A novel hybrid process re-engineering approach with the integration of smart structured data for manufacturing optimization.
Ashik is a member of our Engineering Analysis Simulation and Tribology (EAST) Research Group and also part of our Future Cities Research Network.
Process re-engineering and transformation of manufacturing data into readable, accessible and smart structured data is a big challenge in manufacturing organizations as shown by the high rate of failure. This research aims to design a re-engineering process which will have a better success rate. This research integrates process re-engineering and manufacturing data to identify process interdependencies. It proposes a new hybrid data-driven process re-engineering method integrated with cause and effect algorithm to transform data into smart structured data as a procedure for process identification and mapping and incorporates process verification to analyse the changes made in a manufacturing process. The research will provide a new process re-engineering methodology and a cause and effect algorithm for manufacturing optimization.
Khan, M.A., Mebrahtu, H., Shirvani, H. and Butt, J., 2017. Manufacturing optimization based on agile manufacturing and big data. Advances in Manufacturing Technology XXXI, IOS press: London, UK, pp.345-351.
Khan, M., Butt, J., Mebrahtu, H., Shirvani, H. and Alam, M., 2018. Data-Driven Process Re-engineering and Optimization Using a Simulation and Verification Technique. Designs, 2(4), p.42.
International conference for manufacturing research- ICMR-2017, London