The petrochemical industry is one of the most influential sectors in the economies of oil-producing countries, particularly Iran, as well as 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 consider existing operational constraints. Subsequently, strategic objectives should be established in accordance with the macro-level policies of the industry, enabling organizations to compete effectively in the global market and move toward sustainable future development. Data Envelopment Analysis (DEA) is a powerful retrospective performance evaluation tool, whereas Inverse Data Envelopment Analysis (Inverse DEA) is a forward-looking approach capable of estimating the required input or output values while preserving efficiency. The integration of these two powerful methodologies enables managers to develop flexible and informed plans for future decision-making. Furthermore, data encountered in real-world applications are often characterized by uncertainty and imprecision; therefore, it is essential to extend conventional network DEA models to accommodate fuzzy data in order to obtain more realistic and reliable results. This study proposes a novel fuzzy arithmetic-based inverse Network Data Envelopment Analysis (NDEA) model that estimates the required input values while maintaining both the overall process efficiency and cost efficiency at constant levels. Meanwhile, output values are adjusted according to managerial preferences and organizational objectives. The proposed model is applied to a manufacturing workshop in the oil and petrochemical industry, and the results demonstrate its high effectiveness and practical applicability.