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Supplier selection is a strategic decision and calls for serious attention in both academia and industry daily. It is the process of selecting suitable suppliers, who offer necessary products or se...

Supplier selection is an important and strategic decision of supply chain. Identifying and selecting the right suppliers to provide specialty chemicals, spares parts, oils and lubricant in an oil and gas industry impacts oilfield operations, supply chain performance and competitiveness of the firm. The supplier evaluation and selection is based on multiple quantitative and qualitative criteria. Thus, it is a multi-criteria group decision making (MCGDM) problem which involves importance ratings of suppliers with respect to criteria by group of decision makers (DMs) which are vague and imprecise. In this paper, a supplier's evaluation and selection based on nine criteria has been demonstrated using integrated fuzzy entropy (FEntropy) and fuzzy complex proportional assessment (FCOPRAS) methods in an Indian oil and gas company. Fuzzy entropy method is used for objective weight determination of criteria while FCOPRAS for suppliers ranking and selection.

Supplier evaluation and selection is an important step during the manufacturing process for a product to meet quality requirements. This paper proposes a new supplier evaluation and selection model based on quality matchable degree. In this model, the quantitative and qualitative indicators are involved to describe the quality assurance of suppliers and the manufacturer. The matchable degree of the supplier is calculated according to the quality level of the manufacturer. The multivariate quality loss function method is used to determine the score of each supplier. The implementation of the proposed model is also given in details. An example is presented to illustrate the implementation of the proposed model. The proposed model is compared with other methods are summarised. [Submitted 05 August 2018; Accepted 30 September 2018]

The Indian automotive industry is now the fastest growing industry in the world ranking at fourth position replacing Germany. For any manufacturing industry, it is not only the customers but suppliers also play a very vital role in success. The proper selection of suppliers is necessary. In this research perception of 40 experts from the car manufacturing industry in India obtained about supplier selection scorecard parameters. The ranking is done based on preference. This ranking is then compared with the ranking of key parameters obtained from the literature survey and Dickson's (1966) ranking. Dickson's study is referred to as the base of research in this area since 1966 by most of the researchers. This research helps - understanding how Indian car manufacturing industry's perceptions are different than global perceptions based on literature survey and Dickson's 23 criteria. It provides the Indian car manufacturing industry's preference rankings for these parameters.

Sourcing represents one of the most upstream supply chain activities, which often dictates the rest of downstream supply chain activities. As such, a poor sourcing decision has a significant trickle-down effect on the effectiveness of a buying firm's manufacturing and logistics decisions. Considering such significance, it is imperative for the firm to make a wise, systematic decision in evaluating and selecting suppliers. To support a critical sourcing decision, we propose a supplier evaluation decision-aid tool within the framework of business analytics, which takes in to account the multiple facets of supplier performance evaluation criteria. As a core of such a tool, we develop a relatively new model that is capable of considering both objective and subjective performance criteria simultaneously using a combination of data envelopment analysis (DEA) and analytic hierarchy process (AHP). To verify the practicality and validity of the proposed analytics-based tool, we applied it to an actual problem facing a US aerospace company.

As companies increasingly focus on their core competences, the relationship with suppliers becomes more crucial. In complex supply chains, it is vital to continuously evaluate suppliers to aid the ...

The history of military activities known as logistics and supply chain goes back to the history of the wars. The military supply chain management (MSCM) involves coordinating production, inventory and transportation between the elements of a supply chain, to provide the best combination of response and efficiency for the base that feeds it. In fact, it must operate with mutual understanding of military issues such as strategy, tactics, military intelligence, training, human resources and financial support, because the military's focus is more on response to operational requirements. In this paper, a multi objective programming (MOP) model has proposed to determine the quality, quantity and the risk factors of the best suppliers in the military supply chain. The designed model solved by the MOPSO algorithm and the best answer determined after ranking the Pareto solution set from the TOPSIS. The results of the tests prove that this model has high efficiency.

In recent years, companies pay more attention to ethic and ethical issues have become a key determinant of performance in the business arena. This is not an area which has been systematically examined. From the literature, it appears to be assumed that the supplier selection is multiple attribute decision making/multiple criteria decision making (MADM/MCDM) problem, information and performances usually incomplete and uncertain, and supplier ethics is becoming another key factor to consider, too. The question typically posed is; how ethical factor can be modelled formally for an established MCDM models? In this paper, we propose a new interval TOPSIS-based procedure to resolve this problem. So, the proposed method has a capability to identify a measure of unethical performance with input and output system balancing, and consist of the two-step. In the first step, a new interval TOPSIS-based approach is used, results of this step are input for conventional interval TOPSIS method in the second step. The results indicate that the accuracy of the decision has been increased by considering the ethical factors.

In order to optimise a supply chain, the effective management of suppliers plays a key role. Firms seeking competitive advantage are participating in cooperative supply chain arrangements, such as strategic partnerships, which combine their individual strengths and unique resources. Supplier selection is a multi-criteria decision making problem which includes both qualitative and quantitative factors. In the present scenario environmental criteria should also be given due importance, this further increases the challenge of supplier selection. Generally, decision makers' judgments are often uncertain and cannot be estimated by an exact numerical value. The grey systems theory is a powerful tool to deal with uncertain information. This paper presents a structured model for evaluation and selection of strategic supplier. Stepwise Weight Assessment Ratio Analysis (SWARA) is applied to decision making in order to calculate the relative importance of the criteria. The COmplex PRoportional ASsessment of alternatives with grey relations (COPRAS-G) method is used for supplier evaluation and selection of strategic supplier. A case study is presented to demonstrate the applicability of the proposed model to select a strategic green supplier.

Supplier selection is the process of finding the appropriate suppliers who can provide the buyer with the right quality products and/or services at the right price, in the right quantities, and at the right time. In this paper, a comprehensive overview of the literature on supplier selection criteria and supplier evaluation approaches is presented. More than 25 major production, manufacturing, operations management, and control journals published in 1995-2018 are reviewed. The literature review is divided into three fields, namely criteria in supplier selection, approaches in supplier selection, and a summary of the survey. This work emphasises the historical progression in these fields and offers the latest findings in this area in an attempt to make it easier for supply chain and logistics engineers to find a convenient principle or technique for supplier selection in various scenarios. Finally, future research directions are suggested.

Strategies of market diversification push companies to provide novel products and services to customers, belonging to new geographic and demographic segments. Additionally, market development strategies targeting non-buying customers in selected segments or new buyers in new segments may be paired with increased product diversification and improved business agility. To fulfil the requirements associated with manufacturing, a wider range of products and increased customised demands imply having a wider set of competences available. Most companies find it increasingly difficult to have all required competences in their internal structures; therefore, they need to rely on strategic business partnerships and suppliers to be successful. In this paper, we discuss a data fusion decision approach for supplier and business partner evaluation, which includes past, current and forecast information about business partners. This approach may prove vital for companies to establish strong collaborative business networks., This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the project scope: UID/CEC/00319/2013.

Today, the global market is confronted with short-lifecycle products or highly demanding customers that calls a lot of focus on supply chain. Among the activities of the supply chain, activities of effective purchasing are the most important activity to proper selection of suppliers since of supplier evaluation and selection problem is a multi-criteria decision-making problem that is along with high degree of ambiguity and uncertainty in the real world decision making. Since the information on real world decision-making are often imprecise and expressed verbally therefore the fuzzy sets theory can be effectively used to solve such problems. This paper first provides an overview on the supplier evaluation and selection problem, and then presents a decision making model that integrates interval-valued intuitionistic fuzzy AHP (IVIF-AHP) and interval-valued intuitionistic fuzzy TOPSIS (IVIF-TOPSIS) to solve such problems, and in the end, provides a numerical example to demonstrate the use of the proposed approach.

In supply chain management one of the most important research areas is upstream management. Most studies and proposed models on this issue were with partial solutions and some were specifically academic hypotheses. This paper wraps three issues; supplier selection decision criteria, supplier evaluation and selection and purchase allocation to selected supplier(s) with methodological framework and examples. The findings and subsequent discussions were aimed to provide practical guidance for SCM researchers to manage the upstream of supply chain. To cope with the complexity, manufacturers are considered as coupling point and the whole chain is divided into upstream and downstream. Vast decision criteria were handled statistically and in later two OR tools, AHP and LP, were used to select the best supplier panel and to maximise the purchaser's satisfaction. The model ensures optimal satisfaction of manufacturers with a proper management of their upstream players and will contribute to dig out the optimal satisfaction of supply chain.

This study explores consumer perceptions of supply chain partners. In particular, we analyse whether a supplier's corporate ability affects consumer evaluations of a manufacturer. We also examine the moderating effect of a supplier's market position, either the top-dog (strong) or underdog (weak) position. Using a vignette-based experiment, we find that a supplier's high corporate ability does not affect how consumers evaluate the manufacturer. However, a supplier's low corporate ability leads consumers to devalue the manufacturer. We also find that the effect of a supplier's corporate ability is moderated by the supplier's market position: the link between a supplier's low corporate ability and consumers' negative evaluations of a manufacturer becomes stronger when the supplier is perceived as in a top-dog position.

A good supplier is a key resource for an organisation in reducing costs, improving productivity and quality of the product. Hence, selection of potential suppliers has become an important task of supply chain management. Traditionally, procurement is considered as buying the material at cheapest price and maximises the buyer's profit. But this does not necessarily lead to an optimal situation for all members of a supply chain. This paper attempts to propose an analytical framework for supplier evaluation and selection using analytical hierarchy process (AHP) and linear programming problem (LPP). Supplier selection process includes evaluation of multiple criteria and various constraints associated with them. A case of auto parts manufacturing firm is analysed. After analysing the case, the suppliers were ranked based on their performance using AHP technique and LPP is then adopted to determine the optimum quantity to be procured from each of the best ranked supplier.

The objective of this paper is to identify and investigate the various supplier selection (SS) and supplier evaluation (SE) benefits, SS and SE criteria, supplier development (SD) benefits, SD criteria and SD activities, and rank them according to the level of importance. This paper reports the findings of a survey carried in 150 Indian manufacturing organisations over five sectors namely automobile, machine tool/equipment, electrical/electronics, process and fast-moving consumer goods (FMCG) through a well designed and validated instrument/questionnaire. The results of this survey give the top ten SS and SE benefits, SS and SE criteria, SD benefits, SD criteria and SD activities according to their level of importance. The practitioners and academicians working in this area can use the results of this survey to decide the importance level of these factors in their organisation. This study can be precise for a specific sector or type of industry by modifying the instrument/questionnaire, based on the opinion of experts from the specific sector or type of industry.

Supply chain management SCM is a system that contains multiple entities, processes and activities from suppliers to customers. In a supply chain, raw materials and information flow from the supplier to the manufacturer, before final distributions to customers as finished products or services. The capability of collaboration and information sharing is essential to improve the overall effectiveness of the SCM. Collaboration involves devising a set of strategies in which external and internal actors with different capabilities achieve their common goals and capabilities to face global competition. Small and medium enterprises SMEs are more sensitive to environmental changes of market as compared to large organisations. To achieve competitive advantage SMEs should strategically implement collaboration and information sharing practices in their supply chains. In this study, authors have used a survey-based approach to study collaboration and information sharing practices for SCM in Indian SMEs. The results revealed that supplier selection and evaluation, involvement of customers in decision making and use of IT for different tools has significant impact on business performance of Indian SMEs.

Sole purpose of supplier selection is not limited to get supply at low cost and at right time. Supplier selection is a strategic decision to fulfil company’s goal for long period of time at low risk. To accomplish this objective companies are moving from reactive buying to proactive buying to give more priority to co-creation of wealth with supplier/s. Considering this issue an attempt has been made in this paper to give systematic review of supplier selection and evaluation process from 2005 to 2012 to answer three main questions: (i) Which method is more appropriate for supplier selection? (ii) Which evaluating criteria were most cited? (iii) Is present trend of research is adequate enough to support proactive buying? In this regard, 78 papers are classified into 10 categories to identify factors affecting supplier selection and evaluation process. Statistical analysis has been conducted with software “R” to have better insight on the trend of research. Recommendations and future work is also included to verify inadequacy of existing methods, if any, to support proactive buying process.

The majority of Hong Kong's trading companies' involvement in the supply chain is to bid orders from global distributors and retailers, and then search for appropriate contract manufacturers to produce the goods. This paper is on the establishment of a sustainable procurement model for trading companies. The model is defined with a hierarchy of sustainable procurement criteria with the incorporation of economic, environmental and social aspects. A two-stage ranking and selection approach is implemented in the proposed sustainable procurement model. The pre-screening mechanism is to shortlist suppliers to enter the second stage which is the conventional AHP-based selection process. The original list of criteria is also consolidated into a refined criteria hierarchy after pre-screening. The tedious pairwise comparisons in the AHP-based final selection stage are reduced significantly as only a few shortlisted suppliers are to be evaluated, while the performance indicators are also reduced in the refined criteria hierarchy.

Supplier selection and evaluation have become one of the major decisions for managers in production and operations management sections for both industry and service environments. This research presents a new model that is gained by combination of house of quality (HOQ) which is a part of quality function development (QFD) utilising analytical network process (ANP) to select the appropriate supplier. This study begins with identification of features that purchased products should have them (WHAT’s, internal variable) to satisfy companies requirements, then create criteria to evaluate suppliers (HOW) to rank suppliers based on merit index to determine relative importance of factors in QFD used ANP.

The supplier selection is critical towards the success of a company. This paper compares four different methods to support decision makers in selecting the most appropriate supplier. When using Fuzzy logic, the importance of each criterion gets influenced by the level of decomposition in the hierarchical model. Fuzzy logic does not have the capability to measure the level of consistency in the judgments provided by a decision maker. On the other hand, analytic hierarchy process (AHP) cannot capture subjectivity (or fuzziness) of human judgements as the verbal assessments are converted into crisp values. Fuzzy analytic hierarchy process (FAHP) is a merger of the two methods, Fuzzy logic and the Analytic Hierarchy Process (AHP), which inherits the advantages of both and, therefore, addresses the above mentioned problems. The FAHP method is useful in identifying a suitable supplier and to evaluate its performance as demonstrated in the described case study.

Supplier selection is considered as the most important step in the course of supply chain management, which helps in reducing the costs, improve the quality and deliver the products on time, thereby sustaining the intense pressure in the present global competing environment. Many researchers have done their research on supplier selection and evaluation, the processes which determine the success of any supply chain and proposed new ideas and methods for the same. Around 225 articles from various journals have been reviewed. Based on the research in the previous literature, all the papers have been classified into three categories – conceptual approach, empirical study and analytical models. In this paper, we present a review of the past literature on supplier selection from 134 articles which include only the categories of conceptual approach and empirical study.

Recently, supply chain management (SCM) has been considered by many researchers. Supplier evaluation and selection plays a significant role in establishing an effective SCM. One of the techniques that can be used for selecting suppliers is data envelopment analysis (DEA). In some situations, to select suitable suppliers, purchasing managers deal with undesirable outputs, dual-role factors and imprecise data. The objective of this paper is to propose a new Russell model for selecting the best suppliers in the presence of undesirable outputs, dual-role factors and imprecise data. A case study demonstrates the application of the proposed method.

In the present dynamically changing markets, for an efficient and effective supply chain to take place, it is necessary to focus on the stage of supplier selection. Supplier selection helps in reducing the costs, improve the quality and deliver the products on time, thereby enabling the organisations to survive the rising competition in the current scenario of global competing environment. Many scholars have done their research on supplier selection and evaluation, the processes which determine the success and efficiency of any supply chain proposing various models and using different criteria which influence the supplier selection. Based on this, around 225 articles by various researchers from various journals have been reviewed. Based on the research in the previous literature, all the papers have been classified into three categories – conceptual approach, empirical study and analytical models. In this paper, we present a review of the past literature on supplier selection from 89 articles which include only the category of analytical approach.

In today’s turbulent business environment it is evident that a business must be agile as well as efficient. Supply chains can help in achieving this through enhancing the ability to respond quickly to customer demand and by reducing operating costs. One of the biggest challenges facing organisations today is the need to respond to ever increasing levels of volatility in demand. Agility is defined as the capability of surviving and prospering in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer designed high-quality, high-performance, products and services (Chandna, 2008). Agile supply chains need to be highly flexible in order to reconfigure quickly in response to changes in their environment. Supplier lies in the first node of the supply chain. Rational supplier evaluation (and selection) is really important to the entire supply chain’s agility. An effective supplier selection process is indeed essential for this. To this end, present work highlights an integrated performance appraisement module towards suppliers’ evaluation in agile supply chains. Apart from evaluating suppliers’ overall performance index; the study has been extended to identify ill-performing areas in which suppliers should prosper in the future. Fuzzy logic has been adapted here to facilitate the said appraisement modelling.

With growing concern about environmental protection, carbon emission has become an important factor for global supplier evaluation. Most of the earlier models have focused on cost, quality, geographical location, risk, etc., issues but not given enough importance to carbon emission for global supplier evaluation. Few studies have addressed sustainability but not succeeded to quantify carbon footprint for outsourcing. This paper quantifies the outsourcing carbon footprint that is used as objective function in multi-objective fuzzy linear programming for global supplier evaluation and quota allocation. As specific contribution, different carbon management factors and outsourcing carbon cap are introduced in the model. In multi-objective formulation, total cost, quality rejection, late delivered item, carbon footprint, and total value of purchasing (TVP) are considered as five objectives that have to be optimised simultaneously. AHP is used to calculate the performance of suppliers’ on qualitative criteria. ...

To model decision problems properly has a direct effect on the quality of the solution. The aim of this study is to minimise the errors arising from the decision maker during modelling for increasing problem-solving efficiency of analytic network process (ANP) method. In order to achieve this aim, a two-stage approach was proposed in this study: 1 the decision problem was modelled by ANP based on experts’ opinions 2 the model is assessed by structural equation modelling (SEM) for evaluate its validity and to purify it from expert sourced errors. To prove that, a supplier evaluation problem examined and modelled with ANP. Then the ANP model is discussed as a structural model and verified statistically by SEM. After evaluation, better representation of the decision problem has been provided. This study is the first to statistically evaluate the ability of modelling of ANP approach that is widely used in literature.

Suppliers’ evaluation and selection is a very important part of the supply chain management of a company. A suitable supplier can increase the effectiveness of the supply chain of a company and thus improve its competitiveness. This work suggests a fuzzy multiple criteria decision making (MCDM) model to evaluate and select suppliers, where membership function of each final fuzzy evaluation value can be developed. A Riemann integral-based mean of removals is applied to rank all the final fuzzy evaluation values and ranking procedure can be formulated. A numerical example demonstrates the feasibility of the proposed model.

There has been numerous number of studies related to the supplier selection and problems which adopted a variety of approaches to solve them, however, based on our knowledge none of them has been considered the major decisions related to supplier selection and order allocation in the presence of imprecise data. In an attempt to fulfil this gap, we present an integrated framework which involves two stages including supplier evaluation and order allocation. An augmented imprecise data envelopment analysis (AIDEA) is proposed for supplier evaluation as the basic IDEA model has two main drawbacks, i.e., unrealistic input-output weights and poor discriminating power among all suppliers. Then, an integer programming with fuzzy objectives and constraints is formulated to assign optimal quantity of order to allocated suppliers. The efficiency of suppliers derived from evaluation stage is an important link which integrates these two stages. Finally, the integrated methodology is implemented in a case study organisation.

Green supplier selection is a key phase in green supply chain management. Green supply chain management is defined as integrating environmental thoughts into supply-chain management, including product design, material sourcing and selection, manufacturing processes, delivery of the final product as well as end-of-life management of the product after its useful life. With worldwide growing awareness of environmental protection, green production is becoming an important issue for every manufacturing company and will determine the sustainability of a company in the long term. A performance evaluation system for green suppliers is felt indeed necessary to determine the suitability of suppliers to cooperate ‘greenly’ with the firm. In this paper, a methodology for the evaluation of suppliers’ environmental performances (green supplier selection) has been reported in order to evaluate and select the best candidate supplier. An interval-valued fuzzy set coupled with VIKOR method has been utilised. A case study h...

This paper purports to assess the adoption of low carbon and green supply chain management practices by the industrial supply chain sectors in India, which is inundated with manufacturing and service-oriented industries. The Indian organisations in both manufacturing and service-oriented sectors were sampled for empirical research study and structural equation modelling was used as a path analysis model to verify the hypothetical construction of the study. It was revealed in the research findings that low carbon and green supply chain management (LGSCM) practice is essentially a quality management initiative that requires bilateral efforts of the need and constraints for the integration of the whole supply chain along with the setting-up supplier evaluation methods ensuring sustainable environmental operations.

In supply management, the supplier-buyer strategic relationship (SBSR) activities are critical elements to a firm’s competitive advantage. The purpose of this research is to introduce strategic purchasing (SP) and supplier evaluation system (SES), and uncertain business environmental factors (UBEF) as constructs that could have the potential to contribute to the success of supplier-buyer strategic relationship efforts (SBSR). Based on the relational view of the firm, the authors propose that SP is an antecedent of SES, and UBEF practices and can create value for the buying firm in terms of better purchasing performance. Hypotheses derived from the key features of SP, SES and UBEF practices are tested using structural equation modelling through field research on a sample of 270 senior purchasing executives of 54 manufacturing companies in India. Findings from this study indicate that there is significant evidence to support the hypothesised model in which SP exerts direct influences on SES practices and UBEF, as well as a direct impact on purchasing performance mediated through SES.

Supplier evaluation and selection problems are inherently multi-criteria decision problems. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems. Data Envelopment Analysis (DEA) has been used to evaluate suppliers’ performance when there are multiple inputs and outputs in the supplier selection problem. The DEA determines the relative efficiencies of multiple suppliers. These relative efficiencies are then used to provide benchmarking data for reducing the number of suppliers. The DEA models used for supplier selection require numerical data for all the inputs and outputs for all the suppliers. However, this information may not be readily available in real-world problems. In this paper, we propose a novel DEA model that addresses this gap in the supplier evaluation literature. The proposed model can measure suppliers’ efficiency in problems exhibiting: the presence of undesirable outputs; the lack of input variables and the presence of zero or negative values in the data set. We also present a case study at Saipa, Iran’s second-largest car maker, to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms.

In conventional usage of Data Envelopment Analysis (DEA) for supplier selection, it is assumed that producing more outputs relative to fewer inputs is a criterion of efficiency. However, in the presence of undesirable outputs, suppliers with more good (desirable) outputs and less bad (undesirable) outputs relative to fewer inputs should be recognised as efficient. In addition, to get a complete ranking among suppliers and also eliminate unrealistic weighting schemes among them, this paper proposes a cross-efficiency formulation of DEA, which can treat undesirable outputs. A numerical example demonstrates the application of the proposed model in supplier ranking context.

The need to gain a global competitive advantage on the supply side has forced businesses to search for effective supply network strategies. The effective selection of suppliers is the key ingredient for the success of supply networks. A variety of analytical methods ranging from simple weighted techniques to complex mathematical programming approaches have been proposed for supplier selection. However, these models are generally aimed at supporting a decision maker (DM) in the final selection phase and they have failed to consider a holistic view of the supplier selection process. Supplier evaluation and selection problems are inherently multi-criteria decision problems. Supply networks are now not only configured by suppliers, but also consist of manufacturers, retailers and customers. Therefore, a holistic and comprehensive approach for evaluating these elements in supply networks is required. We propose a multi-objective mathematical programming approach to select the most appropriate supply network elements. The process performance index (PPI) is used as an assessment tool for the supply network elements and the analytic hierarchy process (AHP) is used to integrate the objectives of the proposed mathematical programme to a single one. The efficacy and applicability of the proposed methodology is demonstrated with a numerical example.

Supply chain management (SCM) is regarded as a means to maximise the overall value generated. SCM includes all functions involved in receiving and filling a customer request by forging close relationships with customers and suppliers. Strong relationships and mutual trust between buyer and suppliers are essential for the successful implementation of SCM. This paper is based on the data collected from a survey of 66 Indian textile-related organisations carried out to ascertain issues related to supplier selection and evaluation. The findings indicate that the main criteria for supplier selection in Indian textile organisations are service provided, quality and on-time delivery of material. Effect of supplier selection on performance of organisation and factors influencing development of trust between buyer and suppliers are also discussed.

This study proposes a mechanism to evaluate suppliers for involvement during product development process. The proposed methodology is discussed within a multinational automobile firm and preliminary analysis indicates that the approach provides an effective mechanism for selecting suppliers involved in the product development process. To remedy to supplier integration problems, a clustering approach – fuzzy adaptive resonance theory (Fuzzy ART) – is used. The proposed methodology is explained using a case study that is realised in an automobile firm. Eventually, the results are presented comparatively, which obtained by current supplier evaluation system of the firm and provided from Fuzzy ART methodology. [Submitted 14 July 2009; Revised 16 November 2009; Accepted 17 November 2009]

The selection of appropriate supplier is one of the most critical decisions in the competitive business environment. In this study a decision support model has been developed for supplier selection using analytic hierarchy process by considering both qualitative and quantitative criteria. The proposed model is illustrated with a case study of an electroplating industry to demonstrate the effectiveness of this model. Through the proposed model, it is promising to effectively combine the specialised knowledge, experience and the quantitative data to select the best suppliers.

Case study research is conducted using Kaizen events to study learning mechanisms in process improvement initiatives. It is found that cumulative production experience is not the significant factor that has been traditionally supposed. A forward-looking model of learning is proposed that replicates empirical results at least as well as a classical learning curve. This model has implications for managing buyer-seller relationships on a forward-looking basis.

In the face of acute global competition, supplier management is rapidly emerging as a crucial issue to any companies striving for business success and sustainable development. To optimise competitive advantages, a company should incorporate ‘suppliers’ as an essential part of its core competencies. Supplier evaluation, the first step in supplier management, is a complex multiple criteria decision making (MCDM) problem, and its complexity is further aggravated if the highly important interdependence among the selection criteria is taken into consideration. The objective of this paper is to suggest a comprehensive decision method for identifying top suppliers by considering the effects of interdependence among the selection criteria. Proposed in this study is a hybrid model, which incorporates the technique of analytic network process (ANP) in which criteria weights are determined using fuzzy extent analysis, Technique for order performance by similarity to ideal solution (TOPSIS) under fuzzy environment is adopted to rank competing suppliers in terms of their overall performances. An example is solved to illustrate the effectiveness and feasibility of the suggested model.

Supplier selection is one of the most critical activities of purchasing because of the key role of supplier’s performance on cost, quality, delivery and service. Depending on the purchasing situation, the criteria have varying importance and there is a need to weight criteria. Practically in supplier selection problems, most of the input information is not known precisely. An analytical way to reach the best decision is more preferable in many such business platforms. Analytical hierarchy process (AHP) is one of the best ways for deciding among the complex criteria in different levels. AHP converts the qualitative factors of suppliers into quantitative measure reliably. This paper suggests the use of AHP, one of the strong methods in multi-criteria decision making to evaluate and select the supplier using criteria such as quality, cost, delivery, service, technology, capacity, reliability, lead-time and responsiveness. Here 9 attributes, 27 sub-attributes and 7 alternatives are evaluated to select the best among them. Case study on supplier selection for a leading textile machinery manufacturing industry in South India is presented in this paper.

In today’s competitive environment, an effective supplier selection process has a significant place in the success of manufacturing companies. Selecting the right suppliers help to reduce the purchasing cost and increase the competitiveness of the company. An analytical way to reach the best decision is more preferable in many business platforms. Analytical hierarchy process (AHP) is one of the best ways for deciding among the complex criteria structure in different levels. In this paper, a new method is presented to deal with the performance evaluation and selection of supplier for a manufacturing industry using fuzzy alpha cut analysis. It suggests the use of triangular fuzzy number to express the subjective preferences of evaluators. A usability evaluation of the AHP-based model with seven suppliers is discussed and a hypothetical supplier selection problem is solved. Fuzzy analytical hierarchy process (FAHP) is a new multi-criteria evaluation method evolved from Saaty’s AHP when there is fuzziness in the decision making. The results of this paper provide valuable suggestions to managers on the improvement needed with respect to each criterion when selecting suppliers.

Increasingly, consumers and professional buyers are reluctant to purchase goods and services from firms that produced those goods and services in irresponsible ways, including the use of child and forced labour, unsafe working conditions, and processes which damage the environment. The reputations of firms that operate or source from irresponsible factories are increasingly at risk and, consequently, firms are taking steps to mitigate those risks. Supplier codes of conduct are part of a set of tools used to ensure that business practices at suppliers’ facilities are acceptable to buying firms and their stakeholders. Given the relatively recent existence and rapid growth of conduct codes, this paper addresses how their content has changed over time. A longitudinal analysis indicates that supplier codes of conduct are: becoming more explicit in defining transgressions and out-of-compliance situations, incorporating a broader array of human rights provisions, and using language that leads to working together to fix problems, rather than moving business out of suppliers.

Supplier evaluation and selection problems are inherently multicriteria decision problems. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems. However, traditional supplier selection models too often fail to consider the interaction and the capacity interdependency among the suppliers. Suppliers may exhibit internal interactions if the evaluation criteria used for one supplier are believed to be significantly affected by the evaluation criteria used by one or more of the other suppliers in the group. We propose a new branch-and-bound algorithm that generates portfolio alternatives based on Data Envelopment Analysis (DEA). The DEA model proposed in this study evaluates alternative supplier portfolios with a multicriteria model that considers possible interactions among the suppliers.

As environmental issues are of increased concern, many enterprises have participated in some forms of green-purchasing initiatives. Beside the traditional criteria, the supplier's environmental consciousness, environmental mission, advances in developing environmentally friendly goods, etc., should be evaluated. The attitude difference among supply chain partners will create potential problems for their cooperation and collaboration. A conflict evaluation method is proposed based on measuring their inconsistent attitudes toward important issues such as the environmental ones. A model is built for discovering weight parameters from experience. A pre-qualification process is suggested for screening the suppliers by eliminating those conflicts with the buyer firm.

This study developed a simple two-stage decision framework for evaluating suppliers of high-value and critical items with reference to a heavy engineering organisation by employing the Analytic Hierarchy Process (AHP). The first stage involved examining the qualifying criteria of the items on quality, while the second stage was concerned with identifying all other relevant attributes, including quality concerning high-value and critical items applicable to the organisation under study, and with finding out the relative importance of the same. The attributes were organised in a two-tier hierarchy showing four major criteria in the first tier and 17 subcriteria in the second tier. This enabled us to employ AHP to find out the relative importance of each individual subcriterion through pair-wise comparison between all major criteria and subcriteria by eliciting opinions from three experts. The findings indicated the quality/reliability of the items to be the most important criterion, followed by price and then technological capability of the suppliers. A group of five suppliers was evaluated on all 17 subcriteria with regard to their performance and the overall performance of the suppliers was compared on the factor scores obtained by each supplier.

The supplier evaluation is a key section of the intelligent internet supplier selection and evaluation system. The model used for supplier evaluation is back propagation neural network model which is introduced in the paper. The paper started with the brief introduction of the intelligent internet supplier selection and evaluation system. It provides an outline of the research project and then it concentrated to introduce the application of the BP NN model for supplier evaluation. The application introduced in the paper will include the design of the BP NN model, training of the BP NN model and test results.

For any supply chain management activity to operate effectively, there must be some method of monitoring supplier performance and the metrics in place to provide visibility of performance. Many different methods have been developed over the years to aid in this effort. In the literature, there are numerous examples for the application of various decision tools to aid in the selection of new suppliers or the evaluation of multiple suppliers of the same commodity. This study is an attempt to evaluate and score a focal company's existing suppliers of various commodities. The goal is to rate the suppliers comparatively using common measurable characteristics. An anonymous midsize manufacturing company in the aerospace industry has provided a full year of historical data on its 30 largest Tier 1 suppliers to aid this work. This study applies Data Envelopment Analysis (DEA) to the supplier data and results in the comparative rankings of supplier performance.