Integration of Industry 4.0 technologies into Lean Six Sigma DMAIC: a systematic review

This review examines which Industry 4.0 (I4.0) technologies are suitable for improving Lean Six Sigma (LSS) tasks and the benefits of integrating these technologies into improvement projects. Also, it explores existing integration frameworks and discusses their relevance. A quantitative analysis of 692 papers and an in-depth analysis of 41 papers revealed that “Analyse” is by far the best-supported DMAICs phase through techniques such as Data Mining, Machine Learning, Big Data Analytics, Internet of Things, and Process Mining. This paper also proposes a DMAIC 4.0 framework based on multiple technologies. The mapping of I4.0 related techniques to DMAIC phases and tools is a novelty compared to previous studies regarding the diversity of digital technologies applied. LSS practitioners facing the challenges of increasing complexity and data volumes can benefit from understanding how I4.0 technology can support their DMAIC projects and which of the suggested approaches they can adopt for their context

DMAIC 4.0 - innovating the Lean Six Sigma methodology with Industry 4.0 technologies

Lean Six Sigma (LSS) is a continuous improvement methodology that emerged around 2000 (George and George, 2002; Snee, 2010). It combines the strengths of two methodologies, Lean and Six Sigma, into an effective process and quality improvement framework. Although many organisations have successfully applied LSS over the past two decades, over 60% of Lean and Six Sigma implementations have failed (Albliwi et al., 2014; Sony et al., 2020c), and, accordingly, a significant number of improvement projects. Consequently, researchers have investigated the reasons behind these failures and revealed numerous failure factors, criticisms, impediments, and barriers that jeopardise the success of LSS initiatives. These reasons, also recognised as LSS limitations, represent the problem addressed in this research. On the other hand, the Industry 4.0 (I4.0) era, entailing machine connectivity, big data technologies and artificial intelligence, offers new opportunities for data-driven quality improvement strategies such as LSS. Therefore, this study explored how I4.0 technologies can enhance the traditional LSS methodology by following a Design Science Research (DSR) approach. The aim was to design a solution integrating I4.0 data-driven tools into the traditional DMAIC framework to enhance the success and effectiveness of LSS projects. DMAIC stands for Define, Measure, Analyse, Improve, and Control, representing project phases executed in a prescribed order. The designed solution is a DMAIC 4.0 framework that should help organisations overcome the limitations of LSS by exploiting modern technologies and techniques. This study adopts the DSR process described by Peffers et al. (2007), combined with qualitative methods suggested by Offermann et al. (2009). There are three main phases: (1) Problem Identification, (2) Solution Design and (3) Evaluation. Expert interviews were conducted in phase 1 to confirm the problem and underpin its relevance. The design built in phase 2 is based on existing knowledge and field experience. In phase 3, the researcher successfully evaluated the framework’s utility and effectiveness within a German manufacturing organisation through action research. Additionally, a Delphi study demonstrated that the design presented is relevant and applicable to various industries. Upon Delphi panel feedback, a roadmap was created to guide organisations in implementing the new framework. To the authors’ knowledge, this is the first DMAIC 4.0 framework presented in the academic literature thus far. Knowledge and novel contributions were generated through the design and evaluation process. The validated framework includes 42 LSS tasks enhanced by I4.0 technologies. It incorporates knowledge from extant research related to LSS, DMAIC and I4.0. Furthermore, it focuses on tools and tasks and is more detailed than previously presented frameworks integrating I4.0 with LSS. Unlike conceptual frameworks, it is empirically validated, which should motivate LSS practitioners to innovate their projects. Clearly, there is still room for expansion as there are many more tools in both areas, LSS and I4.0. Researchers and practitioners can customise and apply the framework in various contexts to establish a new standard for DMAIC