Purpose: – Assessing a measure of sustainable supply chain management (SSCM) performance is currently a key challenge. The literature on SSCM is very limited and performance measures need to have a systematic framework. The recently developed balanced scorecard (BSC) is a measurement system that requires a balanced set of financial and non-financial measures. The purpose of this paper is to evaluate the SSCM performance based on four aspects i.e. sustainability, internal operations, learning and growth, and stakeholder. Design/methodology/approach: – This paper developed a BSC hierarchical network for SSCM in a close-loop hierarchical structure. A generalized quantitative evaluation model based on the Fuzzy Delphi Method (FDM) and Analytical Network Process (ANP) were then used to consider both the interdependence among measures and the fuzziness of subjective measures in SSCM. Findings: – The results of this study indicate that the top-ranking aspect to consider is that of stakeholders, and the top five criteria are green design, corporate sustainability, strategic planning for environmental management, supplier cost-saving initiatives and market share. Originality/value: – The main contributions of this study are twofold. First, this paper provides valuable support for supply chain stakeholders regarding the nature of network hierarchical relations with qualitative and quantitative scales. Second, this paper improves practical performance and enhances management effectiveness for SSCM.

Purpose: This paper aims to illustrate the use of data envelopment analysis (DEA) in measuring internal supply chain performance.Design/methodology/approach: Two DEA models were developed – the technical efficiency model and the cost efficiency model. The models are further enhanced with scenario analysis to derive more meaningful business insights for managers in making resources planning decisions.Findings: The information obtained from the DEA models helps managers to identify the inefficient operations and take the right remedial actions for continuous improvement. More importantly, the opportunity cost (forgone profit) calculated serves as a good reference to managers to make efficient decisions on resource allocations.Research limitations/implications: Results are based on the deterministic data set. Future enhancement of the study would be to look into the possibility of modeling DEA in a stochastic supply chain environment (non‐deterministic) due to the fact that supply chain operates in a dynamic environment.Practical implications: The proposed DEA‐based approach provides useful managerial implications in the measurement of supply chain efficiency. The study proves the usefulness of DEA as a decision‐making tool in supply chain.Originality/value: This paper provides useful insights into the use of DEA as a modeling tool to aid managerial decision making in measuring supply chain efficiency.

The negative effect of financial crimes on financial institutions has grown dramatically over the years. To detect crimes such as credit card fraud, several single and hybrid machine learning approaches have been used. However, these approaches have significant limitations as no further investigation on different hybrid algorithms for a given dataset were studied. This research proposes and investigates seven hybrid machine learning models to detect fraudulent activities with a real word dataset. The developed hybrid models consisted of two phases, state-of-the-art machine learning algorithms were used first to detect credit card fraud, then, hybrid methods were constructed based on the best single algorithm from the first phase. Our findings indicated that the hybrid model Adaboost + LGBM is the champion model as it displayed the highest performance. Future studies should focus on studying different types of hybridization and algorithms in the credit card domain.