The petrochemical industry is one of the most influential industries in the economy of oil-rich countries such as Iran, as well as in the global economy. To make informed managerial decisions in this field, managers must not only evaluate the past performance of organizations and identify sources of inefficiency, but also take existing constraints into account. Subsequently, they should set appropriate targets in line with the macro policies of the relevant industry and participate intelligently in global competition while moving toward the future. Data Envelopment Analysis (DEA) is a powerful retrospective tool, whereas Inverse Data Envelopment Analysis (IDEA) is a forward-looking approach capable of estimating data. The combination of these two powerful tools enables managers to plan flexibly and knowledgeably. On the other hand, in the real world, data are often fuzzy. Therefore, efforts have been made to extend conventional network DEA models to the case of fuzzy data in order to obtain more realistic results, and to investigate and propose novel solution approaches. This paper proposes, based on the fuzzy arithmetic approach, a model that estimates inputs while keeping the overall process efficiency as well as cost efficiency constant, and allows outputs to change according to managerial preferences based on organizational objectives. The study has been conducted for a manufacturing workshop in the oil and petrochemical industry, and the obtained results indicate the high effectiveness and capability of the proposed model.