Supplier Performance Evaluation Research Articles

Evaluation of Supplier Performance in Commercial Aircraft Manufacturing Industry

Evaluation of Supplier Performance in Commercial Aircraft Manufacturing Industry

A supplier performance evaluation framework using single and bi-objective DEA efficiency modelling approach: individual and cross-efficiency perspective

In view of complexities associated with supplier performance evaluation based on traditional business criterions (such as costs, quality levels, and delivery timelines) and emerging criterions (such as those related to environmental sustainability), we in this research evolve two different supplier efficiency measurement models that unify such criterions possessing characteristics of both desirable and undesirable outputs. The first model is a single-objective DEA efficiency assessment model wherein both types of outputs are integrated into a single composite efficiency measure. Using data from suppliers of Hyundai Steel Company, we determine composite efficiencies of each of these suppliers thus ranking them in terms of an overall efficiency score that would be useful as far as the first-cut supplier discrimination is concerned. However, due to the relative inability of evolved single-objective efficiency model to perform trade-offs amongst desirable and undesirable outputs and, owing to unidimensionality aspects, we evolve a goal programming based bi-objective efficiency model wherein trade-offs can be performed between both conventional and emerging dimensions criterions leading to different supplier evaluations for varied scenarios. We also integrate our evolved models with the cross-efficiency view of efficiency determination in order to enable the decision-makers to achieve peer-to-peer evaluation and maximum discrimination amongst suppliers.

A dynamic multiple attribute decision making model with learning of fuzzy cognitive maps

This paper concerns with a new multiple attribute decision making (MADM) model to cope with temporal performance of alternatives during different time periods. Dynamic MADM problems has grabbed remarkable attention of decision analysis community in recent years. In parallel with the recent advances in information technologies, firms steadily recognize the importance of data, and business analytics solutions is about to become standard business practice. Majority of the dynamic MADM literature deals with combining past and present data by means of aggregation operators. There is a research gap in developing data-driven methodologies to capture the patterns and trends in the historical data, and provide decision makers with meaningful insights in decision making practices. Analogous with the fact that style of decision making evolving from intuition-based to data-driven, this study proposes a new dynamic MADM model by learning of fuzzy cognitive maps (FCMs) to support decision makers in making informed decisions by considering future performance of alternatives. According to proposed model, Jaya algorithm, a simple and effective metaheuristic optimization method, is used to train FCMs to capture the patterns in historical data. Then, short-, medium-, and long-term future decision making matrices are generated. Finally, past, current and future decision making matrices are taken into consideration, and ranking of alternatives are obtained based on closeness coefficients. The proposed model is realized in a real-life supplier performance evaluation problem.

A Vendor Recommendation Model using Neural Networks

Suppliers today are considered as a logical extension to a firm that provide critical inputs to its processes. Supplier performance evaluation is vital for any firm to carry out supplier selection and to practice effective supply chain management. Further, the supplier performance measurement system should continuously evolve in a company as a process for improving the capabilities, execution effectiveness and efficiency. Though the supplier performance systems have been well investigated in the literature, very few studies have proposed an order-based supplier selection approach that takes into account the changing priorities of the customer. The research tries to address this gap by attempting to establish a dynamic linkage between customer order priorities and supplier characteristics. A neural network model for the order driven vendor recommendation system that ranks the suppliers as per the requirements of the order has been proposed.

An intelligent supplier evaluation model based on data-driven support vector regression in global supply chain

Supplier evaluation is an important issue in supply chain management. Most existing studies rely on expert experience to evaluate supplier performance. In order to alleviate the pressure on experts in global supply chain, an intelligent supplier evaluation model based on data-driven support vector regression (SVR) is proposed in this paper. Two methods are used in the construction process of the proposed intelligent model for supplier evaluation. The integrated multiple criteria decision making (MCDM) is employed to obtain the label of each supplier instead of the manual label. Then the obtained labels are used to train the SVR. Genetic programming (GP) is adopted to set three critical parameters of SVR without prior knowledge, which are kernel function k(·), the penalty parameter C, and the tolerable deviation ε. The performance of the proposed intelligent model is evaluated with the commercially available ARCIC data set. Simulation results show that the accuracy and robustness of proposed intelligent model are superior when compared with existing models.

The Management Innovation Classified Performance Study on Supply Chain Vendor Manufacturing Enterprise

With the arrival of knowledge economy era and the shortening of product life cycle, information technology and the rapid development of e-commerce with each passing day, and customer gradually increasing expectations, the competition among enterprises are gradually entering the supply chain competition, Now enterprises have begun to realize that effective supply chain management, which would have to assess supply chain performance. However, most of the evaluation methods used in our country using expert analysis to evaluate the contractor's financial indicators to determine the final contractor. This method is not only too subjective, but also cuts off the correlation between the indicators. A quantitative management innovation classified performance model of supplier performance evaluation based on clustering and K-NN classification is proposed to classify suppliers. Then on the basis of above analysis, the contractor of misclassification is identified by the K-NN method. Finally, an evaluation and selection case is presented to verify the practicability and effectiveness of the model. The method not only overcomes the above shortcomings, but also reflects the contractor's comprehensive level objectively and fairly. The results show that it can provide an important reference for the fair and impartial evaluation of the project contractor as an important method for decision makers to select the contractor.

Providing a Model for Ranking Suppliers in the Sustainable Supply Chain Using Cross Efficiency Method in Data Envelopment Analysis

Goals: This research seeks to identify the basic indices of sustainability in three dimensions (economic, social and environmental) in the Iranian automotive industry suppliers by reviewing previous research and ranking the suppliers using a cross efficiency approach. In this paper, the performance and ranking of sustainable supply chain suppliers are evaluated by presenting a secondary objective model in terms of cross efficiency.
 Design / Methodology / Approach: In the first step of this research, a preliminary screening of the identified criteria is carried out. the data on the final criteria is collected using a questionnaire. Finally, the evaluation and ranking of suppliers in sustainable supply chain of the automotive industry in Iran is done by the cross-efficiency model presented in this paper.
 Results: The results showed that, according to the criteria of the triple profit model (including 3 dimensions, 7 criteria), supplier No. 8 was identified as the most efficient decision maker unit (DMU) among 12 suppliers of Iran Khodro Company.
 Limitations of the investigation: The main constraints include the timeliness of information gathering and the lack of cooperation of suppliers in providing information.
 Originality / Value: Using the cross efficiency model in data envelopment analysis technique in the field of evaluating supplier performance is a very practical and unrestricted approach.

Supplier Performance Evaluation in Construction Projects: Challenges and Possible Solutions

AbstractSupplier performance analysis can enlighten a construction company about improvement possibilities for better project outcomes. However, for systematic evaluation of a supplier across proje...

Supplier Structure and Performance Evaluation of Supplier Network Phase Rice Supply Chain Management in South Sumatra

This study aims to identify the mapping, measure performance, and analyze problems in the structure of rice supply chain management on the supplier network phase in South Sumatra. Afterward, the result of this study is used as the basic consideration to improve the rice supply chain performance on indicators that have problems. Data collection was conducted by direct interview, questionnaire, and observation. This study used descriptive qualitative and several analysis tools as the data such as Supply Chain Operation Reference Model (SCOR), Analytical Hierarchy Process (AHP), Objective Matrix (OMAX) and Traffic Light System (TLS). According to the rice supply chain mapping on supplier network phase, there are three rice supply chains. Based on the interview, the farmers’ main problem as the main producers of the rice supply chain in South Sumatra is the limitation of unhusked rice price information in harvesting in the financial flow of rice supply chain. Based on the weighting result by OMAX scoring system, the total index value is 7.28, or in the yellow category which means that the performance of LICM rice- industry company in South Sumatra has not achieved the expected performance in terms of rice supply chain even though the result is close to the pre-determined target.

An integrated approach for performance evaluation of suppliers in manufacturing industries

The supplier selection is a multi-criterion problem which includes both qualitative and quantitative factors. The model presented in this thesis aims at aiding the selection process and consists of three phases. In the first phase, criteria for evaluation of suppliers are selected based on literature and analytic hierarchy process (AHP) is applied. The AHP is used to analyse the structure of the problem and weights of each criterion are determined. The second phase involves clustering the available data set into required number of clusters by K-means algorithm. K-means is commonly preferred partitional clustering method which generates required number of clusters. The best cluster is selected based on the weighted sum of ratings of the elements. In the third phase, technique for order preference by similarity to ideal solution (TOPSIS) is performed on the selected cluster. Potential suppliers belonging to the best cluster are ranked according to performance. A sensitivity analysis is performed to determine the robustness of the model.

An integrated approach for performance evaluation of suppliers in manufacturing industries

An integrated approach for performance evaluation of suppliers in manufacturing industries

Predicting group membership of sustainable suppliers via data envelopment analysis and discriminant analysis

Given the fierce competition between large companies, the sustainable supply chain has been recognized as a key component of corporate responsibility in recent years. Classification of suppliers can facilitate the selection of a suitable supplier for management, which saves time and costs for the company. Data envelopment analysis (DEA) has become one of the most frequently applied tools for measuring the relative efficiency of suppliers. Standard DEA models assume that the data are deterministic. But, in many real life applications not all inputs and/or outputs are deterministic, some could be stochastic. Additionally, existence of zero data in stochastic DEA models can be a new assumption in performance evaluation of suppliers. In this paper we proposed a novel super-efficiency stochastic DEA model for measuring relative efficiency of suppliers in presence of zero data. By proposed model, all suppliers are classified into two efficient and inefficient groups based on their efficiency score. Then, to predict group membership of new supplier, a novel Stochastic MIP model is presented. The results of this study indicate the high accuracy of prediction by the proposed model. In order to application of the proposed approach, a case study is presented.

INTEGRATING SUSTAINABILITY INTO SUPPLIER SELECTION: A GREY-BASED TOPSIS ANALYSIS

Sustainable supplier selection plays an important role in sustainable supply chain management operations and implementation. In this paper a novel formal modeling approach is conceptually developed and presented to address sustainable supplier selection. Grey theory and TOPSIS, a distance based multiple criteria method, are used for the integration and evaluation of sustainable supplier performance for sustainable supplier selection. From a research perspective, TOPSIS is improved to more effectively deal with grey numbers by integrating a degree of likelihood rather than converting grey numbers into crisp numbers functions, and it provides more flexible supplier rankings. This methodology strengthens the sustainable supplier selection process, and can be applied to other multiple criteria decision making problems. Illustrative calculations are made using data on sustainable supplier selection and evaluation published by Bai and Sarkis (2010). The technique is relatively accurate, matching well with results from a published grey rough set approach. The methodology easily implementable with minimal complex calculations required. It can also provide support for sustainable supplier selection, benchmarking, and improvement decisions. This is one of the first papers to integrate a broad set of sustainability factors for grey-based TOPSIS and supplier selection.

A green supplier evaluation system based on a new multi-criteria sorting method: VIKORSORT

Depleting natural resources and limited amount of landfill areas have forced many governments to impose stricter measures on environmental performance. In order to comply with those measures and to have a better environmental image, companies are investing heavily in environmental, social and economic responsibility issues. Moreover, they continuously track the environmental performance of their suppliers. Many green supplier evaluation and ranking methodologies were proposed in the literature in order to assist companies in the environmental performance evaluation of suppliers. However, the number of studies on the sorting of suppliers based on environmental criteria is very limited. In this study, we fill this research gap by proposing a novel VIKOR-based green supplier sorting methodology called VIKORSORT. This methodology evaluates the environmental performance of suppliers and sorts them into the predefined ordered classes. The proposed methodology can easily be embedded into an expert system which can suggest a suitable green supplier development program for each class.

Analytical Hierarchy Process to Evaluate Supplier Performance in Timber Industry

This descriptive and cross - sectional research was conducted to determine the priority criteria in supplier selection and the best supplier for Timber Industry as the unit of analysis. Data were collected through interview and questionnaire with the operational Manager of the company. The analysis method used was Analytical Hierarchy Process (AHP) that could assist decision making with criteria, sub-criteria, and several alternatives that had been determined by the company. The researchers find that there are four criteria which become the consideration in selecting supplier those are price, product, service, and delivery. From the analysis, it is foundthat the company should prioritize price compared to other criteria in selecting the supplier. Meanwhile, the other criteria (product, delivery, and service) sequentially become the second, third, and fourth priority criteria in selecting the suppliers. The other result of this research is the company can find the order of the best suppliers which can be recommended to the company.

Evaluation of supplier performance of high-speed train based on multi-stage multi-criteria decision-making method

In the process of developing the industry of high-speed train manufacturing (HSTM), a key problem is to evaluate the performance of suppliers by considering the special characteristics of suppliers. To address this problem, a multi-stage multi-criteria decision-making (MCDM) method is proposed based on the evidential reasoning approach. A criteria framework of the supplier performance evaluation (SPE) problem is constructed first. To describe quantitative and qualitative assessments with uncertainty, belief distributions are adopted to model the problem. Then, the rule-based information transformation technique is introduced to unify quantitative data and qualitative information. Suppose that SPE is performed at multiple stages and a decision maker may prefer the suppliers with the performance that is continuously increasing at these stages. On this assumption, stage weights are determined objectively by considering the dynamic nature of SPE at multiple stages. A real SPE problem in the HSTM enterprise located in Changzhou, Jiangsu Province, China is investigated to verify the validity and applicability of the proposed multi-stage MCDM method.

An empirical approach to the evaluation of defence contractor performance

ABSTRACTSupplier assessment and selection is critical in the world of international logistics and supply chain management. Despite this criticality, related processes and methodologies are largely subjective and anecdotal. A more quantitative empirical approach is needed. Improving the way government agencies and commercial businesses select suppliers can clearly result in cost savings, better schedule adherence, and higher-quality products. A key aspect of supplier selection for large government projects is effective evaluation and use of suppliers’ past performance data. However, organisations continue to search for better ways to adequately assess and utilise that data. This study uncovers new ways to more effectively use the United States government’s primary supplier performance evaluation system, called the Contractor Performance Assessment Reporting System. Based on the results of the study, a strong correlation has been shown to exist between contractors’ past and subsequent scores in terms of cost, schedule, and technical performance. The findings further indicate that the global commercial logistics community can use similar quantitative measures of contractors’ past performance to improve the effectiveness of their own supplier selection decisions.

A business process decision model for fresh-food supplier evaluation

PurposeFrequent food safety incidents caused widespread consumer concerns. Even though food safety is one of the weakest links in the fresh food supply chain and influences consumer food choice in ways different from the quality dimension, this factor is hardly proposed as one of the key traditional supplier selection criteria (e.g. quality, delivery, and price) in the literature. The purpose of this paper is to develop a business process decision model to assess the non-compensating food safety sub-criteria in order to disqualify fresh food suppliers that cannot reach the minimum threshold for low probable food safety failure. The preferred fresh food suppliers can minimize the risk of food safety failure and the associated huge food safety failure costs spanning from private consumer anguish to social distress that cause unbearable costs of sales loss and damage to brand image in business.Design/methodology/approachThis study proposes a novel approach that combines several well-established multi-criteria decision making (MCDM) techniques, including fuzzy AHP (FAHP), TOPSIS, and ELECTRE, and innovatively apply to analyze supplier performance and prioritize potential fresh food suppliers. This hybrid business process model can enforce compliance to all the five non-compensatory sub-criteria of food safety. Since ELECTRE is a non-compensatory MCDM method, it is therefore particularly applicable for disqualifying high risk fresh food suppliers from further full scale supplier performance evaluation by FAHP and TOPSIS. This hybrid business process decision model is able to capitalize on the strengths of these MCDM methods and offset their deficiencies.FindingsThis study uses data of an international supermarket chain to validate feasibility of the proposed model. Results indicate that this model is able to assess the non-compensating food safety sub-criteria via the ELECTRE method in order to disqualify fresh food suppliers that cannot reach the minimum threshold for low probable food safety failure. Only the preferred suppliers with the required food safety capability can proceed to the second stage of the supplier selection process. Assessment via the TOPSIS method reveals the ranking order of those top performing suppliers according to their relative scores along all the supplier selection criteria. The TOPSIS ranking results with the selection of the suppliers C, E, A, and F are robust and consistent across all the different scenarios.Practical implicationsApplication to the fresh food industry is possible with the aid of the MCDM methods. The contribution to the body of knowledge in this teaching and research field demonstrates the importance of first identifying the order qualifier for disqualifying those suppliers that do not satisfy the food safety requirements via the ELECTRE method. The proposed assessment procedure complies with the regulatory policy on food safety, and would influence public policy in applying the best practice of food safety regulation. Without first qualifying the potential suppliers on the basis of food safety, wrong decision can be made to select those high food risk suppliers that have relatively higher overall scores in other supplier selection criteria. Using the assessment results has positive economic and commercial impact on the purchasing managers to formulate appropriate purchasing and supplier development strategy to enhance supplier’s food safety performance, whilst maximizing the overall supplier portfolio performance. The improved supplier’s food safety performance will certainly benefit the society’s quality of life as well.Originality/valueBased on the analytical MCDM methods of FAHP, TOPSIS, and ELECTRE, purchasing managers can operationalize the Hill’s framework of order qualifier and winner that has primarily been used in the literature and manufacturing industry. This study represents the first move to innovatively apply the FAHP, TOPSIS, and ELECTRE methods to operationalize the Hill’s framework of order qualifier and winner that has primarily been used in the literature and manufacturing industry. Application to the fresh food industry to validate the feasibility of the proposed model has been conceived and implemented in this study. Analysis of the data inputs of a supermarket chain via the three MCDM methods generate the results that fulfill the purpose of achieving the research objective of identifying and managing the supplier base that can deliver the best supplier performance, conditional on first passing the fresh food safety test.

A modified TOPSIS method based on vague parameterized vague soft sets and its application to supplier selection problems

In this paper, we propose an intuitively straightforward extension of the vague soft set model called the vague parameterized vague soft set (vp-VSS). This model generalizes the vague soft set by including the opinions of an expert or a moderator regarding the values of the membership function for the parameters that are considered, in the form of a vague set. The values provided by the experts indicate the threshold values for the membership functions of the elements, i.e., the minimum values that must be ideally satisfied by all the elements for each parameter. This provides a clear indication to the users of these information, and forms a pertinent component of the model, particularly in the decision-making process. Subsequently, we define some operations for this model and examine its properties. Subsequently, we introduce two algorithms based on a modified TOPSIS approach and a weighted aggregation operator approach, both of which are based on our proposed vp-VSS model. These algorithms are then applied in two multi-attribute decision-making problems involving supplier selection and the evaluation of supplier performance. The performance and utility of these algorithms are compared and contrasted in terms of the computational complexity and discriminative power of the algorithms.

Investigating critical criteria for supplier quality development

Identifying critical criteria for supplier quality development is vital for improving the performance of suppliers. In this paper, we determine critical criteria for supplier quality development based on review of literature and discussion with supplier quality experts from industry. The criticality of the criteria is determined based on: (a) Kano’s model (must haves, satisfiers, dissatisfiers); (b) Hill’s manufacturing strategy (order winners and order qualifiers); and (c) criteria weights. The results of the study yield the following as the must-be and order winning elements for supplier quality evaluation: price; delivery performance; service; environment; health and safety; ISO 9000; European Foundation for Quality Management; quality management policy; understanding of customer requirements; supplier product quality; process control; quality inspection programs; process for handling complaints; and a system for corrective action. For supplier quality development, the must-haves and order winning elements are: honoring outstanding suppliers; involving suppliers early in product and process development; and sending instructors and technical consultants to the supplier’s site. The proposed results have strong practical applicability and can be used by decision makers for supplier performance evaluation, setting targets for performance improvement, resource allocation for supplier quality development, and designing necessary operations for improving the performance of their suppliers.