Traditional Supplier Research Articles

Transforming Supplier Audits with AI: A Risk-Based Approach in Medical Devices

The medical device industry, highly regulated and sensitive to quality standards, relies on rigorous supplier audits to ensure compliance and mitigate risks. However, traditional supplier audits are often resource-intensive, inconsistent in quality, and lack a clear focus on the most critical risk factors. This paper explores how Artificial Intelligence (AI) can revolutionize the supplier audit process by enabling a risk-based approach that enhances accuracy, efficiency, and regulatory compliance. AI technology, with its advanced data processing, predictive analytics, and machine learning capabilities, can analyze vast amounts of supplier data in real-time, generating risk scores that prioritize high-risk suppliers for more frequent and thorough audits. This shift allows medical device companies to better allocate resources, focusing on high-impact areas and enhancing overall supply chain security. By considering factors such as supplier compliance history, product criticality, regional compliance laws, and performance trends, AI provides a dynamic and data-driven assessment model that minimizes the reliance on subjective audit practices. The paper introduces an AI-powered risk-based audit framework specifically tailored for medical device companies. This framework utilizes a multi-faceted AI-driven risk scorecard, categorizing suppliers by risk levels (high, medium, low) and enabling targeted audit strategies. It also demonstrates how AI can process a wide array of audit-related data inputs—such as supplier compliance, regional geopolitical risks, supply volume, and product criticality—to generate a holistic and accurate risk assessment. Through case studies and industry examples, this research underscores the advantages of integrating AI into supplier audit processes, highlighting cost savings, improved compliance outcomes, and reduced risk of quality issues or product recalls. Furthermore, it examines future trends and ethical considerations of AI in supplier audits, advocating for industry-wide adoption of AI-powered tools to support more efficient and effective supplier management. This paper concludes that adopting a risk-based, AI-driven audit approach in the medical device sector not only supports regulatory compliance but also builds resilience within the supply chain, creating a safer and more efficient landscape for medical device production.

THE POTENTIAL FOR PEER-TO-PEER ELECTRICITY TRADING IN GEORGIA

The global energy transition is fundamentally reshaping electricity markets, moving from centralized systems reliant on fossil fuels to decentralized, renewable energy solutions. Distributed energy resources such as solar panels and storage batteries are empowering consumers to become active participants in the energy market. Peer-to-peer (P2P) electricity trading, enabled by blockchain technology, presents a new model for consumers to trade surplus energy directly with one another, bypassing traditional suppliers. This article explores the potential of P2P electricity trading in Georgia, a country with significant renewable energy resources but limited experience with decentralized energy systems. As Georgia aligns its energy policies with European regulations, the development of a P2P trading market could enhance energy security, empower prosumers, and drive local economic development. Through an analysis of global trends, technological innovations, and the current Georgian energy landscape, this article assesses the opportunities and challenges in implementing P2P trading. By building the necessary infrastructure and fostering public participation, Georgia has the potential to transform its energy market and contribute to a more resilient and sustainable energy future.

Multi-criteria group decision analytics for sustainable supplier relationship management in a focal manufacturing company

In the context of sustainable development, while the benefits of incorporating environmental, social, and governance (ESG) considerations into supplier management have been widely discussed in recent years, a significant gap in the literature remains. Specifically, there is a lack of a standardized decision analytics framework for ESG-oriented supplier relationship management (SRM) processes in the manufacturing sector. To address this gap, this study introduces a multi-criteria supplier analytics framework (MCSAF) towards sustainable SRM. Through referring traditional supplier evaluation criteria as well as emerging ESG criteria, a novel decision hierarchy for supplier segmentation is developed. The proposed system utilizes the Bayesian best-worst method and the Canopy-k-means clustering algorithm to formulate different supplier segments for strategic development. The proposed MCSAF has been successfully implemented in a German solar water pump manufacturer. The results show that the five highest-ranked criteria are ‘Quality’, ‘Impact and importance on product quality and business growth’, ‘Environmental policy/management’, ‘Customer and product responsibility’, and ‘Risk and crisis management’. 52 suppliers are categorized into five segments, leading to the formulation of ESG-enabled supplier development strategies are subsequently formulated. The successful implementation of the MCSAF showcases its effectiveness in advancing ESG practices in sustainable supplier management.

A Literature Survey on Application of MCDM Mathematical Optimization Models for the Sustainable Supplier Selection in Manufacturing Industries

Organizations need to entertain and motivate their ability on sustainable supply chain activities based on environment protection regulation activities. GSCM needs multi-dimensional techniques and methods. Thus multi-criteria decision-making (MCDM) techniques adopted for performance assessment of suppliers in Green supply chain management (GSCM). Supplier selection is important and it is one of the important operational function for the development of GSCM. In order to select the most suitable suppliers, many economic and environmental criteria must be taken account in the decision process. Green supplier choice is a very hard work that needs to consider sustainable factors incorporated into the traditional supplier selection. This paper aims to survey the existing different MCDM approaches which are applied in GSCM for supplier selection and evaluation and identify most significant and effective MCDM approach in comparison with some other MCDM approaches.

To Transfer or Not? Analysis of the Original Equipment Manufacturer's Strategic Outsourcing Strategy in the Presence of the Supplier's Innovation Effort

Recently, a new trend has arisen in which many original equipment manufacturers (OEMs) outsourced to traditional low-cost manufacturing bases (e.g., China) have started to transfer production to new emerging regions with even lower costs (e.g., Southeast Asia). However, it is still unclear whether and when OEMs should transfer to the new environment, especially when taking the traditional supplier's initial innovation effort into consideration. To address this question, we consider a sourcing game where one OEM sources from a traditional low-cost supplier that can be either competitive (CS) or noncompetitive (NS) initially. Moreover, both of these types of suppliers have the ability to innovate by themselves. Driven by the increasing cost pressure, the OEM may transfer to a new supplier (LS) that has an even lower cost but suffers yield uncertainty. The equilibrium results show that the OEM is less likely to transfer when its brand value is large enough. Interestingly, we find that the competition between the CS and OEM does not always prompt the OEM to transfer. When the brand advantage is large enough and the traditional supplier's unit production cost for the OEM is moderate, the OEM should not transfer under the CS scenario but should transfer under the NS scenario. Finally, we find that when the OEM's brand advantage increases, the effectiveness of innovation in preventing the OEM from transferring decreases under the CS scenario, while it increases under the NS scenario.

Optimal strategies for green supply chains with competition between green and traditional suppliers

We consider a green supply chain that consists of a traditional supplier, a green supplier, and a manufacturer. We investigate the optimal strategies for the suppliers’ raw material prices, and the manufacturer’s production and green raw material ordering. In particular, we consider three price competition models: the traditional supplier-led model, the green supplier-led model, and the Nash game model. The results indicate that the more dominant the green supplier, the more likely the green material price will be higher than the traditional material price. In addition, the manufacturers’ product greenness level is the lowest under the green supplier-led model and the highest under the traditional supplier-led model. We also find that, as the manufacturer’s yield uncertainty increases, the prices of traditional and green raw materials both decrease with respect to the manufacturer’s product greenness level. Moreover, as customers’ green preference increases, the price of green raw materials increases, the price of traditional raw materials decreases, and the product greenness level increases.

TOPSIS method based on q-rung orthopair picture fuzzy soft environment and its application in the context of green supply chain management

<abstract> <p>Green supplier selection has been an important technique for environmental sustainability and reducing the harm of ecosystems. In the current climate, green supply chain management (GSCM) is imperative for maintaining environmental compliance and commercial growth. To handle the change related to environmental concern and how the company manages and operates, they are integrated the GSCM into traditional supplier selection process. The main aims of this study were to outline both traditional and environmental criteria for selecting suppliers, providing a comprehensive framework to assist decision-maker in prioritizing green supplier effectively. In order to address issue to simulate decision-making problems and manage inaccurate data. A useful technique of fuzzy set was proposed to handle uncertainty in various real-life problems, but all types of data could not be handled such as incomplete and indeterminate. However, several extensions of fuzzy set were considered, such as intuitionistic fuzzy set, Pythagorean fuzzy set, q-rung orthopair fuzzy set, and q-rung orthopair fuzzy soft set considering membership and nonmember ship grade to handle the uncertainty problem. However, there was a lack of information about the neutral degree and parameterization axioms lifted by existing approaches, so to fill this gap and overcome the difficulties Ali et al. proposed a generalized structure by combining the structure of picture fuzzy set and q-rung orthopair fuzzy soft set, known as q-rung orthopair picture fuzzy soft sets, characterized by positive, neutral and negative membership degree with parameterization tools and aggregation operator to solve the multi criteria group decision-making problem. Additionally, the TOPSIS method is a widely utilized to assist individuals and organizations in selecting the most appropriate option from a range of choices, taking into account various criteria. Finally, we demonstrate an illustrative example related to GSCM to enhance competitiveness, based on criteria both in general and with a focus on environmental consideration, accompanied by an algorithm and flow chart.</p> </abstract>

Modelling of barriers in implementing sustainable manufacturer-supplier collaboration and coping strategies

Finding new, creative ways to improve the performance of their supply chains is one of the biggest problems that manufacturing organizations confront in the age of globalization and competition. To achieve better performance, companies should engage in networking activities such as integrating with other supply chain participants. Manufacturing businesses also face sustainability issues caused by suppliers' practices that fail to meet sustainability criteria. In this sense, developing a collaborative relationship with a supplier is essential for successfully implementing sustainable practices and gaining a competitive edge. Therefore, it is imperative for manufacturing companies to effectively manage their relationships with their suppliers by strengthening their suppliers' commitment to sustainability. The concept of sustainable supplier collaboration (SSC) emerges from the combination of sustainability and supplier relationship building. SSC, in general, broadens the traditional supplier management system by incorporating long-term partnerships into the sustainable aspects. Companies have begun to acknowledge the value of SSC and are taking initiatives in this regard. The adoption has been extremely slow, despite the assurances of sustainable collaboration with suppliers. Hence, research is required to determine how supplier collaboration with economic, environmental, and social promises has been hindered. It is important to acknowledge any hurdles that manufacturing organizations may have while developing SSC. Also, a thorough analysis of these barriers is essential so effective and sustainable collaboration with suppliers can be achieved. Therefore, the primary goal of the study is to identify and analyze barriers to sustainable collaboration with suppliers. The integrated methodology of Total Interpretive Structural Modelling-Decision Making Trial and Evaluation Laboratory (TISM-DEMATEL) is used to achieve the purpose of study. An extreme case scenario involving one manufacturer of home appliances and its suppliers is used in a dyadic India based case study. The findings of this study reveal that the most significant barriers to SSC are lack of manufacturer-supplier communications for sustainable standards and appropriate regulations, lack of trust between manufacturer and supplier, lack of scope and focus for sustainable collaboration, unwillingness to share risks and rewards, lack of top management involvement, lack of combined training programs, and lack of consistent and adequate performance measurement systems. These barriers not only contribute directly to hindering SSC efforts but also influence other barriers at intermediate and lower levels. Additionally, the study offers detailed explanations of each barrier, thereby providing valuable insights for better understanding and effective implementation of sustainable manufacturer-supplier collaboration (SMSC).

Supplier Selection and Evaluation: A Case Study in an Apparel Manufacturing Organization

The supply chain is incomplete without a supplier. A manufacturer is completely reliant on its suppliers, and it is critical for an apparel manufacturing company to choose the proper ones in order to maintain uninterrupted operations. The impacts of traditional supplier selection have been demonstrated in this research. In addition, the best provider was chosen using a proposed supplier selection and evaluation approach. However, four supplier selection and assessment procedures have been used to determine the study's outcome. In addition, the dimensional analysis approach, the cost ratio analysis method, and the weighted point method were used to choose the trim supplier. Benefits increased by 16 percent after making orders with the proper supplier, according to this report. The main finding of this study is that 94 percent of materials are received on time from the best selected source, compared to 50 percent in the past when using the traditional method.

Green-based supplier selection using BWM and VIKOR methods in the Indonesian manufacturing sector

The selection of green-based suppliers remains a challenge for manufacturing firms in Indonesia. In today's business landscape, it is crucial to consider green aspects as part of sustainability practices when choosing suppliers. However, there is a limited number of studies providing frameworks for selecting suppliers based on green factors, particularly in developing countries. This study aimed to propose a framework for selecting suppliers using multicriteria decision-making (MCDM) techniques. A case study was conducted in an Indonesian metal manufacturing company that considers carbon reduction and incorporates traditional supplier selection factors such as quality, cost, and capacity. The study integrated two MCDM techniques, namely the best-worst method (BWM) and Višekriterijumsko Kompromisno Rangiranje (VIKOR), to evaluate and select green-based suppliers. BWM was employed to determine the weights of the criteria, while VIKOR ranked the suppliers. Seven criteria were identified, including cost (C1), quality (C2), delivery time (C3), technology application (C4), compliance with environmental regulations (C5), green practice certification (C6), and green efforts (C7). Nine suppliers were screened and ranked accordingly. The results indicate that quality, delivery time, and cost are the top three ranked criteria based on their importance. Further, this study includes nine suppliers in which supplier S7 demonstrates the best performance compared to the others. This study highlights that in Indonesia, most organizations tend to prioritize traditional criteria, while green factors are still overlooked. However, the company examined in this study has taken the initiative to include environmental factors in its decision-making process. The findings of this study contribute to the development of a framework for selecting green suppliers in a developing country context.

Dynamic small-series fashion order allocation and supplier selection: a ga-topsis-based model

The fashion industry is currently confronted with significant economic and environmental challenges, necessitating the exploration of novel business models. Among the promising approaches is small series production on demand, though this poses considerable complexities in the highly competitive sector. Traditional supplier selection and production planning processes, known for their lengthy and intricate nature, must be replaced with more dynamic and effective decision-making procedures. To tackle this problem, GA-TOPSIS hybrid model is proposed as the methodology. The model integrates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) evaluation into the fitness function of Genetic Algorithm (GA) to comprehensively consider both qualitative and quantitative criteria for supplier selection. Simultaneously, GA efficiently optimizes the order sequence for production planning. The model's efficacy is demonstrated through implementation on real orders, showcasing its ability to handle diverse evaluation criteria and support supplier selection in different scenarios. Moreover, the proposed model is employed to compute the Pareto front, which provides optimal sets of solutions for the given objective criteria. This allows for an effective demand-driven strategy, particularly relevant for fashion retailers to select supplier and order planning optimization decisions in dynamic and multi-criteria context. Overall, GA-TOPSIS hybrid model offers an innovative and efficient decision support system for fashion retailers to adapt to changing demands and achieve effective supplier selection and production planning optimization. The model's incorporation of both qualitative and quantitative criteria in a dynamic environment contributes to its originality and potential for addressing the complexities of the fashion industry's supply chain challenges

Performance Evaluation of Healthcare Supply Chain in Industry 4.0 with Linear Diophantine Fuzzy Sine-Trigonometric Aggregation Operations

The concept of linear Diophantine fuzzy set (LDFS) theory with its control parameters is a strong model for machine learning and data-driven multi-criteria decision making (MCDM). The sine-trigonometric function (STF) has two significant features, periodicity and symmetry about the origin that are very useful tools for information analysis. Keeping in view the characteristics of both STF and LDFS theory, this article introduces the sine-trigonometric operations for linear Diophantine fuzzy numbers (LDFNs). These operational laws lay a foundation for developing new linear Diophantine fuzzy sine-trigonometric aggregation operators (LDFSTAOs). The integration of Industry 4.0 technology into healthcare has the potential to revolutionize patient care. One of the most challenging tasks is the selection of efficient suppliers for the healthcare supply chain (HSC). The traditional suppliers are not efficient in accordance with Industry 4.0, with particular uncertainties. A new MCDM framework is presented based on LDFSTAOs to examine the HSC performance in industry 4.0. A credibility test, sensitivity analysis and comparative analysis are performed to express the novelty, reliability, and efficiency of the proposed methodology.

Supplier evaluation and selection in the constrained environment of advance purchasing

PurposeThe purpose of this study is to address supplier evaluation and selection in a constrained environment of advance purchasing. The study presents the potential solutions to supplier evaluation and selection issues in the Indian automobile sector where advance purchases are carried out to fulfill the supply chain demand.Design/methodology/approachBased on the literature review and expert elicitation, nine major factors which are responsible for the successful implementation of supplier selection in a constrained environment of advance purchasing are identified. This paper explores supplier selection in constrained environment issues based on an integrated method based on Shannon entropy, analytic hierarchy process (AHP) techniques and failure mode effects analysis (FMEA).FindingsAnalysis of the results of the study suggests that traditional suppliers are not suitable in advance purchasing scenarios; hence, criteria developed in this paper to accommodate the requirement of advance purchasing with possible risk considerations are of high importance. This research paper is an original attempt to develop supplier selection criteria for advance purchasing with special identification of deliverability, flexibility, innovation and productivity factors through a case demonstration.Research limitations/implicationsThis study uses data from secondary sources, literature reviews and expert opinions. It formalizes the important factors of successful supplier selection in the constrained environment of advance purchasing in the automotive industry context.Practical implicationsThe paper shows how the engagement of suppliers through advance purchasing helps automotive companies in developing a competitive advantage. The integrated approach of Shannon entropy, AHP techniques and FMEA is an effective and useful method that can be applied to the supplier selection process.Originality/valueThe proposed FMEA-AHP method integrated with Shannon entropy used for evaluation represents a useful tool to embrace the suitable functioning tactics for efficient supplier selection. The study is unique as supplier evaluation and selection in a constrained environment of advance purchasing is not investigated much and has good industry applicability.

Q-Rung orthopair fuzzy hypersoft ordered aggregation operators and their application towards green supplier

Green Supply Chain Management (GSCM) is essential to ensure environmental compliance and commercial growth in the current climate. Businesses constantly look for fresh concepts and techniques for ensuring environmental sustainability. To keep up with the new trends in environmental concerns related to company management and procedures, Green Supplier Selection (GSS) criteria are added to the traditional supplier selection processes. This study aims to identify general and environmental supplier selection criteria to provide a framework that can assist decision-makers in choosing and prioritizing appropriate green supplier selection. The development and implementation of decision support systems aimed to solve these difficulties at a rapid rate. In order to manage inaccurate data and simulate decision-making problems. Fuzzy sets introduced by Zadeh, are a useful technique to handle the imperfectness and uncertainty in different problems. Although fuzzy sets can handle incomplete information in different real worlds problems, but its cannot handle all type of uncertainty such as incomplete and indeterminate data. Therefore different extensions of fuzzy sets such as intuitionistic fuzzy, pythagorean fuzzy and q-rung orthopair fuzzy sets introduced to address the problems of uncertainty by considering the membership and non-membership grade. However, these concepts have some shortcomings in the handling uncertainty with sub-attributes. To overcome this difficulties Khan et al. developed the structure of q-rung orthopair fuzzy hypersoft sets by combining q-rung orthopair fuzzy sets with hypersoft sets. A remarkable and beneficial research work is done in the field of q-rung orthopair fuzzy hypersoft sets, and then we think about the application. In this paper, we use the structure of q-rung orthopair fuzzy hypersoft in multi-criteria supplier selection problems. For this, we present aggregation operator to solve multi-criteria decision-making (MCDM) problems with q-rung orthopair fuzzy hypersoft (q-ROFH) information, known as ordered weighted geometric aggregation operator. Since the uncertainty and vagueness is an unavoidable feature of multi-criteria decision-making problems, the proposed structure can be a useful tool for decision making in an uncertain environment. Further, the expert opinions were investigated using the multi-criteria decision-making (MCDM) technique, which helped identify interrelationship and causal preference of green supplier evaluation aspects that used aggregation operators. Finally, a numerical example of the proposed method for the task of Green Supplier Selection is presented.

Integrating Subjective–Objective Weights Consideration and a Combined Compromise Solution Method for Handling Supplier Selection Issues

The development of artificial intelligence and the recent COVID-19 pandemic as well as the emergence of other diseases has led to dramatic changes in the overall supply chain development. The choice of a suitable supplier will be the key to ensuring sustainable development of the company and the normal operation of the overall supply chain. However, the evaluation data of the supplier selection contains both qualitative and quantitative data simultaneously. In addition, the evaluation information provided by experts often includes some incomplete and hesitant information. These reasons lead to the complexity of supplier selection. Traditional supplier selection calculation methods ignore the objective weight considerations and thus lead to biased assessment results. The main goal of this study is to overcome the limitations of conventional supplier selection methods, fully consider the subjective and objective weights of the evaluation criteria and deal with incomplete information for providing more correct supplier ranking results. A stepwise weight assessment ratio analysis (SWARA) method, the 2-tuple linguistic representation method, and the combined compromise solution (CoCoSo) were applied in this study to solve the problem of supplier selection. To verify the rationality and correctness of the proposed method, the third-party logistics supplier selection was used as the illustrated example in the numerical validation section. The simulation results confirm that the proposed method can effectively deal with supplier selection with unclear information and can provide more reasonable supplier ranking results.

Time Bomb from the West: Video, the Advertising Industry, and Consumer Culture in Late-Socialist Czechoslovakia

Time Bomb from the West: Video, the Advertising Industry, and Consumer Culture in Late-Socialist Czechoslovakia

Spherical Linear Diophantine Fuzzy TOPSIS Algorithm for Green Supply Chain Management System

Enhancing competitive pressure is one of the most significant roles of supply chain management. The competitive environment and customer perception have shifted in favour of an ecological mentality. As a result, green supplier selection (GSS) has emerged as a critical problem. The challenge of green supplier selection striving for agility, durability, ecological sensitivity, leanness, and sustainability is tackled in this paper. In terms of recycling applications, environmental applications, carbon footprint, and water consumption, the environmental parameters evaluated in GSS and traditional supplier selection differ. Because of the form of the problem, a resolution is defined, which comprises an algorithm entrenched in the spherical linear Diophantine fuzzy sets (SLDFSs) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique. Before discussing the approach of the SLDF model, some background information on SLDF sets is provided. To assure the uniqueness of this robust extension, different operations on SLDFSs are described, along with some concise interpretations to help the reader comprehend these ideas. A robust TOPSIS approach has been utilized in the issue of GSS by taking into consideration the multicriteria decision-making (MCDM) technique particularly useful in several areas, like analyzing and choosing traditional and environmental conventionalities. Due to linguistic criteria and the inability to assess all criteria, the fuzzy technique must be used with the TOPSIS method to lessen the consequences of instability and ambiguity. The spherical linear Diophantine fuzzy TOPSIS approach is employed, as it simplifies the evaluation of decision-makers and criteria. The hybrid technique resulting from integrating the SLDFS and TOPSIS is extremely successful in selecting which provider is more suited among the alternatives established on the criteria set by the order of significance, and this method may also be incorporated into similar issues.

New mobility technologies as incentive to location decisions: relocation strategy in the automotive industry

PurposeThe purpose of this paper is to analyze the new technologies that condition location strategy and enable the reindustrialization of European countries involved in the automotive industry. Business relocation has seen an upsurge in recent years. The development of the technologies linked to new mobility (connected, autonomous and electric vehicles) in European countries is increasing interest in relocating high value activities in origin countries to preserve high quality jobs and maintain competitive advantage.Design/methodology/approachThe paper analyzes the changes in location factors and relocation trends through qualitative research based on a multiple case study methodology of first level suppliers in the automotive industry.FindingsThis study reveals the capacity technology has to reconfigure the automotive value chain and to change traditional location factors and relocation trends. The results show relevant differences between the traditional components production divisions characterized by offshoring processes, in a continuous decrease of activity and deindustrialization, and the new technologies divisions linked to new mobility with strong growth, reshoring of the high value activities, reindustrialization plans and links with research and development. For these new mobility divisions, protection of innovation to prevent the core knowledge transfer and strong collaboration with local research institutions are key in this highly technological location context.Originality/valueThe research analyzes the impact of new mobility technologies on the relocation processes in the automotive industry through an original approach based on the evolution of traditional suppliers with divisions linked to new mobility.

Assessing the impact of supplier benchmarking in manufacturing value chains: an Intelligent decision support system for original equipment manufacturers

This research aims to aid original equipment manufacturers (OEMs) to model, analyze, evaluate, and benchmark potential design and manufacturing suppliers based on respective product engineering teams’ efficiencies. The product engineering efficiency in this study is modeled in terms of product engineering-related attributes such as commercial lead time, number of parts, number of green features, number of end products developed, and so forth. Essentially, these parameters capture more complex interactions than simple traditional supplier selection criteria such as cost, quality, delivery, and flexibility. Due to the presence of information uncertainty in terms of bounds related to the suppliers’ related parameters, a number of data envelopment analysis (DEA) efficiency measurement models have been deployed. The proposed decision support system is novel because it models both the self-assessment type and cross-efficiency type using DEA such that maximum discrimination can be achieved amongst suppliers in the presence of interval data. The study is demonstrated for ten different sheet-metal cabin suppliers. Comparison with some well-known, relevant methods is also carried out to illustrate the validity of the proposed method. The research can specifically help supply chain managers to align the evaluation of potential suppliers with their firm's commercial considerations in the presence of information uncertainty.

Investment and Production Strategies of Renewable Energy Power under the Quota and Green Power Certificate System

In order to study the impact of a renewable energy quota and green power certificate system on the strategies of energy suppliers, this paper constructs a multi-stage game model of renewable energy power investment and production from the renewable energy interest chain and its stakeholders. Through the calculation and solution of the model, the optimal renewable energy utilization level, pricing and production strategies of renewable energy power of energy suppliers are calculated under the scenarios of direct sale of power and purchase and sale by power grids. The results show that the quota ratio, green certificate price and investment cost are the key influencing factors of energy suppliers’ strategies, and changes in the values of the three factors will completely change the renewable energy investment, pricing and production levels of energy suppliers in equilibrium. In addition, the study found that the impact of the renewable energy quota on renewable energy utilization levels of energy suppliers depends on the relative size of investment cost and green power certificate price. At the same time, it was also found that with a change in investment cost, green power certificate price and user preference, the market share and renewable energy utilization level of traditional energy suppliers and new energy suppliers also change.