Supply Chain Partner Selection Research Articles

Bilateral matching process and decision analysis for QoS-based manufacturing supply chain partner selection

Manufacturing industry is the material foundation of national economy, the traditional supply chain in a single value creation mode makes the manufacturing industry in the global value chain competition in the “low-end lock” predicament, to create a highly efficient, intelligent supply chain, is the manufacturing industry in the global competition to gain an advantage in the key. In the process of “Digital to Intelligent Reform”, the matching mechanism and decision-making method of supply chain heterogeneity partner selection is the focus of manufacturing supply chain transformation to be solved urgently. This paper takes the manufacturers and suppliers of manufacturing secondary supply chain as the research object, takes service quality as the important influence factor of partner selection, and explores the supply chain partner matching evaluation model under the three modes of unilateral decision-making, bilateral decision-making, and optimization of bilateral decision-making. The study shows that the bilateral matching decision-making mechanism based on QoS can obtain a higher matching completion rate, while digital transformation helps to improve the efficiency and stability of supply chain partner selection. The study can provide a decision basis for heterogeneous partner selection in manufacturing supply chain, and then improve the overall performance of manufacturing supply chain.

Research on Improvement of Hotel Supply Chain Resource Management Decision in the Era of Big Data

Hotel supply chain management (SCM) refers to the material service that takes a hotel as the core object. At present, the main method is centralized SCM, which uses integer programming or mixed integer programming to establish and solve the allocation management of various resources among hotels in the supply chain. In this article, based on ant colony algorithm (ACA), the hotel SCM mode is studied and optimized. The research shows that the convergence path is A-S4-M3-L3-D1-R1-T. Therefore, the optimal partner combination meeting the requirements of the supply chain is S4, M3, L3, D1 and R1. The experiment shows that the max-min ACA is effective to solve the problem of partner selection in hotel supply chain. By using enterprise management technology, information technology, network technology and SCM technology under ACA, the effective rules and control of information flow, logistics, capital flow, business flow and value flow in the whole supply chain can be achieved, and the maximum benefit of the whole hotel supply chain can be realized.

Research on Supply Chain Decision-making Considering Fairness Concerns and Green Technology Innovation

A supply chain consisting of a manufacturer and a retailer is constructed. On this basis, the Stackelberg game between the upper and lower levels of the supply chain is adopted, Consider the balance between manufacturers and retailers in the development of green technology innovation under the concern of fairness. The research shows that green technology innovation has a significant positive impact on the whole supply chain system. Retailers' fair concern behavior will have a certain negative impact on the whole supply chain system and increase the potential conflict among the members of the supply chain. Enterprises need to actively incorporate environmental protection into supply chain management and partner selection decisions, and should not ignore the negative impact of fairness concerns on team cooperation.

How Does Supply Chain Resilience Affect Supply Chain Performance? The Mediating Effect of Sustainability

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.

Sustainable supply chain partner selection and order allocation: A hybrid fuzzy PL-TODIM based MCGDM approach

Sustainability, as a trend of social development and the embodiment of corporate social responsibility, has begun to receive more attention. To achieve this goal, sustainable supplier selection (SSS) and order allocation (OA) are seen as the crucial activities in corporate management. In the process of SSS, the psychological behavior of decision-makers (DMs) could play a critical role in the evaluation results. Therefore, introducing it into the decision-making process may lead to decision in line with the actual situation. In the uncertain multi-criteria group decision-making (MCGDM) problem described by probability linguistic term sets (PLTS), the DMs can evaluate the criteria of each supplier based on his own preference and hesitation, which is useful to avoid the loss of information. For this reason, this study develops a novel multi-criteria group decision-making combined with fuzzy multi-objective optimization (MCGDM-FMOO) model for SSS/OA problems by considering the triple bottom line (TBL) in which includes economic, environmental and social factors. The proposed method includes four stages. (1) the best-worst method (BWM) and entropy weight method are utilized to assign the weights of criteria to obtain the comprehensive weight. According to the output weights, the an acronym for interactive and multi-criteria decision-making in Portugese (TODIM) approach is applied to rank the suppliers under PLTS environment; (2) a FMOO model that can effectively deal with uncertainties and dynamic nature of parameter is formulated for allocating optimal order quantities; (3) two novel approaches are utilized to solve the FMOO model in order to obtain the richer Pareto frontier; and (4) the final OA solution is achieved by technique for order preference by similarity to ideal solution (TOPSIS) method. Finally, the validity and practicability of proposed MCGDM-FMOO model are verified by an example and comparative analysis with other classical MCGDM methods.

On a holistic view of supply chain financial performance and strategic position

Measuring corporate financial performance is an essential task in many supply chain decisions, such as supply chain strategic positioning and partner selection. This study introduces an analytical approach that can quickly scan financial data of many companies and produce a summary measure for each company. The approach offers organizations a less wearing way to obtain a holistic view of all target companies’ financial performance patterns, which imply the underlying supply chain strategies. The strategy map, a two-dimensional representation of the summary, provides a comprehensible means to apprehend the relative strategic position and measure the similarity between companies. The approach relies on three popular machine learning models, forecasting, clustering, and classification. It takes multi-year, multi-variate financial time series from the three standard financial statements, learns the patterns from the data, and tunes model parameters to configure the final settings for future applications. The input data needed are relatively easy to obtain and the self-learning modules only require modest domain knowledge to apply the approach. Its noise reduction, outlier detection, and feature selection functions ensure a consistent and robust performance. The empirical test using data from all US manufacturers and traders listed on NYSE and NASDAQ demonstrates the efficacy of the approach.

A Novel Approach Integrating AHP and COPRAS Under Pythagorean Fuzzy Sets for Digital Supply Chain Partner Selection

Digital supply chains (DSCs) can be useful in unlocking supply chains to gain competitive advantage as a driver of growth, while enabling rapid response, supporting novel technology-driven approaches, and creating innovative products and services. Partner selection (PS) is one of the crucial tasks in the successful digitalization of supply chain. However, the selection of a suitable partner is not an easygoing process and is mostly associated with complexity. Various criteria are considered during the partner evaluation process. This paper delivers an efficient evaluation process to assess alternative DSC partners. The proposed approach integrates the Pythagorean fuzzy sets (PFSs), analytic hierarchy process, and complex proportional assessment under a group decision making environment for the first time in the literature. PFSs can depict experts' evaluations with a richer structure, allowing for a more representative decision making. A case study from Turkey is conducted to validate proposed approach. This study contributes to the existing literature by developing a new evaluation model to improve DSC PS process and by proposing a new hybrid PFSs-based framework. This paper can be useful to practitioners and researchers to better understand DSC PS problem and designing effective DSC partner evaluation systems.

A model for partner selection criteria in energy from waste projects

Energy from Waste is being deployed in both developed and developing economies as a route to reduce dependency on fossil fuels whilst making positive use of resources which might otherwise be landfilled. Energy from Waste supply chains are complex, with a rich diversity of partners and stakeholders involved. For this purpose, the selection of appropriate criteria to guide supply chain design, and in particular the selection of suppliers, is critical for success. In this study, a three-stage process was conducted to identify, refine and validate an evidence-based model. The evidence based model proposed comprises seven categories of criteria used in the design of these supply chains, namely Economic, Environmental, Location, Operations management, which has a sub-category of Plant operation, Political/Legal and Social. The work reported here supports practitioners and researchers involved in supply chain partner selection to systematise their thinking in relation to the criteria that may impact their study or project.

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. Because partner selection is critical to supply chain management, focal firms now need to select supply chains 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, in the partner evaluation process. 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.

Construction of partner selection criteria in sustainable supply chains: A systematic optimization model

With the recent emphasis on sustainable supply chain management (SSCM) by both companies, consumers and governments, selecting eco-economic partners is more important than ever. Multiple aspects play critical roles in partner selection, including so many social, environmental, and economic attributes, but considering all of them simultaneously has proved impossible due to limitations in terms of evaluating resources and efficient decision making. Hence, a crucial first step of partner selection in an SSCM context is to decide which criteria to focus on and which to disregard. However, previous literature on partner selection usually treat the establishment of criteria as a given, and also neglect the interrelationships between them. To bridge this research gap, the present study develops a systematic four-stage optimization model for constructing partner selection criteria, based on Dempster-Shafer theory, the improved non-dominated sorting genetic algorithm-II (NSGA-II), and the decision-making trial and evaluation laboratory (DEMATEL) method. Our model integrates quantitative and qualitative methodologies to identify, group, filter, and analyze potential alternative criteria for partner selection in sustainable supply chains. We demonstrate the feasibility and effectiveness of our proposed approach with a case study of a Chinese electronic and equipment manufacturer.

基于DEA和FCA的旅游供应链合作伙伴的评价与选择

基于DEA和FCA的旅游供应链合作伙伴的评价与选择

Research on supply chain partner selection method based on BP neural network

In the process of establishing supply chain partnership, partner selection is the key and main step. If the enterprise selects the appropriate supply chain partner, in the material equipment unit price, the material equipment production and the supply ability, the product quality appraisal, the brand meets the demand, the new product development ability, has the bad record, the historical project performance, management system and management level, service level, human resource level, internal information processing level, historical cooperation situation, cooperation will, corporate culture and strategic fit degree, financing support ability, information transmission ability, Enterprise green idea propaganda, product energy consumption or energy consumption ratio, green building project participation in construction, environmental protection and energy-saving product development investment, toxic and harmful raw materials use, green matter The use of flowing green packaging, the use of recycled materials/product recycling, and the treatment of industrial “three wastes” will all produce a series of advantages that cannot be matched by traditional relationships. Then, the whole supply chain competitiveness will be improved. This paper studies the selection and evaluation of the partners in the supply chain environment from the point of view of the current research situation of the cooperative relationship in the supply chain environment at home and abroad. Based on the relevant research results at home and abroad, according to certain principles and methods, combined with experts’ evaluation of the future trend of qualitative indicators, a set of evaluation index system of supply chain partners is constructed. The standardized treatment of evaluation index is given. By comparing and analyzing the advantages and disadvantages of the common evaluation methods, the mature BP neural network is applied to the artificial neural network by using the artificial neural network evaluation method. MATLAB software is used to construct neural network. BP neural network is used for training and the trained neural network is used to evaluate an example. The results show that this method can solve the problem of partner selection and evaluation in supply chain environment, and improve the evaluation efficiency.

Enhancing the partner selection process in a Sustainable Partner Network

Purpose: The purpose of this paper is to introduce how to describe in a unified way the business models of different sectors companies based on VAC/EPC notations, and to contribute to the lean based partner selection mechanism for sustainable partner network. Design/methodology/approach: The authors introduce the unified business process modelling approach for collaborative companies modelling, which supports the lean based sustainable partner selection. Where lean correspond to the characteristics of cheap (cost effective), better (lower risk) and fast (less time).Findings: The authors defend that appropriate partner selection is a vital success factor in any collaboration. Existing supplier (partner selection) tools do not include the unified approach for business processes modelling of partners organisations and do not apply the lean principles for sustainable partner selection. Existing collaborative networks such as conventional supply chains spend considerable time and money to search for suitable partners, decrease the number of successful initiation of a project and completed collaborative projects.Research limitations/implications: The authors have designed a unified methodology for the business process modelling of manufacturing and machinery sectors companies applied for the formation of a sustainable partner network. After implementation, the solution applies the lean based approach for partner selection, to decrease the time and cost spend with simultaneous improvement of collaborative project sustainability. Nevertheless, the proposed approach is adaptable to other fields.Practical implications: To evaluate the achieved results the authors have performed the simulation study of Sustainable Partner Network (SPN) formation followed by partner’s selection that supports lean principles. The paper includes a feasibility case study for the approval of findings, where twenty-seven small and medium enterprises (SMEs) from the manufacturing and logistics fields collaborate to achieve a common goal. The time required for collaborative partner selection in the conventional supply chain is compared to the partner selection in Sustainable Partner Network was found to less.Originality/value: The main idea is to collect information about the available resources of SMEs into a new temporary entity and to utilise those resources for the realisation of the tasks of a particularly large project while meeting customer expectations. This work also suggests a methodology for faster business process modelling and evaluation (assessment) based on lean principles for a particular business process. Partners can use the SPN resources with the attributes of fast, cheap and less risk. The suggested solution enables to select the most reliable partners and contributes to the improvement of collaborative networks sustainability.

A Method for Partner Selection of Supply Chain Using Interval-Valued Fuzzy Sets — Fuzzy Choquet Integral and Improved Dempster–Shafer Theory

Supply chain partner selection (SCPS) is an important issue for firms to remain highly competitive. SCPS is generally formulated as a fuzzy multi-criteria decision-making problem, of which the most crucial step is how to cope with the fuzzy correlations of multi-criteria and individual differences of experts. To gain more exact results, we adopt interval-valued intuitionistic fuzzy sets to express the fuzziness of multi-criteria. Fuzzy Choquet integral model is developed to explain correlations of multi-criteria and calculate the evaluation results of each expert. An improved Dempster–Shafer theory is utilized to reconcile differences and synthesize each expert decision results. Additionally, an example is presented to illustrate the potential application of the proposed method.

Supply Chain Partner Selection under Cloud Computing Environment: An Improved Approach Based on BWM and VIKOR

The wide application of cloud computing technology makes supply chain partner share information effectively and increase corporate profits. However, in cloud computing environment, the evaluation process of supply chain partner selection is dynamic and fuzzy, and its evaluation index is uncertain. To solve these problems, this paper proposes a new method combed with rough number, best-worst multicriteria decision-making method (BWM), and compromise ranking method (VIKOR). Firstly, Rough number is used to integrate data from different evaluation matrix. Secondly, rough number improves a new method, BWM, to get RBWM criteria weight methods. Thirdly, the rough numbers are introduced into VIKOR and combined with RBWM to evaluate and sort the supply chain partners. Finally, an example is given to demonstrate the rationality and feasibility of the method.

A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains

The selection of suitable supply partners is a strategic issue for managers working in humanitarian operations and has received little attention in the literature. In humanitarian operations, complexity characterizes the continuous-aid procurement operations, and the selection criteria can differ from those used in commercial supply chain settings. This paper advances knowledge by introducing a supply partner selection framework for continuous-aid procurement. A proposed multi-criteria decision-making model uses selection criteria attributes verified by the extant literature and by field experts. A fuzzy Analytic Hierarchy Process is then used to compute criterion weights, and a fuzzy Technique for Order Performance by Similarity to Ideal Solution is used to rank supply partner alternatives. Even with elevated levels of subjectivity, these techniques enable humanitarian operation stakeholders to select the best supply partner effectively. An actual case illustrates how the proposed framework efficiently identifies the most suitable continuous-aid supply partner for the prevailing situation.

Research on supply chain partner selection and task alloca-tion based on fuzzy theory under an uncertain environment

Nowadays the enterprises pay the closer attention to the relationship among suppliers, manufacturers and distributors due to the global competitive market economy. And they manage the supply chain through establishing strategic cooperative partnership, which can greatly enhance the competitive advantage and obtain greater overall profits. In this paper, the fuzzy theory is applied to study the supply chain partner selection and the task coarse allocation problem in under multi-attribute fuzzy comprehensive decision-making and fuzzy constraints. Finally, the fuzzy comprehensive decision of supply chain network structure was verified through the case of Shaoxing textile.

Partner Selection in Supply Chain of Vietnam’s Textile and Apparel Industry: The Application of a Hybrid DEA and GM (1,1) Approach

Vietnam is currently among the top-five textile and apparel exporters, and the industry is considered quite attractive to foreign investors. Nevertheless, the global textile and garment industry is experiencing important changes. The three main producing regions in the world are China, Southwest Asia (India, Pakistan, Bangladesh, and Turkey), and ASEAN. In order to maintain its positioning and to establish stable and sustainable Vietnam textile and apparel development, there must be radical changes. Due to this necessity, the authors conducted this study by using grey forecasting to predict and reflect the condition of businesses in the period of 2017–2020, together with combining a DEA model to help businesses select the most appropriate strategic partner in the supply chain in order to achieve economic goals and promote the strength of the businesses partaking in the association. Besides, this helps businesses exploit market opportunities and take advantage of the capabilities of the textile and apparel industry.

A System Dynamics model for joint upstream and downstream partner selection in a supply chain consisting of suppliers and retailers

Firms no longer compete as autonomous entities and prefer to join in a supply chain alliance to take advantage of highly competitive business situation. Supply chain coordination has a great impact on strategic partnering and success of a firm in competitive business environment. In this paper, we propose a system dynamics simulation model for strategic partner selection in supply chain. Our model addresses a supply chain including suppliers and retailers. It presents an approach to simulating the tendency of each supplier (retailer) to select downstream (upstream) partner and the impact of their policies on the whole supply chain.

Partner selection in green supply chains using PSO – a practical approach

Partner selection is crucial to green supply chain management as the focal firm is responsible for the environmental performance of the whole supply chain. The construction of appropriate selection criteria is an essential, but often neglected pre-requisite in the partner selection process. This paper proposes a three-stage model that combines Dempster–Shafer belief acceptability theory and particle swarm optimisation technique for the first time in this application. This enables optimisation of both effectiveness, in its consideration of the inter-dependence of a broad range of quantitative and qualitative selection criteria, and efficiency in its use of scarce resources during the criteria construction process to be achieved simultaneously. This also enables both operational and strategic attributes that can be selected at different levels of hierarchy criteria in different decision-making environments. The practical efficacy of the model is demonstrated by an application in Company ABC, a large Chinese electronic equipment and instrument manufacturer.