# Industry 4.0 and Lean Six Sigma Integration: A Systematic Review of Barriers and Enablers
## 2 Theoretical Background
### 14.0 and LSS Integration  
Two recurrent ideas in the literature on integration are that (1) LSS creates the conditions for I4.0 implementation, and (2) I4.0 and LSS support each other during their respective deployments. Regarding the first idea, LSS tools and techniques facilitate the adoption of I4.0 technologies through process standardisation and human error reduction [32; 33], as well as through process variability reduction [23]. Furthermore, using LSS techniques, such as VSM, can help select I4.0 technologies for a company, as these facilitate the identification of areas where these technologies can contribute the most [33]. Regarding the second integration perspective, I4.0 technologies can, for example, reduce the required effort to maintain L [34] and benefit SS by enabling the collection and analysis of large volumes of data in a shorter time [35; 36]. Likewise, LSS can boost the performance of I4.0 during its execution by providing practical uses for the data collected (through I4.0 technologies) and facilitating their interpretation and analysis (see [10; 37]).  
Multiple examples of I4.0-LSS solutions have been proposed in the literature; however, most research has focused on I4.0 technologies that may support L techniques and tools [36; 38; 39; 40; 41; 42; 43]. For instance, [44] pointed out that the IoT is beneficial in conjunction with poka-yoke and Andon, ensuring \"zero defects\" in production. RFID tags and sensors may allow real-time data collection from the production process to feed the VSM [45]; in [43], the impact of JIT was stressed by integrating it with RFID, cloud technologies, BDA, and augmented reality (AR). With the help of these technologies, JIT can reduce inventory in production processes by monitoring materials and enabling accurate delivery within the system [41]. Furthermore, a study with 46 companies in India found that Machine Learning can effectively identify the correct level of manufacturing flexibility to guarantee a lean operation [46]. Regarding SS techniques, in [47], the benefit of integrating BDA with SPC was studied. According to the authors, it can quickly address quality and delivery issues by enabling data tracking. BDA and SS integration was also discussed in [35] and [48]. They pointed out its potential to handle data variability and complexity, and provide in-depth process knowledge, helping to improve decision-making accuracy. Finally, regarding Design-for-Six-Sigma, in [49], the authors showed how Artificial Neural Networks (ANN) could be used to enhance the performance of the DMADV approach through a case study. The reader is referred to [23] for more examples of I4.0-LSS solutions.  
Other aspects of the integration that have received little or moderate attention in the literature include the contact points between I4.0 and LSS [16], reference architectures to enable integration [50], and the combined impact of I4.0, LSS, and Quality Management Systems (e.g., ISO 9001) on organisational performance [51]. Furthermore, in a review in [13], themes such as motivations, challenges, benefits, and critical success factors were identified as recurrent in the literature on I4.0 and LSS integration. However, in most cases, these themes have been addressed only from the I4.0 and L integration perspective or have appeared in the I4.0 and LSS integration literature as ancillary topics (as opposed to core topics), implying a need for a systematic review of them. To the best of our knowledge, only the benefits of integration have been addressed in recent work by [10]. Encouraged by this research gap, we propose a research design to systematically assess the literature on enablers of and barriers to I4.0 and LSS integration in the next section.