A systematic review of the integration of Industry 4.0 with quality-related operational excellence methodologies

Figures & data

Figure 1. SLR process (Tranfield, Denyer, and Smart 2003; Thomé, Scavarda, and Scavarda 2016).

Figure 2. Publications per year.

Figure 3. Publications per country.

Table 1. Most prolific authors.

Primary authors	Article count	Topics
Yadav N.	2	Impact of Industry 4.0, LSS, and quality management systems on organizational performance; Critical success factors for LSS in quality 4.0
Mishra S.	2	Automation’s human and process characteristics; automation’s technological dimensions

Figure 4. Publications per journal.

Table 2. Research methodologies.

Methodology	Article count
Literature review	11
Survey	8
Case study	6
Interviews	2
Mixed methods	1
Grounded theory	1
Case study, action research	1
Delphi technique	1
Simulation	1
Action research	1
Ethnography	1

Table 3. Summary of TQM and Industry 4.0 integration.

Integration theme	Core content	Exemplary contributions	Knowledge gaps
Big data and integration	Cloud, IIoT, sensors, RFID, CPS, wireless networks, and automated and enhanced data collection and processing	Chiarini and Kumar (2021); D’Orazio, Messina, and Schiraldi (2020); Rosin et al. (2020); Pagliosa, Tortorella, and Ferreira (2019); Santos et al. (2021)	Concepts for incorporating new techniques into current processes and systems Involved resources and capabilities
Industry 4.0 technologies	Augmented reality, robotics, and additive manufacturing	Chiarini and Kumar (2020); Pagliosa, Tortorella, and Ferreira (2019)

Table 4. Summary of LSS and I4.0 integration.

Integration theme	Core content	Exemplary contributions	Knowledge gaps
The use of big data in LSS projects	Big data, real-time data, IIoT, sensors, linking multiple systems, RFID tags, or condition monitoring, new analytical capabilities, integration, architectures (e.g., RAMI 4.0), modification of ERP/MES/SCADA systems, PDM/PLM	Antony et al. (2019); Ganjavi and Fazlollahtabar (2021); Santos et al. (2021)	Limited sample sizes in terms of regions and firm sizes, mostly manufacturing; integration of LSS with Industry 4.0
Integration of Industry 4.0 features and DMAIC stages in LSS projects; Algorithms and machine learning in LSS projects		Chiarini and Kumar (2021)	Investigate when Industry 4.0 and LSS should be integrated; investigate the skills that LSS practitioners should develop
As part of a holistic management system: LSS, Industry 4.0, and TQM; New success factors	Employee adaptability (change readiness), cross-functional understanding, and technological affinity, as well as decision-making kinds, knowledge worker availability, and social interaction	Ganjavi and Fazlollahtabar (2021); Yadav, Shankar, and Singh (2021)	Creating a hierarchical link between new success criteria

Table 5. Allocation of Industry 4.0 elements to DMAIC (Chiarini and Kumar 2021).

I4.0 elements	LSS DMAIC phases
MES/SCADA	Define-Measure-Control
PDM/PLM	Define-Measure-Control
Big data collection and analytics	Measure-Analyze-Improve
AI and machine learning	Define-Measure-Analyze-Improve-Control
3D printing, additive manufacturing	Improve
Smart products and customer interaction	Measure-Analyze-Improve-Control
RFID	Measure-Control
Smart sensors	Measure-Control
Collaborative and Autonomous Mobile Robots	Improve
AR and Smart Human Interfaces	Measure-Analyze-Improve-Control

Table 6. Summary of BPM and Industry 4.0 integration.

Integration theme	Core content	Exemplary contributions	Knowledge gaps
BPM as an integration moderator	Process modeling, ERP, and MES integration	Xu, Xu, and Li (2018); Pagliosa, Tortorella, and Ferreira (2019); Rey et al. (2021); Santos et al. (2021)	Integrating diverse models into a unified framework for vertical and horizontal process integration
Process automation and workflow management	Workflow management software, automation software (Celonis, UIpath), ERP connectivity, and standardized/flexible procedures	van der Aalst, Bichler, and Heinzl (2018); Xu, Xu, and Li (2018); Mishra, Sree Devi, and Badri Narayanan (2019a); Mishra, Sree Devi, and Badri Narayanan (2019b)
Process mining and optimization	Data gathering, simulation	Pagliosa, Tortorella, and Ferreira (2019); Tran, Ruppert, and Abonyi (2021)	Concepts of operationalization
Agile BPM	Processes that are adaptable for rapid and continual development	Sehlin, Truedsson, and Cronemyr (2019); Baiyere, Salmela, and Tapanainen (2020)	Methods for adaptive process modeling, infrastructure flexibility, actor capabilities, and generalization

Table 7. Modes of action in integrating QM with Industry 4.0.

Cluster	Core content
Implementation strategies	Problem-solving methodologies (e.g., PDCA, DMAIC), Industry 4.0 implementation on a point-by-point basis, pilot-based approaches
Barriers	Financial constraints, a lack of management support, a lack of awareness, hesitant behavior, digitalization or automation of wasteful activities, technological complexity introduced to shopfloor processes, lack of resources and competencies, time, data quality, digital knowledge and skills, governance structures
Success factors	Behavioral and cultural change, leadership support, interfunctional team development, training activities, employee involvement and empowerment, process changes without advanced coding skills, balance between process modeling and flexibility, efficient and reliable data warehouse systems, data gathering and storage resources, computational power, resource availability, management competence
Capabilities	Organization: Organizational structures of ambidexterity, adaptability, and agility, innovation/continuous improvement culture, data-driven decision-making, increased communication from and to workers, supportive learning environment and leadership style, employee awareness Resources: Automatic data gathering and analysis, process modeling and notations (e.g., BPMN) vs light touch processes, infrastructural flexibility, profound business processes, network integration, interoperability, orchestrate ability, retrofit-ability of machines, preparation/availability of digitally experienced managers, workforce transformation (development vs recruiting), cross-functional team composition, processes for balancing exploitation and exploration Capabilities: Coding/software development, data-based and complex problem solving, data science, algorithm operationalization, data/process mining, digitalization/IT, simulation, BPM-related skills, data protection and IT security, production technologies, modern interfaces, routines for integrating, reconfiguring external and internal competencies and resources, readiness of technologies, processes, and people

Figure 5. Integration process.

Table 8. Future research opportunities.

Focus	Gap/limitation	Future research opportunities
What	Primarily higher-level concepts and less reflecting process and routine adaptations, limited sample sizes, manufacturing focus, not all constituting elements (e.g., TQM) reflected yet	Expand research status and further explore the integration of QM and Industry 4.0 on the level of its constituting elements; Including soft aspects
How	Lack of generalization, only vague frameworks/operational concepts for executing an integration	Explore integration pathways, expand modes of action, involved resources, processes, and capabilities in concrete frameworks

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