Developing a Parallel Network Slack-Based Measure Model in the Occurrence of Hybrid Integer-Valued Data and Uncontrollable Factors

This study develops an alternative approach to the parallel network Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model, offering a more accurate and informative assessment of performance within a network system. Traditional DEA models solely focus on the input utilization and the outputs produced when assessing efficiency, disregarding the operation of internal processes within a network system. In addition, these approaches do not assess the concurrent requirement of hybrid integer-valued data and uncontrollable factors on efficiency measures. To address these gaps, we propose a novel approach to parallel network SBM DEA model that integrates hybrid integer-valued data with uncontrollable factors, aiming for a more precise evaluation. Both requirements were initially integrating into the existing method. Subsequently, the optimal solution for the proposed method was achieved by converting its fractional form into a linear one. Therefore, the measures of the proposed approach can now deal directly with controllable hybrid integer- valued input and output slacks. We applied this model to a dataset of 26 faculties in a Malaysian public university, followed by a comparative analysis with existing models. Empirical findings indicate that four (4) faculties are found to be overall effective, as all of their internal processes are effective, while the other faculties are ineffective since not all of their internal processes are effective. The results from our model enable decision-makers to identify ineffectiveness within network processes, thereby facilitating targeted improvements in system performance. By concentrating on the appropriate processes, management can enhance their overall effectiveness and internal effectiveness.