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

Rashidi, Kamran

doi:10.1108/bij-11-2019-0505

Cited by 12 publications

(10 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The case for the proposed DEA model becomes strong in the view presented by Mahdiloo et al (2014), when supplier benchmarking becomes even more difficult when some of the inputs and output parameters become common. This is also supported by Rashidi (2020), where author has extensively compared AHP and DEA models in the context of supplier selection and concluded that DEA is suitable for firms that want to consider suppliers who can make significant performance improvements while making their businesses more sustainable. In case of e-commerce enterprises, it is a very difficult assignment to select the best set of suppliers that will contribute toward the overall profit of the firm.…”

Section: Methodsmentioning

confidence: 81%

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

Pratap

Daultani

Dwivedi

et al. 2021

BIJ

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 81%

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

Pratap

Daultani

Dwivedi

et al. 2021

BIJ

View full text Add to dashboard Cite

show abstract

“…This method is mainly applied when it is difficult to identify and compare direct relationships among multiple inputs and outputs and can be used to develop a more suitable decision-making model than AHP in environments with insufficient information. AHP Process chain companies and their performances have been compared to verify the practical effectiveness of each technique [34]. According to the findings, the AHP technique can provide detailed and gradual decision-making results through pairwise comparison in cases with large amounts of information, whereas the DEA method can provide effective decision-making strategies for situations with insufficient decision-making factors in a new environment.…”

Section: Data Envelopment Analysis (Dea) Methodsmentioning

confidence: 99%

Assessment Framework of Smart Shipyard Maturity Level via Data Envelopment Analysis

et al. 2021

View full text Add to dashboard Cite

The fourth industrial revolution (“Industry 4.0”) has caused an escalating need for smart technologies in manufacturing industries. Companies are examining various cutting-edge technologies to realize smart manufacturing and construct smart factories and are devoting efforts to improve their maturity level. However, productivity improvement is rarely achieved because of the large variety of new technologies and their wide range of applications; thus, elaborately setting improvement goals and plans are seldom accomplished. Fortunately, many researchers have presented guidelines for diagnosing the smartness maturity level and systematic directions to improve it, for the eventual improvement of productivity. However, most research has focused on mass production industries wherein the overall smartness maturity level is already high (e.g., high-level automation). These studies thus have limited applicability to the shipbuilding industry, which is basically a built-to-order industry. In this study, through a technical demand survey of the shipbuilding industry and an investigation of existing smart manufacturing and smart factories, the keywords of connectivity, automation, and intelligence were derived and based on these keywords, we developed a new diagnostic framework for smart shipyard maturity level assessment. The framework was applied to eight shipyards in South Korea to diagnose their smartness maturity level, and a data envelopment analysis (DEA) was performed to confirm the usefulness of the diagnosis results. By comparing the DEA models, the results with the smart level as an input represents the actual efficiency of shipyards better than the results of conventional models.

show abstract

“…Following the proposed analysis of eigenvalue and eigenvector used in a matrix calculation, ref. [29] describe a simple model, which is adapted for this research:…”

Section: Tvm and Sustainability Trade-off Analysismentioning

confidence: 99%

“…Refs. [29,55] provide details of the AHP model validation and the application of their mathematical models.…”

Section: Tvm and Sustainability Trade-off Analysismentioning

confidence: 99%

“…Qualitative methods are also well defined in academic literature [1,[22][23][24]. To achieve more effective results, some authors explore the integration of multi-criteria and multi-objective decision methods [25][26][27][28][29]. In strategic, tactical, and operational performance dimensions, applying methods to ensure effectiveness of performance dimensions is a common practice.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Operational Decisions and Sustainability: A Brazilian Case of a Drugs Distribution Center

Campos¹,

Vivas²,

Ferreira³

et al. 2020

Sustainability

View full text Add to dashboard Cite

Recently, the supply chain in the pharmaceutical sector, which is important economically to the healthcare industry worldwide, has received special attention owing to different factors involved in the distribution of drugs. Furthermore, it has an important role in global sustainability as organizations base their efficient decisions on the results from performance analysis of economic indicators. Thus, the sustainability of operations decisions must be analyzed to achieve better decision efficiency. This study integrates analytical methods of operational activities evaluation for a drug distribution center in a pharmaceutical logistic organization to analyze the sustainability of its operations. Furthermore, a proposed framework incorporates time variability management (TVM) decisions into a trade-off analysis of triple bottom-line (TBL) sustainability dimensions and operations managers’ decisions. The framework is a real-time data-gathering decision system that evaluates processes using stochastic simulation and process efficacity based on control-chart analysis and analyzes the trade-off performance. Managers’ decisions on time variability is modeled using an Analytical Hierarchy Process. The results of the trade-off analysis of sustainability and TVM indicate that economic dimensions have a higher impact on an organization than social and environmental dimensions. Managers assume that social and environmental impacts are less important to organizations’ performance. Environmental and social dimensions have different impacts on time variability decisions, where managers assume that operations’ time reduction has more impact on the social dimension, while operations’ time increase has a higher environmental impact. Thus, the framework is an effective tool for analyzing the sustainability of operations decisions, which is associated with variability analysis.

show abstract

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

Cited by 12 publications

References 69 publications

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

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

Assessment Framework of Smart Shipyard Maturity Level via Data Envelopment Analysis

Operational Decisions and Sustainability: A Brazilian Case of a Drugs Distribution Center

Contact Info

Product

Resources

About