Chain Environment Research Articles

THE ELECTRONIC TRADING OF WAREHOUSE RECEIPTS ON GLOBAL AGRICULTURAL EXCHANGES. CONCLUSIONS FOR POLAND

The aim of the study is to assess the use of Electronic Warehouse Receipt (EWR) as an instrument to support the trade of agricultural raw materials on global agricultural markets, to analyze the possibilities and potential of introducing this system in Poland, as well as to initiate a discussion on the restoration of the system of licensed storage and electronic warehouse receipt. Statistical data published by the Futures Industry Association and data obtained from commodity exchanges, as well as available literature, were used as source material. The analyses carried out show that a properly implemented EWR provides a proven solution to problems occurring in the food economy: the financing of current operating costs of running a farm and farm liquidity, and the financing of trade between participants in the supply chain. For a EWR system to be successful, it is necessary to understand the value chain, market and business environment. It is also important to assess risk, competitiveness, relationships and processes, justification and financing needs of chain participants. Thanks to this approach, a warehouse receipt can constitute loan security for all participants in the value chain, following the goods. Dematerialization of the warehouse receipt enables the takeover of trading in this document by a licensed exchange, and thus enables not only trading in a futures instrument, as on the CBOT or MATIF exchange, but also in a cash instrument with physical delivery, as on the TMEX or e-NAM exchange. The warehouse receipt system in Poland has been abolished, but the problems it solves remain. Taking into account the advantages of the warehouse receipt system, it should be restored in Poland to provide the agricultural sector with effective and proven solutions.

Harnessing Technology for Enhanced Efficiency in Supply Chain Operations

The international supply chain environment is quickly changing due to innovations that are reinventing not just the method companies operate. Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, And Automation are some technologies that change the supply chains with more efficiency, better decisions, and live views. The technologies simplify processes like inventory management, demand forecasting, order fulfillment, and logistics optimization, which can keep businesses ahead of the competition in a fast-paced global marketplace. In this paper, we look at how organizations use these technologies to drive operational excellence, entitlement, and responsiveness to customer demand. The report presents some case studies of the successful applications of these technologies and talks about what companies are facing when incorporating these new systems into their existing supply chain process. It also explores strategic best practices in addressing these challenges, beginning with defining clear goals, aligning technology, and ensuring their workforces are ready for change. Finally, the paper looks at the future for supply chain technology. With burgeoning trends such as hyper-automation (driven by AI and IoT), we can expect things to keep improving and improving supply chain performance in years to come. The results show that despite challenges, successfully implementing these technologies increases efficiency and reduces costs, improving customer satisfaction and providing firms with a sustainable comparative advantage. Keywords Supply Chain Management, Technology, Artificial Intelligence, Internet of Things, Blockchain, Automation, Operational Efficiency, Demand Forecasting, Logistics, Inventory Management, Hyper-Automation.

Partner selection for supply chain collaboration: New data envelopment analysis models

Partner selection is crucial for ensuring successful supply chain collaboration. This study focuses on selecting the best partner for a predefined two-stage supply chain using data envelopment analysis to assess the performance of collaborative systems. We distinguish between two levels of supply chain collaboration: chain-to-chain and stage-to-stage collaboration. The former involves partner selection within the same supply chain across two stages, while the latter allows for selected partners from different supply chains across two stages. We incorporate the technology learning effect and introduce three degrees of collaboration (minor, major, and medium) for both chain and stage collaboration levels. Solutions are provided for each collaboration level and degree, with comparative analysis indicating that major collaboration in stage-to-stage level yields superior performance. A numerical example and a real-world case study are presented to illustrate our models and findings, demonstrating that our approach offers superior benefits and more flexible options compared to existing methods. Thus, the proposed approach not only contributes to advancing theoretical understanding but also provides practical implications for optimizing collaborative relationships within complex multi-stage supply chain environments.

Leveraging Multi-Agent Reinforcement Learning for Digital Transformation in Supply Chain Inventory Optimization

In today’s volatile supply chain (SC) environment, competition has shifted beyond individual companies to the entire SC ecosystem. Reducing overall SC costs is crucial for success and benefits all participants. One effective approach to achieve this is through digital transformation, enhancing SC coordination via information sharing, and establishing decision policies among entities. However, the risk of unauthorized leakage of sensitive information poses a significant challenge. We aim to propose a Privacy-preserving Multi-agent Reinforcement Learning (PMaRL) method to enhance SC visibility, coordination, and performance during inventory management while effectively mitigating the risk of information leakage by leveraging machine learning techniques. The SC inventory policies are optimized using multi-agent reinforcement learning with additional SC connectivity information to improve training performance. The simulation-based evaluation results illustrate that the PMaRL method surpasses traditional optimization methods in achieving cost performance comparable to full visibility methods, all while preserving privacy. This research addresses the dual objectives of information security and cost reduction in SC inventory management, aligning with the broader trend of digital transformation.

Higher order approximations in quantum speed limit time of a qubit coupled to XXZ spin chain environment

Higher order approximations in quantum speed limit time of a qubit coupled to XXZ spin chain environment

Photonics of High-Entropy Polymers Revealing Molecular Dispersion via Polymer Mixing.

Blending multiple polymers together to form the so-called "high-entropy polymers (HEPs)" can generate the effects of molecular dispersion in addition to suppressing polymer phase separation. We embedded a semiconducting polymer (conjugated polymers, CPs) in an optically inert matrix composed of n polymer species and found that a molecule-level dispersion is attained in HEPs defined as n ≥ 5. In the regime of dilute CP concentrations, the photonic properties vary widely in the n = 1 matrices owing to diverse solubility parameters, but the distribution narrows with n, and the CP starts to exhibit behaviors of molecule-level dispersion at n ≥ 5, where the matrix polymers compete with each other to exert direct influences on the embedded CP. Specifically, for MEH-PPV, increasing n reduces the fluorescence redshift and spectral width from diminishing aggregation. For the rigid PFO molecules, increasing n creates a dilution effect facilitating formation of the low-energy planar β-phase. For the flexible regioregular P3HT-rr, HEPs offer well-dispersed amorphous chains highly susceptible to chain environments, thus influencing ηR's in the quasi-fixed amorphous-crystalline energy transfer landscape. The HEP effects continue for greater CP concentrations, consistent with the matrix dispersing behaviors in the dilute regime. This work demonstrates a molecular-level dispersion by HEPs, offering a method of molecular tailoring for polymer research and applications via simple mixing.

Supply Chain Management for the Engineering Procurement and Construction (EPC) Model: A Review and Bibliometric Analysis

China’s “Going Out” strategy and the “Belt and Road” initiative have significantly propelled the expansion of overseas contractual engagements, with a pronounced impact in the Engineering, Procurement, and Construction (EPC) sector. EPC contracting is increasingly recognized for its superior resource allocation and integration efficiency. Within the EPC framework, procurement is pivotal, exerting a direct influence on project cost, schedule, and quality parameters. The biblio-metric analysis of this paper reveals the research trend of EPC projects in the past decade, especially in supply chain integration, procurement management, and stakeholder management. This indicates an increasing interest in efficient project management and supply chain optimization in international engineering EPC projects. Although the application of supply chain integration in the manufacturing industry has been a successful experience, its practical application in construction and EPC projects is still insufficient. Integration strategies mentioned in the literature, such as achieving supply chain transparency through digital platforms, reducing operating costs, and optimizing delivery time, are directions that need to be further explored in this field. Effective supply chain integration and increased maturity of procurement management in EPC projects are still less involved areas. In addition, there is also a lack of research on how to manage high-risk and unpredictable risks in large international engineering projects. This article suggests that future research should focus on developing flexible supply chain management tools and practices to adapt to a project-oriented supply chain environment.

Modification of the existing maximum residue levels for propamocarb in radishes (roots and small leaves).

In accordance with Article 6 of Regulation (EC) No 396/2005, the Federal Public Service (FPS) Health, Food chain Safety and Environment submitted a request to the competent national authority in Belgium (evaluating Member State, EMS) to modify the existing maximum residue levels (MRLs) for the active substance propamocarb in radishes for the residues occurring in radish roots and in small radish leaves (proposed to be classified under the subgroup of Roman rocket/rucola). The data submitted in support of the request were found to be sufficient to derive MRL proposals for radishes and small radish leaves. Adequate analytical methods for enforcement are available to control the residues of propamocarb on the commodities under consideration at the validated limit of quantification (LOQ) of 0.01 mg/kg. Based on the risk assessment results, EFSA concluded that the short-term and long-term intake of residues resulting from the use of propamocarb according to the reported agricultural practice is unlikely to present a risk to consumer health. The chronic consumer risk assessment shall be regarded as indicative since affected by uncertainty related to the data gaps identified in the MRL review.

Strategies for effective high pressure germination or inactivation of Bacillus spores involving nisin.

Extremely resistant spore-forming bacteria are widely distributed in nature. They infiltrate the food chain and processing environments, posing risks of spoilage and food safety. Traditional heat-intensive inactivation methods often negatively affect the product quality. HP germination-inactivation offers a potential solution for better preserving sensitive ingredients while inactivating spores. However, the presence of ungerminated (superdormant) spores hampers the strategy's success and safety. Knowledge of strategies to overcome resistance to HP germination is vital to progress mild spore control technologies. Our study contributes to the evaluation and development of mild preservation processes by evaluating strategies to enhance the HP germination-inactivation efficacy. Mild preservation processes can fulfill the consumers' demand for safe and minimally processed food.

An Algorithm for Part Input Sequencing of Flexible Manufacturing Systems with Machine Disruption

Because disruption happens unpredictably and generates serious impact in supply chain and production environments in the real world, it is important to develop approaches to handle disruption. This paper investigates disruption handling in part input sequencing of flexible manufacturing systems (FMSs). An algorithm is proposed for FMS part input sequencing to handle machine breakage. Evaluation is performed for the proposed algorithm by simulation experiments and result analyses. Finally, conclusions are summarized with managerial implications discussed and further research works suggested.

Algorithm aversion during disruptions: The case of safety stock

Algorithm aversion occurs when organizations or individuals reject optimal analytical decision support in favour of informal, subjective decisions. This phenomenon has been observed in many practical decision-making scenarios and is generally believed to negatively impact decision quality. However, its existence and effect in volatile supply chain environments has not been empirically tested in the literature. Safety stock buffering demand volatility is an important decision in supply chain management, making it an ideal lens to observe algorithm aversion. In this paper, we empirically investigate algorithm aversion behaviour in the context of safety stock settings. We collect data from a case retail company across a range of stockkeeping units (SKUs), encompassing both pre-disruption and post-disruption time stages with varying levels of volatility. We introduce a simulation model to determine whether algorithm aversion exists for safety stock decisions and to assess how algorithm adoption and adaptation affects performance. Our findings indicate that algorithm aversion occurs during supply chain disruptions, with algorithmic decisions significantly outperforming human judgment. Based on interview results and theories of information systems, we propose a theory to explain and generalize the above findings. This theory attributes algorithm aversion behaviour to reduced sense of fitness among algorithm users and lack of slack resources for both users and developers. It also offers insights into how the adoption and adaptation of algorithms influence decision performance during disruptive events.

B-ERAC: Blockchain-Enabled Role-Based Access Control for Secure IoT Device Communication

Security risks are increasingly concerning as the Internet of Things (IoT) expands. Authentication, access control, and authorization present significant challenges for resource-constrained IoT devices. Traditional authentication methods often require enhancements for these devices, but Blockchain technology presents a potential solution. Decentralized and distributed, Blockchain eliminates a single point of failure and relies on Elliptic Curve Cryptography (ECC) for robust security. We have introduced a cutting-edge solution to fortify communication security within IoT devices across supply chain ecosystems. By harnessing the power of Blockchain technology, our framework incorporates smart contracts, adheres to ES256 encryption standards, and seamlessly integrates with Infura API. These components establish stringent access controls, ensure data integrity, and enhance transparency throughout supply chain processes. The framework’s robust architecture facilitates swift and secure transactions, bolsters traceability efforts, and effectively mitigates potential security risks. With its scalable design and reliable functionality, this framework emerges as a pivotal asset for optimizing IoT device communication within dynamic supply chain environments. The use of ProVerif in our analysis provides a formal guarantee of the correctness of our access control mechanisms.

Enhancing SME Competitiveness Through Coopetition Networks: A Service-Dominant Logic Approach

In the era of rapid globalization and digital transformation, small and medium-sized enterprises (SMEs) in the manufacturing sector encounter significant challenges in scaling operations, enhancing operational efficiency, and fostering innovation. This study focuses on manufacturing SMEs, where coopetition– a strategic blend of competition and cooperation– addresses these challenges. Despite its potential to drive innovation and value creation, coopetition networks often struggle to succeed, particularly under the pressures of advancing technology and the need for digital collaboration. To address these issues, this research develops a comprehensive framework that integrates Service-Dominant (S-D) Logic with technology-enabled coopetition networks. Utilizing the Design Science methodology, the study presents a framework that underscores the crucial role of technology in fostering value co-creation within SME networks. This framework is designed to enable sustainable and effective engagement in coopetition, significantly mitigating the risk of network failure by aligning technological innovation with coopetition principles. The proposed framework was evaluated through a targeted survey of managers from SMEs in the Portuguese stone manufacturing sector. The survey results con firmed the practical applicability and potential to promote robust and sustainable coopetition strategies. By integrating theoretical insights with practical applications, this research offers a roadmap for SMEs to effectively manage the complexities of coopetition within the digital supply chain environment.

A critical review of machine learning applications in supply chain risk management

This article examines the transformative role of Machine Learning (ML) in Supply Chain Risk Management (SCRM), emphasizing its ability to enhance risk prediction and mitigation through real-time, data-driven insights. It demonstrates how ML applications like demand forecasting, inventory optimization, supplier risk assessment, and fraud detection improve supply chain resilience by analyzing large datasets to identify patterns, predict risks, and recommend proactive actions. However, the article also highlights key challenges, including data quality, availability, and privacy issues, which limit the effectiveness of ML models. Integrating ML with legacy systems poses additional technical and financial difficulties, particularly for smaller businesses. High implementation costs and scalability constraints further hinder widespread adoption. Ethical concerns, such as data bias and privacy, still need to be explored, raising questions about responsible ML use in supply chains. Future research should address these gaps by improving data governance frameworks for accuracy and privacy, developing scalable ML models suited to various supply chain environments, and exploring synergies with technologies like blockchain and the Internet of Things (IoT). These advancements will help realize ML’s full potential, fostering more agile, transparent, and resilient supply chains capable of navigating complex global risks. By tackling these challenges, ML can shift SCRM from reactive to proactive, ensuring long-term operational continuity and a competitive edge in the evolving global market.

Supply chain digitalization and firms' green innovation: Evidence from a pilot program

Green innovation is essential for realizing environmental sustainability and achieving long-term ecological balance. This paper investigates the influence of supply chain digitalization on firms' green innovation in the manufacturing industry. China's 2018 Supply Chain Innovation and Application Pilot program as a quasi-natural experiment, we employ a difference-in-differences (DID) estimation approach to reach the following findings: (1) supply chain digitalization significantly enhances the quantity and quality of enterprise green innovation, with a more substantial impact on quality. (2) supply chain financing capacity and firm environmental awareness play a partial mediating role in the relationship supply chain digitalization and the quality and quantity of corporate green innovation. (3) the heterogeneity analysis reveals that the impact of supply chain digitalization on green innovation is notably heightened in firms with managers who have overseas experience, weak internal controls, and poor regional supply chain environments. This study enriches the theoretical understanding of enterprise green innovation from the perspective of supply chain digitalization, providing a theoretical basis and practical insights for green innovation practices in enterprises.

Understanding the survival strategies: organization resilience and innovative capabilities in post-pandemic East Asia

ABSTRACT The COVID-19 pandemic has significantly and globally impacted and even disrupted corporate operations to a considerable extent, compelling businesses to confront changing supply chain environments, unstable market demands and internal contradictions. We argue that East Asian enterprises that have survived the pandemic have learned from their experiences, transforming these into key strategic capabilities in innovation, operational efficiency, customer relationships and employee management to ensure resilience in volatile and hostile environments. One universal lesson learned is that the survival of businesses integrates resilience, sustainability and a people-oriented perspective. This special issue focuses on the business organizations and innovation capabilities in the post-pandemic East Asian region, compiling the core viewpoints and hypotheses from the collection on topics such as strategic innovation and adaptive changes, diverse teams and leadership and stakeholder relationship management. These findings enrich our understanding of East Asian enterprises’ comprehensive capability enhancement and business model transformation, especially after the COVID-19 pandemic.

Prevalence of Listeria monocytogenes in Milk and Dairy Product Supply Chains: A Global Systematic Review and Meta-analysis.

Listeria monocytogenes, one of the main foodborne pathogens, is commonly found in milk and dairy products. This study aimed to estimate the presence of L. monocytogenes in milk and dairy product supply chains using a meta-analysis based on PubMed, Embase, Web of Science, and Scopus databases. A total of 173 studies were included in this meta-analysis. The pooled prevalence in the supply chain environment was 8.69% (95% confidence interval [CI]: 5.30%-12.78%), which was higher than that in dairy products (4.60%, 95% CI: 1.72%-8.60%) and milk products (2.93%, 95% CI: 2.14%-3.82%). Subgroup analysis showed that L. monocytogenes prevalence in raw milk (3.44%, 95% CI: 2.61%-4.28%) was significantly higher than in pasteurized milk (0.60%, 95% CI: 0.00%-2.06%). The highest prevalence of L. monocytogenes in milk and dairy products was observed in North America (5.27%, 95% CI: 2.19%-8.35%) and South America (13.54%, 95% CI: 3.71%-23.37%). In addition, studies using culture and molecular methods (5.17%, 95% CI: 2.29%-8.06%) had higher prevalence than other detection methods. Serogroup 1/2a and 3a (45.34%, 95% CI: 28.74%-62.37%), serogroup 1/2b and 3b (14.23%, 95% CI: 6.05%-24.24%), and serogroup 4b/4e (13.71%, 95% CI: 6.18%-22.83%) were dominant in these studies. The results of this study provide a better understanding of the prevalence of L. monocytogenes in milk and dairy product supply chains and suggest a potential foodborne pathogen burden.

Hybrid machine learning-based model for evaluating the performance of agile-sustainable supply chains in the context of industry 4.0: a case study

In today’s world, businesses and, in general, supply chains have undergone extensive transformations, and relying solely on traditional metrics such as cost and quality cannot provide a comprehensive and complete evaluation of companies active in various sections of supply chains. One of the main concerns of supply chain managers is to create an integrated and comprehensive structure for evaluating the performance of active branches. In this context, this study presents a structure that, by simultaneously considering agility and sustainability metrics within the context of the industry 4.0, which has brought about fundamental changes in the supply chain environment in recent years, aims to evaluate the active branches in the dairy product supply chain. On the other hand, the increase in the volume of data produced in the supply chain environment and the development of the applications of machine learning algorithms in various fields, which offer better applications compared to intuitive approaches, have led this study to use hybrid data-driven approaches, which are a combination of expert-based methods and documented organizational data, to evaluate the performance of supply chain branches. Therefore, this study is innovative in terms of the evaluation metrics and the data-driven approach developed. In the first step, evaluation metrics appropriate to the dimensions of agility, sustainability, Industry 4.0, and general metrics were identified, and then the fuzzy best-worth method (FBWM) approach was used to weight the metrics. According to the findings, data-driven, marketing, overhead costs, delivery timeframe, and product quality were selected as the most important metrics. Subsequently, using the developed artificial neural network algorithm, which calculates the input weights of the metrics using the FBWM method, a model for evaluating the supply chain was presented, and the findings show that the developed approach performs better than other algorithms on the problem data with more than 92% accuracy.

Multi-population genetic algorithm with crowding-based local search for fuzzy multi-objective supply chain configuration

Supply chain configuration is often fuzzy and involves multiple objectives in real-world scenarios, but existing researches lack the exploration in the fuzzy aspect. Therefore, this paper establishes a fuzzy multi-objective supply chain configuration problem model to minimize the lead time and product cost oriented towards real supply chain environments. To solve the fuzzy problem, the theories of membership and closeness degree in fuzzy mathematics are adopted, and a multi-population genetic algorithm (MPGA) with crowding-based local search method is proposed. The MPGA algorithm uses two populations for optimizing the two objectives separately and effectively, and is characterized by three main innovative aspects. Firstly, a radical-and-radial selection operator is designed to balance the convergence speed and diversity of population. In the early stage of the algorithm, two populations are both optimized towards the ideal knee point, and then are separately optimized towards the two ends of the Pareto front (PF). Secondly, an elitist crossover operator is devised to promote information exchange within two populations. Thirdly, a crowding-based local search is proposed to speed up convergence by improving the crowded solutions, and to enhance diversity by obtaining new solutions around the uncrowded ones. Comprehensive experiments are tested on a fuzzy dataset with different sizes, and the integral of the hypervolume of PF is used for the evaluation of the fuzzy PF. The results show that MPGA achieves the best performance over other comparative algorithms, especially on maximum spread metric, outperforming all others by an average of 39 % across all test instances.

E-procurement optimization in supply chain: A dynamic approach using evolutionary algorithms

The increasing dynamism of global markets, coupled with the occurrence of unpredictable events, has introduced substantial challenges in formulating efficient supply chain strategies. The inherent dynamic nature of logistic networks necessitates a departure from traditional supply chain methodologies. This study proposes an advanced solution for dynamic e-procurement utilizing evolutionary algorithms (EAs). In conventional supply chains involving buyers and suppliers, a critical challenge is identifying cost-efficient suppliers capable of fulfilling consumer demands amidst fluctuating prices and quantities. Traditional optimization techniques often fail to perform effectively under these dynamic conditions. Moreover, detecting changes during the optimization process is an additional hurdle in dynamic optimization problems. Recent advancements have demonstrated the efficacy of EAs in solving a variety of real-world dynamic optimization issues.This research introduces a novel evolutionary algorithmic framework that integrates the Hybrid Multipopulational Reinitialization Strategy (HMRS), with a proposed hybrid change detection mechanism (named Smirnov-based Multi-sensor Detection Mechanism (SMDM)) to address the dynamic e-procurement problems. The proposed framework enhances the algorithm’s adaptability and responsiveness to real-time changes within the e-procurement environment. By effectively detecting and responding to these variations, the framework aims to optimize procurement processes, ensuring efficiency and robustness in managing fluctuating requirements and conditions inherent to dynamic e-procurement scenarios. The empirical analysis presented underscores the superiority of Differential Evolution (DE) variants over Genetic Algorithm (GA) variants within the procurement context. The detailed empirical study validates the effectiveness of the proposed dynamic approach in addressing the challenges associated with dynamic e-procurement. Considering real-world parameter fluctuations, the proposed approach demonstrates significant resilience, positioning it as a robust and efficient solution for optimizing the e-procurement process and adeptly managing the complexities of the supply chain environment.