Data-Driven Process Reengineering and Optimization Using a Simulation and Verification Technique

Abstract

Process reengineering (PR) in manufacturing organizations is a big challenge, as shown by the high rate of failure. This research investigated different approaches to process reengineering to identify limitations and propose a new strategy to increase the success rate. The proposed methodology integrates data as a procedure for process identification (PI) and mapping and incorporates process verification to analyze the changes made in a specific process. The study identifies interdependency within the manufacturing process (MP) and proposes a generic process reengineering approach that uses simulation and analysis of production line data as a method for understanding the changes required to optimize the process. The paper discusses the methodology implementation technique as well as process identification and the process mapping technique using simulation tools. It provides an improved data-driven process reengineering framework that incorporates process verification. Based on the proposed model, the study investigates a production line process using the WITNESS Horizon 21 simulation package and analyse the efficiency of data-driven process reengineering and process verification in terms of implementing changes.