in China.

This paper takes formulation process of Suning Appliance Group’s 10-year strategy (2010–2020) as the research case, designs the research issues, propositions and analysis unit of the case study, and uses the data collection and analysis methods in the grounded theory to realize the theoretical development from data to viewpoint conceptualizing and to proposition categorizing.

There are four key concepts that affect the judgment of overall strategic legitimacy of super-large enterprises: Emerging-market opportunities and strategic operational positioning, legitimacy perspective mainly manifests as legitimacy judgment of strategic direction within organization. Positioning of core resources (including intangible resources) and their value identification methods or value evaluation criteria, the legitimacy perspective is mainly reflected in the organization's internal legitimacy judgment of functional planning, especially implementation path. The impact factors of the key performance of each SBU are positioned, and the legitimacy perspective is mainly reflected in the organization’s internal judgment on the legitimacy of strategic supporting measures, especially the resources needed for the implementation of the strategy and capacity development. The periodical strategic objectives and performance measurement indicators of each SBU are mainly reflected in the organization’s internal legitimacy judgment on strategic alignment and specific action plans for strategic operational units. The legitimacy of these four key concepts is strongly influenced by the rationality of these strategic concepts, which are closely related to their shaping patterns driven by right-brain and left-brain thinking modes.

This case is a longitudinal study of the strategic decision-making process, not a longitudinal follow-up of the actual implementation of the strategy. In addition, given that the case enterprise was facing the emerging market at that time and focused on pushing firms to seize opportunities, not much research has been done on the impact of external legitimacy on the strategic formulation process, a variable that is increasingly being focused on today.

This model has guidance significance and practical demonstration role for a large number of enterprises that are implementing the “+Internet” strategic change under traditional offline operation.

According to the summary of the connection between data and propositions in several rounds, this paper constructs a theoretical model of left and right brain thinking mode driving key concepts to achieve the internal legalization process of strategic changes.

In the analysis process, the legalization theory and the sense-making method are introduced into enterprises’ strategy making process. Based on this analysis framework, this paper analyzes in detail that the top decision-making level and the middle and high executive level form key strategic concepts to promote the internal legalization process of strategic decision-making driven by the right-brain intuitive thinking mode and the left-brain rational thinking mode, which greatly improves the quality of strategy formulation and the operability of strategy implementation.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

The purpose of this paper is to illustrate the growing role of robots in the logistics industry.

Following an introduction, which identifies key challenges facing the industry, this paper discusses robotic applications in warehouses, followed by sections covering transportation and delivery and conclusions.

The logistics industry faces a number of challenges that drive technological and operational changes. Robots are already playing a role within the warehouse sector and more complex applications have recently arisen from developments in artificial intelligence-enabled vision technology. In the transportation sector, autonomous trucks are being developed and trialled by leading manufacturers. Many major logistics companies are involved and limited services are underway. Last-mile delivery applications are growing rapidly, and trials, pilot schemes and commercial services are underway in Europe, the USA and the Far East. The Chinese market is particularly buoyant, and in 2019, a delivery robot was launched that operates on public roads, based on Level-4 autonomous driving technology. The drone delivery sector has been slower to develop, in part due to regulatory constraints, but services are now being operated by drone manufacturers, retailers and logistics providers.

This paper provides details of existing and future applications of robots in the logistics industry.

To delve into the integration of global logistics and supply chain networks amidst the digital transformation era. This study aims to investigate the potential role of China’s Belt and Road Initiative (BRI) in facilitating the integration of global flows encompassing both tangible goods and intangibles. Additionally, the study seeks to incorporate third-party logistics activities into a comprehensive global logistics and supply chain integration framework.

Prior research is synthesised into a global logistics and supply chain integration framework. A case study was undertaken on Yuan Tong (YTO) express group to investigate the framework, employing qualitative data analysis techniques. The study specifically examined the context of the BRI to enhance comprehension of its impact on global supply chains. Information was collected in particular to two types of supply chain flows, the physical flow of goods, and intangible information and cash flows.

The proposed framework aligns well with the case study, leading to the identification of global logistics and supply chain integration enablers. The results demonstrate a range of ways BRI promotes global logistics and supply chain integration.

The case study, with multiple examples, focuses on how third-party logistics firms can embrace global logistics and supply chain integration in line with BRI. The case study approach limits generalisation, further applications in different contexts are required to validate the findings.

The framework holds promise for aiding practitioners and researchers in gaining deeper insights into the role of the BRI in global logistics and supply chain integration within the digital era. The identified enablers underscore the importance of emphasising key factors necessary for success in navigating digital transformation within global supply chains.

This study identifies and characterizes configurations of generic business models for logistics service providers (LSPs) in the context of industrial additive manufacturing (AM). A literature-based framework of the AM service supply chain (SC) is developed to embed the generic configurations in their SC context.

Following an exploratory research design, 17 interviews were conducted with LSPs, LSPs' potential partners and customers for industrial AM services.

Six generic configurations are identified, the LSP as a Manufacturer, Landlord, Logistician, Connector, Agent and Consultant. The authors outline how these configurations differ in the required locations, partners and targeted customer segments.

The current discussion of reshoring and shorter, decentralized AM SCs confronts LSPs with novel challenges. This study offers guidance for managers of LSPs for designing business models for industrial AM and raises awareness for LSPs' resource and SC implications.

This study contributes to the scarce literature on AM business models for LSPs with in-depth empirical insights. Based on the six identified configurations, this study sets the ground for theorizing about the business models, in particular, the value creation, value proposition and mechanisms for value capture of the business models. In addition, this study suggests how the generic configurations fit the features of specific types of LSPs.

Regarding the retail internal supply chain (SC), both retailers and research are currently focused on reactive food waste reduction options in stores (e.g. discounting or donations). These options reduce waste after a surplus has emerged but do not prevent an emerging surplus in the first place. This paper aims to reveal how retailers can proactively prevent waste along the SC and why the options identified are impactful but, at the same time, often complex to implement.

The authors follow an exploratory approach for a nascent topic to obtain insights into measures taken in practice. Interviews with experts from retail build the main data source.

The authors identify and analyze 21 inbound, warehousing, distribution and store-related options applied in grocery retail. Despite the expected high overall impact on waste, prevention measures in inbound logistics and distribution and warehousing have not been intensively applied to date.

The authors provide a structured approach to mitigate waste within retailers' operations and categorize the types of barriers that need to be addressed.

This research provides a better understanding of prevention options in retail operations, which has not yet been empirically explored. Furthermore, this study conceptualizes prevention and reduction options and reveals implementation patterns.

This study aims to document students’ supply chain solutions developed through the internship hackathon program. The study profiled innovative solutions developed by university students in Kenya to solve health supply chain logistics challenges during and beyond COVID-19. This is done by exploring students’ experience in developing sustainable logistics and supply chain management capacity-building programs in a low-middle-income country (LMIC).

This study used a qualitative approach to explore the experiences and perceptions of students and mentors who participated in a hackathon program. The study followed a cross-sectional descriptive survey design, collecting data from the participants through online questionnaires. The data were analyzed and presented using thematic analysis and narrative techniques.

Findings provide preliminary evidence for narrowing the gap between theory and practice through a hackathon internship blended with a mentorship program. Assessment of this program provides evidence for developing solutions toward ensuring the availability of essential medicine in LMICs during a pandemic such as COVID-19 by students. The profiled solutions demonstrate a broader perspective of innovative solutions of university students, mentors and potential opportunities for a triple helix approach to innovation for health supply chain system strengthening.

This original study provides evidence for advancing contribution to developing innovative solutions through partnerships between investors, universities and industry practitioners interested in mentoring students in the health-care supply chain during COVID-19 in LMICs. Specifically, contingency factors that affect the implementation of innovative programs during and beyond global pandemics such as COVID-19 by students’ innovators are identified, and implications for policy action are discussed based on the praxis of sensemaking.

This study examines a novel approach that combines internship, mentorship and hackathon projects for logistics and supply chain students in LMICs. The approach aims to bridge the gap between theory and practice and to create innovative solutions for essential medicines during and after COVID-19. The study urges more resources for supporting such programs, as they benefit both academia and industry. The study also argues that hackathon internship programs can help the logistics and supply chain industry adapt to the post-pandemic era. The study offers insights for investors, universities and practitioners in the health-care industry.

This study shows how to develop innovative solutions for the health-care supply chain during COVID-19 in an LMIC through partnerships between investors, universities and industry practitioners who mentor students. The study identifies the contingency factors that influence the success of such programs during and beyond global pandemics such as COVID-19 and discusses the policy implications based on the sensemaking praxis of the student innovators.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.