Search results

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.

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.

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.

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.

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.

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

The purpose of this paper is to promote the integration of quality function deployment (QFD) with quantitative methods in an attempt to enhance its effectiveness and resolve issues like the failure of prioritizing customer requirements and determining proper importance levels.

This paper discusses the combination of QFD with analytic hierarchy process‐analytic network process (AHP‐ANP) and DEAHP‐DEANP methodologies to prioritize selection criteria in a service context. DEAHP is a hybrid method, developed by Ramanathan. Data envelopment analysis (DEA) is embedded in the AHP methodology and potentially replaces the eigenvalue method. In this paper DEAHP is used for completing the relationship matrix of house of quality (HOQ). Furthermore, in accordance with the DEAHP method, a methodological framework for completing the roof matrix of HOQ is developed, based on DEA and ANP techniques‐DEANP. The implementation of QFD along with the above mentioned quantitative approaches is studied utilizing data from the Greek banking sector, and a comparative analysis is conducted.

The illustrative implementation offers support to the compatibility of the two alternative approaches demonstrating both applicability and ease of use. Results suggest that the QFD‐DEAHP‐DEANP methodology provides superior information since it offers more robust sensitivity analysis outcomes.

The novelty of this research lies in the innovative idea to use QFD in conjunction with DEAHP and in the development of the DEANP method. The proposed methodology is a valuable tool for identifying strategic opportunities and gaining sustainable advantage since it helps service providers to capture the mix of services that customers are expecting them to offer.

The main intention of this paper is to analyze various factors hindering the growth of the agricultural supply chain and several industry 4.0 technologies to eliminate the same. In addition to a detailed assessment on the implementation of these technologies in agriculture, this manuscript also presents a priority list providing a rank to them based on the relative efficiency of these advancements in addressing these obstacles.

This research proceeds with a two-step process. The particular barriers in the agriculture supply chain and industry 4.0 technologies are determined in the first step. Next, the proposed framework, a combination of data envelopment analysis (DEA) and analytic hierarchy process (AHP), i.e. DEA-AHP, is used to determine a hierarchical structure for the factors and the relative productive efficiencies of the alternatives. The DEA methodology gives a performance analysis of various decision-making units. At the same time, AHP helps in evaluating alternatives weights based on numerous criteria, allowing us to categorize their importance further.

This study reveals how the involvement of technological advancements in agriculture can help manage the supply chain more efficiently. It also justifies how the large quantities of data generated can handle these increasing challenges in the agricultural supply chain.

The results of this study provide a priority list of alternatives based on their final weights. This ranking system can help farmers and the government select the best-suited technology for bringing automation into the agricultural supply chain.

This research is unique as it analyes the general factors hindering the development of the agriculture supply chain while simultaneously providing a list of alternatives based on their relative efficiencies. The study enriches existing literature by providing an analytic approach to determine the weightage of various critical success factors that can help improvise and entrust the real and undeniable requirements of consumers, suppliers and producers.

The purpose of this study is to present a hybrid analytic network process (ANP) and data envelopment analysis (DEA) method for ranking organizational units as well as prioritizing organization’s human capital management criteria.

In the proposed DEA model, human capital management criteria are considered as the inputs and organizational commitment considered as the output of DEA model. Afterward, the organizational unit’s efficiency and the weight of human capital management criteria are evaluated through DEA and the data are considered as the basis of ANP model.

The results of prioritizing the drivers of human capital management were showed that leadership practices, human capital management and learning capacity are the most important ones. Also, the findings proved that the proposed integrated DEA/ANP can be helpful in managerial issues.

Filling the super-matrix and designing the questionnaire of ANP have always been the challenge of scholars because of the large number of data it requires. One of the main advantages of the proposed hybrid method in this research is that it resolves the above-mentioned problem of ANP.

The results of the proposed method may provide managers this opportunity to better analyze the condition of organizational units from human capital management perspective and focus on the most important activities in this context. Moreover, the proposed hybrid method can help scholars to better use both DEA and ANP techniques and obtain more reliable findings.

In this study, DEA efficiency measures of units, and the weights of inputs and outputs of DEA model are used to fill the super-matrix of ANP. In addition to having a logical approach, this method provides more reliable results and enjoys the advantages of both ANP and DEA techniques.

The purpose of this paper is to model the choice parameters for selection of third party logistics service providers in global lead logistics provider (LLP) environments.

Analytical hierarchy process (AHP) modeling has been carried out after questionnaire‐based survey, results of which were moderated with inputs from experts from industry and academics.

The findings of this study revealed the under lying sub‐parameters which come into play while rating/choosing or evaluating service providers in the global LLP situations of today.

The number of service providers, selection criteria and the number of members of the AHP assessment team have been identified as some of the limitations of the present work.

The study has significant practical implications for the service providers and for the organizations which have to choose them. The fourth party logistics scenario entails this choice to be made by the LLP.

This paper deals with inputs from the Indian LLPs and the Indian service providers. With India emerging as the second most important outsourcing destination after China, the assessment of parameters specific to this country while modeling is of practical importance to LLPs beyond the political boundaries of India also.

The application of correct contractor selection strategies leads to the selection of a qualified contractor and, as a result, the on-time delivery of the project with the desired quality and within the predetermined budgetary constraints. For this reason, evaluating and qualifying contractors before reviewing the proposed prices has been considered an important issue. One factor that disrupts the project completion process and the failure to achieve pre-planned goals effectively is the occurrence of contractors' disputes and claims in projects. To this end, the present study explores claim-reduction strategies for selecting effective contractors in an uncertain environment to reduce possible problems.

The two-step grey data envelopment analysis (GDEA) approach was used to measure efficiency as a powerful tool in selecting efficient contractors during tenders. This approach can extend the applications of multi-criteria decision-making (MCDM) models. In other words, given some uncertainties, the unavailability of some data, and the problems with the DEA model, the two-step GDEA model was used to rank the contractors. The data confirmed the satisfactory outcomes from the selected model.

The preliminary assessment of contractors is a pre-tendering process and a step in categorizing contractors, excluding contractors lacking required qualifications, and selecting efficient contractors. At first, it will help the employer to exclude inexperienced and unqualified contractors, save resources and time, reduce threats, replace opportunities with threats, and reduce material and non-material costs during the completion of the project until the projects are put into operation. Consequently, this approach reduces claims to a minimum level and increases the organization's effective material and non-material profit.

Oil and gas plans and projects have a significant, sensitive, and decisive role in the economic, social, political, cultural, infrastructural, and all-round development of Iran; This is while most of the financial resources needed to implement the development and programs across the country come from oil revenues. Studies have indicated that despite the importance of these plans and projects, many of them are not completed successfully, and this causes irreparable losses to the country's economy and development in various fields.

1. The findings of this study can be used by organizations to select more effective contractors to assign projects and plans to them.

2. The preliminary assessment of contractors is a pre-tendering process and a step in categorizing contractors, excluding contractors who lack required qualifications, and finally selecting efficient contractors.

3. At first, it will help the employer to exclude inexperienced and unqualified contractors, save resources and time, reduce threats, replace opportunities with threats, and reduce material and non-material costs during the completion of the project until the projects are put into operation.

4. This approach also gives credit to the employer during the execution period and contributes to assessing unqualified contractors and reducing the temptation to hand over the project to an unqualified contractor but with a lower bid price.

5. Consequently, this approach reduces claims to a minimum level and increases the effective material and non-material profit of the organization.

6. Moreover, it provides an extra-organizational evaluation for contractors, motivating them to upgrade their capabilities and optimally allocate material and non-material resources, especially human resources.

The findings of this study can be used by organizations to select more effective contractors to assign projects and plans to them.

The preliminary assessment of contractors is a pre-tendering process and a step in categorizing contractors, excluding contractors who lack required qualifications, and finally selecting efficient contractors.

At first, it will help the employer to exclude inexperienced and unqualified contractors, save resources and time, reduce threats, replace opportunities with threats, and reduce material and non-material costs during the completion of the project until the projects are put into operation.

This approach also gives credit to the employer during the execution period and contributes to assessing unqualified contractors and reducing the temptation to hand over the project to an unqualified contractor but with a lower bid price.

Consequently, this approach reduces claims to a minimum level and increases the effective material and non-material profit of the organization.

Moreover, it provides an extra-organizational evaluation for contractors, motivating them to upgrade their capabilities and optimally allocate material and non-material resources, especially human resources.

The purpose of this research is to first explore various third party logistic service provider supply chain enablers. Thereafter the interrelationship amongst the various supply chain enablers has been studied using ISM Methodology. Despite the complex relationships third party logistic service providers (3PLs) share with their clients or firms, they often attract a demand owing to the flexibility and competitive edge they provide to their client firms in adapting to the rapidly changing market conditions, focusing on their core competencies and developing long-term growth strategies for them. Choosing and evaluating the right third-party logistic service provider is an important responsibility for logistic managers. This largely depends on selecting appropriate 3PLs supply chain enablers that assess the 3PLs on different fronts.

This paper presents an ISM approach for studying the interrelationships between various 3PLs enablers and accordingly constructing a hierarchical structure of them.

The results suggest that delivery, service reliability and risk and uncertainty factors have the highest importance.

Selection of the 3PLs is a critical issue when they are required to be selected by the company at the global level. This often requires doing a comparative study for both domestic as well as global service providers. Choosing appropriate supply chain enablers as the basis for selection of 3PLs therefore will serve as a research topic to be further explored both by researchers as well as company managers. Further studying the inter-relationships amongst various supply chain enablers will provide basis to managers to justify their choice.

The novelty of the research lies in the application of methodology to the case of third-party logistic service provider selection

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.

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.

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.

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.

Quality function deployment (QFD) has been used to translate customer requirements into engineering characteristics of a product, while benchmarking was developed to search for the best industry practices, which will lead to exceptional performance through the implementation of these best practices. However, no attempt has been made to integrate QFD with benchmarking to identify the best practices of QFD model. This paper aims to classify the QFD models and thereby applying benchmarking process to propose the best practices of QFD model.

The fundamental benchmarking model developed by Camp has been used to benchmark the existing QFD models available in the literature.

Benchmarking the QFD models revealed about 36 QFD steps in the first phase of the house of quality. The tools used in solving for each practice are also reported.

The proposed model is conceptual and it requires validation by implementing the same in an organization to understand its effectiveness.

Utilizing the benchmarking process to develop the best practices of QFD model is an original concept.