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Purpose – The purpose of this paper is to study the role of business intelligence (BI) in achieving agility in supply chain context by examining the relationship between BI competence, agile capabilities, and agile performance of the supply chain. Design/methodology/approach – A theoretical framework is developed drawing on the resource-based view, the dynamic capabilities perspective, and the competence-capability relationship paradigm, as well as an extensive review of the literature. Structural equation modeling is employed to analyze the data collected from Iranian manufacturers in the automotive industry. Findings – The empirical results support the conceptualization of supply chain BI competence as a multi-dimensional construct comprising managerial, technical, and cultural competence, and confirm that it is a key enabler of supply chain agility in terms of both agile capabilities and agile performance. The results also provide support for partial mediation of agile capabilities on the relationship between BI competence and agile performance of the supply chain. Originality/value – This paper provides a response to the identified need for empirical evidence on the benefits derived from BI, especially in the supply chain context. It also contributes to the existing supply chain agility literature by providing insight into the value and role of BI in enhancing agile capabilities and performance in the inter-organizational supply chain.

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This paper presents a multi-objective possibilistic programming model to design a second-generation biodiesel supply chain network under risk. The proposed model minimizes the total costs of biodiesel supply chain from feedstock supply centers to customer centers besides minimizing the environmental impact (EI) of all involved processes under a well-to-wheel perspective. Non-edible feedstocks are considered for biodiesel production. Variable cultivation cost of non-edible feedstock is assumed to be non-linear and dependent upon the amount of cultivated area. New formulation of possibilistic programming method is developed which is able to minimize the total mean and risk values of problems with possibilistic-based uncertainty. To solve the proposed multi-objective model, a hybrid solution approach based on flexible lexicographic and augmented ɛ-constraint methods is proposed which is capable to find appropriate efficient solutions from the Pareto-optimal set. The performance of the proposed possibilistic programming method as well as the developed solution approach are evaluated and validated through conducting a real case study in Iran. The outcome of this study demonstrates that high investment cost is required for improving the environmental impact and risk of sustainable biodiesel supply chain network design under risk. Decision maker preferences are required for suitable trade-off among total costs, risk values and environmental impact.