AHP-based approaches for supplier evaluation: Problems and perspectives

Creating resilient supply chains through a culture of measuring

The COVID-19 pandemic and the ensuing disruptions to global supply chains have brought risk management to the fore. While guidance on risk management is proliferating, an area that is largely untapped is risk measurement. The pandemic has made us realize the criticality of risk measurement and the need to develop a culture of continuous measuring. Based on our interviews with purchasing and supply management (PSM) professionals about how they measure and manage risk, we offer a framework integrating how to rethink risk measurement, how to continuously measure risk, how to translate measurement into action, and how to establish a culture of continuous measuring. It captures a shift in mindset that is needed to truly take risk measurement to the next level. Once this is accomplished, it can help PSM professionals build more resilient supply chains in a post-pandemic world, and serve as a foundation for PSM scholars to provide further guidance enabling practitioners to “walk the talk” when it comes to risk management.

Modelling supply chain viability during COVID-19 disruption: A case of an Indian automobile manufacturing supply chain

In recent years, supply chains seem to be moving more towards reconfiguring their networks to become more profitable. In the times of COVID-19, where the whole supply chains have been disrupted, suppliers are unable to supply, and manufacturers are unable to manufacture because of lockdowns in the various regions around the world. This pandemic can be compared to past earthquakes and tsunamis, as the coronavirus is also a natural disaster. Due to these past disruptions, organizations have taken many precautions and developed risk mitigation strategies to manage them. Because the COVID-19 outbreak shows the importance of new business perspectives like repurposing a viability strategy, that comes with sustainability and reconfigurability. Where reconfiguration focuses on adaptation, which directly means changes in resources and capabilities and repurposing focuses on a quick response solution to address the shortage. In this paper, a study has been done in two phases to model viability in an automobile supply chain during the COVID-19 times. In the first phase, a hybrid Multi-criteria Decision Making (MCDM) approach is used to get the best criteria and alternatives with sustainability and reconfigurability under consideration. The multi-objective mixed-integer linear programming (MOMILP) model has been developed in the second phase. Suppliers' weight that is obtained will be used to get the optimal order and allocation. This model will help develop supply chain strategies to cope with situations that hinder the firm's competitiveness. A case study of an Indian automobile manufacturer has been taken to show the applicability and effectiveness of the proposed methodology using GAMS/CPLEX solver.

Structural transformation of fuzzy analytical hierarchy process: a relevant case for Covid-19

Covid-19 has posed difficult and challenging situations to the supply chains and companies are in fix how to choose the vendors under the uncertainty and complexity in recent years. Therefore, this research aims to incorporate structural transformation of the fuzzy analytical hierarchy process (FAHP) that is most appropriate for the uncertainty and disruption caused by Covid-19 like situation for ensuring supplies from vendors. The conventional approaches for vendor selection and evaluation use numerous multi-criteria decision-making tools that may not ensure reliability in a dynamic situation caused due to Covid-19. In this research, Fleiss’ Kappa method ensures the reliability of responses from eight respondents by using pairwise comparisons and assigning weights as envisaged in FAHP. In addition to determine the reliability of responses, a step under FAHP has been altered. This alteration is demonstrated in the vendor selection case in the Covid-19 scenario. The research suggests a plausible system required to address the uncertainties associated with Covid-19 to select and evaluate vendors by modifying a FAHP. The proposed altered mechanism can be incorporated in a similar type of other decision-making circumstances such as Covid-19, where the decision-makers are more than one, and the situation is very dynamic. The study is likely to facilitate information management, algorithmic development in decision making, or machine-driven decisions in uncertain conditions. The study offers managerial implications to purchase managers to accommodate and combine multiple factors and responses concerning the vendor performances for their evaluation, thus making a process more reliable.

Supplier Selection Risk: A New Computer-Based Decision-Making System with Fuzzy Extended AHP

Supplier risks have attracted significant attention in the supply chain risk management literature. In this article, we propose a new computational system based on the ‘Fuzzy Extended Analytic Hierarchy Process (FEAHP)’ method for supplier selection while considering the relevant risks. We sought to evaluate the opportunities and limitations of using the FEAHP method in supplier selection and analyzed the support of the system developed through the real case of a Brazilian oil and natural gas company. The computational approach based on FEAHP automates supplier selection by determining a hierarchy of criteria, sub-criteria, and alternatives. First, the criteria and sub-criteria specific to the selection problem were identified by the experts taking the relevant literature as a starting point. Next, the experts performed a pair-wise comparison of the predefined requirements using a linguistic scale. This evaluation was then quantified by calculating the priority weights of criteria, sub-criteria, and alternatives. The best decision alternative is the one with the highest final score. Sensitivity analysis was performed to verify the results of the proposed model. The FEAHP computer approach automated the supplier selection process in a rational, flexible, and agile way, as perceived by the focal company. From this, we hypothesized that using this system can provide helpful insights in choosing the best suppliers in an environment of risk and uncertainty, thereby maximizing supply chain performance.

Suppliers selection based on intuitionistic fuzzy dimensional analysis

Supplier selection is a crucial activity in the supply chain management, and fundamentally it is a multi-criteria decision-making problem. However, the interaction of the criteria, the uncertain regarding to subjectivity and the imprecise information generally present in the provider selection process is still an open issue. The main purpose of this paper is to arise a new method to support the provider selection process. In this sense, this paper introduces the intuitionistic fuzzy version of dimensional analysis under a group decision making and multi-criteria environment applied to suppliers selection. Likewise, a set of decision makers reveal their preferences about the criteria and options in evaluation by mean of a linguistic grade that are mapped via intuitionistic numbers. Consequently, the importance of each decision maker and criteria is considered in the method. Hence, the methodology assumes that exist a choice better than the rest which is called ideal alternative. Each choice in evaluation, is compared with that ideal to induce an index of similarity. Additionally, the alternative with highest index of similarity is proposed as solution to the supplier selection problem. The method offer a systematic mode to deal with the interrelationship between the criteria attached into the decision making sphere. Finally, a numerical example is addressed to supplier selection to facilitate the understanding of the proposed method.

Integrated analytical hierarchy process and grey relational analysis approach to measure supply chain complexity

Purpose

The purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity (SCC).

Design/methodology/approach

Through extensive literature review, the authors discussed various drivers of SCC. These drivers were classified into five dimensions based on expert opinion. Moreover, a novel hybrid mathematical model was developed by integrating analytical hierarchy process (AHP) and grey relational analysis (GRA) methods to measure the level of SCC. A case study was conducted to demonstrate the applicability of the developed model and analyze the SCC level of the company in the study.

Findings

The authors identified 22 drivers of SCC, which were further clustered into five complexity dimensions. The application of the developed model to the company in the case study showed that the SCC level of the company was 0.44, signifying that there was a considerable scope of improvement in terms of minimizing complexity. The company that serves as the focus of this case study mainly needs improvement in tackling issues concerning government regulation, internal communication and information sharing and company culture.

Originality/value

In this paper, the authors propose a model by integrating AHP and GRA methods that can measure the SCC level based on various complexity drivers. The combination of such methods, considering their ability to convert the inheritance and interdependence of drivers into a single mathematical model, is preferred over other techniques. To the best of the authors' knowledge, this is the first attempt at developing a hybrid multicriteria decision-based model to quantify SCC.

AHP versus DEA: a comparative analysis for the gradual improvement of unsustainable suppliers

Purpose

Data envelopment analysis (DEA) and analytical hierarchy process (AHP) are two widely applied methods to evaluate and rank suppliers in terms of sustainability. In this study, to investigate the extent to which potential differences in the outcomes of these two methods influence the benchmarking strategies, a comparative analysis based on a common set of data gathered from 19 logistics service providers is implemented.

Design/methodology/approach

As suppliers' sustainability cannot be improved in a single-step process due to several limitations, improvement needs to proceed gradually. Therefore, using the self-organising map method, the suppliers were classified into clusters within a novel framework for gradually improving their sustainability. Then, the two processes of gradual improvement based on the outcomes of DEA and AHP were compared.

Findings

The findings show that although the rankings of suppliers guided by the methods correlated to a high degree, the benchmarking strategies provided by the methods for gradually improving the sustainability of suppliers differed considerably. In particular, whereas AHP suggests a benchmarking policy better suited for unsustainable or less sustainable suppliers with limited access to resources, DEA proposes one for suppliers able to dramatically boost their sustainability with few quick, significant leaps in performance.

Originality/value

First, this study revealed a novel gradual improvement framework using the self-organising map method. Second, it clarified the extent to which the benchmarking policies are influenced by the type of evaluation method.

Green Supplier Selection Criteria: From a Literature Review to a Comprehensive Knowledge Base

The supplier selection problem is one of the most important competitive challenges used by modern enterprises. Due to the fact that companies have to improve their practices in the management of a green supply chain that aims to respect green practices and technologies to increase sustainability, selecting the optimal green supplier is a challenging multi-dimensional problem. While most of the research has focused on the development and improvement of new methods, relatively limited attention has been paid to the identifying sets of criteria and capturing the domain knowledge. This issue is significant because a correctly identified set of criteria plays a vital role in supporting the decision-making process. The approach presented creates a gap between classical assumption of decision making and knowledge-based problem structuring. The current paper presents a methodological and practical background for capturing and handling knowledge about green supplier selection criteria, supported by a formal guideline for their appropriate acquisition. In addressing this research challenge, the aims of this paper are twofold: providing meta-analysis to reveal a collection of key data supported by a formal and trustworthy bibliometric analysis, and capturing knowledge in one place in the form of ontology for enabling selection and evaluation criteria of green suppliers is proposed. The proposed ontology is available for public use.

Strategic partner evaluation criteria for logistics service provider networks

Purpose

The purpose of this paper is to establish criteria for evaluating strategic partners in a network of logistics service providers (LSPs) to show how analytical network process (ANP) can be used to identify the weights of these criteria on a case-specific basis, and to investigate whether the ANP model can be used as a starting point to evaluate strategic partners for other LSP networks.

Design/methodology/approach

Based on a literature review of vertical cooperation, the authors develop an overview of criteria for the evaluation of partners in a network of LSPs. The authors then apply ANP at LSP1 to validate the criteria, identify weights for these criteria and to validate model outcomes. Furthermore, the authors investigate whether the ANP model developed for LSP1 can be applied to another LSP with similar characteristics (LSP2). In-depth interviews are used to draw conclusions on the modeling approach and the model outcomes.

Findings

The research shows that evaluation criteria for partners in vertical partnerships between shippers and LSPs are applicable to LSP partners in horizontal partnership networks. The ANP model with criteria weights provides a good starting point for LSPs to customize the evaluation framework according to their specific needs or operating environments.

Originality/value

Limited research is available on evaluating LSP partners in horizontal partnerships. To the best of the authors’ knowledge, this paper is the first to bring forward horizontal LSP partner evaluation criteria to develop an ANP model for LSP partner evaluation and to apply this to two cases, and to provide a starting point for evaluating partners in similar horizontal LSP networks.

Comparative Analysis of Factors for Supplier Selection and Monitoring: The Case of the Automotive Industry in Thailand

The integration of the global market makes the supply chain more complex and has great impacts on efficient supplier management strategies. The aim of this study is to present a systematic supplier management framework to integrate supplier selection and monitoring phases, which are not independent of each other. However, only a few previous studies have pointed out the differences between the two phases. The proposed methodology integrates a quantitative and qualitative approach, formulating multi-criteria decision making (MCDM) using the analytic hierarchy process (AHP) to evaluate the priorities of these criteria. This research explores the difference in the set of criteria for supplier selection and supplier monitoring. The results provide comprehensive insights into the criteria to help decision-makers, managers, and practitioners select appropriate suppliers and monitor suppliers’ performances in the automotive industry. Based on the result, it can be said that a company should integrate the supplier selection and monitoring process. Furthermore, the purchasing and manufacturing manager should continuously collaborate and synchronize the relative weights for the critical factors.

Dynamic supplier selection approach for mining equipment company

Purpose

This paper aims to address the supplier selection (SS) problem under dynamic business environments to optimize the procurement cost of spare-parts in the context of a mining equipment company (MEC). Practically, involved parameters’ value does not remain constant as planning periods due to fluctuation in the demand and their market dynamics. Therefore, dynamicity in the parameter is considered as an important factor when a company forms a responsive chain through most eligible suppliers with respect to planning periods. This area of study may be considered for their complexities to the approaches toward order-allocations with bi-products of unused and repair spare-parts.

Design/methodology/approach

An integrated methodology of analytic hierarchy process (AHP) and mixed-integer non-linear programming (MILP) is implemented in the two stages during each planning periods. In the first stage, AHP is used to obtain the relative weights with respect to each spare-parts of each criterion and based on that, the ranking is evaluated in accordance with case considered. And in the second stage, MILP is formulated to find the allocations of each spare-part with two distinct approaches through Model-1 and Model-2 separately. Moreover, Model-1 and Model-2 are outlined based on the ranking and efficient parameters-value under cost, limited capacities, quality level and delay lead time respectively.

Findings

The ranking and their optimal order-allocation of potential suppliers are obtained during consecutive planning periods for both unused and repair spare-parts. Subsequently, sensitivity analysis is conducted to deduce the key nuggets with the comparison of Model-1 and Model-2 in the changing of capacity, demand and cost per spare-parts. From this analysis, it is found that suppliers who have optimal parameter settings would be better for order-allocations than ranking during the changing planning period.

Practical implications

This paper points out the situation-specific approach for SS problem for a mining industry which often faces disruptive supplying environments. The managerial implication between ranking and parameters are highlighted through Model-1 and Model-2 by sensitivity analysis.

Originality/value

It provides useful directions for managers who are involved in the procurement of spare-parts in the mining environment. For this, suppliers are selected for order-allocation by using Model-1 and Model-2 in the dynamic business environment. The solvability of the model is presented using LINGO 17. Furthermore, the case company selected in this study can be extended to other sectors.

Comparison of performance measurement in different purchasing and supply management practices

Purpose

The purpose of this paper is to compare the characteristics of performance measurement in cross-functional and supplier-oriented purchasing and supply management (PSM) practices. It clarifies the purposes and content of performance measurement in three PSM practices.

Design/methodology/approach

A multiple case study approach is utilized in this study. Each of the cases is related to a contemporary performance measurement development project during the period 2015–2016. Interviews are conducted to obtain a pre-understanding of the research questions at hand. The case studies continue as an action research including measurement development workshops.

Findings

Identification of causalities between measurement objects and the measurement value benefits are contemporary issues of performance measurement development in the PSM context. The findings indicate that the measurement of outputs and outcomes instead of inputs and measurement supporting supplier relationship management is increasingly important in the service context. Measurement purpose affects the characteristics of PSM performance measurement. This study identifies that more informal control and non-standard measures are needed in interactive PSM practices such as cross-functional integration.

Research limitations/implications

The study provides practical examples of performance measurement in service purchasing, classifies the characteristics of performance measurement and highlights the need to extend the scope of measurement in the PSM context. The findings of the case studies reported support practitioners in developing performance measurement that satisfies contemporary managerial needs.

Originality/value

Earlier studies are often not explicit with the purpose of performance measurement. This study contributes to the existing literature by linking performance measurement characteristics to the understanding of PSM practices and providing a wider overview of the varying characteristics of performance measurement supporting strategic purchasing of services.

Intelligent evaluation of suppliers using extent fuzzy TOPSIS method

Purpose

In a developing economy like India, the contribution of small- and medium-sized enterprises (SMEs) to the national gross domestic product is significant. This sector creates immense employment opportunities and produces economic products and services. To survive in the globalized marked condition, it is essential for SMEs to be competitive on several fronts such as quality, cost, delivery, lead time, flexibility, etc. Hence, it is imperative for them to have a sound supplier base. Therefore, supplier selection problem (SSP) has a vital role to play in supply chain management of SMEs. The paper aims to discuss these issues.

Design/methodology/approach

However, SSP has now become a significant challenge to address due to the complexity, vagueness and various criteria involved in it. Recently, fuzzy Technique for Order Performance by Similarity to Ideal Solution method has been widely used to tackle such problems.

Findings

The present paper aims at developing an intelligent system for SSP, which can consider the multiple criteria and the uncertainty aspects in the decision process. A case study of a small-scale manufacturing company has been presented.

Practical implications

This study provides a guideline for SME sector to implement intelligent systems for supplier selection decision-making problems. Case application concludes that this model improves firm’s decision making and suppliers’ performance.

Originality/value

The proposed intelligent model can provide the guidelines and directions for the decision makers to effectively choose suppliers in the current competitive environment. And it also provides an opportunity for supplier improvement.

Prerequisites for performance measurement supporting purchaser-supplier collaboration

Purpose

The purpose of this paper is to clarify the prerequisites for performance measurement supporting purchaser-supplier relationships and value co-creation. It also explains the causes for the limited use of collaborative measurement.

Design/methodology/approach

Four case companies representing different contextual settings are studied. The primary source of empirical material is an interview study addressed to 24 interviewees. The empirical data are analyzed according to the constructs created as a result of the literature review.

Findings

The results reveal that prevailing performance measurement practices represent a more transactional than relationship-oriented approach to purchaser-supplier collaboration. The technical prerequisites for collaborative performance measurement are mostly not fulfilled, inhibiting the use of performance measurement in a collaborative manner. It is proposed that the differentiation between project and process production types has implications on the importance of collaborative performance measurement.

Research limitations/implications

The paper illustrates the desirable characteristics of performance measurement supporting collaboration. It also presents an application of collaborative performance measurement in a single case context. The research reveals the need to develop non-financial performance measures further in order to facilitate the more proactive use of performance measurement supporting true value co-creation between purchaser and supplier companies.

Originality/value

The empirical research on the topic of performance measurement in purchasing and supply management (PSM) is often limited to intra-organizational measurement and highlights transactional approach to collaboration between parties, although PSM research has otherwise acknowledged the importance of value creation and relationships between organizations.

Fuzzy Multi-Objective Supplier Selection considering Production Requirements in Resilient Supply Chain

One of the substantial concerns in any large scale enterprises is the process of evaluating, selecting and improving suppliers due to the involvement of many uncontrollable and unavoidable factors. To address this issue in industry, suppliers are selected in the context of a resilient supply chain since it is one way to overcome disruptions. Selection of suppliers involves a variety of both qualitative and quantitative attributes. This research proposes the five main criteria of supplier selection which are chosen based on vulnerable areas and production requirements to develop flexibility in procuring material. In order to select appropriate suppliers for optimal order allocation the fuzzy multi-objective linear programming (FMOLP) is proposed which overcomes the vagueness of the information. Also to calculate the importance of each criterion fuzzy analytic hierarchy process (FAHP) is used. Finally, the practical usefulness of the proposed method is ascertained through a case study in steel industry.

Multi-criteria supplier selection model using the analytic hierarchy process approach

Purpose

– The purpose of this paper is to propose a supplier selection model for an automobile company using analytic hierarchy process (AHP) approach. Supplier selection is now growing to be an important but complex issue, as it involves a large number of factors and decisions. These factors have complicated operational and financial implications.

Design/methodology/approach

– A case study of an Indian automobile company is used to illustrate and propose a supplier selection model using AHP approach.

Findings

– Key criteria of the supplier selection problem are identified based on the literature review and case study. Then, a model is proposed for supplier selection using AHP approach followed by a detailed sensitivity analysis.

Practical implications

– This paper contributes to supplier selection process, and points out the importance of supplier selection. It proposes a supplier selection model for an automobile industry which often faces heterogeneous supply environments. The proposed model provides key criteria for supplier selection in Indian context. This model may have high acceptability, where a large number of suppliers are available to supply the materials or provide the services.

Originality/value

– The proposed model can provide guidelines and directions for managers involved in purchasing function to effectively select suppliers on the basis of the identified criteria in the heterogeneous manufacturing environment.

Multi-criteria decision making for supplier selection using fuzzy AHP approach

Purpose

– The purpose of this paper is to propose a multi-criteria supplier selection model using fuzzy analytical hierarchy process (FAHP) approach for a leading automobile company in India.

Design/methodology/approach

– FAHP approach followed by a sensitivity analysis has been used.

Findings

– In this study, a FAHP-based supplier selection model is proposed to provide useful insights in choosing appropriate suppliers in dynamic situations in order to enhance long-term relationship with them.

Practical implications

– This study proposes a supplier selection model for an automobile industry which often faces heterogeneous supply environments. This model may have a high acceptability where a large number of suppliers are available to supply the materials or provide the services. As analytic hierarchy process is the most widely used methodology for supplier selection, however, it becomes less efficient in case of inconsistencies observed in the data. However a FAHP-based approach may overcome this difficulty.

Originality/value

– It contributes to supplier selection process and points out the importance of supplier selection problem, especially in the context of multi-criteria decision-making in Indian scenario.

Multi-period demand allocation among supplier in a supply chain

Purpose

– This paper aims to address the problem of optimal allocation of demand of items among candidate suppliers to maximize the purchase value of items. The purchase value of the items directly relates to cost and quality of raw materials purchased from the supplier. In an increasingly competitive environment, firms are paying more attention to selecting the right suppliers for procurement of raw materials and component parts for their products. The present research work focuses on this issue of supply chain management.

Design/methodology/approach

– This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

Findings

– The supply chain network is witnessing a changing business environment due to government policies aimed at promoting new small manufacturing enterprises (small and medium-sized enterprises) for intermediate parts and components. Hence, the managers have an option to select a new group of suppliers and allocate the optimal multi-period demand among the new group of suppliers to maximize their purchase value. In this context, the proposed hybrid model would be beneficial for the managers to operate their supply chain effectively and efficiently. The present research work will be helpful for the managers who are interested to reconfigure their supply chain under the failure of any supply chain partner or in a changing business environment. The model provides flexibility to the managers for evaluation of the different available alternatives to take a decision of optimal demand allocation among the suppliers. The proposed hybrid (fuzzy, TOPSIS and MILP) model provides more objective information for supplier evaluation and demand allocation among suppliers in a supply chain. The managers can use the proposed model to the analysis of other management decision-making problems.

Originality/value

– This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. This hybrid algorithm prioritizes the suppliers and then allocates the multi-period demand among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

An application of hybrid data envelopment analytical hierarchy process approach for supplier selection

Purpose

– The problem of supplier selection gets complicated when a company looks for various criteria to evaluate different suppliers. The decision criteria used for supplier selection process can be different for different organizations due to a large number of factors. Hence, it can be said that supplier selection is basically, a multiple criteria decision-making (MCDM) problem. The purpose of this paper is to propose a hybrid data envelopment analytical hierarchy process (DEAHP) approach to solve the supplier selection problem for an automobile company.

Design/methodology/approach

– In this study, the data envelopment analysis (DEA) approach is embedded into analytic hierarchy process (AHP) methodology. Literature review suggested that majority of researches found it appropriate using DEA and AHP methodologies for supplier selection process; hence it is felt that a hybrid DEAHP would be a useful methodology to offer a MCDM model for supplier selection problem.

Findings

– First, the key criteria of the supplier selection problem for the company are identified. Then a model is developed and implemented for supplier selection using DEAHP approach. This study concluded that quality, cost and service are the most crucial criteria for an automobile company operational in a developing country like India. Sensitivity analysis further helped to evaluate suppliers based on each criterion.

Research limitations/implications

– As this analysis and findings are based on only one case study of an Indian automobile company, and this necessitates caution in interpreting the results. The limited number of interviewed managers in a company restricts the generalizability of the results. Though the company selected for this study is typical of developing country businesses, the findings of the paper may not be readily extensible to other companies. Second, this study used retrospective settings, based on the interviewed feedback after the events had occurred. This method naturally poses limitations due to respondent recall and the accuracy of information provided. Third, the problem chosen for this study is based in a single country context and further additional research will be required to examine if the findings could be extended to other automobile companies in other developing nations. Also in some cases technique used in the study may pose some extra computational efforts.

Practical implications

– This study points out the importance of the supplier selection problem. It provides key criteria for supplier selection in Indian context also proposes a framework to deal with multiple criteria. proposed model deals with two crucial criteria long term relationships and flexibility which were relatively less discussed and considered in the literature in past.

Originality/value

– The proposed MCDM model can provide the guidelines and directions for the decision makers to effectively choose suppliers in the current competitive environment.