Supplier selection with Shannon entropy and fuzzy TOPSIS in the context of supply chain risk management

Supplier selection is the process of finding the right suppliers, at the right price, at the right time, in the right quantities, and with the right quality. The aim of this paper, is supplier selection in the context of supply chain risk management. Thus nine criteria of quality, on time delivery and performance history and six risks in the supply chain including supply risk, demand risk, manufacturing risk, logistics risk, information risk and environmental risk considered for evaluating suppliers. Shannon entropy is used for weighing criteria and fuzzy TOPSIS is applied for ranking suppliers. Findings show that, in the spare parts supplier selection problem, demand risk is the most important factor

Clustering sustainable suppliers in the plastics industry: A fuzzy equivalence relation approach

Nowadays, pure economic supply chain management is not commonly contemplated among companies (especially buyers), as recently novel dimensions of supply chains, e.g., environmental, sustainability, and risk, play significant roles. In addition, since companies prefer buying their needs from a group of suppliers, the problem of supplier selection is not solely choosing or qualifying a supplier from among others. Buyers, hence, commonly assemble a portfolio of suppliers by looking at the multi-dimensional pre-determined selection criteria. Since sustainable supplier selection criteria are often assessed by linguistic terms, an appropriate clustering approach is required. This paper presents an innovative way to implement fuzzy equivalence relation to clustering sustainable suppliers through developing a comprehensive taxonomy of sustainable supplier selection criteria, including supply chain risk. Fifteen experts participated in this study to evaluate 20 suppliers and cluster them in the plastics industry. Findings reveal that the best partitioning occurs when the suppliers are divided into two clusters, with 4 (20%) and 16 (80%) suppliers, respectively. The four suppliers in cluster one are performing better in terms of the capability of supplier/delivery, service, risk, and sustainability criteria such as environment protection/management, and green innovation. These factors are critical in clustering and selecting sustainable suppliers. The originality of this study lies in developing an all-inclusive set of criteria for clustering sustainable suppliers and adding risk factors to the conventional supplier selection criteria. In addition to partitioning the suppliers and determining the best-performing ones, this study also highlights the most influential factors by analysing the suppliers in the best cluster

Interrelations among leadership competencies of BIM leaders: A fuzzy DEMATEL-ANP approach

© 2020 by the authors. The use of new, digitally enabled innovations, such as building information modeling (BIM), raises issues such as the delineation of a competent leader. Even though BIM-based competency assessment models have become essential tools for maximizing the potential values of BIM implementation, the current competency models provide limited focus on leadership aspects that facilitate and enhance the BIM implementation efforts. This paper seeks to identify the specific competencies required for BIM implementation and examines the relationships between these competencies. Thirty-two experts from around the globe investigated a total of 15 leadership competencies under three categories pertaining to intellectual, managerial, and emotional leadership. Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) was implemented to examine the cause-and-effect relationships among the BIM leadership competencies and fuzzy analytic network process (ANP) was performed to weigh those competencies. Findings show that the intellectual competencies act as the cause group, while managerial and emotional competencies are the effect groups. Moreover, the involving leadership is found to be the more suitable leadership style for BIM professionals, given the current capability and maturity levels of BIM implementation, in order to deal with the required changes throughout the BIM implementation process. This study contributes to the existing body of knowledge in the BIM domain to examine the associated leadership competencies by using the multi-criteria decision-making (MCDM) technique. The results of this research show the relative importance of criteria and sub-criteria, which contributes to further improvement of BIM leadership

Sustainability in construction projects: A systematic literature review

This paper aims to identify the major research concepts studied in the literature of sustainability in construction projects. Two bibliometric analysis tools—(a) BibExcel and (b) Gephi, were used to analyze the bibliometrics indices of papers and visualize their interrelations as a network, respectively. Therefore, a research focus parallelship network (RFPN) analysis and keyword co-occurrence network (KCON) analysis were performed to uncover the primary research themes. The RFPN analysis clustered the studies into three major categories of evaluating sustainability, project management for sustainability, and drivers of sustainable construction. The KCON analysis revealed that while each paper had a different focus, the underlying concept of all clusters was sustainability, construction, and project management. We found that while ‘sustainability’ was the leading keyword in the first cluster, i.e., evaluating sustainability, it was the second top keyword with the eigenvector centrality of over 0.94 in the other two clusters. We also found that the concept of sustainability should be included in construction projects from the early stages of design and feasibility studies and must be monitored throughout the project life. This review showed that previous researchers used a variety of statistical and mathematical techniques such as structural equation modelling and fuzzy decision-making methods to study sustainability in construction projects. Using an integrated approach to identifying the research gaps in this area, this paper provides researchers with insights on how to frame new research to study sustainability in construction projects

Cross-docking: A systematic literature review

This paper identifies the major research concepts, techniques, and models covered in the cross-docking literature. A systematic literature review is conducted using the BibExcel bibliometric analysis and Gephi network analysis tools. A research focus parallelship network (RFPN) analysis and keyword co-occurrence network (KCON) analysis are used to identify the primary research themes. The RFPN results suggest that vehicle routing, inventory control, scheduling, warehousing, and distribution are most studied. Of the optimization and simulation techniques applied in cross-docking, linear and integer programming has received much attention. The paper informs researchers interested in investigating cross-docking through an integrated perspective of the research gaps in this domain. This paper systematically reviews the literature on cross-docking, identifies the major research areas, and provides a survey of the techniques and models adopted by researchers in the areas related to cross-docking

Forecasting project success in the construction industry using adaptive neuro-fuzzy inference system

Project managers often find it a challenge to successfully manage construction projects. As a result, understanding, evaluating, and achieving project success are critical for sponsors to control projects. In practice, determining key success factors and criteria to assess the performance of construction projects and forecast the success of new projects is difficult. To address these concerns, our objective is to go beyond the efficiency-oriented project success criteria by considering both efficiency- and effectiveness-oriented measures to evaluate project success. This paper contributes to existing knowledge by identifying a holistic and multidimensional set of project success factors and criteria using a two-round Delphi technique. We developed a decision support system using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast the success of mid- and large-sized construction projects. We gathered data from 142 project managers in Australia and New Zealand to implement the developed ANFIS. We then validated the constructed ANFIS using the K-fold cross-validation procedure and a real case study of a large construction project in Western Australia. The forecasting accuracy measures R2=0.97461, MAPE = 2.57912%, MAE = 1.88425, RMSE = 2.3610, RRMSE = 0.03149, and PI = 0.01589 suggest that the developed ANFIS is a very good predictor of project success

Common weights analysis of renewable energy efficiency of OECD countries

Arising from the recent COP26 climate change conference, many governments have emphasised carbon neutrality targets for environmental sustainability by increasing renewable energy efficiency. This study develops a common set of weights (CSW) model for the additive model in data envelopment analysis using goal programming to analyse the energy efficiency of the Organisation for Economic Co-operation and Development (OECD) countries. The CSW model, which has better discrimination power, places Iceland as the most renewable energy-efficient member country in the OECD, followed by Luxemburg and Norway. Our findings suggest that OECD countries should increase their renewable energy consumption and reduce municipal waste and CO2 emissions. Investment subsidies should be provided to support the development and adoption of energy-efficient technologies and promote awareness in the community and industry to improve the efficiency of renewable energy to combat climate change