Table 1: Timeline of Industrial Revolutions
(Source: Horn, 2016)

The evolution of industry has traversed four distinct phases, with each era introducing significant advancements in production efficiency and productivity. The current era builds upon these advancements, converging the digital and physical worlds to create a highly interconnected, data driven manufacturing environment. This convergence has far-reaching implications for the manufacturing sector, enabling significant productivity gains, improved product quality, and accelerated time-to-market. To fulfill the research objectives, the study was conducted with focus on answering following research questions:

• What are the key drivers and barriers to the adoption of IMS and smart factories?
• How do these systems impact manufacturing efficiency, product quality, and customization capabilities?
• What are the implications of this convergence for the future of work, skill gaps, and workforce training?

The hypothesis tested is that the convergence of IMS, smart factories, and Industry 4.0 will have a positive impact on manufacturing efficiency, product quality, and customization capabilities.

The convergence of IMS, smart factories, and Industry 4.0 is a debatable topic, with some arguing that it will lead to significant productivity gains and improved product quality, while others raise concerns about job displacement, skill gaps, and potential risks. This study aims to contribute to this debate by providing a nuanced understanding of the implications of this convergence for the manufacturing sector. The study’s findings offer valuable insights for manufacturers, policymakers, and researchers seeking to navigate the complexities of this convergence.

This study provides a foundation for future research in several areas, including investigating the impact of IMS and smart factories on supply chain management and logistics. Other potential research areas include examining the role of emerging technologies like blockchain, 5G, and edge computing in enabling Industry 4.0 applications, and developing frameworks and models for assessing the economic, social, and environmental sustainability of IMS and smart factories.

2. LITERATURE REVIEW

The advent of Industry 4.0 has revolutionized the manufacturing landscape, ushering in an era of IMS, smart factories, and digital transformation. This paradigm shift from traditional automated production to fully integrated, digitized manufacturing systems has transformed the industrial ecosystem. The integration of technologies such as AI, the IoT, and DA into production processes has enabled the creation of self-monitoring, self-learning, and adaptive systems. As a result, manufacturers are now able to achieve significant productivity gains, improve product quality, and accelerate time-to-market. However, this transformation also raises important questions about the
future of work, skill gaps, and the potential risks associated with increased reliance on digital technologies.

2.1 Broad Reviews
Several studies have provided comprehensive overviews of IMS, smart factories, and Industry 4.0. For instance, Oztemel (2019) presented a general overview of IMS, smart factories, and Industry 4.0, highlighting their key components, challenges, and opportunities. Similarly, Gautam et al. (2024) provided a broad review of intelligent manufacturing, discussing its components, challenges, and opportunities. These reviews have helped to establish a foundation for understanding the concepts, technologies, and applications of IMS, smart factories, and Industry 4.0. However, they often lack critical evaluations of the existing literature and fail to provide a nuanced understanding of the complex relationships between these concepts. This is essential for understanding the concepts, technologies, and applications of IMS, smart factories, and Industry 4.0. However, they often lack critical evaluations of the existing literature and fail to provide a nuanced understanding of the complex relationships between these concepts.

2.1.1 Overview of IMS
Oztemel (2019) presented a comprehensive overview of IMS, smart factories, and Industry 4.0. This study provided a detailed analysis of the key components, challenges, and opportunities of IMS, smart factories, and Industry 4.0. Oztemel (2019) highlighted the importance of digital technologies such as AI, the IoT, and DA in enabling IMS and smart factories. The study also discussed the potential benefits of Industry 4.0, including increased productivity, improved product quality, and reduced costs.

2.1.2 Critical Review of Smart Manufacturing or SM

Iqbal, Khan and Badruddin (2024) conducted a critical review of SM, focusing on its CPS, industrial IoT, and AI applications. The study analyzed the current state of SM, highlighting the importance of digitalization, decentralization, and real-time DA. Specifically, the authors examined the role of edge computing, fog computing, and Cloud Computing or CA in enabling SM. They also discussed the benefits of SM, including predictive maintenance, quality control, and supply chain optimization. However, the study also identified several challenges, including data security, interoperability, and the need for standardized communication protocols.

2.1.3 Conceptual Framework for SM Systems

Zheng et al. (2018) presented a conceptual framework for SM systems, integrating concepts from Industry 4.0, lean manufacturing, and Six Sigma. The framework focused on the development of a SM system architecture, comprising five layers: sensing, data processing, DA, decision-making, and action. The authors discussed the role of technologies such as Radio-Frequency Identification or RFID, Wireless Sensor Networks or WSNs, and CA in enabling SM. They also highlighted the benefits of the proposed framework, including improved production efficiency, reduced waste, and enhanced product quality. Furthermore, the study discussed the challenges of implementing SM
systems, including the need for significant investments in technology and training, as well as concerns about data security and privacy.

2.1.4 Big DA or BDA in IMS

Wang et al. (2022) conducted a comprehensive review of BDA in IMS, focusing on the applications, challenges, and future directions of BDA in this domain. The study analyzed the current state of BDA in intelligent manufacturing, highlighting the importance of Machine
Learning or ML, DL or deep learning, and NLP or Natural Language Processing in analyzing large datasets. Specifically, the authors examined the role of BDA in predictive maintenance, quality control, and supply chain optimization. They also discussed the challenges of implementing BDA in IMS, including data quality issues, scalability, and interpretability. Furthermore, the study highlighted the future directions of BDA in intelligent manufacturing, including the integration of edge computing, fog computing, and CA.

2.1.5 Knowledge Framework for IMS

Jardim-Goncalves et al. (2011) presented a knowledge framework for IMS, focusing on the development of a unified knowledge representation model for manufacturing systems. The framework integrated concepts from ontology, semantic web, and knowledge management to enable the sharing and reuse of knowledge in manufacturing systems. Specifically, the authors discussed the role of ontologies in representing manufacturing knowledge, including product design, process planning, and production scheduling. They also highlighted the benefits of the proposed framework, including improved collaboration, reduced errors, and enhanced decision making. Furthermore, the study discussed the challenges of implementing the knowledge framework, including the need for standardized ontologies, data integration, and knowledge management.

2.1.6 Engineering Framework for Service-Oriented IMS
Giret, Garcia and Botti (2016) presented an engineering framework for service-oriented IMS, focusing on the development of a modular and flexible architecture for manufacturing systems. The framework integrated concepts from Service-Oriented Architecture or SOA, Multi-Agent Systems or MAS, and semantic web services to enable the creation of IMS. Specifically, the authors discussed the role of service-oriented architecture in enabling the integration of heterogeneous systems, including production planning, scheduling, and control. They also highlighted the benefits of the proposed framework, including improved flexibility, scalability, and reconfigurability. Furthermore, the study discussed the challenges of implementing the engineering framework, including the need for standardized service interfaces, data integration, and semantic interoperability.

2.2 Critical Evaluation
This review has examined the above studies on IMS, SM, and Industry 4.0. While these studies have been proved useful in terms of offering valuable insights into the current state of intelligent manufacturing, several limitations and research gaps have been identified. Firstly, the studies reviewed primarily focused on the technological aspects of intelligent manufacturing, with limited consideration of the social and organizational implications. For instance, the studies by Hiremath et al. (2025) and Iqbal, Khan and Badruddin (2024) highlighted the benefits of SM, but failed to examine the potential impacts on workforce skills and organizational structures.

Secondly, the studies reviewed relied heavily on conceptual frameworks and literature reviews, with limited empirical evidence to support their claims. For example, the study by Zheng et al. (2018) proposed a conceptual framework for SM systems, but did not provide any case studies or empirical data to validate the framework.

Thirdly, the studies reviewed primarily focused on the manufacturing sector, with limited consideration of the broader supply chain and industry context. For instance, the study by Wang et al. (2022) inspected the applications of BDA in intelligent manufacturing, but failed to examine the potential implications for supply chain management and industry competitiveness.

Lastly, the studies reviewed highlighted several technical challenges associated with intelligent manufacturing, including data integration, interoperability, and cybersecurity. However, these challenges were not examined in sufficient depth, and further research is needed to develop effective solutions.

2.3 Identification of Research Gap
A significant research gap exists in the development of standardized frameworks for integrating Industry 4.0 technologies, such as AI, blockchain, and the IoT, in intelligent manufacturing. The lack of standardized frameworks can lead to interoperability issues and hinder the widespread adoption of Industry 4.0. Furthermore, there is a need for more research on human-machine collaboration, including the design of intuitive user interfaces, the development of effective training programs, and the examination of the impact of automation on workforce skills. Additionally, the impact of Industry 4.0 on supply chain management is not well understood, and more research is needed to examine the potential benefits and challenges of using technologies such as blockchain and IoT in supply chain management. Finally, the cybersecurity implications of Industry 4.0 are a significant concern, and more research is needed to develop effective cybersecurity protocols for Industry 4.0 technologies.

3. QUALITATIVE ANALYSIS: THEMATIC

The study has identified the following themes and subthemes:

3.1 Theme 1: Industry 4.0 and SM

3.1.1 Subtheme 1.1: Enabling Technologies

• References: Oztemel (2019), Zheng et al. (2018), Thoben, Wiesner & Wuest (2017)
• Codes: Industry 4.0, SM, Enabling Technologies, Digitalization

3.1.2 Subtheme 1.2: Business Models and Value Creation

• References: Marr (2020), Chen et al. (2017)
• Codes: Business Models, Value Creation, Industry 4.0, Innovation, Competitiveness

3.1.3 Subtheme 1.3: Challenges and Future Directions

• References: Hiremath et al. (2025), Iqbal, Khan and Badruddin (2024), Wang et al. (2022)
• Codes: Challenges, Future Directions, Industry 4.0, SM, Sustainability

3.2 Theme 2: IMS

3.2.1 Subtheme 2.1: System Architecture and Design

• References: Barari et al. (2021), Giret, Garcia and Botti (2016), Jardim-Goncalves et al. (2011)
• Codes: System Architecture, Design, Intelligent Manufacturing, CPS

3.2.2 Subtheme 2.2: Human-Machine Collaboration

• References: Gautam et al. (2024), Hiremath et al. (2025), Iqbal, Khan and Badruddin (2024)
• Codes: Human-Machine Collaboration, Intelligent Manufacturing, Industry 4.0, Workforce Development

3.2.3 Subtheme 2.3: Data-Driven Decision Making

• References: Qi and Tao (2018), Wang et al. (2022)
• Codes: Data-Driven Decision Making, Intelligent Manufacturing, Industry 4.0, BDA

3.3 Theme 3: CPS and Digital Twin

3.3.1 Subtheme 3.1: Concept and Framework

• References: Lee, Bagheri and Kao (2015), Qi and Tao (2018)
• Codes: CPS, Digital Twin, Industry 4.0, SM

3.3.2 Subtheme 3.2: Applications and Case Studies

• References: Christo and Cardeira (2007), Gautam et al. (2024), Wang et al. (2016)
• Codes: Applications, Case Studies, CPS, Digital Twin, Industry 4.0

3.3.3 Subtheme 3.3: Challenges and Future Directions

• References: Marr (2020), Chen et al. (2017)
• Codes: Challenges, Future Directions, CPS, Digital Twin, Industry 4.0, Sustainability

3.4 Brief Description Analysis of Themes
The thematic analysis revealed three primary themes: Industry 4.0 and SM, IMS, and CPS and Digital Twin. These themes are interconnected and highlight the complex and multifaceted nature of Industry 4.0 and SM.

3.4.1 Theme 1: Industry 4.0 and SM
This theme highlights the importance of Industry 4.0 and SM in transforming the manufacturing sector. The subthemes of enabling technologies, business models and value creation, and challenges and future directions emphasize the need for a holistic approach to implementing Industry 4.0 and SM. The references cited in this theme, such as Oztemel (2019) provided a comprehensive overview of the current state of Industry 4.0 and SM.

3.4.2 Theme 2: IMS
This theme focuses on the design and implementation of IMS. The subthemes of system architecture and design, human-machine collaboration, and data-driven decision making highlight the importance of integrating technological, organizational, and human factors in designing IMS. The references cited in this theme, such as Barari et al. (2021) and Gautam et al. (2024), provide insights into the design and implementation of IMS.

3.4.3 Theme 3: CPS and Digital Twin
This theme emphasizes the importance of CPS and digital twin in enabling Industry 4.0 and SM. The subthemes of concept and framework, applications and case studies, and challenges and future directions highlight the need for a comprehensive understanding of CPS and digital twin. The references cited in this theme, such as Qi and Tao (2018), provide a thorough overview of the current state of CPS and digital twin.

The thematic analysis highlights several implications and future directions for research and practice:

1. Integration of Technological, Organizational, and Human Factors: The analysis emphasizes the need for a holistic approach to implementing Industry 4.0 and SM.
2. Data-Driven Decision Making: The analysis highlights the importance of data-driven decision making in IMS.
3. CPS and Digital Twin: The analysis emphasizes the importance of CPS and digital twin in enabling Industry 4.0 and SM.
4. Human-Machine Collaboration: The analysis highlights the need for effective human machine collaboration in IMS.

Overall, the thematic analysis provides a comprehensive overview of the current state of Industry 4.0 and SM. The analysis highlights several implications and future directions for research and practice, emphasizing the need for a holistic approach to implementing Industry 4.0 and SM.

4. PRACTICAL EXAMPLES AND REAL-LIFE CASE STUDIES OF INTELLIGENT MANUFACTURING AND SMART FACTORIES

4.1 Case Study 1: Siemens Industry
4.1.1 Background
Siemens Industry, a leading industrial conglomerate, sought to revolutionize its production processes and product quality by embracing intelligent manufacturing practices (Wolf & Lepratti, 2020).

4.1.2 Implementation
Siemens integrated a suite of intelligent manufacturing technologies, including digital twin platforms, IoT devices, AI, and ML algorithms, as well as robotics and automation (Wolf & Lepratti, 2020). By leveraging digital twin platforms, Siemens achieved a 20% reduction in
production time. The incorporation of IoT devices facilitated real-time data collection, enabling data-driven decision-making and resulting in a 15% decrease in energy consumption. AI and ML algorithms were applied to analyze data and optimize production processes, yielding a 10% improvement in product quality. Furthermore, robotics and automation technologies were implemented to optimize production processes, resulting in a 25% reduction in labor costs.

4.1.3 Results
The implementation of intelligent manufacturing practices led to enhanced efficiency, improved product quality, and reduced costs (Wolf & Lepratti, 2020).

4.2 Case Study 2: Smart Production Planning and Control
4.2.1 Background
A manufacturing company aimed to optimize its production planning and control processes by leveraging smart technologies (Oluyisola et al. 2022).

4.2.2 Implementation
The company implemented a smart production planning and control system, integrating DA, ML algorithms, IoT devices, and CA (Oluyisola et al. 2022). DA and ML algorithms were utilized to analyze production data and optimize production planning, resulting in a 12% reduction in lead times. IoT devices were integrated to collect real-time data on production processes, enabling data driven decision-making and resulting in a 10% reduction in energy consumption. CA facilitated real-time data sharing and collaboration across departments and supply chain partners, resulting in a 15% improvement in supply chain efficiency.

4.2.3 Results
The implementation of the smart production planning and control system led to improved production planning, reduced lead times, and enhanced product quality (Oluyisola et al. 2022).

4.3 Case Study 3: Semiconductor Intelligent Manufacturing
4.3.1 Background
A semiconductor manufacturing company sought to optimize its production processes and improve product quality by embracing the Industry 3.5 framework (Chien, Wang & Fu, 2018).

4.3.2 Implementation
The company implemented an Industry 3.5 framework, integrating smart sensing and data acquisition technologies, DA, and decision-making algorithms, as well as CPS (Chien, Wang & Fu, 2018). Smart sensing technologies were utilized to collect real-time data on production processes, enabling data-driven decision-making and resulting in a 10% reduction in defect rates. DA and decision-making algorithms were applied to analyze production data and optimize production processes, yielding a 12% improvement in product quality. CPS were implemented to integrate physical systems with computational algorithms and networks, resulting in a 15% improvement in production efficiency.

4.3.3 Results
The implementation of the Industry 3.5 framework led to enhanced efficiency, improved product quality, and reduced costs (Chien, Wang & Fu, 2018).

5. QUESTIONNAIRE: ADOPTION OF IMS

5.1 Section 1: Industry Information
I. Name: _____________________
II. Location: _____________________
III. Industry Type (e.g., Automotive, Aerospace, Electronics): _____________________

5.2 Section 2: Adoption of IMS

I. Is your company currently using IMS? (Yes/No)
II. If yes, what mode of intelligent manufacturing system are you using? (Select one or more)

• AI
• IoT
• Robotics
• CA
• CPS (CPS)
• Other (please specify)

III. What is the primary driver for adopting IMS in your company? (Select one)

• Cost reduction
• Increased efficiency
• Improved product quality
• Enhanced customer satisfaction
• Competitive advantage
• Other (please specify)

5.3 Section 3: Benefits and Challenges

I. What benefits have you experienced since adopting IMS? (Select one or more)

• Improved productivity
• Reduced waste
• Enhanced supply chain management
• Increased flexibility
• Better decision-making
• Other (please specify)

II. What challenges have you faced during the adoption of IMS? (Select one or more)

• High upfront costs
• Complexity of implementation
• Cybersecurity concerns
• Skills gap in workforce
• Integration with existing systems
• Other (please specify)

5.4 Section 4: Future Plans

I. Do you plan to expand your use of IMS in the next 2 years? (Yes/No)
II. If yes, what areas of your operations do you plan to focus on? (Select one or more)

• Production planning
• Quality control
• Supply chain management
• Maintenance and repair
• Other (please specify)

6. DATASET OF A QUESTIONNAIRE GATHERING EXPERT OPINIONS FROM 50 DIFFERENT MANUFACTURING INDUSTRIES AROUND THE WORLD