An intelligent framework for performance optimisation of integrated management system and resilience engineering in pharmaceutical plants

Abstract

The objective of this study is to present an intelligent framework for integrated performance evaluation and optimisation of integrated management system (IMS) and resilience engineering (RE) in pharmaceutical plants. To this end, the required data are collected via valid questionnaires in the quality department of the pharmaceutical plants. Questionnaires are verified and validated by Alpha-Cronbach and t-test. Adaptive neuro-fuzzy inference system and fuzzy data envelopment analysis are employed in the intelligent framework to optimise the performance of the pharmaceutical plants. Moreover, the preferred model is selected in the proposed framework by perturbation analysis and correlation experiment. In addition, the impacts and weights of indicators on performance are identified by sensitivity analysis. Similarly, a design of experiment is performed to prioritise the factors in the order of importance. Finally, it can be argued that integration with regard to the RE leads to performance improvement in the presented framework. In this study, a novel integrated framework based on IMS and RE is presented for performance evaluation and optimisation of pharmaceutical plants. Fuzzy mathematical programming and fuzzy intelligence are used to achieve the objective of the stated framework due to data uncertainty. Also, critical success factors are identified.

Keywords:

• integrated management system (IMS)
• resilience engineering (RE)
• adaptive neuro-fuzzy inference system (ANFIS)
• fuzzy data envelopment analysis (FDEA)
• performance optimisation
• design of experiment
• critical success factors (CSFs)

Additional information

Funding

This work was supported by University of Tehran, Iran.