Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method

PurposeDue to increasing uncertainty in the global business scenario, research on supply chain resilience is gaining significance. The outbreak of the COVID-19 pandemic has accelerated and magnified the issues already pertaining in the supply chain thereby increasing the vulnerabilities in the network. This study attempts to build the concept of pseudo-resilience in supplier selection and evaluation for supply chain sustainability.Design/methodology/approachA combination of multi-criteria decision-making methods AHP and R is adopted, and an integrated method called Combined AHP–R method is used to identify and include the property of pseudo-resilience into supplier selection processes.FindingsThe authors identified various factors contributing to pseudo-resilience considering supplier selection process and found the most important attribute. Using the combined AHP–R method, the suppliers were evaluated, considering the attributes contributing to the pseudo-resilience of supply chains and best supplier was selected.Originality/valueTo the best of our knowledge, this is the first study addressing a supplier selection problem for sustainable supply chains, considering pseudo-resilience. Also, this is the first study to apply the AHP–R method for supplier selection in the resilience or sustainability context.

Purpose This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature. Design/methodology/approach This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations. Findings The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service. Research limitations/implications The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties. Practical implications The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly. Social implications The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains. Originality/value Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.

An integrated group fuzzy best-worst method and combined compromise solution with Bonferroni functions for supplier selection in reverse supply chains

Supplier risks have attracted significant attention in the supply chain risk management literature. In this article, we propose a new computational system based on the ‘Fuzzy Extended Analytic Hierarchy Process (FEAHP)’ method for supplier selection while considering the relevant risks. We sought to evaluate the opportunities and limitations of using the FEAHP method in supplier selection and analyzed the support of the system developed through the real case of a Brazilian oil and natural gas company. The computational approach based on FEAHP automates supplier selection by determining a hierarchy of criteria, sub-criteria, and alternatives. First, the criteria and sub-criteria specific to the selection problem were identified by the experts taking the relevant literature as a starting point. Next, the experts performed a pair-wise comparison of the predefined requirements using a linguistic scale. This evaluation was then quantified by calculating the priority weights of criteria, sub-criteria, and alternatives. The best decision alternative is the one with the highest final score. Sensitivity analysis was performed to verify the results of the proposed model. The FEAHP computer approach automated the supplier selection process in a rational, flexible, and agile way, as perceived by the focal company. From this, we hypothesized that using this system can provide helpful insights in choosing the best suppliers in an environment of risk and uncertainty, thereby maximizing supply chain performance.

Along with advances in technology and the advent of the information age, supply chain competition will become the core strategy of enterprises that are in pursuit of a competitive advantage. Supplier selection and evaluation are key issues in the success of a competitive enterprise. Supplier selection for an enterprise is a typical multicriteria decision-making problem that includes qualitative and quantitative criteria. However, some input information can be missing or nonexistent in selecting suppliers, rendering it more difficult to choose the best supplier. To this end, the traditional supplier selection approach does not consider the ordered weights of the values of attributes, causing biased conclusions. Moreover, there is a significant amount of fuzzy and intuitionistic fuzzy information in real-world situations, for which the traditional approach in choosing the best supplier becomes no longer applicable. To solve these issues, this study proposes a novel supplier selection method, integrating the intuitionistic fuzzy weighted averaging method and the soft set with imprecise data, in identifying the best supplier in a supply chain. To illustrate our proposed method, a numerical example of the supplier selection problem is adopted. This paper also compares the results of the fuzzy weighted averaging, intuitionistic fuzzy weighted averaging, and intuitionistic fuzzy dependent aggregation operator methods in dealing with missing or nonexistent data. Based on our results, the proposed method is reasonable, effective, and better reflects real-world situations with regard to supplier selection.

There is a growing interest in the food service industry at present. The problem of supplier selection brings forward the important issue of supply chain management. The paper tries to find how to select the upstream suppliers. This has become the main problem to develop the supply chain in food service industry. In this paper data envelopment analysis would be used for supplier selection in supply chain. A supplier selection index system based on chain catering industry was developed, and the relevance between indexes was reduced through factor analysis. Suppliers can be analyzed with the decision making model. Furthermore, to achieve the dynamic selection of supplier, life cycle of enterprise development was introduced, which made contribution to the integration of supply chain. Finally, the method of supplier dynamic selection was tested and verified by a numerical example. The result shows that this supplier selection method would be applicable to chain catering industry and the dynamic management of suppliers.

PurposeThis paper aims to address the supplier selection problem based on a developed framework capturing the essence of the supply chain operations reference (SCOR) model, sustainability and providing services to customers. Specifically, the authors consider planning, manufacturing, delivery, sustainability and customer service attributes to evaluate and select suppliers.Design/methodology/approachRelevant literature is reviewed, a framework capturing the essence of major supply chain functions was developed and suitable measurement attributes were identified. An integrated fuzzy analytic hierarchy process and fuzzy technique for order performance by similarity to ideal solution method are employed to obtain the final ranking of the attributes and suppliers. The proposed methodology is illustrated through a real case of an Indian automobile company.FindingsThe authors observed that planning, manufacturing, customer service, sustainability and delivery are preferred in decreasing order to select component suppliers for an automotive company. The impact of suppliers on planning and manufacturing is most important to consider while assessing suppliers. Interestingly, concerns about sustainability and delivery are the least cared factors when selecting suppliers. The top five criteria contain measures of operational efficiency rather than purchasing cost.Originality/valueThis paper proposes and demonstrates a supplier selection framework harmonizing supply chain functions of the SCOR model, sustainability and customers service that adds a valuable wing to literature that expounds on the connection of purchasing strategy to corporate strategy. A case study in an automotive company throws unique and valuable managerial implications for purchasing and supply chain performance.

In recent years, interruption or failure events have occurred due to frequent natural disasters, the outbreak of COVID-19, policy environment turbulence, an increasingly complex business environment, and the increasingly fragile global supply chain. This has reduced the efficiency of supply chains and customer service quality and increased operating costs, creating new requirements for supply chain flexibility and sustainability. When investigating 21 companies based on 200 questionnaires and a structural equation model analysis, the results showed that the elasticity of the supply chain for supply chain sustainability, economic sustainability, social sustainability, and environment sustainability has an obvious positive effect: supply chain sustainability has an obvious positive effect on supply chain performance. Supply chain resilience has no direct positive effect on supply chain performance, but it has a strong indirect effect on supply chain performance under the mediating effect of supply chain sustainability. In view of this, in order to create sustainable supply chain development and improve the performances of supply chains, it is necessary to establish the awareness of risk prevention, root the risk culture in supply chain network organization, and improve supply chain resilience in multiple dimensions. Enterprises in the supply chain should continue to build their resilience and establish effective strategies to integrate supply chains. The intermediary role of sustainability in supply chains and of supply chain flexibility in supply chain performance shows the influence of economically, socially, and environmentally sustainable angles, such as the implementation of supply chain management, the maximization of the interests of the whole supply chain, improving the ability of supply chain enterprises to innovate and develop, establishing customer awareness, and enhancing humanistic ideas. Dynamic selection of supply chain partners while focusing on their green performance promotes the green development of supply chain enterprises.

Structured methodology for supplier selection and evaluation in a supply chain

Multi-attribute Decision Model of Green Supplier Selection under the Low-carbon Economy

: There has been a tremendous burgeoning of interest in artificial intelligence (AI) over the last few years. Investments of commercial and government sponsors reflect a widespread belief that AI is now ready for practical applications. The area of AI currently receiving the greatest attention and investment is expert system technology. Most major 'high tech' corporations have begun to develop expert systems, and many software houses specializing in expert system tools and applications have recently appeared. The defense community is one of the heaviest investors in expert system technology, and within this community one of the application areas receiving greatest attention is C3I. Many ESD programs are now beginning to ask whether expert system applications for C3I are ready for incorporation into ESD-developed systems, and, if so, what are the potential benefits and risks of doing so. This report was prepared to help ESD and MITRE personnel working on acquisition programs to address these issues and to gain a better understanding of what expert systems are all about. The primary intention of this report is to investigate what expert systems are and the advances that are being made in expert system technology for C3I applications. The report begins with a brief tutorial on expert systems, emphasizing how they differ from conventional software systems and what they are best at doing.

Many research experts and industry executives point out that the industry competition is not only between enterprises, but also between supply chains. With tight connections and cooperation between supply chain members, the supply chain can increase values for end customers and gains competitive advantage. Meanwhile, such relationships also create win-win situations among supply chain members. Thus, selecting supply chain members which includes supplier selection is a very important task in the supply chain management. Many studies have mentioned the importance of supplier selection in supply chain management, and have established methods and criteria for supplier selection. However, few studies considered the supplier selection from the perspective of the entire supply chain. Thus, this study proposes a new supplier selection approach that considers the entire supply chain in order to maximize the performance of the entire supply chain. We believe that our approach can be served as a supplier selection tool for improving supply chain performance.

This review article provides a comprehensive overview of supplier quality management (SQM) in the context of modern supply chain management (SCM). SQM is a methodical strategy aimed at examining and managing the dependability and performance of suppliers who provide components, raw materials, or services essential to the manufacturing process. The article discusses the objectives of SQM, including boosting procurement accuracy, simplifying supplier assessments, improving communication with suppliers, and promptly addressing non-compliance issues. It highlights the importance of SQM in ensuring product quality, reducing risks, and enhancing consumer satisfaction. The article also delves into supplier selection and evaluation methods, supplier evaluation criteria, risk assessment and mitigation strategies, quality assurance agreements, continuous improvement practices, and regulatory compliance challenges in SCM processes. By providing insights into these key aspects, this review article aims to offer a comprehensive understanding of SQM and its significance in contemporary supply chain operations.

Negotiation is a key factor influencing supply chain management, as it is a critical competency that is directly impacting long term strategic success and operational performance. It is determining critical decisions like supplier selection, contract terms, pricing, warranty, liability and dispute resolution becoming key activity in aligning with stakeholders objectives and mitigating risks in complex global markets. The aim of this paper is to explore the multitude of impacts that negotiation has on supply chain performance, with a focus on cost optimisation, operational resilience and supplier relationship management. Using qualitative research method through an analysis of recent qualitative and quantitative research, including meta-analysis and case studies, the author identifies negotiation as a strategic leverage that increases supply chain reactivity and efficiency. Research hypothesis one is that engaging in effective negotiation practices drives both financial savings and increase collaboration and trust within the supply chain, advocating for accountability and stability even in volatile market conditions. Second research hypothesis states that strategic negotiation allows organizations to foresee and react to diverse challenges such as supply disruptions, inflation, price volatility, demand variation and quality issues by acting with precision and agility, without jeopardising the relationship with different stakeholders. The results and findings confirm the two research hypothesis and highlight the importance of implementing a structured approach in negotiation, focusing significant efforts on preparation, adaptability and mutual value creation. Literature review conducted underscores a series of tools that can be used to enhance the outcome of negotiation, while data analytics, scenario planning, cultural specificities and dedicated training to support continuous improvement of negotiation skills of supply chain professionals are most impactful as studied in more than ten years experience of the author’s practical supply chain negotiation. This study brings additional value to the research field as it is synthesising key findings in academic research and industry practices with a goal to raise awareness and provide guidance to supply chain proffesionalls about the role of negotiation in improving supply chain efficiency offering practical recommendations on how to integrate negotiation as a central component of supply chain strategy. The findings offer supply chain managers the knowledge and tools to beneficiate from the full potential of negotiation,therefore driving sustainable growths and competitive advantage.

PurposeThe oil and gas industry is a crucial economic sector for both developed and developing economies. Delays in extraction and refining of these resources would adversely affect industrial players, including that of the host countries. Supplier selection is one of the most important decisions taken by managers of this industry that affect their supply chain operations. However, determining suitable suppliers to work with has become a phenomenon faced by these managers and their organizations. Furthermore, identifying relevant, critical and important criteria needed to guide these managers and their organizations for supplier selection decisions has become even more complicated due to various criteria that need to be taken into consideration. With limited works in the current literature of supplier selection in the oil and gas industry having major methodological drawbacks, the purpose of this paper is to develop an integrated approach for supplier selection in the oil and gas industry.Design/methodology/approachTo address this problem, this paper proposes a new uncertain decision framework. A grey-Delphi approach is first applied to aid in the evaluation and refinement of these various available criteria to obtain the most important and relevant criteria for the oil and gas industry. The grey systems theoretic concept is adopted to address the subjectivity and uncertainty in human judgments. The grey-Shannon entropy approach is used to determine the criteria weights, and finally, the grey-EDAS (evaluation based on distance from average solution) method is utilized for determining the ranking of the suppliers.FindingsTo exemplify the applicability and robustness of the proposed approach, this study uses the oil and gas industry of Iran as a case in point. From the literature review, 21 criteria were established and using the grey-Delphi approach, 16 were finally considered. The four top-ranked criteria, using grey-Shannon entropy, include warranty level and experience time, relationship closeness, supplier’s technical level and risks which are considered as the most critical and influential criteria for supplier evaluation in the Iranian oil and gas industry. The ranking of the suppliers is obtained, and the best and worst suppliers are also identified. Sensitivity analysis indicates that the results using the proposed methodology are robust.Research limitations/implicationsThe proposed approach would assist supply chain practicing managers, including purchasing managers, procurement managers and supply chain managers in the oil and gas and other industries, to effectively select suitable suppliers for cooperation. It can also be used for other multi-criteria decision-making (MCDM) applications. Future works on applying other MCDM methods and comparing them with the results of this study can be addressed. Finally, broader and more empirical works are required in the oil and gas industry.Originality/valueThis study is among the first few studies of supplier selection in the oil and gas industry from an emerging economy perspective and sets the stage for future research. The proposed integrated grey-based MCDM approach provides robust results in supplier evaluation and can be used for future domain applications.