Purpose: Despite the proliferation of supply chain risk management (SCRM) studies, a theoretically supported and empirically validated study on justifying the antecedents and measurement dimensions of supply chain resilience (SCRE) is rare. Therefore, drawing on extensive literature review, this study aims to explore and validate the antecedents and the measurement dimensions of SCRE.Design/methodology/approach: This study uses positivist paradigm using quantitative method. However, it also uses qualitative approach in the form of field study to contextualize the research model. The quantitative study is conducted by operationalising a survey research. Partial least square-based structural equation modelling has been used to analyze the data.Findings: Study results suggest that the psychometric properties of the SCRE dimensions, supply chain readiness, response and recovery, are reliable and valid. It also affirms that supply chain orientation (SCO), learning and development and supply chain risk management culture (SCRMC) significantly influence the SCRE. Further, SCRMC mediates the relationship between SCO and SCRE.Practical implications: The findings of this study will assist the supply chain managers in taking decision on readiness capability development and reducing the decisional uncertainty during response and recovery.Originality/value: Drawing on extensive extant literature on crisis management and supply chain management, this study develops and validates the measurement dimensions of SCRE in terms of readiness, response and recovery, as well as justifies the antecedent factors of SCRE, which is a novel attempt in SCRM literature.

Artificial intelligence (AI) and smart automation are revolutionizing the global supply chain ecosystem at an accelerated pace, providing tremendous potential for resilience, innovation, efficacy, and profitability. This paper examines how AI, machine learning (ML), and robotic process automation (RPA) influence supply chain operations to adjust to the risks and vulnerabilities. It focuses on how AI and other relevant technologies will enhance forecasting to predict actual demand, expedite logistics, increase warehouse efficiency, and promote instantaneously making decisions. This study utilizes thematic analysis to find AI-driven supply chain applications, including logistics optimization, forecasting demand, and risk mitigation, among 383 peer-reviewed articles (2017–2024). It provides a strategic framework for dealing with vulnerabilities, operational excellence, and resilient solutions. Additionally, the research investigates how AI contributes to supply chain resilience by predicting disruptions and automating risk mitigation strategies. This paper identifies critical success factors and challenges in adopting intelligent automation by analyzing real-world industry implementations. The findings will propose a strategic framework for organizations aiming to leverage AI to achieve operational excellence, agility, and real-time information flow for effective decision-making.

The sustainability concept is commonly used in many domains. However, the assessment of reflective and formative measurement has been ignored largely. As a result, sustainability factor scales are specified wrongly and this might lead to reduced scale validity. The aim of the study is find out the nature of sustainability factors either reflective or formative by investigating three distinct industrial settings in Bangladesh. A quantitative research design is used and the data is analysed through Partial Least Square (PLS) analysis. PLS analysis validates the indicators and factors. Sustainability factors in context of microbusiness and supply chain found reflective in nature whereas in e-business it was formative. The study suggests that sustainability factor is a context specific phenomena and it can be treated either reflective or formative.