The Application of Machine Learning to Consolidate Critical Success Factors of Lean Six Sigma

Abstract

Lean six sigma (LSS) is a quality improvement phenomenon that has captured the attention of the industry. Aiming at a capability level of 3.4 defects per million opportunities (Six Sigma) and efficient (lean) processes, LSS has been shown to improve business efficiency and customer satisfaction by blending the best methods from Lean and Six Sigma (SS). Many businesses have attempted to implement LSS, but not everyone has succeeded in improving the business processes to achieve expected outcomes. Hence, understanding the cause and effect relationships of the enablers of LSS, while deriving deeper insights from the functioning of the LSS strategy will be of great value for effective execution of LSS. However, there is little research on the causal mechanisms that explain how expected outcomes are caused through LSS enablers, highlighting the need for comprehensive research on this topic. LSS literature is overwhelmed by the diverse range of Critical Success Factors (CSFs) prescribed by a plethora of conceptual papers, and very few attempts have been made to harness these CSFs to a coherent theory on LSS. We fill this gap through a novel method using artificial intelligence, more specifically Natural Language Processing (NLP), with particular emphasis on cross-domain knowledge utilization to develop a parsimonious set of constructs that explain the LSS phenomenon. This model is then reconciled against published models on SS to develop a final testable model that explains how LSS elements cause quality performance, customer satisfaction, and business performance.

Iterative histogram equalization using discrete wavelet transform in low-dynamic range

“…After exiting the iteration, a particular 6-sigma rule 32 is applied in which the range is between [−3,3], where the maximum contrast enhancement is obtained in case of sigma is 3 and the minimum contrast enhancement is obtained in case of sigma is −3 rx(i)={xiQF,false|xifalse|>3ex(μhighfreq+numOfSigma×1L−1∑i=1Lfalse(false|xifalse|−μhighfreqfalse)23ex)xi,otherwise,where QF∈[0,10] and numOfSigma∈[−3,+3]. The numOfSigma is multiplied by the standard deviation of the bins.…”

“…After exiting the iteration, a particular 6-sigma rule 32 is applied in which the range is between ½−3; 3, where the maximum contrast enhancement is obtained in case of sigma is 3 and the minimum contrast enhancement is obtained in case of sigma is −3 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 4 ; 1 1 6 ; 3 4 0 r x ðiÞ ¼…”

Organizational change towards Lean Six Sigma implementation in the manufacturing supply chain: an integrated approach

“…2.2 Identification of the key factors to Lean Six Sigma implementation in manufacturing supply chains LSS implementation factors are complex and abundant (Mabrouka et al, 2021), and some of the reported LSS implementation failures are attributed to a poor understanding of cause and effect relationships between the LSS enablers (Perera et al, 2021). The relationship between LSS factors with the supply chain performance in manufacturing industries has been proven significant (Ahmad et al, 2019;Gultom and Wibisono, 2019;Ali et al, 2020).…”

“…LSS implementation factors are complex and abundant (Mabrouka et al. , 2021), and some of the reported LSS implementation failures are attributed to a poor understanding of cause and effect relationships between the LSS enablers (Perera et al. , 2021).…”

Application of six sigma through deep learning in the production of fasteners

“…To understand the cause-and-effect relationships of factors that make lean six sigma (LSS) effective and to carry out LSS effectively, Perera et al have introduced a new method that uses artificial intelligence and machine learning, specifically natural language processing (NLP). It was stated that this model would contribute to developing final testable models that reveal how LSS elements affect quality performance, customer satisfaction and business performance (Perera et al , 2021). In their study, Elboq et al have used Lean six sigma tools together with deep learning in a textile company.…”