Title: 'DMAICS 2 CRISP DM' approach for improving and optimising the performance of an industrial mining production process

Authors: Ilham Battas; Hicham Behja; Laurent Deshayes

Addresses: Engineering Research Laboratory (LRI), Modeling System Architecture and Modeling Team (EASM), National and High School of Electricity and Mechanic (ENSEM), Hassan II University, Casablanca, 8118, Morocco; Research Foundation for Development and Innovation in Science and Engineering, Casablanca, 16 469, Morocco; Innovation Lab for Operations, Mohammed VI Polytechnic University, Benguerir, 43150, Morocco ' Engineering Research Laboratory (LRI), Modeling System Architecture and Modeling Team (EASM), National and High School of Electricity and Mechanic (ENSEM), Hassan II University, Casablanca, 8118, Morocco ' PLM-CCI Academy of Vichy-France, France

Abstract: In order to meet the challenges of the economic world, mining companies are always trying to improve the performance of their production chains by optimising production to the maximum possible extent. Until now, some of the most powerful and effective tools to achieve positive and sustainable operational results in organisations around the world to improve the performance of a production chain are Lean Six Sigma (LSS) and knowledge discovery in database (KDD). Therefore, using a combination of these two proven process improvement approaches (Lean Six Sigma and KDD) for the development of a mining chain efficiency prediction system will help mine managers to rapidly, and continuously improve their production chain. Indeed, the system will be an effective and efficient tool allowing each interested mining company to project itself over time, to predict the performance of its activity, to identify management alerts in advance, and to control its overall production system.

Keywords: Six Sigma; lean; DMAICS; knowledge discovery in database; KDD; CRISP DM; data analysis; prediction system; decision-making support system; mining industrial efficiency; optimisation.

DOI: 10.1504/IJSSCA.2023.134444

International Journal of Six Sigma and Competitive Advantage, 2023 Vol.14 No.4, pp.408 - 436

Accepted: 12 Jan 2023
Published online: 23 Oct 2023 *

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