Industry 4.0 and Lean Six Sigma Integration: History
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Contributor:
Jaime Macias-Aguayo
,
Lizzi Garcia-Castro
,
Kleber F. Barcia
,
Duncan McFarlane
,
Jorge Abad-Moran

In recent years, Industry 4.0 (I4.0) has been a recurrent theme in the literature on Lean Six Sigma (LSS), given the synergies that can arise from their combination. However, several factors can affect the integration of I4.0 and LSS in practice. This article presents a systematic literature review of the barriers to integration and the critical success factors (i.e., enablers) involved in this process.

  • barriers
  • enablers
  • Industry 4.0
  • integration

1. Industry 4.0 Technology

Since its introduction in 2011, multiple authors have attempted to conceptualise Industry 4.0 [1]. However, to date, there has been no consensus on its definition. This fact makes it challenging to conduct academic studies on the topic [2] and has given rise to the incorrect assumption that I4.0 covers almost every technology. To shed light on this issue, the researchers begin by reviewing some of the most cited articles that provide descriptions of I4.0. The researchers then analyse these descriptions and identify a set of technologies that enable their implementation.
Table 1 summarises the descriptions of I4.0, listed chronologically, to show how this notion has been shaped over the years. From a pure technology perspective, these descriptions show that, at a fundamental level, I4.0 is characterised by the confluence of the concepts of the Internet of Things (IoT) and cyber-physical systems (CPSs) [3]. The IoT focuses on the interconnectivity of physical objects, allowing their communication and collaboration [4]. In this regard, wireless communication technologies can be seen as enablers of this concept. Cyber–physical systems are integrations of physical objects, cloud technology, and algorithms, which allow the analysis and control of physical processes [5]. In this regard, technologies that allow data collection from physical objects, such as radio-frequency identification (RFID) tags and sensors, can be seen as enablers of such integrations (i.e., CPSs). A similar argument can be made in favour of technologies that facilitate data storage from physical objects (in the cloud) and technologies that allow data analysis. Examples of the latter include (but are not limited to) Big Data and Analytics (BDA), machine learning, artificial intelligence, simulation, and digital twins [6]. Furthermore, the control dimension of the CPSs means that the “computations” can affect the physical objects being monitored (and vice versa) [7], leading to a need for actuators as part of such cyber–physical systems.
Table 1. A summary of relevant works.
Authors Description Year Article Citations
[3] Industry 4.0, or the fourth stage of industrialisation, is a technological evolution in the manufacturing domain enabled by cyber-physical systems (CPSs) and the Internet of Things and Services. 2013 3825
[8] “Industry 4.0 collectively refers to a wide range of current concepts, whose clear classification concerning a discipline as well as their precise distinction is not possible in individual cases. […] Fundamental concepts are: Smart Factory, Cyber-physical systems, self-organisation, new systems in distribution and procurement, adaptation to human needs, and corporate social responsibility”. 2014 3806
[5] “The term Industry 4.0 refers to the fourth industrial revolution and is often understood as the application of the generic concept of cyber-physical systems (CPSs) to industrial production systems (cyber-physical production systems).” 2015 139
[4] Industry 4.0 is based on the principles of interconnection (i.e., a collaboration between physical objects and humans), information transparency (i.e., availability of digital data of the physical world and their exploitation using analytics), decentralised decisions (enabled by CPSs), and technical assistance. 2017 708
[9] “I4.0 is aimed at creating intelligent factories where manufacturing technologies are upgraded and transformed by Cyber-physical systems (CPSs), Internet of Things (IoT), and cloud computing”. 2017 1728
[10] “Industry 4.0 represents the current trend of automation technologies in the manufacturing industry, and it mainly includes enabling technologies such as the cyber-physical systems (CPSs), Internet of Things (IoT) and cloud computing”. 2018 1990
From the perspective of deployment, according to [3], I4.0 may be implemented in organisations through a combination of horizontal and vertical integrations, as well as the integration of engineering from beginning to end along the full value chain. Horizontal integration relates to the cooperation of information technology (IT) systems at different stages of the value chain, as well as their cooperation with the IT systems of other companies’ value chains. Such cooperation entails security issues related to data and information sharing [11]. In this regard, technologies that enable secure data sharing, such as blockchain, are usually considered enablers of I4.0. In contrast, vertical integration relates to the hierarchical integration of IT systems, which requires the actuator and sensor signals to be fully integrated digitally across all levels, all the way up to the ERP level [3]. In this regard, systems for supervisory control and data acquisition (SCADA), manufacturing execution systems (MES), and enterprise resource planning modules (ERP modules) can be considered as part of the baseline technological infrastructure for I4.0 implementation.
From a design perspective, in [4], I4.0 was characterised using the principles of interconnection, information transparency, decentralised decisions, and technical assistance. According to these authors, the latter characteristic is based on the notion that, in the “Smart Factory”, the staff needs (1) technologies aggregating and displaying data for decision-making, such as smartphones and other wearables (e.g., augmented/virtual reality glasses) and (2) technologies automating time-consuming or safety-critical activities, such as robots.

2. Lean Six Sigma

The origins of LSS date back to the early 2000s, when the principles of Lean (L) began to be integrated into Six Sigma (SS) [12]. While the emphasis of SS has been on variability minimisation and defect eradication, mainly in manufacturing [13], the primary focus of L has been on removing all Muda, or waste, from all places and processes inside the system [14]. Hence, both approaches can be considered complementary.
Different LSS implementation frameworks have been proposed in the literature, but so far, there has been no agreement on this or a definition [15][16]. However, there appears to be a consensus that, at a basic level, LSS implies adopting the problem-solving approach DMAIC (Define–Measure–Analyse–Improve–Control) and incorporating L and SS techniques in each phase of this approach [17][18].
Multiple tools and techniques are used as part of Lean and SS. According to work in [11], the Lean techniques and tools with the highest synergistic connection with I4.0 technologies are value stream mapping (VSM), Lean office, visual management, just-in-time (JIT), heijunka, 5Ss, jidoka, kanban, cellular manufacturing, single-minute exchange of die (SMED), total productive maintenance (TPM), and poka-yoke. Furthermore, according to [13], SS involves the use of the DMAIC approach for achieving operational excellence and DMADV (Define–Measure–Analyse–Design–Verify) for achieving excellence in the design process of new products and services (an area known as Design For Six Sigma), as well as the use of statistical tools, such as statistical process control (SPC), regression, design of experiments (DOE), failure mode and effects analysis (FMEA), and management tools, such as SIPOC (Suppliers-Inputs-Process-Outputs-Customers), Critical to Quality (CTQ) trees, or the Voice of the Customer (VOC), to name a few.

3. I4.0 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 [19][20], as well as through process variability reduction [11]. 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 [20]. Regarding the second integration perspective, I4.0 technologies can, for example, reduce the required effort to maintain L [21] and benefit SS by enabling the collection and analysis of large volumes of data in a shorter time [22][23]. 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 [24][25]).
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 [23][26][27][28][29][30][31]. For instance, [32] 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 [33]; in [31], 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 [29]. 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 [34]. Regarding SS techniques, in [35], 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 [22] and [36]. 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 [37], 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 [11] 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 [38], reference architectures to enable integration [39], and the combined impact of I4.0, LSS, and Quality Management Systems (e.g., ISO 9001) on organisational performance [40]. Furthermore, in a review in [41], 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. Only the benefits of integration have been addressed in recent work by [24]. Encouraged by this research gap, the researchers propose a research design to systematically assess the literature on enablers of and barriers to I4.0 and LSS integration in the next section.

This entry is adapted from the peer-reviewed paper 10.3390/app122211321

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