Candidate Suppliers Research Articles

A federated learning approach to automated and secure supplier selection in cyber manufacturing as-a-service

The emergence of cyber or platform-based manufacturing as-a-service is rapidly disrupting the way discrete parts are sourced and manufactured. However, the centralized business model of cyber manufacturing as-a-service platforms raises concerns about data ownership and access control of independent manufacturing suppliers. Contrary to centralized platforms, cyber manufacturing as-a-service aims to connect designers with geographically distributed manufacturers by serving as a broker who matches the query part design requirements with the manufacturing capabilities of candidate suppliers in its network. One of the key challenges in realizing the vision of cyber manufacturing as-a-service is the lack of a computationally efficient method for manufacturing capability search while maintaining data security of the proprietary datasets of the suppliers in the network. In this paper, we propose a federated learning approach that utilizes a deep unsupervised part retrieval model (FL-DUPR) to learn a federated embedding of suppliers’ manufacturing capabilities without directly accessing their proprietary datasets. We demonstrate through two case studies that this approach yields a supplier selection accuracy of 89 % when the manufacturing capabilities of the suppliers do not overlap, and a multi-label supplier selection accuracy of 87 % when there are significant overlaps in the suppliers’ manufacturing capabilities. We also show that our unsupervised learning approach outperforms the baseline supervised learning classification model trained under the same federated learning framework. The results demonstrate the promise of the proposed federated embedding approach for automated identification of the required manufacturing capabilities offered by various suppliers without directly accessing their proprietary data, thus paving the way for a more secure cyber manufacturing as-a-service business model.

Research on Supplier Selection of Pharmaceutical Enterprises under the Dual Carbon Target

With the proposal of national carbon peak and carbon neutrality targets, green supply chain management has become one of the important means of reducing carbon emissions. Based on elaborating on the connotation of new quality productivity, this article analyzes the supplier selection problem of pharmaceutical enterprises under the dual carbon target. A pharmaceutical supplier evaluation index system has been established from five aspects related to low-carbon and environmental protection, including technological innovation, research and development capabilities, digitization, intelligent production, environmental protection technology, and so on, with five primary indicators and 18 secondary indicators. Secondly, a game theory combination weighting method combining the G1 method and entropy weight method is used to determine the weights of indicators. A pharmaceutical supplier evaluation model is established by combining the matter element extension model. A company is used as an example to evaluate its 8 candidate suppliers and select the best supplier. Finally, corresponding measures are proposed to promote the healthy and sustainable development of pharmaceutical enterprises.

Optimisation method of product family supplier selection based on data mining under carbon neutral policy

The carbon neutral policy restrains the carbon emissions of product family manufacturers, which incurs additional costs for manufacturers. In previous studies of product family supplier selection, the dilemma of carbon neutrality faced by manufacturers was rarely researched. Therefore, this study puts forward an intelligent optimisation method for product family supplier selection under carbon neutral constraints, which assists manufacturers in satisfying carbon neutrality constraints while minimising total production costs. Firstly, the data representation models of product family are constructed by using information ontology technology, and then the candidate suppliers that meet the manufacturer’s performance requirements are mined by semantic similarity. Secondly, by calculating the configuration cost and carbon footprint of the product family, the configuration cost-carbon neutral cost model (CC-CNC model) of the manufacturer is established with the goal of minimising the manufacturer’s total cost, and then the genetic algorithm is employed to solve the model, thus the optimal supplier selection scheme is mined. The feasibility of the proposed method is demonstrated by supplier selection for water-cooled fan product family, which guides the manufacturer to minimise the cost by selecting the best supplier scheme and taking reasonable carbon neutral measures, contributing to the manufacturer’s low-carbon production and economic benefits.

Incorporating Cost Risk in Supplier Selection for Long Term Contracts

Purchasing firms naturally select the supplier with the lowest total cost. Often overlooked is the impact of the duration of long-term contracts (common in the automotive industry). We provide a method to quantify how risk escalates with contract duration. Our method applies to virtually all cost elements. For clarity, we demonstrate it on logistics costs that can change over a long horizon, resulting in a change in the relative attractiveness of candidate suppliers. We simulate a case study to estimate the likelihood that each supplier will provide the lowest cost over the contract duration. Finally, we quantify the risk reduction afforded by dividing a long contract into a sequence of shorter contracts.

A Two-Stage Sustainable Supplier Selection Model Considering Disruption Risk

The global spread of the pandemic has changed many aspects of life and placed the supply chain at risk of disruption. To solve the problem of supplier selection under the risk of supply chain disruption, in this paper, we propose a two-stage evaluation model to address the issue of supplier selection in the context where a pandemic requires a lockdown. First, we incorporate the lead time into the epidemic model that predicts the evolution of the pandemic to identify suppliers that have a high risk of disruption caused by the pandemic’s evolution. Second, we propose a best–worst method combined with regret theory to rank candidate suppliers. Our model provides a dynamic link between the pandemic’s evolution and supplier selection, and it allows selecting suppliers according to various criteria while avoiding supply chain disruptions due to inappropriate supplier selection. We validate the proposed model on a real case study with epidemic data from China. This paper is the first to consider the impact of lockdowns during the pandemic on supplier selection. We develop a novel MCDM model BWM-RT for supplier selection; our model can be an effective decision support approach to help decision makers better cope with the risk of supply chain disruptions.

Optimizing Supplier Selection and Order Lot-Sizing Decisions in a Two-Stage Supply Chain

This paper analyzes different lot-sizing policies for the supplier selection and order allocation problem in a two-stage supply chain. The supply chain consists of multiple candidate suppliers and a single buyer. In this system, selected suppliers produce a product in batches at finite production rates, ship it to the buyer, and the buyer sells it to the market at a constant demand rate. Our goal is to evaluate two lot-sizing policies and select the one that optimizes the supply chain by minimizing the total cost and maximizing supplier efficiency. A bi-objective mixed-integer nonlinear programming (BOMINLP) model is proposed. The first objective consists of the development of a coordination mechanism for supplier selection and order allocation that minimizes the entire supply chain cost, and the second objective comprises a data envelopment analysis (DEA) approach to evaluate the overall performance of suppliers to optimize supplier efficiency. Then, the lot-for-lot and order frequency policies are applied to the BOMINLP model separately to determine the set of selected suppliers as well as the corresponding order quantities and number of orders allocated to each selected supplier per replenishment cycle. Numerical examples that illustrate the solution approach and compare the two lot-sizing policies are provided.

Assesment of the Candidate Suppliers’ Fluency in ISO GPS Standards Essential Principles, Rules and Indications

2. MORSE E.P., SHAKARJI C.M., SRINIVASAN V., 2018, A Brief Analysis of recent ISO Tolerancing Standards and Their Potential Impact on Digitization of Manufacturing, Procedia CIRP, 75, 11–18. https://doi.org./10.1016/ J.Procir.2018.04.080. Google Scholar

A hybrid group decision-making approach involving Pythagorean fuzzy uncertainty for green supplier selection

Green supplier selection is a significant issue in green supply chain management field and has drawn worldwide attention from academia, industry, and governments. Numerous methodological models have emerged for correctly choosing an appropriate green supplier, but some inadequate aspects of these models, such as the use of high-order fuzzy uncertainty, incompatible designed criteria, the weight methods used by decision experts, and consensus on distinct ranks, should be improved. This research propounds a hybrid approach that combines the analytic hierarchy process (AHP), technique for order preference by similarity to ideal solutions (TOPSIS), and linear assignment method (LAM) under Pythagorean fuzzy (PF) uncertainty to assess candidate suppliers. A new hierarchical evaluative criteria system with green factors that run throughout procurement, production, and transaction is built. The ordered weighted averaging operation predicated on a normal distribution is adopted to figure out the significance of the relevant decision experts, and the PF-AHP technique is exploited to recognize relevant criteria significance. To find the best green supplier, the PF-TOPSIS technique employs the most recent generalized distance measure and magnitudes. The LAM is exploited to arrive at a consensus on distinct ranks depending on differentiated distance parameter values in the distance formula for the PF sets, and then the optimal green supplier can be obtained. Additionally, the presented PF-AHP-TOPSIS-LAM approach was applied to an illustrative case, and the outcomes were shown to be more beneficial and efficacious than the solutions by other existing techniques. Hence, our presented hybrid approach provides a better methodological framework, establishes a critical foundation for artificial intelligence applications within a decision support field, and contributes to such competitiveness of microenterprises and the long-term development of society.

Blockchain and Satisfiability Modulo Theories for Tender Systems

A tender process consists in competing offers from different candidate suppliers or contractors. The tender winner is supposed to supply or provide a service in better conditions than competitors. Tenders are developed using centralized unverified systems, which reduce transparency, fairness and trust on the process, it also reduces the ability to detect malicious attempts to manipulate the process. Systems that provide formal verification, decentralization, authentication, trust and transparency can mitigate these risks. Satisfiability Modulo Theories provides a formal analysis to prove correctness of tender offers properties, verified properties ensures system reliability. In addition, one technology that claims to provide decentralization is Blockchain, a chain of distributed and decentralized records linked in a way such that integrity is ensured. This paper presents a formal verified and decentralized proposal system, based on Satisfiability Modulo Theories and Blockchain technology, to make electronic procurement tenders more reliable, transparent and fair.

Sustainable Supplier Selection and Evaluation for the Effective Supply Chain Management System

With increased environmental protection awareness, sustainability has been incorporated into supply chain management. Sustainable supplier selection and evaluation have become an acritical part of supply chain management. They can significantly improve the supply chain’s operational performance and enhance enterprises’ competitiveness. Based on trapezoidal interval type-2 fuzzy numbers (TIT2FNs) and cloud probability dominance relations (PDR), manufacturers can make more tangible and environmentally friendly decisions in the SSSE process. In this paper, a SSSE indicator system is first established using the necessary economic, environmental, and social factors. The importance of the indicators described in linguistic terms is transformed into TIT2FNs, and the weight of each indicator is calculated. In order to prevent candidate suppliers from promoting performance maliciously, different weights are given according to the impact of the enterprise’s historical performance on the present. Finally, the cloud PDR method is used to determine the optimal sustainable supplier. A case study and analysis are provided to show the feasibility and superiority of the proposed method.

A Hesitant Fuzzy Method for Evaluating Risky Cold Chain Suppliers Based on an Improved TODIM

Enterprises need sustainable development in order to reduce costs and increase income. The cold chain logistics industry needs to promote sustainable supply chains more. As the beginning of the supply chain, the choice of suppliers is particularly important. Considering the risky attitude of decision-makers, an improved hesitant fuzzy TODIM approach is adopted to select suppliers. In order to calculate a more objective indicator weight, the generalized Shapley function of the hesitant fuzzy measure is adopted by analyzing the relationships among indicators. The uncertain supplier evaluation information given by decision-makers is obtained by using hesitant fuzzy information. The improved Interactive and Multi-criteria Decision-Making (TODIM) method based on hesitant fuzzy numbers is used to analyze the psychological behavior of decision-makers under different market prospects and comprehensively rank the candidate suppliers. Finally, a case study of selecting cold chain logistics suppliers is provided to verify the effectiveness and feasibility of the method in this paper.

Development of a Rough-MABAC-DoE-based Metamodel for Supplier Selection in an Iron and Steel Industry

In the context of supply chain management, supplier selection can be defined as the process by which organizations score and evaluate a range of alternative suppliers to choose the best possible one who can provide superior quality of raw materials at cheaper rate and lesser lead time. It is a decision making process with multiple trade-offs between various conflicting criteria which in turn helps the organizations identify the suitable suppliers that would establish a robust supply chain assisting in maintaining a competitive edge. The main objective of supplier selection is thus focused on reducing purchase risk, maximizing overall value to the organization, and developing closeness and long-term relationships between the suppliers and the organization. In this paper, while selecting the most suitable supplier for gearboxes in an Indian iron and steel industry, assessments of three decision makers on the performance of five candidate suppliers with respect to five evaluation criteria are first aggregated using rough numbers. The definitive distances of those rough numbers are then treated as the inputs to a 25 full-factorial design plan with the corresponding multi-attributive border approximation area comparison (MABAC) scores as the output variables. Finally, a design of experiments (DoE)-based metamodel is formulated to interlink the computed MABAC scores with the considered criteria. The competing suppliers are ranked based on this rough-MABAC-DoE-based metamodel, which also easies out the computational steps when new suppliers are included in the decision making process.

Evaluation and selection of green suppliers for papermaking enterprises using the interval basic probability assignment-based intuitionistic fuzzy set

With the vigorous development of the global economy, modern enterprises have agreed to implement green supply chain management. The evaluation and selection of green suppliers plays a vital role in successful green supply chain management. As a multi-attribute group decision-making green supplier selection problem, it is difficult to fully describe the evaluation factors using precise numerical values and classical fuzzy sets, so it cannot assist enterprise managers in making completely reasonable decisions. Therefore, this paper proposes a green supplier evaluation and selection method based on interval BPA-based intuitionistic fuzzy sets. First, build a green supplier evaluation index system based on the characteristics of the papermaking enterprise's suppliers. Then, the intuitionistic fuzzy number is used to express the indicators of the evaluator for each supplier, and the intuitionistic fuzzy information is integrated through the organic combination of the IIFWA integration operator and the structural entropy weight to obtain the comprehensive evaluation value of the evaluator. Finally, the interval BPA-based intuitionistic fuzzy set method ranks the candidate suppliers and determines the best green supplier. Applying this method to the field of green supplier evaluation and selection can provide an effective method for enterprises to select suppliers scientifically and reasonably, which has practical reference value and practical significance.

Portfolio optimisation of material purchase considering supply risk – A multi-objective programming model

For the sake of better coping with the problem of material procurement, a multi-objective optimisation model was established in a systematic analysis framework for material procurement considering supply risk. First, this paper combs and identifies supply risk factors and constructs a supply risk evaluation system from the dimensions of quality, price, delivery, service and technology. Second, based on the linguistic scale and fuzzy theory, this paper measures the supply risk of candidate suppliers and estimates the relevant parameters of the multi-objective optimisation model by using the triangular fuzzy numbers. In addition, an improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized in this paper to solve the multi-objective model, since traditional intelligent algorithms have slow convergence speed and are easily trapping into local optimisation. Finally, this paper conducts simulation experiments by setting three types of decision-makers with different risk preferences and provides material procurement combination schemes in different scenarios. Through numerical simulation experiments, it was verified that the optimisation model established in this paper was feasible and useful for the selection of candidate suppliers and the portfolio optimisation of material procurement.

INTEGRATING BWM AND ARAS UNDER HESITANT LINGUISTIC ENVIRONMENT FOR DIGITAL SUPPLY CHAIN FINANCE SUPPLIER SECTION

In the era of intelligence and informatization, digital supply chain finance (DSCF) has become one of the important trends in the development of supply chain finance. With the gradual increase of DSCF suppliers and various requirements of small and medium-sized enterprises for suppliers in providing financing services, selecting the most suitable DSCF supplier is of great significance for most small and medium-sized enterprises to expand reproduction and improve competitiveness. To address such a decision-making problem, this paper proposes a new multi-expert multiple criteria decision-making model by integrating the Best Worst Method (BWM) and Additive Ratio ASsessment (ARAS) method under the hesitant fuzzy linguistic environment, in which the hesitant fuzzy linguistic BWM method is applied to determine the weights of criteria while the hesitant fuzzy linguistic ARAS method is proposed to rank the candidate suppliers. A case study is given to demonstrate the procedure of the proposed method for the selection of optimal DSCF suppliers, which shows the feasibility of the proposed method. Finally, sensitivity analysis and comparative analyses are provided to testify the applicability and superiority of the proposed method.

Multicriteria supplier selection using acceptability analysis

The supplier selection problem has been largely explored in the extant literature and attracted considerable attention of academics and purchasing managers. Practical supplier selection problem is usually revolved around multicriteria and a committee of experts. However, even using the exact values of the input data, certain experts may generate uncertain evaluation results on a supplier, because the exact weights with respect to each criterion are extremely difficult to reach a group consensus. In this article, first the interval data to describe all experts’ evaluation on all suppliers are formulated and then a stochastic multicriteria acceptability analysis (SMAA-2) is applied to provide a full rank of all candidate suppliers. SMAA-2 method is considered as an effective instrument to deal with stochastic decision-making problems. The rank acceptability indices and holistic rank indices are obtained to support the supplier selection. A numerical example drawn from the previous paper is recalculated to show the effectiveness of our approach.

Dynamic Strategic Procurement from Capacitated Suppliers

Many industries are characterized by large buyers and suppliers whose decisions are influenced by the strategic interactions among these parties. Here we present an economic model for a monopolistic buyer that needs to procure a critical item from two candidate suppliers competing to win the buyer’s account. The buyer’s objective is to design a procurement process that minimizes its total cost. We analyze this problem under one static and two dynamic procurement design choices and investigate the impact of these choices and capacities on supplier profits and buyer’s procurement cost. All three processes are modeled as non‐cooperative games and solved under suitable equilibrium concepts. We have found that when the buyer can choose purchase quantities independent of quoted prices, dynamic procurement can lower its cost by as much as 50% when compared to static procurement. However, when the buyer is constrained to set a precommitment quantity and award sales starting with the low priced supplier, the savings obtained from dynamic procurement diminish, but the buyer can still save as much as 12.5%.

A new integrated MCDM model for sustainable supplier selection under interval-valued intuitionistic uncertain linguistic environment

Sustainable supply chain management (SSCM) plays a great part in enterprise production operation management due to strict government regulations and increased public awareness. Selecting the best sustainable supplier is essential for companies to promote SSCM, which, as indicated in many studies, is a multi-criteria decision making (MCDM) problem. Besides, decision makers tend to utilize linguistic terms for expressing their evaluations owing to their fuzzy knowledge. This paper reports an innovative MCDM model for sustainable supplier selection by integrating best-worst method (BWM) and alternative queuing method (AQM) within the interval-valued intuitionistic uncertain linguistic setting. The novel approach allows to capture the uncertainty and vagueness of decision makers’ judgements with the aid of interval-valued intuitionistic uncertain linguistic sets. Furthermore, the BWM method can obtain the optimal weights of criteria by constructing a nonlinear programing model. The AQM is reliable and intuitive to generate the ranking of candidate suppliers. Finally, a watch manufacturer is used as an example for illustrating the practicability and effectiveness of the proposed sustainable supplier selection model.

A note on Group Selection with multiple quality characteristics: power comparison of two methods

The Group Selection problem is an essential problem in the supplier selection process. The objective of the problem is to select a subset of suppliers containing the best among multiple candidate suppliers. Manufacturers should procure parts from the selected suppliers to produce high-quality products. Lin, C.J., W.L. Pearn, J.Y. Huang, and Y.H. Chen [2017. “Group Selection for Processes with Multiple Quality Characteristics.” Communications in Statistics – Theory and Methods. doi:10.1080/03610926.2017.1364392] considered the problem under multiple quality characteristics, and proposed the Modified Bonferroni method and the Multiple Comparisons with the Best (MCB) method to tackle the problem. The two methods, however, may select different subset containing the best depending on the magnitude of the differences among the k estimated index values. In this paper, we derive the power function for the Modified Bonferroni method, and compare the power of the two methods with extensive simulations. The results show that the MCB method is more powerful than the Modified Bonferroni method when the actual number of the best process is one. On the other hand, the Modified Bonferroni method significantly outperforms the MCB method when the actual number of the best process is greater than one. The results provide practitioners with useful reference about the properties of the two methods for supplier selection.

A parts supplier selection framework of mechanical manufacturing enterprise based on D-S evidence theory

Parts supplier selection (PSS) is an important part of supply chain management of manufacturing enterprise. In the PSS process, the values of evaluation indicators are often uncertain and incomplete and the importance degrees of evaluation indicators are often instable. To solve this problem, a PSS framework of manufacturing enterprise based on D-S evidence theory is proposed. The indicator system for PSS is established, and the indicators are divided into three categories: quantitative, comprehensive qualitative and direct qualitative. The initial indicator values are processed by membership grade method to obtain the tendency degree. A two-order weighted D-S evidence theory model is constructed to evaluate the screened candidate suppliers. A manufacturing enterprise application case is given finally to illustrate the correctness and feasibility of the proposed framework.