Supplier selection and evaluation in e-commerce enterprises: a data envelopment analysis approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saurabh Pratap ◽  
Yash Daultani ◽  
Ashish Dwivedi ◽  
Fuli Zhou
Keyword(s):  
Data Envelopment Analysis ◽  
Supplier Selection ◽  
Management Practices ◽  
Real Life ◽  
Supplier Management ◽  
Supplier Evaluation ◽  
Business Practices ◽  
Data Envelopment ◽  
Content Type ◽  
Customer Service Level

PurposeE-commerce refers to the facilitation and delivery of goods and services to the customers employing an electronic arrangement. For an e-commerce firm, the customer service level provided by its suppliers can make or break the firm. The purpose of this research is to help e-commerce enterprises in addressing the vast challenge of complex supplier selection and evaluation process that must be performed vigilantly.Design/methodology/approachThe present study utilizes a three-pronged approach that integrates supplier management practices with the operational business practices of an e-commerce enterprise. In the first step, key performance factors for e-commerce capable suppliers are identified through an expert opinion and existing supplier management literature. Further, Data Envelopment Analysis (DEA) is employed to obtain the efficiency score for each supplier that enables their ranking on various performance parameters. Lastly, the suppliers are classified into different categories based on their performance and efficiency.FindingsUnder the proposed classification scheme, top five suppliers, i.e. supplier 1, 7, 9, 11 and 17 are categorized as HE (High Performance and Efficient). It is suggested that e-commerce enterprises must build long-term relationship with the identified top performing suppliers. The study also provides real insights into supplier's performance on a number of objective criteria. Further, the present study enhances the overall performance and productivity of an e-commerce firm by achieving input cost minimization and output quality maximization, simultaneously.Research limitations/implicationsThe results are valid for e-commerce enterprises in general. However, the present DEA model can be further evolved when applied in case of any particular e-commerce enterprise depending upon the internal capabilities of that firm. The nuances related to a firm's own supply capability development can be further explored by practitioners and researchers.Practical implicationsThe proposed approach is expected to motivate decision-makers to consider using more sophisticated approached like DEA in supplier evaluation processes. Also, as a benchmarking technique, the proposed supplier classification approach is expected to be highly useful for practitioners in real-life settings.Originality/valueThe novel contribution of this study includes the supplier evaluation, ranking and classification for e-commerce enterprises based on the real-life data. The insights would help the practitioners to formulate novel strategies for appropriately investing in supplier relationships.

Fuzzy preference programming formulation in data envelopment analysis for university department evaluation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dyanne Brendalyn Mirasol-Cavero ◽  
Lanndon Ocampo
Keyword(s):  
Data Envelopment Analysis ◽  
Real Life ◽  
The Philippines ◽  
Data Envelopment ◽  
Data Accessibility ◽  
Content Type ◽  
Proposed Model ◽  
University Department ◽  
Fuzzy Dea ◽  
Fuzzy Preference

Purpose University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the efficiency of academic departments in data envelopment analysis (DEA) deals with crisp data, which may be, often, imprecise, vague, missing or predicted. Current literature offers various approaches to addressing these uncertainties by introducing fuzzy set theory within the basic DEA framework. However, current fuzzy DEA approaches fail to handle missing data, particularly in output values, which are prevalent in real-life evaluation. Thus, this study aims to augment these limitations by offering a fuzzy DEA variation. Design/methodology/approach This paper proposes a more flexible approach by introducing the fuzzy preference programming – DEA (FPP-DEA), where the outputs are expressed as fuzzy numbers and the inputs are conveyed in their actual crisp values. A case study in one of the top higher education institutions in the Philippines was conducted to elucidate the proposed FPP-DEA with fuzzy outputs. Findings Due to its high discriminating power, the proposed model is more constricted in reporting the efficiency scores such that there are lesser reported efficient departments. Although the proposed model can still calculate efficiency no matter how much missing and unavailable, and uncertain data, more comprehensive data accessibility would return an accurate and precise efficiency score. Originality/value This study offers a fuzzy DEA formulation via FPP, which can handle missing, unavailable and imprecise data for output values.

Forecasting sustainability of supply chains in the circular economy context: a dynamic network data envelopment analysis and artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hadi Shabanpour ◽  
Saeed Yousefi ◽  
Reza Farzipoor Saen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Envelopment Analysis ◽  
Supply Chains ◽  
Circular Economy ◽  
Real Life ◽  
Network Data ◽  
Data Envelopment ◽  
Content Type ◽  
First Time

PurposeThe objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.Design/methodology/approachIt is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.FindingsA practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.Originality/valueWe propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.

scholarly journals A methodology for supplier selection under the curse of dimensionality problem based on fuzzy quality function deployment and interval data envelopment analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253917
Author(s):  
Xi Bao ◽  
Fenfen Li
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Supplier Selection ◽  
Quality Function Deployment ◽  
Interval Data ◽  
Curse Of Dimensionality ◽  
Supplier Evaluation ◽  
Data Envelopment ◽  
Quality Function ◽  
Fuzzy Quality Function Deployment

Supplier selection is an important decision-making problem, which involves many quantitative and qualitative factors incorporating vagueness and imprecision. This study proposes a novel fuzzy multi-criteria decision-making framework for supplier selection, which integrates quality function deployment (QFD) and interval data envelopment analysis (DEA). The proposed methodology allows for considering the relationships among the product features and supplier evaluation criteria (SEs) and the impacts of inner dependence among SEs by constructing a house of quality (HOQ). Considering that the number of supplier evaluation indicators is greater than the number of suppliers in some cases, the curse of dimensionality problem usually exists. To solve this problem, we combine the HOQ, interval DEA models, and forward-stepwise selection approach to screen supplier evaluation indicators and select the best supplier(s). Through the two-stage supplier selection method, we can achieve the double screening of indicators and determine the final supplier(s). Finally, the application of the proposed framework is demonstrated through a numerical example and a sensitivity analysis is also carried out to verify the stability of the proposed methodology. This study focuses on supplier selection based on the combination of fuzzy QFD and interval DEA, and also provide a new two-phase methodology for DEA indicator screening.

A data envelopment analysis approach by partial impacts between inputs and desirable-undesirable outputs for sustainable supplier selection problem

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Nemati ◽  
Reza Farzipoor Saen ◽  
Reza Kazemi Matin
Keyword(s):  
Data Envelopment Analysis ◽  
Supplier Selection ◽  
Selection Problem ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Content Type ◽  
Dea Model ◽  
Sustainable Supplier Selection ◽  
Overall Efficiency ◽  
Supplier Selection Problem

PurposeThe objective of this paper is to propose a new data envelopment analysis (DEA) model for assessing sustainability of suppliers with partial impacts between inputs, desirable outputs and undesirable outputs.Design/methodology/approachThis paper examines partial impacts of inputs on desirable and undesirable outputs and applies weak disposability assumption to propose a novel DEA model to determine the sustainability of suppliers.FindingsThis paper shows the type of resource sharing in DEA models and takes into account sustainable development and sustainability assessment concepts for sustainable supplier selection problem and develops a DEA model for selecting the most sustainable suppliers with partial sharing of resources. To select the most sustainable suppliers, this model helps managers to consider aggregate efficiency, overall efficiency and bundle efficiency. The paper introduces the supplier which is efficient at all levels as the most sustainable supplier.Originality/valueFor the first time, this paper suggests a new DEA model by partial impact between inputs and good outputs/bad outputs for selecting sustainable supplier and deals with the situations in which each supplier has several subunits. The new model calculates aggregate efficiency, overall efficiency and subunit efficiency of supplier. paper introduces the supplier which is efficient in all levels including aggregate efficiency, overall efficiency and subunit efficiency as the best supplier.

Supplier evaluation and classification in a Colombian motorcycle assembly company using data envelopment analysis

2019 ◽  
Vol 32 (2) ◽  
pp. 159-180 ◽  
Author(s):  
Rodrigo Restrepo ◽  
Juan G. Villegas
Keyword(s):  
Data Envelopment Analysis ◽  
Current Trend ◽  
Superior Performance ◽  
Just In Time ◽  
Supplier Evaluation ◽  
Data Envelopment ◽  
Content Type ◽  
Ordinal Variables ◽  
Using Data

Purpose The purpose of this paper is to present a case study in which data envelopment analysis (DEA) is used to evaluate and classify the suppliers of a Colombian motorcycle assembly company. This tool allows the integration of several attributes into single performance measures (cross-efficiency and diversity efficiency) and subsequent classification based on the values obtained for these two metrics. Design/methodology/approach The classification uses a methodology based on two main tools. The first is an input-oriented cross-efficiency DEA model with ordinal variables to evaluate the suppliers’ performance, and the second is a classification of these into categories that identifies those with good performance for features that make them outstanding. Findings The assembly company segments its suppliers according to supply frequency. The results show that suppliers working under a just-in-time system achieve superior performance with respect to other suppliers. Practical implications The application of this methodology in a real-world case illustrates how DEA can be a useful tool to support the evaluation and classification of suppliers (a process of increasing complexity given the current trend to include multiple strategic measures together with classical operational measures). Moreover, the methodology illustrated in the study can be adapted to other similar settings. Originality/value The main contributions of this paper are twofold. First, to the best of our knowledge, this is the first study to illustrate the use of DEA in a real case related to supplier evaluation. Second, the presence of ordinal variables (e.g. quality or environmental ratings) gives rise to DEA variants seldom used in this context.

Achieving supply chain excellence through supplier management

2018 ◽  
Vol 25 (9) ◽  
pp. 4084-4102 ◽  
Author(s):  
Mohammad Asif Salam ◽  
Sami A. Khan
Keyword(s):  
Management System ◽  
Supplier Selection ◽  
Real Life ◽  
Supplier Management ◽  
Management Program ◽  
Strategic Decision ◽  
Case Study Methodology ◽  
Content Type ◽  
Supplier Performance

PurposeThe purpose of this paper is to develop a supplier selection and management program to improve overall supplier performance.Design/methodology/approachSupplier performance is measured in terms of quality and delivery within a fast moving consumer goods (FMCG) business of a multinational company based in Thailand using a case study methodology. The quality and delivery related data were collected from daily deliveries at the manufacturing plant both before and after implementing the supplier management program.FindingsFindings of the study suggest that the selection of suppliers based on their performance is important for manufacturing firms. Moreover, the supplier selection and management program can contribute effectively to improving suppliers’ performance.Research limitations/implicationsThis case study has been conducted based on a single company within the FMCG industry. Hence, it limits the generalizability of the findings across industries.Practical implicationsThe study provides a real-life tool for practitioners to learn about the importance of strategic decision-making process pertaining to the supplier selection and management program.Social implicationsThis study demonstrates that through a transparent supplier evaluation process, the firms can develop trust and long-term relationship with their suppliers for pursuing the goals of product development and innovation.Originality/valueImplementing a supplier management system is a critical step in enhancing an organization’s overall competitiveness. To develop an effective supplier management system firms must have objective measures and share those with their suppliers. Developing metrics for suppliers’ evaluation is the key to achieving continuous improvement as evidenced in this case.

An application of network data envelopment analysis with fuzzy data for the performance evaluation in cargo sector

2018 ◽  
Vol 31 (4) ◽  
pp. 492-509 ◽  
Author(s):  
Berna Simsek ◽  
Fatih Tüysüz
Keyword(s):  
Performance Evaluation ◽  
Data Envelopment Analysis ◽  
Performance Measurement ◽  
Real Life ◽  
Process Efficiency ◽  
Network Data ◽  
Data Envelopment ◽  
Fuzzy Data ◽  
Content Type ◽  
Network Data Envelopment Analysis

Purpose The purpose of this paper is to present a methodology which enables to measure and analyze the performance of sub-process and overall system of a cargo company. Design/methodology/approach Network data envelopment analysis method with fuzzy data is used for performance measurement which considers the sub-process efficiency simultaneously together with the overall efficiency and also the uncertainty included in input–output data. Findings A real-life application of the proposed model is presented for Turkey. The application results show the efficiency scores of ten branches according to each sub-process and also the overall system. Although the obtained results are case specific, the application results indicate that the inefficient branches can achieve efficiency either by decreasing circulation ratio input for human resources sub-process or by increasing closed complaint output or by decreasing open complaint output for customer relationship management sub-process. Originality/value The study presented provides insights into the performance measurement applications in cargo sector. The methodology presented provides the flexibility of removing or adding some new sub-processes and also decision-making units which enables the approach to be used for other performance evaluation problems.

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