Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the capabilities of ML across different segments of the food supply chain, encompassing pre-harvest agricultural activities to post-harvest processes and delivery to the consumers. Three specific examples of applying cutting-edge ML to advance food science are detailed in this chapter, including its use to improve beer flavor, using natural language processing to predict food safety incidents, and leveraging social media to detect foodborne disease outbreaks. Despite advances in both theory and practice, application of ML to smart food safety still suffers from issues such as data availability, model reliability, and transparency. Solving these problems can help realize the full potential of ML in food safety. Development of ML in smart food safety is also driven by social and industry impacts. The improvement and implementation of legal policies brings both opportunities and challenges. The future of smart food safety lies in the strategic implementation of ML technologies, navigating social and industry impacts, and adapting to regulatory changes in the AI era.

Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, and robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs of efficient order processing and system stability. This study focuses on three key stages of RMFSs: order allocation and sorting, shelf selection, and coordinated robot scheduling. For each stage, mathematical models are established and the corresponding solutions are proposed. Unlike traditional methods, DRL technology is introduced to solve these problems, utilizing a Genetic Algorithm and Ant Colony Optimization to handle decision making related to large-scale orders. Through simulation experiments, performance indicators-such as shelf access frequency and the total processing time of the RMFS-are evaluated. The experimental results demonstrate that, compared to traditional methods, our algorithms excel in handling large-scale orders, showcasing exceptional superiority, capable of completing approximately 110 tasks within an hour. Future research should focus on integrated decision-making modeling for each stage of RMFSs and designing efficient heuristic algorithms for large-scale problems, to further enhance system performance and efficiency.

The challenges posed by unsustainable practices in today\'s economy underscore the urgent need for a transition toward a circular economy (CE) and a holistic supply chain (SC) perspective. Benchmarking plays a pivotal role in managing circular SCs, offering a metric to gauge progress. However, the lack of consensus on the optimal benchmarking approach hampers effective implementation of circular business practices. To address this gap, we conducted a systematic review of the literature, identifying 29 pertinent publications. The analysis revealed 30 unique attributes and sub-attributes for benchmarking circularity, which were clustered into five main attributes. The main attributes are goals, subjects, key performance indicators (KPIs), data sources, and evaluation methods, while the sub-attributes are organised as features of the main attributes and depicted as a feature model. Drawing from selected publications, we illustrated each feature with examples. Our model offers a comprehensive benchmarking reference for circularity and will be a valuable tool for managers in the transition toward circularity. Supply chains seeking to benchmark their transition to circularity can apply the reference model to ensure that their benchmarking strategy is consistent with state-of-the-art knowledge. By providing a generic circularity benchmarking approach that is valid for diverse economic sectors, our findings contribute to theoretical efforts to address the lack of generic frameworks for CE.

UNASSIGNED: Effective supply chain management (SCM) of point-of-care (POC) tests for diseases like severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) requires active participation from diverse stakeholders, government entities, and regulatory bodies. The responsibility for overseeing various aspects of POC tests, including procurement, quality assurance, storage, inventory management, distribution, and human resource capacity, lies with national, provincial, and local levels of government. This study aimed to collaboratively develop an innovative approach to enhance SCM for SARS-CoV-2 POC diagnostic services in resource-limited settings, using the Mopani District in Limpopo province, South Africa, as a case study.
UNASSIGNED: Key stakeholders were invited to participate in an online workshop using purposive sampling. The study employed the nominal group technique (NGT) for data collection, which consisted of two phases. Phase 1 focused on identifying barriers in the supply chain of COVID-19 rapid tests, while phase 2 aimed to devise strategies to overcome the priority barriers identified in phase 1. Participants used a Likert scale of 1-5 to rank barriers and strategies, and an overall ranking score was calculated for each. The participants were provided with the results of the ranking exercise for their feedback.
UNASSIGNED: Eleven key stakeholders from national (n = 1), provincial (n = 4), and local government (n = 2) levels, research entities (n = 3), and non-governmental organizations (n = 1) took part in the study. Participants identified significant barriers in the supply chain, such as the availability of testing kits, unknown demand, information on SCM during a pandemic, methods of controlling stock, and procurement processes. Strategies suggested by key stakeholders included monitoring stock levels and optimizing stock visibility systems to improve test availability, enhancing information visibility and consistent data updates to address unknown demand and improve SCM during a pandemic, employing data capturing and digitization for effective stock control, and implementing demand planning and standardized procurement processes at the national level to enhance stock procurement.
UNASSIGNED: The successful collaboration with key stakeholders, facilitated by the NGT, resulted in the co-creation of a novel approach to enhance SCM for COVID-19 diagnostic services in resource-limited settings. This study holds the potential to support the provision of COVID-19 diagnostic services in such settings. A recommended follow-up study would assess the feasibility of implementing this approach.

The global community is actively pursuing alternative energy sources to mitigate environmental concerns and decrease dependence on fossil fuels. Biodiesel, recognized as a clean and eco-friendly fuel with advantages over petroleum-based alternatives, has been identified as a viable substitute. However, its commercialization encounters challenges due to costly production processes. Establishing a more efficient supply chain for mass production and distribution could surmount these obstacles, rendering biodiesel a cost-effective solution. Despite numerous review articles across various renewable energy supply chain domains, there remains a gap in the literature specifically addressing the biodiesel supply chain network design. This research entails a comprehensive systematic literature review (SLR) focusing on the design of biodiesel supply chain networks. The primary objective is to formulate an economically, environmentally, and socially optimized supply chain framework. The review also seeks to offer a holistic overview of pertinent technical terms and key activities involved in these supply chains. Through this SLR, a thorough examination and synthesis of existing literature will yield valuable insights into the design and optimization of biodiesel supply chains. Additionally, it will identify critical research gaps in the field, proposing the exploration of fourth-generation feedstocks, integration of multi-channel chains, and the incorporation of sustainability and resilience aspects into the supply chain network design. These proposed areas aim to address existing knowledge gaps and enhance the overall effectiveness of biodiesel supply chain networks.

In the context of sustainable development, market competition is intensifying, and financial constraints have emerged as a significant hindrance to green project investment. Green Supply Chain Finance (GSCF), characterized by long-term collaboration, has emerged as a crucial financial approach to mitigate corporate financial limitations and channel capital flows into environmentally friendly industries. We propose a two-echelon supply chain with one supplier and two competing retailers over a single period and investigate ordering, sales, and financing decisions simultaneously under competition. Retailers constrained by financial considerations may secure GSCF or traditional bank financing (BF) loans. This study investigates the influence of competition on pricing and sales strategies during the selling season. The results demonstrate that retailers select between clearance and responsive selling strategies based on the level of market competition. During the ordering season, retailers share the product market equally when interest rates are uniform, and the supplier formulates a supply chain contract while considering the financing interest rate. In the presence of differential interest rates, retailers may not always opt for the GSCF, even when they offer an interest rate advantage, due to the comprehensive impacts of operational and financial strategies. Remarkably, competitive retailers do not choose the GSCF when their initial green investment capital surpasses a certain threshold.

Avocado production is mostly confined to tropical and subtropical regions, leading to lengthy distribution channels that, coupled with their unpredictable post-harvest behavior, render avocados susceptible to significant loss and waste. To enhance the monitoring of \'Hass\' avocado ripening, a data-driven tool was developed using a deep learning approach. This study involved monitoring 478 avocados stored in three distinct storage environments, using a 5-stage Ripening Index to classify each fruit\'s ripening phase based on their shared characteristics. These categories were paired with daily photographic records of the avocados, resulting in a database of labeled images. Two convolutional neural network models, AlexNet and ResNet-18, were trained using transfer learning techniques to identify distinct ripening indicators, enabling the prediction of ripening stages and shelf-life estimations for new unseen data. The approach achieved a final prediction accuracy of 88.8% for the ripening assessment, with 96.7% of predictions deviating by no more than half a stage from their actual classifications when considering the best side of the samples. The average shelf-life estimates based on the attributed classifications were within 0.92 days of the actual shelf-life, whereas the predictions made by the models had an average deviation of 0.96 days from the actual shelf-life.

In the context of regular epidemic prevention and control, this paper considers a two-stage tourism supply chain consisting of a scenic spot that attracts tourists through advertising and a travel agency that invests in service improvement and epidemic prevention. By establishing theoretical game models of a tourism supply chain, we investigate how the service level and advertising level can affect the retail price, product service level, and profits of the supply chain. The results show that the service level of travel agencies could improve consumers\' preferences, expand the market demand for tourism products, and improve the efficiency of the supply chain to achieve a win-win situation and increase the profits of the scenic spot and the travel agency. The retailer price, service level, promotion level, and supply chain profit all increase as the service coefficient and advertising coefficient increase, and the speed of the increase is higher for the centralized model than for other models. Some valuable information could be provided for supply chain enterprises to develop collaborative strategies and promote tourism supply chain management practices.

This comprehensive bibliometric study investigates Sustainable Supply Chain Innovation (SSCI) research, examining its evolution, identifying key contributors, and unveiling emerging trends. Analyzing 1158 English-language SSCI articles using the robust Scopus dataset exposes noteworthy journals, authors, institutions, and global contributions. The findings suggest a consistent increase in research output since 1999, with a notable surge in the past decade. Network analysis and density-based spatial clustering identified six SSCI research clusters: Sustainability and Responsibility in Business, Navigating Innovation and Disruption, Sustainable Business Strategies, Environmental Sustainability and Innovation, Sustainable Food Systems and Environmental Impact, and Sustainable Business Dynamics. These clusters highlight the diverse nature of the evolving Sustainability and Supply Chain Management (SCM) field, contributing to a thorough understanding of the SSCI research landscape and emphasizing interconnections between sustainability and SCM themes, potentially leading to more comprehensive theoretical models. Furthermore, this understanding aids businesses in anticipating emerging trends and implementing optimal practices in SSCI. Moreover, recognizing active institutions and global contributors provides practical insights for fostering strategic collaborations.

Outsourcing is an essential tool in supply management. This study examines the current outsourcing application areas of the public health sector in Türkiye. Application examples are presented, together with the prominent theories in outsourcing (Transaction Cost Economics Theory (TCE), Core Competencies Theory (CCT), Agency Theory (AT), Resource-Based View Theory (RBVT), Relational Theory (RT), and Social Change Theory (SCT), Turkish public procurement legislation, Ministry of Health outsourcing practices, and public hospitals are discussed for review and example. The examination shows that foreign dependency on the public health sector is increasing, and more than one theory is applied in some procurement processes. As a result of this study, examining and comparing which theories come to the fore in outsourcing in other countries is recommended.