RESUMEN

BACKGROUND: The health commodities supply chain is considered an important building block of any functional health system. There is a suboptimal availability of various health commodities in Low and Middle Income Countries (LMIC). Result Based Financing (RBF) is the innovation in health system financing that links financing with results. In Tanzania, a supply chain component was added to the RBF implementation in 2016 with the purpose of improving essential health commodities availability. This study aimed to evaluate the effects of RBF on essential health commodities in Tanzania, i.e., health commodities that have a high impact on the population's priority health care needs. They must be available all the time and be affordable in Tanzania. METHODS: The study employs a retrospective pre- and post-intervention design with a comparison group, ensuring a systematic approach to data collection and analysis. The intervention area was the Medical Store Department (MSD) Mwanza zone, while the comparison was the Moshi MSD zone. Data was extracted from the electronic Logistic Management Information System (eLMIS) on a quarterly basis at the health facilities from April 2016 to September 2018. A Repeated Measure Analysis of Variance (RMA) analysis was carried out. RESULTS: The study showed that the average availability of essential health commodities at the facilities in the RBF implementing zone was higher than in the non-implementing zone (50 vs 33). Similarly, there was a significant effect of RBF on the essential health commodities availability at health facilities F (9, 414) = 12.83, p = 0.0000. CONCLUSION: Result Based Financing has a positive impact on the availability of essential health commodities in the implementing areas.

RESUMEN

The rapid integration of the Internet of Things (IoT) into logistics and supply chain management (SCM) marks a significant transformation towards enhanced efficiencies, security, and sustainability. Through a comprehensive bibliometric analysis of 2680 publications from the Scopus database, this study charts the evolution of IoT within logistics and SCM and reveals a shift from foundational explorations to mature implementations. The research unfolds a complex thematic structure, highlighting the revolutionary impacts of IoT and related technologies such as RFID, the synergy of Industry 4.0 with SCM through Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT), the strategic role of blockchain for enhanced traceability and security, and the advent of novel communication and encryption technologies for secure data exchanges. Further, the analysis categorizes the scholarly discourse into critical areas including big data and IoT optimization in SCM, IoT-driven innovation in the food supply chain, applications of blockchain and smart contracts, digital transformation through Industry 4.0, security advancements with intelligent systems, and the exploration of advanced technologies for Industry 4.0 and 5.0. This review not only delineates the intellectual landscape of IoT applications in logistics and SCM but also identifies emerging research areas such as blockchain integration, 5G potential, and AI-driven optimizations, suggesting pathways for future research to broaden the understanding in this dynamically evolving field. It serves as an essential resource for academics and practitioners, providing insights into the transformative role of IoT in logistics and SCM and proposing directions for future technological and academic endeavors.

RESUMEN

This systematic review addresses a significant gap in the existing literature by examining the intricate relationship between management practices and Industry 4.0 technologies in shaping supply chain sustainability. While prior studies have explored their individual impacts, this review synthesizes and categorizes findings to identify nuanced trends that contribute to supply chain efficiency, waste reduction, and environmental footprints. To achieve the goal of the study, a rigorous search strategy was employed to select peer-reviewed journal articles focusing on total quality management, just-in-time, vendor-managed inventory, lean, manufacturer-led decentralized systems, blockchain, the internet of things, and big data in the context of supply chain sustainability. The selected studies underwent a thorough evaluation to ensure quality and relevance. The findings highlight key insights: the adoption of management practices, particularly total quality management and just-in-time, significantly contributes to reducing waste, enhancing efficiency, and minimizing environmental footprints across supply chains. Simultaneously, the integration of industry 4.0 technologies like blockchain, the Internet of things, and big data empowers data-driven decision-making, transparency, and traceability, amplifying sustainability efforts. In conclusion, this review contributes a novel perspective by synthesizing, categorizing, and analyzing the impact of management practices and Industry 4.0 technologies on supply chain sustainability. Its findings offer valuable insights for addressing contemporary challenges and advancing sustainable practices amid dynamic global scenarios.

RESUMEN

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.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

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.

RESUMEN

Recycling has become a critical response to the goals of reaching a carbon peak and achieving carbon neutrality. This study explores the effects of consumer free-riding behavior, the quality of recycling services, and the costs of channel transfers on the profitability of manufacturers and retailers in a dual-channel closed-loop supply chain (CLSC), focusing on the importance of recycling practices for carbon neutrality. Using consumer utility theory and a Stackelberg game model, we analyze the dynamics among these factors. Our results show that: (i) Consumer free-riding behavior slightly increases market demand and recycling volumes, enhancing profitability for both manufacturers and retailers in the dual-channel CLSC. (ii) The quality of recycling services and the transfer costs associated with retailer free-riding behavior jointly influence the profits of manufacturers and retailers. (iii) The effect of free-riding behavior on recycling services affects both forward sales and reverse recycling channels equally. This study provides valuable insights for decision-making in sustainable development practices in the recycling sector, significantly contributing to the goal of achieving carbon neutrality.

RESUMEN

Fuzzy hybrid models are efficient mathematical tools for managing unclear and vague data in real-world scenarios. This research explores the q-rung orthopair fuzzy soft set (q-ROFSS), which presents incomplete and ambiguous details in decision-making problems. The main intention of this study is to describe and evaluate the characteristics of the correlation coefficient (CC) and weighted correlation coefficient (WCC) for q-ROFSS. Also, the technique for order preference should be enhanced by similarity to the ideal solution (TOPSIS) with extended measures in q-ROFSS settings. Furthermore, we integrated mathematical formulations of correlation obstructions to confirm the consistency of the planned technique. It helps handle difficulties involving multi-attribute group decision-making (MAGDM). Moreover, a numerical illustration is presented to clarify how the advocated decision-making methodology can be implemented in evaluating suppliers in green supply chain management (GSCM). As a result, each alternative is assessed using multiple criteria, such as quality and reliability, capacity and scalability, compliance and certifications, and sustainability practices. The technique proposed in this study retains the selected research's specific structure more effectively than current techniques. A comparative analysis further substantiates the feasibility and effectiveness of the proposed approach over other decision-making techniques.

RESUMEN

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.

Asunto(s)

RESUMEN

BACKGROUND: The Global Fund partnered with the Zimbabwean government to provide end-to-end support to strengthen the procurement and supply chain within the health system. This was accomplished through a series of strategic investments that included infrastructure and fleet improvement, training of personnel, modern equipment acquisition and warehouse optimisation. This assessment sought to determine the effects of the project on the health system. METHODS: This study employed a mixed methods design combining quantitative and qualitative research methods. The quantitative part entailed a descriptive analysis of procurement and supply chain data from the Zimbabwe healthcare system covering 2018 - 2021. The qualitative part comprised key informant interviews using a structured interview guide. Informants included health system stakeholders privy to the Global Fund-supported initiatives in Zimbabwe. The data collected through the interviews were transcribed in full and subjected to thematic content analysis. RESULTS: Approximately 90% of public health facilities were covered by the procurement and distribution system. Timeliness of order fulfillment (within 90 days) at the facility level improved from an average of 42% to over 90% within the 4-year implementation period. Stockout rates for HIV drugs and test kits declined by 14% and 49% respectively. Population coverage for HIV treatment for both adults and children remained consistently high despite the increasing prevalence of people living with HIV. The value of expired commodities was reduced by 93% over the 4-year period. Majority of the system stakeholders interviewed agreed that support from Global Fund was instrumental in improving the country's procurement and supply chain capacity. Key areas include improved infrastructure and equipment, data and information systems, health workforce and financing. Many of the participants also cited the Global Fund-supported warehouse optimization as critical to improving inventory management practices. CONCLUSION: It is imperative for governments and donors keen to strengthen health systems to pay close attention to the procurement and distribution of medicines and health commodities. There is need to collaborate through joint planning and implementation to optimize the available resources. Organizational autonomy and sharing of best practices in management while strengthening accountability systems are fundamentally important in the efforts to build institutional capacity.

Asunto(s)

RESUMEN

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.

RESUMEN

In the era of Industry 4.0 (I4.0), automation and data analysis have undergone significant advancements, greatly impacting production management and operations management. Technologies such as the Internet of Things (IoT), robotics, cloud computing (CC), and big data, have played a crucial role in shaping Logistics 4.0 (L4.0) and improving the efficiency of the manufacturing supply chain (SC), ultimately contributing to sustainability goals. The present research investigates the role of I4.0 technologies within the framework of the extended theory of planned behavior (ETPB). The research explores various variables including subjective norms, attitude, perceived behavior control, leading to word-of-mouth, and purchase intention. By modeling these variables, the study aims to understand the influence of I4.0 technologies on L4.0 to establish a sustainable manufacturing SC. A questionnaire was administered to gather input from small and medium-sized firms (SMEs) in the manufacturing industry. An empirical study along with partial least squares structural equation modeling (SEM), was conducted to analyze the data. The findings indicate that the use of I4.0 technology in L4.0 influences subjective norms, which subsequently influence attitudes and personal behavior control. This, in turn, leads to word-of-mouth and purchase intention. The results provide valuable insights for shippers and logistics service providers empowering them to enhance their performance and contribute to achieving sustainability objectives. Consequently, this study contributes to promoting sustainability in the manufacturing SC by stimulating the adoption of I4.0 technologies in L4.0.

RESUMEN

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.

Asunto(s)

RESUMEN

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.

RESUMEN

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.

RESUMEN

The current study explores the dynamics of business practices in Pakistan's Small and Medium Enterprises (SMEs). This research focusing on how "green supply chain management" (GSCM), Industry 4.0 technologies (IND), total quality management (TQM) and sustainable performance (SP) are interconnected. The study collected data from 382 SMEs owners and managers and data analysis was conducted utilizing by SPSS and SmartPLS. The results indicate that employ GSCM pattern has constructive impact on SMEs overall performance in terms of their social and financial aspects. Interestingly IND act as intermediaries among GSCM and sustainable performance underscoring their potential to translate sustainability efforts into outcomes. Moreover, our survey reveals that TQM have a crucial function in enriching the relationship between Industry 4.0 technologies and sustainable performance by executing as a moderator. It further highlighting the value of adopting a quality focused approach to maximize the causal factor of advancements on sustainability output. The findings of this survey research offering insights for practitioners, SMEs, and policy makers alike by highlighting the significance of integrating practices such as GSCM, IND and effective quality management to enhance SME efficiency. These outcomes further contribute to an understanding of the mechanics at play, within SMEs while offering guidance for organizations maneuvering the changing landscape of sustainable business practices and IND.

Asunto(s)

RESUMEN

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.

RESUMEN

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.

RESUMEN

Background Case studies have highlighted labour rights abuse in the manufacture of several healthcare products, but little is known about the scale of the problem or the specific products involved. We aimed to quantify and compare the overall and product-specific risks of labour rights abuse in the manufacture of healthcare products supplied to high-income settings using multiple datasets on the product country of origin (COO). Methods Public procurement data from South-Eastern Norway (n=23,972 products) were compared to datasets from three other high-income settings: procurement data from Cambridge University Hospitals, trade data from UN Comtrade, and registry data from the US Food and Drug Administration (FDA). In each dataset, the product COO was matched to the International Trade Union Confederation risk rating for labour abuse and deemed high-risk when rated 4, 5, or 5+. Results In the Norway data, 55.4% of products by value had a COO declared, 49.1% of which mapped as high-risk of labour rights abuses. COO was identified for 70/100 products in the Cambridge data, with COO matching high-risk at 59.9% by value. The level of risk for specific medical product categories varied between the Norway, US FDA, and UN Comtrade datasets, but those with higher proportional risk included medical/surgical gloves and electrosurgical products. Conclusion Evidence of high-risk of labour rights abuse in the manufacture of healthcare products present in these data indicates a likely high level of risk across the sector. There is an urgent need for global legislative and political reform, with a particular focus on supply chain transparency as a key mechanism for tackling this issue.