Hicham, R.; Abdallah, L.; Mohamed, M. Risk Management and Assessment Hybrid Framework for Business Process Reengineering Projects: Application in Automotive Sector. Eng2024, 5, 1360-1381.
Hicham, R.; Abdallah, L.; Mohamed, M. Risk Management and Assessment Hybrid Framework for Business Process Reengineering Projects: Application in Automotive Sector. Eng 2024, 5, 1360-1381.
Hicham, R.; Abdallah, L.; Mohamed, M. Risk Management and Assessment Hybrid Framework for Business Process Reengineering Projects: Application in Automotive Sector. Eng2024, 5, 1360-1381.
Hicham, R.; Abdallah, L.; Mohamed, M. Risk Management and Assessment Hybrid Framework for Business Process Reengineering Projects: Application in Automotive Sector. Eng 2024, 5, 1360-1381.
Abstract
This study introduces an integrated method for managing process risks in a Business Process Reengineering (BPR) project, using Robust Data Envelopment Analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA: Severity, Occurrence, and Detection (S-O-D), and incorporating two additional factors (Breakdown Cost and Breakdown Duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an au-tomotive sector company, and the results showed the impact of uncertainty on values by com-paring different approaches such as RPN, PFMEA-DEA, and PFMEA-RDEA. A new reduced risk categorization was achieved, who allowed for decision-makers to focus on necessary actions for reengineering.
Keywords
BPR; DEA; RDEA; Machine Learning; FMEA; PFMEA; Risk Prioritization; ANN
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
