This paper assesses the link between dimensions of agile supply chain, competitive objectives and business performance in the UK North Sea upstream oil and gas industry. A questionnaire was designed and administered covering important criteria of agility identified from the literature. The questionnaire was sent to a sample of 880 supply chain managers within the UK oil and gas industry and a net response rate of 17.8% was achieved. Statistical tests for validity and reliability were carried out. Also, the KS statistical test for normality was undertaken on the data. All the tests affirm that the data came from a normal distribution. Non-response bias analysis was conducted through wave analysis using one-way ANOVA and no statistically significant difference was revealed by the t-test result. By examining the whole supply chain associated with agile practices in an important sector, the paper identifies the most important dimensions and attributes of supply chain agility and provides a deeper insight into those characteristics of agility that are most relevant within the oil and gas industry.

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In recent years, green logistics has received increasing interest due to the rise in greenhouse gases emission from transport operations. It is a promising approach to managing supply chain decisions to reduce environmental damages. Many models have been developed in the literature to study the environmental aspects of routing problems. However, these models have not been implemented in practice, particularly in batch processing industries. In this paper, we developed a two-stage stochastic model which integrates the tactical and operational decision levels to reduce the economic and environmental impacts of transport activities. This model generates optimum vehicle routes and their delivery velocities with the objective of minimizing the total distribution cost and the related emissions. We implemented the proposed model in two case studies drawn from the fast-moving consumer goods industry. This resulted in a cost reduction of up to 13 % of the total related economic and environmental costs compared to the actual situation at the test sites. These results also show the potential of green logistics-based models to improve the current modelling capabilities for batch distribution planning.