This paper introduces a Network Data Envelopment Analysis (NDEA) model that accounts for intermediate products and linking activities within decision-making units (DMUs). Traditional DEA models often neglect these internal activities, leading to an incomplete evaluation of overall efficiency. The proposed NDEA model uses a slacks-based measure (SBM) approach to evaluate divisional and overall efficiencies, allowing for non-proportional changes in inputs and outputs. The model is applicable to various organizational structures, such as electric power companies, hospitals, broadcasting companies, and financial holding companies. The paper discusses the basic framework of NDEA, including notation, production possibility set, efficiency measures, and projection. It also explores the properties of NDEA models, proving that under the variable returns-to-scale (VRS) assumption, every division has at least one divisionally efficient DMU. The paper includes illustrative examples and comparisons with traditional DEA models, highlighting the importance of considering linking activities. Additionally, it addresses the role of the intensity vector and proposes extensions to incorporate link flows and connectivity among divisions. The study concludes with suggestions for future research directions, emphasizing the need for further development and application of NDEA models.This paper introduces a Network Data Envelopment Analysis (NDEA) model that accounts for intermediate products and linking activities within decision-making units (DMUs). Traditional DEA models often neglect these internal activities, leading to an incomplete evaluation of overall efficiency. The proposed NDEA model uses a slacks-based measure (SBM) approach to evaluate divisional and overall efficiencies, allowing for non-proportional changes in inputs and outputs. The model is applicable to various organizational structures, such as electric power companies, hospitals, broadcasting companies, and financial holding companies. The paper discusses the basic framework of NDEA, including notation, production possibility set, efficiency measures, and projection. It also explores the properties of NDEA models, proving that under the variable returns-to-scale (VRS) assumption, every division has at least one divisionally efficient DMU. The paper includes illustrative examples and comparisons with traditional DEA models, highlighting the importance of considering linking activities. Additionally, it addresses the role of the intensity vector and proposes extensions to incorporate link flows and connectivity among divisions. The study concludes with suggestions for future research directions, emphasizing the need for further development and application of NDEA models.