This paper aims to explore key pillars of supply chain competitiveness (SCC) and understand how top supply chains remain competitive in the long term. The research design is divided into two phases. First, an extensive review of the scholarly SCC literature is conducted to identify the core pillars that help achieve SCC. Second, the literature published in practitioner outlets on the five selected companies of Gartner's Supply Chain Top 25 is scrutinized to understand how top supply chains apply the core pillars of SCC in practice. A total of 193 scientific and practitioner articles were analyzed to develop the key pillars of SCC. This study identified six key pillars of SCC in the literature: innovation, sustainability, collaboration, information technology, agility, and flexibility. It has been found that a combination of these pillars, if not all, will be required to remain competitive in the post-COVID-19 era.

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.

This study conducts a systematic meta-literature review in the field of sustainable supplier selection. The number of published papers within the domain of sustainable supplier selection has grown considerably in recent years. Up until now, there has been no attempt quantitatively analyze the content of these published papers using bibliometric and network analysis software. Thus, this paper utilizes Gephi and Bibexcel software to conduct a quantitative review. In total, 4,882 documents were reviewed based on 336 combinations searched in Scopus and the Web of Science from 1990 to March 2018. Bibliometric, co-word and co-citation analysis are applied to quantitatively extract and analyze the content of these papers. The analysis reveals that: 1) There is a gap between industry and academia that needs to be bridged; 2) More studies in the area of global sourcing are needed; 3) Comparing the outcomes of different supplier evaluation methods is required; 4) There has been no major shift or change in the traditional supplier selection practices; 5) The ratio of the applied social criteria is relatively low compared to the total number of criteria; 6) The innovation capability of suppliers needs to be further considered; 7) More studies of sustainable supplier selection are needed in the e-procurement arena, as well as service-based industries such as healthcare, and 8) Evaluating the sustainability of suppliers in a dynamic environment needs to be further studied. The conclusion also reveals that only a limited number of journals exhibit a specific focus on the sustainable supplier selection arena; analytical and mathematical-based methods are the most applied supplier selection tools and there is a misalignment between the applied criteria in the triple bottom line. 

This paper presents a comparative analysis of the outcomes achieved when two widely applied methods for supplier selection—the ‘technique for order of preference by similarity to ideal solution’ (TOPSIS) and ‘data envelopment analysis’—are applied to the problem of identifying the most preferred sustainable suppliers. Both fuzzy DEA and fuzzy TOPSIS are applied to a common dataset of logistics service providers in Sweden. The results reveal that TOPSIS outperforms DEA in terms of both calculation complexity and sensitivity to changes in the number of suppliers. However, output rankings from the two models are found to be less than perfectly correlated. The paper concludes that utilizing both methods, as applied to just a small number of evaluation criteria and a relatively low level of detail in the data, produces a useful pooled shortlist of potential sustainable suppliers. This can then form the basis for a second stage application where either of the methods may be applied to a greater number of criteria that are specified to a higher level of detail. Even more critically, the results also have the potential to point to specific aspects for discussion when negotiating price and service quality commitments with potential sustainable suppliers. 

The objective of this paper is to evaluate the sustainability of operational logistics performance within a sample of OECD nations and to compare this to the most widely known national measure, the Logistics Performance Index (LPI), as developed by the World Bank. Data Envelopment Analysis is applied to derive an efficiency score reflecting the degree to which sustainable operational logistics performance (SOLP) is achieved within each sample nation. A comparison between the national rankings achieved under the LPI and SOLP evaluation regimes reveal that there is a statistically insignificant monotonic correlation between them. While one country may be a top performer with respect to one of the evaluation approaches, it does not emerge as such under the other. However, the results of the analysis suggest that logistics industries of the United States, the Netherlands, Norway and Australia are found to be top performers under both approaches, while that of Greece, Korea, Italy and Portugal are found to be poor performers under both. The paper concludes that the SOLP approach provides useful information that supplements that provided by the LPI, but that it better facilitates performance improvements within a nation's logistics industry by helping to identify the sources of inefficiency and the nations which possess benchmark performing logistics sectors. 

Supplier development plays a significant role in the cost, quality and delivery improvements of construction projects. However, there is limited research on analytical methods of categorizing and prioritizing a high number of suppliers for effective allocation of scarce development resources. This research aims to develop an objective model to categorize a general contractor’s suppliers. To do so, we use three concepts from different research backgrounds–recency, frequency and monetary value (RFM); data envelopment analysis (DEA); and the customer pyramid–and add the number of projects (P) shared with each supplier as a context-related variable to build a novel RFMP model. The model categorizes suppliers into four levels of the supplier pyramid, utilizing historical data on supplier–contractor transactions. To test the model in practice, we adopt a case study of an international construction company in Finland. The results reveal that a supplier’s RFMP score reflects its contribution to the contractor’s business; therefore, development investments should vary, based on a supplier’s position in the supplier pyramid. This research contributes to the knowledge on supply chain management in construction by combining three approaches–RFM, DEA and the customer pyramid–into a single objective model to categorize suppliers for effective development investments.

Sustainable supplier management goes beyond a simple compliance with standards and norms defined by a purchaser or being only effective in terms of economic performance in sustainable supply chain management context. Buying firms are seeking to adopt the sustainable supplier management in their supply chains as they are responsible for their suppliers’ environmental and social performances. Previous researches on sustainable supplier management indicate that evaluation of suppliers’ sustainability has attracted a great attention from practitioners while suppliers’ sustainability development has been widely neglected. One of the development programs for suppliers is performance improvement. Two significant issues need to be considered for implementation of suppliers’ improvement program; first, considering time in evaluation process and continuity in improvement, second, applying stepwise or gradual approach as sudden and huge changes in inputs and outputs may be impossible or troublesome. To this end, we propose a data envelopment analysis (DEA)-based model to incorporate dynamic concept into gradual improvement approach. Given proposed model, carry-overs between two consecutive periods are considered in a dynamic environment and also inefficient suppliers are projected to efficiency frontier step-by-step. An illustrative case study in machinery industry is proposed to indicate applicability of proposed model.

Nowadays, majority of organizations are seeking to achieve sustainable development with respect to "green" concept. One of the main criteria for assessing green performance is eco-efficiency. To identify all aspects of the eco-efficiency, inputs should be divided into energy and non-energy and outputs should be divided into good and bad outputs. To deal with this issue, a data envelopment analysis (DEA) model is developed to divide inputs into both energy and non-energy and outputs into both desirable (good) and undesirable (bad) outputs. Likewise, variables are separated into both discretionary and non-discretionary factors. Accordingly, a bounded adjusted measure (BAM) based on green indicators is developed to calculate the eco-efficiency of decision making units (DMUs). Besides, energy saving potentials and undesirable output abatement potentials are calculated to show correlation coefficient between energy consumption and undesirable output. Finally, proposed model is validated by assessing the eco-efficiency of some selected members of organization for economic cooperation and development (OECD). Australia, Finland, Ireland, New Zealand, and Switzerland are recognized as eco-efficient countries and the rest of countries are inefficient in terms of the eco-efficiency. High and positive Spearman correlation coefficient between energy consumption and undesirable outputs addresses that the more use of energy inputs, the more undesirable outputs.