Many transportation companies in the current environmental corporate socially responsible era have long term objectives to move their operations towards green logistics. This requires them to use more environmental friendly resources as inputs that will produce fewer harmful outputs to the environment. In this regard, converting diesel trucks to operate on Natural Gas (NG) and diesel has recently received significant attention in the transportation sector of logistics. However, initial results in the literature indicate that the performance of a dual diesel/NG engine is not the same as a conventional diesel powered engine. Apart from engine conversion, this demonstrates the significance of engine performance management as one of the important steps to be carried out for achieving the goal of green logistics in this regard. Existing work in the literature has focussed on studying the engine's performance outputs from different types of fuels but not in the area of engine performance management. In this paper, the need to address this gap is highlighted and an approach based on pattern discovery and association mining techniques is proposed that provides knowledge by which an engine's key performance factors can be managed or fine-tuned so that the performance of a dual diesel/NG engine is similar a diesel one in specific operational conditions. The proposed approach will also provide comprehensive analysis from a business perspective including cost-benefit decision-making that will assist transport companies in the changeover decision-making process.