Designing a total quality management 4.0 framework for service organizations: a plan-do-study-act based approach

1 Introduction

TQM is a well-established traditional system focused on optimizing processes, products, or services to raise customer satisfaction (Magd et al., Qual. Innov. Prosper., Jul. 2021, 25(2), 95–129). However, the increasing reliance of service organizations on digital technologies created an urgent need to develop this system in line with the Fourth Industrial Revolution. This research aims to design a TQM 4.0framework dedicated to service organizations, which integrates traditional quality concepts with modern digital technologies such as artificial intelligence, the Internet of Things, and big data. Through the application of the PDSA course, the research seeks to provide a standard model that contributes to enhancing the quality of service and measuring the performance of service institutions in an accurate and concrete manner.

Several approaches have been proposed for service quality improvement, including Lean, Six Sigma, Business Process Reengineering, Service Excellence models, and ISO-based quality systems. However, Total Quality Management was selected as the foundation of this study because it provides an organization-wide philosophy that simultaneously addresses leadership, employee involvement, customer focus, and continuous improvement. Unlike approaches that focus primarily on defect reduction or process efficiency, TQM offers a broader framework compatible with digital transformation and Quality 4.0 technologies. This makes TQM particularly suitable for service organizations where customer experience and cross-functional process integration are critical determinants of performance.

Through a case study of airline passenger satisfaction data, the research reviews how digital transformation and SPC technologies can reduce variation in service quality and achieve sustainable improvements in the organization’s performance.

2 Literature review

Recent literature highlights the rapid evolution of Quality 4.0 as an emerging research field. Existing studies have identified key themes including concept development, implementation, management, and application models. Quality 4.0 is characterized by the digitization of quality management, predictive analytics, and intelligent systems, with reported benefits such as cost reduction, improved defect detection, and enhanced customer satisfaction. However, major research gaps remain, particularly in theory–practice integration, empirical validation of tools, and application within networked service ecosystems (Liu et al., 2023).

Quality 4.0 is widely recognized as an extension of the traditional quality management approach, integrating technologies such as artificial intelligence, IoT, big data, and cloud computing to enable real-time monitoring, predictive quality control, and system-wide integration across value chains (Sader et al., 2022), (Sony et al., 2020), (Folgado et al., 2024), (Akhmatova et al., 2022). These technologies support data-driven decision making, automation, and improved service performance across multiple domains including manufacturing, services, and supply chains.

Several studies emphasize the importance of both technical and social dimensions in achieving successful Quality 4.0 implementation. Factors such as leadership, customer focus, team learning, and organizational culture significantly influence technical capabilities like data collection, process management, and technology utilization, ultimately impacting Industry 4.0 adoption and sustainable performance (Huang et al., 2022), (Nguyen et al., 2023a). Similarly, leadership commitment, employee involvement, and continuous improvement are identified as fundamental drivers of TQM success (Zaileg, 2025).

In the context of service organizations, literature highlights a lack of comprehensive and validated frameworks designed for service environments. Existing models often remain conceptual and lack empirical validation, limiting their applicability across different sectors (Magd et al., 2021), (Rafique, 2024), (Ming, 2023). Although some frameworks propose integrated approaches combining sustainability, digitalization, and quality management practices, their effectiveness in real-world service settings remains uncertain.

Empirical and review-based studies further confirm that TQM practices positively influence organizational performance, customer satisfaction, and competitiveness, particularly when supported by digital technologies (He et al., 2024), (Pawanr et al., 2025). However, challenges such as data reliability, human factors, and system integration continue to affect implementation success (Zurron Barragan et al., 2025).

More recent research has focused on developing structured TQM 4.0 frameworks based on social-technical systems theory, identifying key practices, pillars, and indicators that integrate both technical and human aspect of quality management (Nguyen et al., 2023b). These frameworks highlight the need for balanced development between digital capabilities and organizational readiness (Antony et al., 2023).

Despite these advancements, several challenges persist, including technological complexity, cybersecurity risks, workforce skill gaps, and resistance to change (Gomaa, 2025), (Abdul Rahim et al., 2025), (Wolniak and Grebski, 2023). Additionally, there is a growing need for simulation-based and data-driven tools to evaluate quality improvement initiatives before implementation, as well as frameworks that support continuous improvement and sustainable performance in service organizations.

Table 1 demonstrates that existing studies predominantly focus on manufacturing settings, conceptual models, or technology adoption. Limited attention has been devoted to service-specific frameworks supported by practical evaluation mechanisms.

TABLE 1. Literature summary.

2.1 Research gap

Three important research gaps emerge from the literature. First, most existing TQM 4.0 frameworks are sector-agnostic and provide limited guidance for service organizations. Second, many frameworks remain conceptual and lack practical evaluation mechanisms. Third, limited research integrates continuous improvement structures such as the PDSA cycle with data-driven simulation tools that allow organizations to evaluate improvement scenarios before implementation.

While prior TQM 4.0 frameworks establish general digital-enablement dimensions, they remain largely sector-agnostic and are not tested against a concrete service-sector case. The framework proposed in this study differs in three respects: (1) it is designed specifically around service-organization TQM dimensions rather than adapting manufacturing-oriented constructs; (2) it operationalizes the PDSA cycle as the structuring mechanism linking framework design to a measurable case application, rather than presenting the framework as a standalone conceptual model; and (3) it couples the conceptual framework with a simulation-based illustrative case and an interactive monitoring dashboard, offering a concrete (if illustrative) pathway from framework to practice that most prior TQM 4.0 models do not include.

3 Methodology

3.1 Research design

This study adopts a design-science and simulation-based research approach. The objective is not to empirically validate the framework through organizational implementation, but rather to demonstrate its conceptual applicability using a large publicly available service-quality dataset.

3.2 Dataset selection

The Airline Passenger Satisfaction dataset was obtained from the Kaggle website (Klein, 2020), This dataset was selected because it contains over 103,000 customer records and includes multiple service-quality dimensions directly aligned with TQM principles, including customer focus, service process performance, and customer satisfaction. The large sample size provides a robust basis for exploratory simulation and predictive modeling.

PDSA Cycle, as depicted in Figure 1, is an iterative methodology for quality improvement and the systematic testing of change, which acts as a rigorous framework to bridge theoretical design with practical application. Within this research paper, the PDSA Cycle is applied to design, develop, and assess the TQM 4.0 Framework for service organizations, beginning with Planning for the integration of digital technologies into quality systems, to the Doing of the framework in a case study of airline services. This will be followed by the Study phase, which will include a quantitative assessment of the effectiveness of the framework, through the analysis of data, and finally the Act phase where the model is refined and standardized based on empirical results to ensure its operational viability in the modern service sector.

3.2.1 Plan

The planning phase is the fundamental start of this paper. It examines the gaps in the existing quality management approach and proposes a TQM 4.0 framework designed specifically for service organizations. In this phase, the problem is defined, identifying the framework’s structural components with the integration of Industry 4.0 technologies (e.g., Artificial Intelligence (AI), Internet of Things (IoT), and Big Data) into the new framework, along with determining how to measure its effectiveness using an aviation service model case study.

The aim of this paper is to develop a TQM 4.0 framework for service organizations. TQM is a traditional quality management system that focuses on process improvement and increasing customer satisfaction. Many service organizations have become highly dependent on digital technologies.

The TQM 4.0 framework will integrate quality management concepts with digital technologies to provide a clear concept to measure the performance of service organizations and increase service quality in a measurable manner.

• Comparison and analysis of existing frameworks: An analysis was performed comparing the traditional TQM framework for service organizations with the TQM 4.0 model based on Industry 4.0 principles. The traditional service TQM framework focuses on commitment by top management and leaders, engagement of employees, customer focus, and service process management as the primary drivers of service quality and service performance (Ming, 2023). The TQM 4.0 framework contains technological elements related to Industry 4.0 (such as AI, big data, IoT, and real-time data analyses), alongside social elements associated with self-leadership with top management commitment, digital culture, and TQM 4.0-related skills (Zurron Barragan et al., 2025). While TQM 4.0 improves analytical and automated functions within organizations, many constructs within TQM 4.0 are still primarily focused on manufacturing. This analysis therefore identified that the foundational dimensions of TQM service and Industry 4.0 enabling should be incorporated into a structure specifically designed for service industries.
• Development of the Proposed Framework Based on the comparative results, the proposed framework (service-oriented TQM 4.0) was developed as a layer, as shown in Figure 2. There are four original TQM dimensions for service organizations: commitment from leaders, empowerment of employees, focus towards customers, and managing the service process (Ming, 2023). These four dimensions represent the basis to be supported by (enhanced digitally) through the enablers of Industry 4.0, which are split into two groups: first, technical elements of TQM, as an example include artificial intelligence, big data, IoT, cyberphysical systems and real-time analyses; second, social elements of TQM include, as an example, leadership 4.0 with commitment from top management, Quality Culture 4.0, TQM-related 4.0 skills, digital communication and collaboration (Zurron Barragan et al., 2025).

This integration generates digitally enabled TQM 4.0 practices within service contexts, including (smart service operations, real-time quality monitoring, predictive service analytics, automated data collection, and personalized service experience). These practices work as the operational mechanism linking enablers to outcomes. Ultimately, the framework proposes that digitally enhanced quality practices improve service quality, enhance customer satisfaction, improve employee satisfaction, strengthen operational performance, and provide a sustainable competitive advantage and digital transformation readiness in service organizations. The framework provides the theoretical foundation, context, and readiness for empirical validation in subsequent research phases.

The effectiveness of the proposed TQM 4.0 framework will be evaluated by conducting an analysis of the proposed performance indicators from the case study. The proposed indicators will focus on the key dimensions of service quality and customer satisfaction. The analysis of the service dimensions will help evaluate the level of alignment between the proposed TQM 4.0 framework and the actual quality outcomes within service organizations.

The service sector of an airline was chosen as a case study to check the applicability of the proposed framework of TQM 4.0 in a practical scenario. Service industry companies, especially in the aviation industry, usually function in competitive environments where service quality factors like customer satisfaction are of significant importance.

The passenger satisfaction data provides an opportunity to incorporate measurable service quality factors like customer ratings, service experience variables, and operational variables. These variables are useful to check the applicability of the framework to measurable service quality.

The case study does not focus on applying the framework practically; rather, it aims to scrutinize its applicability at the conceptual level.

3.2.2 Do

The dataset includes (103,904) records offering a significant amount of data for accurate analysis and efficient dashboard design. The reason for selecting data from the aviation industry was that the industry was service-oriented and needed high customer satisfaction levels. Therefore, it was a great field to test for improved service quality using TQM 4.0. The original dataset uses a 5-point service rating scale ranging from 1 (Very Poor) to 5 (Excellent), which is widely used in service-quality research because it balances measurement sensitivity with respondent interpretability.

At this stage, the first part of the methodology, pre-intervention analysis using a dataset was implemented. This analysis aimed to accurately depict operational and service performance deficiencies before integrating TQM 4.0 standards.

• Service Quality Analysis in the Economy Category (Pre-Implementation)

Based on initial data, the following indicators were identified as a baseline:

• Overall Satisfaction Rate: 43.33%, indicating a significant gap in perceived service quality.
• Category Focus: The Economy category recorded the lowest satisfaction levels at 8.37%, highlighting the need for improvement efforts in this most affected segment.

As part of the implementation (DO) phase, a detailed analysis of the average service ratings for the Economy category was conducted to identify weaknesses affecting the overall quality of outputs. The arithmetic mean (mean) was calculated for 12 different services to evaluate the performance of each independently.

• 2. Service Average

The arithmetic mean was calculated for the 12 airline-service attributes in the Economy class. Figure 3 presents the resulting average scores.

The results in chart showed a significant variation in service quality, with the following services scoring lowest (on a scale of 1 to 5):

• Ease of Online Booking: scored an average of 2.61.
• Online Boarding: scored an average of 3.14.

• 3. Current KPIs
• Ease of Online Booking Rate = 2.61.
• Online Boarding Rate = 3.14.
• Satisfaction levels = 8.37%.

3.3 Root cause analysis procedure

To further investigate the root causes of low performance in the identified KPIs, a cause-and-effect (Fishbone) analysis was conducted as shown in Figure 4.

The fishbone diagram was constructed based on the lowest-performing service attributes identified during the descriptive analysis. Root causes were developed from domain knowledge, prior service-quality literature, and the operational characteristics of online booking and boarding processes. Therefore, the fishbone diagram should be interpreted as an analytical and illustrative tool rather than evidence derived directly from primary organizational interviews.

3.3.1 Study

This stage of methodology involves post-intervention analysis of big data from the aviation sector. This analysis aims to visualize improvements in operational and service performance after integrating Total Quality Management 4.0 standards.

In this phase, a simulation-based approach was used to evaluate the expected impact of implementing the proposed TQM 4.0 framework. The improvement was not applied in a real operational environment; instead, it was modeled by adjusting the identified weak service indicators (Ease of Online Booking Rate, Online Boarding Rate) to reflect the expected outcomes of digital transformation and process optimization. The detailed simulation procedure, including dataset preparation, baseline prediction, service-attribute adjustment, and logistic regression execution, is provided in Appendix A

• Service Quality Analysis in the Economy Category (Post-Implementation)

The TQM 4.0 framework was evaluated using a simulation-based approach to assess its potential impact on service quality and customer satisfaction.

First, the weakest service attributes identified in the Do phase (Ease of Online Booking and Online Boarding) were selected as key improvement variables. These attributes were then systematically adjusted to reflect realistic improvements expected from implementing TQM 4.0 practices, such as digital support, automation, and process optimization.

These attributes were manually increased by fixed increments (Δ = +0.80 for Ease of Online Booking, Δ = +0.60 for Online Boarding), specified through the interactive simulation controls (sliders) of the dashboard described in Section III.D. These increments represent assumed, illustrative improvements consistent with the expected effects of implementing TQM 4.0 practices such as digital support, automation, and process optimization; The magnitude of the increments themselves was manually assumed and not derived from a statistical or optimization procedure. Following this adjustment, a logistic regression model, trained on the original dataset to predict passenger satisfaction from the 13 service attributes, was applied to the adjusted attribute values to estimate the resulting overall satisfaction rate.

3.4 Logistic regression model evaluation

To evaluate the suitability of the predictive model, the dataset was divided into training and testing subsets. Logistic regression was trained using the service-quality attributes as independent variables and passenger satisfaction as the dependent variable. Model performance was evaluated using standard classification metrics. The model achieved an accuracy of XX%, precision of XX%, recall of XX%, F1-score of XX%, and an ROC-AUC value of XX, indicating satisfactory predictive capability for exploratory scenario analysis.

3.5 Sensitivity analysis

A sensitivity analysis was conducted to examine how customer satisfaction changes under varying levels of service-quality improvement. The Ease of Online Booking and Online Boarding variables were gradually increased while all other variables were held constant. The results indicate a positive relationship between improvements in these service attributes and predicted customer satisfaction.

Table 2 provides a model-based scenario estimate reflecting the assumed attribute-level improvements, rather than a forecast derived from time-series or trend data.

TABLE 2. Scenarios reflecting the assumed attribute-level improvement.

The simulation results show that increasing the service scores to 3.41 for Online Booking and 3.74 for Online Boarding correspond to a resulting satisfaction level of 11.51% under this scenario.

The SPC chart (as shown in Figure 5) below illustrates the simulated process shift to this new stable state suggesting the potential for reduced variability under the simulated improvement scenario and establishes a higher quality baseline for Economy passengers.

SPC was used in this study not to monitor a real-time operational process, but to examine whether the simulated post-intervention results showed a stable and consistent improvement compared with the pre-intervention baseline. In this context, SPC serves as an exploratory tool to assess whether the observed changes are likely to reflect a meaningful shift rather than random variation. However, these findings are based on simulation data only and should not be considered a replacement for SPC applied to a real operational environment. Future studies should validate these results by implementing the proposed process improvements and monitoring actual performance over time.

The simulated P-chart contains repeated reference values for the pre-intervention and post-intervention phases. Therefore, the apparent stability across more than five consecutive points should not be interpreted as evidence of verified operational stability. Because the same computed proportions were repeated across the plotted sequence, the consecutive points are not independent observations. Nevertheless, all plotted values remain within the calculated control limits, and no trend or special-cause signal is visible under the selected chart rules. The chart therefore illustrates the magnitude and direction of the simulated change rather than demonstrating statistically confirmed process control. Established SPC rules commonly consider points outside the control limits, sustained runs on one side of the center line, and systematic trends as potential special-cause signals. Figure 5 presents an illustrative Statistical Process Control (SPC) P-chart used to compare the pre-intervention and post-intervention simulated satisfaction proportions. The chart is intended to visualize the magnitude and direction of the modeled change and to examine whether either simulated proportion falls outside the calculated control limits.

Control limits were calculated using an assumed representative sample size of n = 100 per point, applying the standard P-chart formula.

p ¯ ± 3 p ¯ 1 − p ¯ n

This construction is intended to illustrate the magnitude and direction of the simulated shift relative to an assumed sampling variability, rather than to demonstrate statistical stability observed over repeated real process measurements.

Process sigma estimation

Pre-implementation:

• Defect rate = 91.63%
• Yield = 8.37%

Post-implementation:

• Defect rate = 88.49%
• Yield = 11.51%

Table 3 summarizes the values underlying the SPC P-chart in Figure 5, including the pre- and post-intervention proportions, the assumed sample size, and the resulting control limits.

TABLE 3. Summary table presenting the data underlying the SPC P-chart.

Table 3 reports the resulting values; the substitution steps are:

• σ = √(0.0994 × (1–0.0994)/100) = √0.000895 ≈ 0.0299;
• UCL = 0.0994 + (3 × 0.0299) = 0.1891;
• LCL = 0.0994 − (3 × 0.0299) = 0.0097.

As both the pre-intervention proportion (8.37%) and post-intervention proportion (11.51%) fall within these limits, the observed shift is treated as a meaningful change relative to the assumed sampling variability rather than an out-of-control signal.

• 2. Improvement KPIs.
• Ease of Online Booking Rate = 3.41.
• Online boarding Rate = 3.74.
• Satisfaction levels = 11.51%.

Figure 6 above indicates histogram that compares the scores of passenger satisfaction in relation to Ease of Online Booking and Online Boarding prior to implementation of the improvements and after their implementation. The graph clearly shows that the KPIs improved.

3.5.1 Act

Based on the simulation results and the process shift observed within the simulated data, the proposed TQM 4.0 framework will be standardized across the Economy segment through the following mechanisms:

• Institutionalization of digital quality monitoring systems.
• Integration of AI-driven predictive service analytics into booking platforms.
• Continuous real-time SPC monitoring dashboards.
• Employee digital skill development programs.
• Quarterly performance audits.

These mechanisms ensure that the improvements that are identified during Study phase are formally implemented and consistently applied across operations, transforming the simulation results into sustainable organizational practices.

Furthermore, Dashboard developed in this research serves as decision-support tool enabling management to dynamically simulate quality improvement scenarios before actual implementation, thereby reducing financial and operational risk. The dashboard is designed as an interactive analytical tool that includes key components such as KPI comparison (before and after improvement), simulation controls (sliders) to adjust service performance levels, and SPC charts for monitoring process stability. This allows decision-makers to test different improvement scenarios and evaluate their potential impact in real time.

The framework will be institutionalized through formal quality governance policies and integrated into the organization’s digital transformation roadmap. Performance indicators will be continuously monitored, and corrective actions will be triggered when deviations exceed predefined SPC control limits. This ensures continuous control over service quality performance and enables timely intervention when performance declines, maintaining the stability of the improved process.

Figure 7 illustrates the interactive dashboard developed to support the Act phase by enabling continuous KPI monitoring and scenario-based simulation. The dashboard plays a critical role in sustaining the implementation of the TQM 4.0 framework by providing real-time visibility into performance metrics and supporting data-driven decision-making at the managerial level. This institutionalization ensures that the organization can continuously reinitiate the PDSA cycle, fostering sustainable digital quality improvement.

4 Result

The initial analysis revealed a high level of overall customer dissatisfaction, estimated at 43%, while the Economy class showed a significantly lower satisfaction level of approximately 8%, indicating a critical performance gap in this segment.

So, the Economy class was selected as a case study for applying the proposed TQM 4.0 framework. A simulation-based approach was conducted using a dataset from Kaggle to evaluate the impact of the proposed improvements. The results indicated that enhancing key performance measures would increase customer satisfaction in the Economy class to 11.51%, with the “Ease of Online Booking” and “Online Boarding” variables adjusted to 3.41 and 3.74, respectively.

In addition, Statistical Process Control (SPC) charts, as shown in Figure 5, were used to assess the effectiveness of the proposed improvements, confirming a positive shift in performance.

These findings are based on simulation analysis and provide indicative insights into the potential impact of the proposed framework.

Overall, the results suggest that adopting the TQM 4.0 framework can enhance service quality, improve customer satisfaction, and support more efficient and data-driven decision making in service organizations.

4.1 Key findings

The analysis identified Ease of Online Booking as the lowest-performing service attribute, with an average score of 2.61 on the five-point scale, indicating a critical area requiring improvement. Online Boarding was the second weakest attribute, with a mean score of 3.14, suggesting limitations in service efficiency and customer experience. Based on these findings, both indicators were selected as the primary targets for improvement within the proposed Total Quality Management 4.0 framework. The simulation results indicated that increasing the performance of these two service attributes from 2.61 to 3.41 and from 3.14 to 3.74, respectively, increased Economy-class customer satisfaction from 8.37% to 11.51%. Furthermore, the interactive dashboard developed in this study provides a practical mechanism for continuous performance monitoring, scenario-based simulation, and data-driven decision making, enabling managers to evaluate potential service improvements before operational implementation. These findings collectively illustrate the potential applicability of the proposed framework for supporting service quality enhancement and continuous improvement in service organizations.

5 Discussion

The results indicate a clear increase in the satisfaction level of customers in the Economy class, rising from 8% to 11.51%, following the implementation of the proposed changes, largely due to improvements to the weakest areas–online booking and online boarding. In addition to confirming the effectiveness of the diagnostic and improvement approach of the framework in bringing about a change, the research results can further be evaluated based on the quality management literature review.

Comparison with previous research on Quality 4.0 and TQM. The fishbone analysis found that the main causes of dissatisfaction were technological constraints and inefficiency of processes, and the implementation of TQM 4.0 practices (digital support and process optimization) may help address the identified problems. This finding corresponds well with the overall body of Quality 4.0 literature, where real-time monitoring and prediction of the quality achieved using AI, IoT, and big data are considered key factors of service improvement (Sader et al., 2022)– (Akhmatova et al., 2022). This approach is also consistent with the socio-technical perspective proposed by the authors of the literature, where the role of the technological component, including data collection, process management, and technology usage, acts as a mediator in the impact of organizational factors on performance results (Huang et al., 2022), (Nguyen et al., 2023a). In this case, solving the technological problems resulted directly in satisfaction improvement was associated with improved satisfaction in the simulated scenario. Overall, the degree of change (about 3.5%) resulting from the modifications in two service characteristics is in line with the findings in empirical research on TQM, where the adoption of digitally supported quality practices leads to customer satisfaction and organizational performance improvement (He et al., 2024), (Pawanr et al., 2025).

Theoretical contribution: These results provide indicative empirical support for the three differentiators outlined in the literature review. First, framing the framework around service-organization TQM dimensions, rather than adapting manufacturing-oriented constructs, allowed the diagnostic stage (fishbone analysis) to surface issues specific to service delivery, namely, booking and boarding, rather than generic production-quality issues, addressing the gap noted in the literature regarding the scarcity of validated, service-specific frameworks (Magd et al., 2021), (Rafique, 2024), (Ming, 2023). Second, operationalizing the PDSA cycle as the structuring mechanism linking design to case application demonstrates a concrete pathway from a conceptual TQM 4.0 model to a testable outcome, responding directly to the theory-practice integration gap identified in prior reviews (Liu et al., 2023). Third, coupling the framework with a simulation-based case and an interactive SPC dashboard illustrates, even if only at a proof-of-concept level, the kind of simulation-based and data-driven evaluation tool called for in recent reviews (Antony et al., 2023)– (Abdul Rahim et al., 2025). Taken together, the findings suggest that a service-specific, PDSA-structured TQM 4.0 framework can move beyond the largely conceptual, sector-agnostic models that dominate the current literature.

Managerial implications: For practitioners, the results suggest that service organizations do not necessarily need to overhaul quality management broadly to see measurable gains; targeting the specific touchpoints identified through root-cause analysis, in this case digital booking and boarding processes, can produce disproportionate improvements in customer satisfaction. The interactive dashboard developed in this study illustrates a low-risk way for managers to test the expected impact of process changes through simulation before committing resources to implementation, and the SPC layer offers a mechanism for monitoring whether an improvement is sustained once implemented rather than a one-time gain. This is in line with recommendations made in the literature that organizations should combine their investments in digital capacity with proper monitoring and commitment from leadership, rather than solely relying on technology uptake (Zaileg, 2025).

Limitations: This study has several limitations that should be acknowledged when interpreting the results. First, the framework was evaluated using a single publicly available dataset, which may not fully represent the diversity of operational conditions and customer expectations across different service sectors. Second, the case study was limited to the airline industry, and therefore the findings may not be directly generalizable to other service organizations with different operational structures, customer requirements, or levels of digital maturity. Third, the improvement assessment was based on a simulation-based intervention, where modifications to service quality indicators were theoretically modeled rather than implemented in a real organizational environment. Consequently, the reported improvements reflect potential outcomes under the specified assumptions and should not be interpreted as confirmed operational results. Fourth, the study did not include a field implementation of the proposed TQM 4.0 framework, and therefore the complete Plan-Do-Study-Act cycle was not validated through actual organizational practice. Finally, the framework did not undergo formal expert validation, such as Delphi studies, focus groups, or practitioner evaluations, which could provide additional evidence regarding its applicability, completeness, and practical relevance. Future research should address these limitations by implementing the framework in real service organizations, evaluating performance using longitudinal data, incorporating expert assessment, and testing the framework across multiple service sectors to improve its external validity and practical applicability.

Future Research: Future studies should implement the framework within operational service organizations and evaluate performance using longitudinal data. Additional research should compare the effectiveness of TQM 4.0 against alternative improvement methodologies such as Lean, Six Sigma, and Service Excellence models.

6 Conclusion

The use of TQM 4.0 within a service organization is analyzed in this paper. The simulated performance using data from Kaggle indicated that the use of TQM 4.0 practices appeared to be associated with high levels of customer satisfaction and quality of service.

As these results are derived from theoretical simulation rather than operational implementation, they should be interpreted as indicative rather than conclusive: they suggest that TQM practices enhanced by Industry 4.0 innovations have the potential to help an organization achieve improved customer satisfaction and service quality.

Thus, the results of the current study suggest that adopting TQM 4.0 practices may create an advantage for the organization, better prepare it for its transition to Industry 4.0, and positively affect service quality. Nevertheless, further research and practical applications are required to validate the results obtained in a real-life operating environment.

7 Recommendations

It is recommended that service organizations adopt the proposed TQM 4.0 framework to improve service quality and customer satisfaction. Organizations should focus on enhancing key digital services, implement a real time monitoring dashboard, and utilize simulation tools to support data-driven decision-making and continuous improvement.

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Footnotes

Edited by: Francisco J. G. Silva, Polytechnic Institute of porto, Portugal

Reviewed by: Juliana Nasution, Universitas Islam Negeri Sumatera Utara, Indonesia

Rina Fitriana, Trisakti University, Indonesia