Next Article in Journal
Prediction of Vehicle Crashworthiness Parameters Using Piecewise Lumped Parameters and Finite Element Models
Previous Article in Journal
Developing Self-Similar Hybrid Control Architecture Based on SGAM-Based Methodology for Distributed Microgrids
Article Menu
Issue 4 (December) cover image

Export Article

Open AccessArticle
Designs 2018, 2(4), 42; https://doi.org/10.3390/designs2040042

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

1
Department of Engineering and the Built Environment, Anglia Ruskin University, Bishop Hall lane, Chelmsford CM1 1SQ, UK
2
Department of Computer Science and Engineering, Royal University of Dhaka, Dhaka-1213, Bangladesh
*
Author to whom correspondence should be addressed.
Received: 28 September 2018 / Revised: 15 October 2018 / Accepted: 21 October 2018 / Published: 27 October 2018
Full-Text   |   PDF [6508 KB, uploaded 29 October 2018]   |  

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. View Full-Text
Keywords: process reengineering (PR); process identification (PI); process verification (PV); process optimization (PO); business process reengineering (BPR); manufacturing process reengineering (MPR) process reengineering (PR); process identification (PI); process verification (PV); process optimization (PO); business process reengineering (BPR); manufacturing process reengineering (MPR)
Figures

Graphical abstract

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 (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Khan, M.A.A.; Butt, J.; Mebrahtu, H.; Shirvani, H.; Alam, M.N. Data-Driven Process Reengineering and Optimization Using a Simulation and Verification Technique. Designs 2018, 2, 42.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Designs EISSN 2411-9660 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top