Bootstrap approach for supplier selection based on production yield

Current manufacturing industries have increased their level of out-sourcing and relied more heavily on their supply chain as a source of competitive advantage. Supplier selection decisions have become an important component of production management. Those decisions have a significant impact on a firm's marketing competition, and suppliers may account for a large portion of the production cost. Production quality is one of the key factors in supplier evaluation. The manual of supplier certification includes a discussion of process capability analysis, which recommends a procedure for evaluating the most prevalent process capability index C _pk. However, the recommended procedure is applicable only when evaluating an individual supplier's performance. In this paper, we apply the bootstrap method to the supplier selection problem. We construct lower confidence intervals for the capability difference and ratio between two given suppliers. Performance comparisons are made among various bootstrap methods in terms of error probability and selection power. For convenience of applications, the sample sizes required for various designated selection power are also tabulated.     

Using integrated fuzzy DEMATEL and fuzzy C: means algorithm for supplier evaluation and selection

Decision-making techniques are used to help evaluate the current suppliers’ aim at classifying performance of individual suppliers against desired levels of performance, so as to design suitable plans to increase the performance and capabilities of suppliers. In this study, an integrated model is introduced and proposed for increasing the supplier selection and evaluation quality. The methodology is composed of two steps. The first stage is fuzzy decision-making trial and evaluation laboratory method in which the interactions between the evaluation criteria and the criteria weight have been computed. At the second stage, performances of suppliers are assessed using both the criteria weights obtained at the first stage and fuzzy c-means clustering algorithm by classifying the vendors according to their performances. Obtained results show that the proposed model is very well suited as a decision-making tool for supplier selection decisions.     

A model for performance monitoring of suppliers

The purchasing function is central to the strategic operations of effective supply chain management. This centrality is mainly due to the significant impact of material costs on profits, increased investments in inter-organizational advanced manufacturing and information technologies, and a growing emphasis on the just-in-time operations philosophy. The critical business processes of the purchasing function include supplier selection, negotiation of supply contracts, monitoring supplier performance, and acting as an interface between an organization and its suppliers. Within these core processes of purchasing, this paper narrows its scope to focus upon the supplier performance evaluation and monitoring process, which assists in maintaining effective customer-supplier linkages. While price has been traditionally considered as the single most important factor in evaluating suppliers, it has been agreed upon that value performance emphasis needs to include other important factors such as quality, delivery and flexibility. Given this evolving business competitiveness environment, the contribution of this research is threefold. First, it applies a new multi-criteria evaluation model for supplier performance evaluation by considering various performance criteria. Second, a proposed application extension of this model serves as a monitoring and control mechanism for the performance of suppliers, thus supporting continuous process improvement to achieve efficient customer-supplier relationships. Third, the paper presents this technique as a new methodological extension to data envelopment analysis research by improving the discriminatory power of an existing variable returns to scale model. The model and its application are demonstrated through a previously published illustrative case example.     

Procedure for supplier selection based on Cpm applied to super twisted nematic liquid crystal display processes

The loss-based process capability index C _pm , sometimes called the Taguchi index, has been proposed to the manufacturing industry as a method to measure process performance. The index C _pm takes into account the targeting degree of the process, which essentially measures process performance based on average process loss. Based on the C _pm index, a mathematically complicated approximation method was developed previously for selecting a subset of processes containing the best supplier from a given set of processes. The present paper implements this method and develops a practical step-by-step procedure to aid supplier selection decisions. The accuracy of the selection method is investigated using a simulation technique. The accuracy study provides useful information about the sample size required for a designated selection power. A two-phase selection procedure is developed to select a better supplier and to examine the magnitude of the difference between the two suppliers. Also investigated is a real-world case on the super twisted nematic liquid crystal display manufacturing process, and it is applied to the selection procedure using actual data collected from the factories to reach a decision in supplier selection.     

Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process

The proposed system illustrates that logic fuzzy can be used to aid management in assessing a supplier's environmental performance in the supplier selection process. A user-centred hierarchical system employing scalable fuzzy membership functions implement human priorities in the supplier selection process, with particular focus on a supplier's environmental performance. Traditionally, when evaluating supplier performance, companies have considered criteria such as price, quality, flexibility, etc. These criteria are of varying importance to individual companies pertaining to their own specific objectives. However, with environmental pressures increasing, many companies have begun to give more attention to environmental issues and, in particular, to their suppliers’ environmental performance. The framework presented here was developed to introduce efficiently environmental criteria into the existing supplier selection process and to reflect on its relevant importance to individual companies. The system presented attempts to simulate the human preference given to particular supplier selection criteria with particular focus on environmental issues when considering supplier selection. The system considers environmental data from multiple aspects of a suppliers business, and based on the relevant impact this will have on a Buying Organization, a decision is reached on the suitability of the supplier. This enables a particular supplier's strengths and weaknesses to be considered as well as considering their significance and relevance to the Buying Organization.     

An integrated fuzzy multi-criteria group decision-making approach for green supplier evaluation

Purchasing is one of the most vital functions within a company and supplier performance evaluation is one of the most important business processes of the purchasing function. Traditionally, companies have considered factors such as price, quality, flexibility, etc. while evaluating suppliers. However, environmental pressures urge them to consider green issues. This study proposes a decision model for supplier performance evaluation by considering various environmental performance criteria. An integrated, fuzzy group decision-making approach is adopted to evaluate green supplier alternatives. More precisely, a fuzzy analytic hierarchy process (AHP) is applied to determine the relative weights of the evaluation criteria and an axiomatic design (AD)-based fuzzy group decision-making approach is applied to rank the green suppliers. Finally, a case study is given to demonstrate the potential of the methodology.     

Two-phase selection framework that considers production costs of suppliers and quality requirements of buyers

Buyers are faced with selecting the optimal supplier, while suppliers are left to consider production costs. In this study, we developed a two-phase selection framework that allows buyers to evaluate the performance of suppliers while taking production costs into account for value maximisation. This scheme is a win-win solution capable of promoting long-term relationships between buyers and suppliers. Under the assumption of normality, the first phase involves constructing a new Six Sigma quality capability analysis chart (SSQCAC) which takes production costs into account. The objective is to evaluate all potential suppliers using the 100?×?(1–α)% upper confidence limit (UCL) of an integrated Six Sigma quality index (SSQI) Q_PIh when dealing with products with smaller-the-better (STB), larger-the-better (LTB), or nominal-the-best (NTB) quality characteristics. According to interval estimation theory, this method can have a significant impact on the consumption of resources; i.e. the production costs of the supplier can be decreased by reducing the production quality to below that required by the buyer. The proposed method also filters out unsuitable suppliers in order to simplify the decision problem and reduce computational demands and operational risks/costs without compromising the quality of the final product. In the second phase, a detailed analysis is conducted using Euclidean distance measure to select the optimal supplier from among the remaining candidates. We conducted a real-world case study to evaluate the efficacy of the proposed method. We also conducted comparisons with existing methods to demonstrate the advantages of the proposed method and its managerial implications. Suggestions for future study are also provided.     

Advanced ART2 scheme for enhancing metrology-data-quality evaluation

The metrology-data-quality-index (DQI_y) algorithm was proposed to perform metrology-data-quality evaluation of the automatic virtual metrology system developed by the authors. The DQI_y algorithm is based on the adaptive-resonance-theory 2 (ART2). ART2 divides data into different patterns according to the similarity of process data, and then calculates the corresponding DQI_y value and its threshold, DQI_yT, for evaluation and judgment. However, in practical applications, the classical ART2 technique still could not cluster process data very precisely. Since some samples with dissimilar process parameters might be sorted into the same cluster, two or more groups could be found in the corresponding metrology-data cluster. This phenomenon may cause invalid DQI_y detection. To solve the problem above, the advanced ART2 scheme is proposed in this paper to enhance the accuracy of the DQI_y algorithm. A large industrial data-set showing both a shift in metrology measurements without a process shift and a process shift that was not captured by the metrology of the actual photo and color-filter production tools of a TFT-LCD factory were adopted as illustrative examples to verify the practicality of the proposed scheme. Experimental results show that the performance of the advanced ART2 is indeed better than that of the original ART2.     

Modeling System Effects in Ballistic Impact into Multi-layered Fibrous Materials for Soft Body Armor

An analytical model is developed to study various ‘system effects’ during impact of a flat-faced, cylindrical projectile into a flexible, multi-layered target with no bonding between layers. Each thin layer is assumed to have in-plane, isotropic, elastic mechanical properties. The model allows variation of the mechanical properties from layer to layer as well as the spacings between the layers in order to study their combined effects on the ballistic performance of the system. In particular, we consider such performance measures as the V₅₀ limit velocity, the number of layers penetrated when impacting below this limit, and the residual projectile velocity after complete penetration above this limit. The V₅₀ performance of the target is found to degrade progressively as the spacings between layers are increased relative to the sum of layer thicknesses without spacing. A second finding is that for a given set of layers with differing mechanical properties, both the V₅₀ and the residual velocity depend on the order of layer placement. A third finding is that among systems with identical layers of a given in-plane tensile strength, the V₅₀ velocity increases with increasing strain-to-failure of the layers. However the relative magnitude of this increase diminishes with increasing target-to-projectile areal density ratio. The model builds on the authors’ previous analysis for impact into a single elastic membrane and the results have important design implications for armor design especially for hybrid material configurations.     

超声协同微球型Bi2O2CO3降解罗丹明B的研究

Bi_2O_2CO_3是一种Bi类半导体催化剂,文章研究了它的超声催化性能。首先,采用水热法制备了微球型的Bi_2O_2CO_3,利用X射线衍射(X-ray Diffraction, XRD)、扫描电子显微镜(Scanning Electron Microscope, SEM)、紫外-可见漫反射光谱对样品的晶体结构、微观形貌、光学特性进行了表征。然后,以罗丹明B(Rh B)作为模型污染物,通过研究超声催化降解罗丹明B来评测Bi_2O_2CO_3的超声催化性能。研究了催化剂的浓度(Ccatalytic)、初始罗丹明B染料的浓度(CRhB)和超声功率(P)等实验因素对超声催化降解效率的影响。得出在Ccatalytic=3 g·L-1,CRhB=10 mg·L-1和P=400 W条件下降解罗丹明B的效率最高,其最高降解效率可以达到91.7%。     

A four-phase AHP–QFD approach for supplier assessment: a sustainability perspective

Recently, companies have become increasingly aware of the need to evaluate suppliers from a sustainability perspective. Introducing the triple bottom line (economic, social, and environmental performance) into supplier assessment and selection decisions embeds a new set of trade-offs, complicating the decision-making process. Although many tools have been developed to help purchasing managers make more effective decisions, decision support tools, and methodologies which integrate sustainability (triple bottom line) into supplier assessment and selection are still sparse in the literature. Moreover, most approaches have not taken into consideration the impact of business objectives and requirements of company stakeholders on the supplier evaluation criteria. To help advance this area of research and further integrate sustainability into the supplier selection modelling area, we develop an integrated analytical approach, combining Analytical Hierarchy Process (AHP) with Quality Function Deployment (QFD), to enable the ‘voice’ of company stakeholders in the process. Drawing on the sustainable purchasing strategy development process, our AHP–QFD approach comprises four hierarchical phases: linking customer requirements with the company's sustainability strategy, determining the sustainable purchasing competitive priority, developing sustainable supplier assessment criteria, and lastly assessing the suppliers. An illustrative example is provided to demonstrate the application of the proposed approach.     

Exponential random graph modeling for diagnosis of out-of-control signals in social network surveillance: Signal interpretation in social networks with ERG modeling

Monitoring of the social networks for detecting anomalous behavior could be vital for the system's survival. This anomalous behavior could raise from any changes in behavior or attributes of a particular individual or groups of individuals in the network and causes structural changes. Multivariate statistical process control charts are effective tools for this purpose while Exponential Random Graph Models are used to model highly interdependent data of the network. So after selecting a model for specific network, T² control charts are used for monitoring the network data to detect any anomalous behavior. Then the Mason, Tracy, and Young method is utilized for interpreting an out-of-control condition. Finally, some real-world examples are used to evaluate the performance of the proposed diagnosis approach. Since complicated dependency in a social network makes different choices in model selection for Exponential Random Graph Models and this causes various results in the evaluation study, if the impact of diagnosis result is not seen in model selection, the appropriate model will not be necessarily selected and this will affect the effectiveness of the whole system. So, in this paper for improving the performance of diagnosis, two indices are introduced and added to model selection criteria and then the appropriate model could be selected based on the decision-maker's preferences.     

A numerical methodology to assess the quality of the design velocity field computation methods in shape sensitivity analysis

The velocity field in shape sensitivity analysis is not uniquely defined although it must meet numerous theoretical and practical criteria. These practical criteria can be used to compare the existing velocity field computation methods which meet the theoretical criteria, but only in qualitative terms. When the FEM is used in design sensitivity analysis (DSA), due to the FE discretization error, the DSA errors will depend on the design velocity field considered. This paper presents a numerical methodology for quality evaluation of design velocity field computation methods in quantitative terms based on the analysis of the DSA discretization error. The sensitivity of the squared energy norm (χ_m = ?∥u∥2/?a_m, a_m being a design variable) has been taken as the magnitude to measure the error of the DSA. For h‐refinements, the squared error in energy norm (∥ e ( u ) ∥²) and the error in χ_m(e(χ_m)) are theoretically related by a constant which is independent of the refinement degree of the FE model. The quality of the design velocity field computation methods can therefore be assessed in terms of the stability of e(χ_m) /∥e(u) ∥2 in sequences of meshes. An example of the use of this methodology, where six design velocity field computation methods are compared, is presented. Copyright © 2004 John Wiley & Sons, Ltd.     

Spatial regression analysis of NOx and O3 concentrations in Madrid urban area using Radial Basis Function networks

This paper discusses the performance of Radial Basis Function networks (RBF) in a problem of spatial regression of pollutants in Madrid. Specifically, the spatial regression of NO_x and O₃ is considered, in such a way that, starting from a set of measuring points provided by the air quality monitoring network of Madrid, the complete surface of the pollutants in the city is obtained. This pollutant surface can be used as an initial step for modeling intra-urban pollution using land-use regression techniques for example. Also, different works has used a pollutant surface to study the patterns of pollution in different cities in the world and also to establish their air monitoring networks under mathematical criteria. The paper is focussed in analyzing the performance of RBF networks to obtain this first pollutant surface, so different RBF training algorithms are tested in this paper. Specifically, evolutionary-based RBF training algorithms are described, and compared with classical training algorithms for RBF networks with Gaussian kernels. The inclusion of meteorological variables in the RBF networks are also discussed in the paper. The experimental part of the article studies real results of the application of RBF networks to obtain a first pollutant surface of NO_x and O₃, using the data of the air pollution monitoring network of Madrid and the meteorological network of the city.     

An integrated multi-objective decision making process for the performance evaluation of the vendors

The objective of the study is to select the best performing supplier among the group according to the prioritisation of performance criterion through the application of techniques like MISM (modified interpretive structural modelling), MICMAC (impact matrix cross-reference multiplication applied to a classification), and AHP (analytical hierarchy process). To understand the interaction between the factors and to prioritise them, MISM technique has been applied by using which weights have been calculated for the performance factors and establishing a contextual relationship between the available factors and then ranking of the factors is done based on the results obtained. In the MICMAC analysis, performance criteria are classified into four clusters depending upon their driving power and dependence power. This helps to find out which criteria are influencing the supplier selection process. AHP is used to rank the supplier to find the best one from the group of suppliers. After ranking the suppliers, sensitivity analysis has been applied to determine the most critical criteria i.e. how sensitive is the ranking of the alternative to the change in weights of the criterion or the alternatives. A study was done in an automotive component manufacturing industry in the southern part of India. Finally, validation of the model is performed by the sensitivity analysis.     

Global supplier selection: a fuzzy-AHP approach

Global supplier selection has a critical effect on the competitiveness of the entire supply chain network. Research results indicate that the supplier selection process appears to be the most significant variable in deciding the success of the supply chain. It helps in achieving high quality products at lower cost with higher customer satisfaction. Apart from the common criteria such as cost and quality, this paper also discusses some of the important decision variables which can play a critical role in case of the international sourcing. The importance of the political-economic situation, geographical location, infrastructure, financial background, performance history, risk factors, etc., have also been pointed out in particularly in the case of global supplier selection. Supplier selection problem related to the global sourcing is more complex than the general domestic sourcing and as a result it needs more critical analysis, which could not be found properly in past available literatures. This paper discusses the fuzzy based Analytic Hierarchy Process (fuzzy-AHP) to efficiently tackle both quantitative and qualitative decision factors involved in selection of global supplier in current business scenario. The fuzzy-AHP is an efficient tool to tackle the fuzziness of the data involved in deciding the preferences of the different decision variables involved in the process of global supplier selection. The triangular fuzzy numbers are used to transform the linguistic comparison of the different decision criteria, sub-criteria and performance of the alternative suppliers. The pairwise comparison matrices help in deciding the synthetic extent value of each comparison and finally, the priority weights of one alternative over another are decided in this paper. An example from a manufacturing industry searching for the global supplier for a critical component is used to demonstrate the effective implementation procedure of proposed fuzzy-AHP technique. The proposed model can provide the guidelines and directions for the decision makers to effectively select their global suppliers in the current competitive business scenario.     

The effects of integrated supply management practices and environmental management practices on relative competitive quality advantage

Superior quality continues to be central to a manufacturing company's success, and firms are constantly striving for quality advantage over their competition. It is therefore crucial for companies to ensure the sound practice of quality management principles. However, companies must also seek additional avenues to enhance the relative competitive quality advantage of their product offerings. In this context, we examine the role of integrated supply management practices (SMP) and environmental management practices (EMP) as drivers of actual and perceived quality. We suggest that these groups of practices, in addition to quality management practices, are crucial in an increasingly transparent, competitive and global business environment. As such, SMP can be leveraged with suppliers providing expertise, capabilities and high quality inputs for the final product, resulting in higher actual quality, but also via the suppliers’ more intangible reputation, generating higher perceived quality. Similarly, EMP can result in process improvements impacting actual quality, but can also create a favourable image of the company among customers, enhancing the perceived quality of the firm's products. Overall, we suggest SMP and EMP as key strategic drivers aiding in the further differentiation of firms. Our hypotheses are grounded in the resource-based view of the firm, and are tested with survey data collected from 434 manufacturing plants.     

A genetic algorithm based grey goal programming (G3) approach for parts supplier evaluation and selection

The problem of part supplier selection is a major concern for all manufacturers when seeking to enhance the products’ quality and productivity. The objective of this paper is to propose an integrated genetic algorithm based grey goal programming (G³) approach to solve the part supplier selection problem. The main factor in part supplier selection is the assembly relation of the parts so as to find the suitable suppliers combination for the parts of a product. We first identify the main factors affected on supplier selection. We then present a grey-based goal programming model to work as the fitness function to evaluate the suppliers with respect to the total deviation the factors have from the ideal values. Since the objective is to find the best solution, a genetic algorithm is used to solve this problem for faster and better evaluation. The novelty of this integrated approach is to apply both qualitative and quantitative factors at once in one model and to use the grey theory to cover the lack of information of qualitative factors in order to find a solution in a near real situation.     

Supplier post performance evaluation: the effects of buyer preference weight variance

We use empirical post performance data of suppliers and buyers to demonstrate that degrees of variability of buyer evaluation team weight preferences can have varying effects on the perception of supplier performance attributes. We first justify then use data envelopment analysis (DEA) as a research tool to integrate buyer performance and buyer weight preferences with supplier performance and then use the results of the dyadic DEA evaluation model to analyse three propositions. This integration is essential to a dyadic approach. Results indicate that buyer preference weights on supplier performance attributes influence the perception of supplier performance sometimes in unexpected ways dependent on the degree of conformity versus disparity in the factor weights that was put into operation using assurance regions. As a result, buyer performance factors and preferences must be measured and their effects understood before post performance supplier evaluation is completed and other components of a relationship management plan are implemented.     

Supplier Selection Fuzzy Model in Supply Chain Management: Vietnamese Cafe Industry Case

Supplier selection is a vital part of the supply chain and is also a current issue that concerns businesses today as supplier quality directly affects the operations of the organization. Choosing the right supplier can help businesses increase productivity, competitiveness in the market, and profits without having to lower the quality of the products. However, choosing a supplier is not a simple matter, it requires businesses to consider many aspects about their suppliers. Therefore, the goal of this study is to propose an integrated model consisting of two models: Fuzzy Analytics Network Process (Fuzzy-ANP) model and Weighted Aggregated Sum Product Assessment (WASPAS) to solve the problem above. The Fuzzy-ANP model was developed to evaluate the weightings of the supplier selection criteria, and the WASPAS Model was used to rank the suppliers. An example of supplier selection in the coffee industry in Vietnam was studied to validate the model, namely 5 main criteria, with 16 sub-criteria, and 7 suppliers. The model test results show that the Fuzzy ANP and WASPAS integration model was suitable. In future, these developing models can apply to other industries or integrate with other models.