Partner selection in sustainable supply chains: a fuzzy ensemble learning model

With the increasing demands on businesses to operate more sustainably, firms must ensure that the performance of their whole supply chain in sustainability is optimized. As partner selection is critical to supply chain management, focal firms now need to select supply chain partners that can offer a high level of competence in sustainability. This paper proposes a novel multi-partner classification model for the partner qualification and classification process, combining ensemble learning technology and fuzzy set theory. The proposed model enables potential partners to be classified into one of four categories (strategic partner, preference partner, leverage partner and routine partner), thereby allowing distinctive partner management strategies to be applied for each category. The model provides for the simultaneous optimization of both efficiency in its use of multi-partner and multi-dimension evaluation data, and effectiveness in dealing with the vagueness and uncertainty of linguistic commentary data. Compared to more conventional methods, the proposed model has the advantage of offering a simple classification and a stable prediction performance. The practical efficacy of the model is illustrated by an application in a listed electronic equipment and instrument manufacturing company based in southeastern China

Sustainable Assessment in Supply Chain and Infrastructure Management

In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management

Essays in financial technology: banking efficiency and application of machine learning models in Supply Chain Finance and credit risk assessment

The financial landscape is undergoing a significant transformation, driven by technological innovations that are reshaping traditional banking practices. This thesis examines the evolving relationship between financial technology (FinTech) and banking, specifically addressing the credit risk aspects within the domains of Supply Chain Finance (SCF) and peer-to-peer (P2P) lending. FinTech has experienced rapid growth and innovation over the past decade. It encompasses a wide range of technologies and services that aim to enhance and streamline financial processes, disrupt traditional banking models, and offer new solutions to consumers and businesses. The status of FinTech and banking is assessed through an extensive review of the current literature and empirical data. Accordingly, FinTech development has significantly impacted the financial landscape, driving innovation, competition, and customer expectations while it has exposed inefficiencies within traditional banking, it has also compelled banks to evolve and embrace technological advancements. The impact of FinTech on traditional banking models, customer behaviours, and market competition is aimed to be explored. This investigation highlights the challenges and opportunities that arise as FinTech disrupts and reshapes the banking sector, emphasizing its potential to enhance efficiency, accessibility, and customer experiences. As Chapter 3 focuses on an empirical analysis of the impact of FinTech on the operating efficiency of commercial banks in China. Further, in the context of credit risk, the thesis focuses on SCF and P2P lending, two prominent areas influenced by FinTech innovation. SCF has witnessed substantial transformation with the infusion of FinTech solutions. Digital platforms have streamlined the flow of funds within complex supply networks, enhancing the liquidity of suppliers and optimizing working capital for buyers. However, this transformation introduces new credit risk challenges. As suppliers' financial data becomes more accessible, the need for accurate risk assessment and predictive modelling becomes paramount. The integration of big data analytics, machine learning, and artificial intelligence (AI) holds the promise of refining credit risk evaluation by offering real-time insights into supplier financial health, thereby improving lending decisions and reducing defaults. Similarly, P2P lending has redefined the borrowing and lending landscape, enabling direct connections between individual borrowers and lenders. While P2P lending platforms offer speed, convenience, and access to credit for previously underserved segments, they also grapple with credit risk concerns. Evaluating the creditworthiness of individual borrowers without sufficient credit history demands innovative risk assessment methodologies. The emergence of data issues, such as imbalanced data issues, feature selection, and data processing, presents challenges in building accurate credit risk profiles for P2P lending participants. FinTech solutions play a pivotal role in creating and implementing these alternative risk assessment models. Note that, few studies in the literature investigate the benchmark of the advanced method of solving the credit risk assessment in emerging financial services. This thesis aims to address this research gap by evaluating the effectiveness of credit risk assessment models in these FinTech-driven contexts, considering both traditional methodologies and novel data-driven approaches. Chapter 4 investigates the credit risk assessment issue in Digital Supply Chain Finance (DSCF) with the Machine Learning approach and Chapter 5 emphasises the issue of data imbalance of credit risk assessment in P2P Lending. By addressing these gaps and issues, this thesis aims to contribute to the broader discourse on FinTech's role in shaping the future of banking. The findings have implications for financial institutions, policymakers, and regulators seeking to harness the benefits of FinTech while mitigating associated risks. Ultimately, this study offers insights into navigating the evolving landscape of credit risk in SCF and P2P lending within the context of an increasingly technology-driven financial ecosystem

HSE management system and efficiency evaluation of construction projects

The strategy of Belt and Road Initiative actively advocates the establishment of economic partnerships with countries along the belt and road, and makes infrastructure construction a field for preferential development. It provides good development opportunities for domestic and foreign engineering contracting enterprises. The "Strategic Implementation Guidance Document" mentions that infrastructure construction and operation should be green and efficient. However, most state owned engineering contracting enterprises do not attach importance to HSE management, which obviously affects the engineering efficiency and hinders the sustainable development of enterprises. However, China's engineering contracting enterprises pay less attention to HSE management, which obviously affects project efficiency and hinders the sustainable develop ment of enterprises. This study takes a typical large project contracting enterprise (Enterprise A) in China as the research object, adopts the SWOT analysis method, analyzes the weakness of project management at the organization level, and consummates th e organization level project HSE management system of the enterprise at the organization layer and the project layer. Through the enterprise interview, questionnaire design and investigation, this study puts forward the suitable indicators and methods of H SE management performance evaluation, and verifies the effectiveness of the methods through the empirical analysis of eight projects. Then, this study proposes incorporating the project HSE management performance into the comprehensive evaluation methods o f project efficiency. The empirical analysis shows that the HSE management system proposed in this study at the organization layer and at the project layer can improve the HSE management performance of the project. Project HSE management performance has a positive impact on project efficiency, that is, the more enterprises attach importance to project HSE management, the higher the degree of completion of project objectives. The above research results are helpful to improve project management system at the organizational level and enhance the competitiveness of project contracting enterprises.A estratégia da "Belt and Road Initiative" defende ativamente o estabelecimento de parcerias económicas com os países ao longo de uma determinada faixa e faz da construção de infraestruturas um campo preferencial para desenvolvimento. Este campo fornece boas oportunidades de desenvolvimento para as empresas de engenharia nacionais e estrangeiras. O "Documento de Orientação de Implementação Estratégica" menciona que a construção e operação de infraestruturas deve ser eficiente e sustentável. No entanto, a grande maioria das empresas públicas contratadas não atribuem a devida importância aos sistemas de gestão de saúde e segurança (HSE) o que, obviamente, afecta a eficiência da engenharia e dificulta o desenvolvimento sustentável das empresas. As empresas privadas chinesas de engenharia ignoram a gestão HSE o que, obviamente, afecta, também, a eficiência dos projetos de sustentabilidade das empresas. Neste trabalho foi considerada uma grande empresa Chinesa de contratação (Empresa A) na China como referência de pesquisa, que adota a análise swot, analisa as fraquezas na gestão de projetos ao nível da organização e consuma a gestão de projetos em HSE ao nível da organização e do projeto em si. Através de entrevistas, questionários e investigação, este projeto apresenta os indicadores e métodos a dequados de avaliação de desempenho e verifica a efetividade dos métodos através da análise empírica de oito projetos. Em suma, este projeto propõe incorporar métodos de avaliação da eficiência através dos indicadores de gestão do HSE. A análise empírica demonstrou que o sistema proposto de gestão HSE, ao nível da organização e de projeto, pode aumentar efetivamente a gestão de performance do HSE em projeto. A gestão da performance do projeto HSE teve um impacto positivo na eficiência do projeto, ou seja, quanto mais as empresas atribuirem importância à gestão de projetos HSE maior será o nível de conclusão dos objetivos sustentáveis do projeto. Os resultados deste projeto são úteis para melhorar o sistema de gestão de projetos ao nível organizacional e aum entar a competitividade das empresas na contratação de projectos

Methods for Using Manpower to Assess USAF Strategic Risk

With limited personnel resource funding availability, senior US Air Force (USAF) decision makers struggle to base enterprise resource allocation from rigorous analytical traceability. There are over 240 career fields in the USAF spanning 12 enterprises. Each enterprise develops annual risk assessments by distinctive core capabilities. A core capability (e.g. Research and Development) is an enabling function necessary for the USAF to perform its mission as part of the Department of Defense (DOD). Assessing risk at the core capability is a good start to assessing risk, but is still not comprehensiveness enough. One of the twelve enterprises has linked its task structure to Program Element Codes (PECs). Planners and programmers use amount of funding per PEC to assess tasks needed to address a desired capability. For the first time, a linkage between core functions, core capabilities, PECs, tasks and manpower has been developed. We now can provide an objective nomenclatured way to compute personnel risk. All resources planned are not programmed (i.e. resource allocated and budgeted); the delta between the two translate into capability gaps and a level of strategic risk. A USAF career field risk demonstration is performed using normal, sigmoid and Euclidean-norm functions. Understanding potential personnel shortfalls at the career field level should better inform core capability analysis, and thus increase credibility and defensibility of strategic risk assessment

Research in Supply Chain Management: Issue and Area Development

Today the study of supply chain management (SCM) is growing rapidly and provides a great opportunity to do research both empirical and theoretical development. Research opportunities in SCM has been reviewed by many researchers and grouped into many categories. This paper contains a review of research SCM and classify into 7 categories, namely (1) SCM Operational Management & Strategy, (2) knowledge management, (3) Relationship Management, (4) Information Technology in SCM, (5) Supply Chain Design, Logistics & Infrastructure, (6) Global Issues, (7) Environment, Legal & Regulations. The issue in each category and research opportunities will be discussed in this paper. Keywords: Supply Chain Management, Research Opportunities in SCM, Issue in SC

An Investigation into Factors Affecting the Chilled Food Industry

With the advent of Industry 4.0, many new approaches towards process monitoring, benchmarking and traceability are becoming available, and these techniques have the potential to radically transform the agri-food sector. In particular, the chilled food supply chain (CFSC) contains a number of unique challenges by virtue of it being thought of as a temperature controlled supply chain. Therefore, once the key issues affecting the CFSC have been identified, algorithms can be proposed, which would allow realistic thresholds to be established for managing these problems on the micro, meso and macro scales. Hence, a study is required into factors affecting the CFSC within the scope of Industry 4.0. The study itself has been broken down into four main topics: identifying the key issues within the CFSC; implementing a philosophy of continuous improvement within the CFSC; identifying uncertainty within the CFSC; improving and measuring the performance of the supply chain. However, as a consequence of this study two further topics were added: a discussion of some of the issues surrounding information sharing between retailers and suppliers; some of the wider issues affecting food losses and wastage (FLW) on the micro, meso and macro scales. A hybrid algorithm is developed, which incorporates the analytic hierarchical process (AHP) for qualitative issues and data envelopment analysis (DEA) for quantitative issues. The hybrid algorithm itself is a development of the internal auditing algorithm proposed by Sueyoshi et al (2009), which in turn was developed following corporate scandals such as Tyco, Enron, and WorldCom, which have led to a decline in public trust. However, the advantage of the proposed solution is that all of the key issues within the CFSC identified can be managed from a single computer terminal, whilst the risk of food contamination such as the 2013 horsemeat scandal can be avoided via improved traceability

Intelligent Multi-Attribute Decision Making Applications: Decision Support Systems for Performance Measurement, Evaluation and Benchmarking

Efficiency has been and continues to be an important attribute of competitive business environments where limited resources exist. Owing to growing complexity of organizations and more broadly, to global economic growth, efficiency considerations are expected to remain a top priority for organizations. Continuous performance evaluations play a significant role in sustaining efficient and effective business processes. Consequently, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency of various industries. Majority of these models focus solely on quantitative criteria omitting qualitative data. However, a thorough performance measurement and benchmarking require consideration of all available information since accurately describing and defining complex systems require utilization of both data types. Most evaluation models also function under the unrealistic assumption of evaluation criteria being dependent on one another. Furthermore, majority of these methodologies tend to utilize discrete and contemporary information eliminating historical performance data from the model environment. These shortcomings hinder the reliability of evaluation outcomes leading to inadequate performance evaluations for many businesses. This problem gains more significance for business where performance evaluations are tied in to important decisions relating to business expansion, investment, promotion and compensation. The primary purpose of this research is to present a thorough, equitable and accurate evaluation framework for operations management while filling the existing gaps in the literature. Service industry offers a more suitable platform for this study since the industry tend to accommodate both qualitative and quantitative performance evaluation factors relatively with more ease compared to manufacturing due to the intensity of customer (consumer) interaction. Accordingly, a U.S. based food franchise company is utilized for data acquisition and as a case study to demonstrate the applications of the proposed models. Compatible with their multiple criteria nature, performance measurement, evaluation and benchmarking systems require heavy utilization of Multi-Attribute Decision Making (MADM) approaches which constitute the core of this research. In order to be able to accommodate the vagueness in decision making, fuzzy values are also utilized in all proposed models. In the first phase of the study, the main and sub-criteria in the evaluation are considered independently in a hierarchical order and contemporary data is utilized in a holistic approach combining three different multi-criteria decision making methods. The cross-efficiency approach is also introduced in this phase. Building on this approach, the second phase considered the influence of the main and sub-criteria over one another. That is, in the proposed models, the main and sub-criteria form a network with dependencies rather than having a hierarchical relationship. The decision making model is built to extract the influential weights for the evaluation criteria. Furthermore, Group Decision Making (GDM) is introduced to integrate different perspectives and preferences of multiple decision makers who are responsible for different functions in the organization with varying levels of impact on decisions. Finally, an artificial intelligence method is applied to utilize the historical data and to obtain the final performance ranking. Owing to large volumes of data emanating from digital sources, current literature offers a variety of artificial intelligence and machine learning methods for big data analytics applications. Comparing the results generated by the ANNs, three additional well-established methods, viz., Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), are also employed for the same problem. In order to test the prediction capability of these methods, the most influencing criteria are obtained from the data set via Pearson Correlation Analysis and grey relational analysis. Subsequently, the corresponding parameters in each method are optimized via Particle Swarm Optimization to improve the prediction accuracy. The accuracy of artificial intelligence and machine learning methods are heavily reliant on large volumes of data. Despite the fact that several businesses, especially business that utilize social media data or on-line real-time operational data, there are organizations which lack adequate amount of data required for their performance evaluations simply due to the nature of their business. Grey Modeling (GM) technique addresses this issue and provides higher forecasting accuracy in presence of uncertain and limited data. With this motivation, a traditional multi-variate grey model is applied to predict the performance scores. Improved grey models are also applied to compare the results. Finally, the integration of the fractional order accumulation along with the background value coefficient optimization are proposed to improve accuracy