# The Implementation of Internet of Things in the Manufacturing Industry: An Appraisal

Charles Okpala, Charles Onyeka, Nkemakonam Chidiebube Igbokwe

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Charles Okpala, Charles Onyeka, Nkemakonam Chidiebube Igbokwe. The Implementation of Internet of Things in the Manufacturing Industry: An Appraisal. International Journal of Engineering Research and Development, 2024, 20 (7), pp.510 - 516. [https://hal-04669400](https://hal-04669400)

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# The Implementation of Internet of Things in the Manufacturing Industry: An Appraisal

Igbokwe Nkemakonam Chidiebube, Okpala Charles Chikwendu, and Nwamekwe Charles Onyeka

Correspondence Address
Industrial/Production Engineering Department
NnamdiAzikiwe University, P.M.B. 5025 Awka
Anambra State - Nigeria.

**Abstract:** The advent of the Internet of Things (IoT) has revolutionized various industries, and the manufacturing sector is no exception. This article provides a comprehensive review of the implementation of IoT in the manufacturing industry, highlighting its impact on processes, efficiency, and overall productivity. The paper also explored key technologies, challenges, and opportunities associated with the integration of IoT in manufacturing, offering insights into the future trajectory of this transformative trend.

**Keywords:** internet of things, manufacturing, sensor technology, connectivity, data analytics, cloud computing, manufacturing processes

## I. Introduction

The manufacturing industry has undergone significant changes due to technological advancements, particularly with the rise of the Internet of Things (IoT) (Yuan et al., 2021). IoT has transformed manufacturing by providing improved connectivity, data exchange capabilities, and automation opportunities (Schröeder et al., 2020). The integration of IoT with technologies like Mixed Reality has been shown to have positive impacts on manufacturing operations, creating value through innovative strategies (Ola, 2023). Utilizing a flexible IoT architecture combined with AI models enables real-time monitoring of labor-intensive manufacturing sites and accurate production forecasting (Yuan et al., 2021). Implementing smart and flexible manufacturing systems with technologies like Autonomous Guided Vehicles (AGVs) and IoT can lead to Industry 4.0 transformation in manufacturing companies (Vlachos et al., 2022).

Studies have emphasized the importance of sustainable manufacturing within the industry 4.0 framework, highlighting the need to align manufacturing practices with sustainability goals (Machado et al., 2019). Furthermore, the integration of Cloud computing with IoT has been explored to efficiently manage IoT resources and services (Botta et al., 2016), enhancing the capabilities of IoT devices and systems in manufacturing settings.

IoT technology plays a critical role in enhancing supply chain visibility, connectivity, and overall performance (Muhamed et al., 2022). By leveraging IoT, companies can achieve better organization, technological management, agility, and customer-centric product and service tailoring (Lončar et al., 2018). And, IoT-enabled intelligent workshops utilizing technologies like RFID have demonstrated improved efficiency in material management (Sahil, 2021).

Okpala, Igbokwe and Nwankwo (2023), posited that in an era characterized by rapid technological advancements, the integration of Artificial Intelligence (AI) into various industries has emerged as a transformative force, which reshapes traditional paradigms and drive unprecedented innovation. They pointed out that one such sector at the forefront of this AI revolution is manufacturing, where intelligent automation, predictive analytics, and machine learning algorithms are redefining the way products are designed, produced, and optimized. The adoption of IoT in manufacturing is a transformative process that offers numerous benefits such as improved operations, enhanced connectivity, and increased efficiency. By effectively leveraging IoT technologies, manufacturing companies can streamline processes, optimize supply chains, and drive innovation in the industry 4.0 era.
## II. Key Technologies Driving IoT in Manufacturing

Sensor technology, connectivity, data analytics, and cloud computing are the essential components driving the IoT in manufacturing.

a. **Sensor Technology:** Sensor technology acts as the foundation of IoT in manufacturing, enabling real-time data collection from machinery, equipment, and products to monitor asset status and performance (Ferreira and Lind, 2022). The backbone of IoT in manufacturing lies in sensor technology. Sensors embedded in machinery, equipment, and even products enable real-time data collection, providing insights into the status and performance of assets.

b. **Connectivity:** Connectivity is a crucial element that supports the functionality of the Internet of Things (IoT) in manufacturing, enabling seamless data exchange among devices. Technologies like 5G, Wi-Fi, and RFID are essential for establishing a robust and efficient network infrastructure within manufacturing facilities (Wójcicki et al., 2022). These connectivity technologies facilitate reliable communication between IoT devices, enabling real-time data transmission and improving operational efficiency in manufacturing processes.

5G technology is particularly important for delivering high-speed, low-latency connectivity, which is vital for meeting the data-intensive demands of IoT applications in manufacturing (Syafrudin et al., 2018). The implementation of 5G networks can significantly enhance the performance of IoT devices, allowing for faster data transfer and increased responsiveness in industrial settings. Wi-Fi technology complements 5G by providing reliable wireless connectivity within manufacturing facilities, supporting IoT devices in seamlessly transmitting data across the network (Syafrudin et al., 2018). On the other hand, RFID technology plays a critical role in asset tracking and identification, enabling efficient inventory management and enhancing supply chain visibility in manufacturing operations (Syafrudin et al., 2018).

The integration of these connectivity technologies in manufacturing environments not only improves data exchange capabilities but also facilitates the adoption of IoT solutions to enhance operational efficiency and productivity. By utilizing 5G, Wi-Fi, and RFID technologies, manufacturers can establish a strong network infrastructure that enables seamless communication among IoT devices, leading to enhanced automation, real-time monitoring, and data-driven decision-making in the industry 4.0 era.

c. **Data Analytics:** The application of data analytics in manufacturing is transforming traditional processes and fostering innovation. Through the use of machine learning and data analytics, manufacturers can achieve operational excellence, enhance productivity, and make data-driven decisions (Chu et al., 2023). By harnessing IoT-generated data and advanced analytics tools, manufacturers can gain valuable insights into their operations, leading to continuous improvement and increased competitiveness in the industry 4.0 landscape.

Data analytics and machine learning algorithms are essential in leveraging the data generated by Internet of Things (IoT) devices in manufacturing. These technologies enable manufacturers to optimize operations, enhance efficiency, and drive innovation (Fuller et al., 2020). By integrating data analytics into manufacturing processes, predictive maintenance, quality control, and supply chain optimization are made possible. Machine learning algorithms can predict equipment failures, leading to reduced downtime and maintenance costs (Schröeder et al., 2020). Real-time monitoring and analysis of production data through data analytics platforms allow for early defect detection, improving product quality and reducing waste (Haricha et al., 2023). Furthermore, data analytics supports supply chain optimization by providing insights into inventory levels, demand forecasting, and production processes, thereby enhancing overall supply chain efficiency (Stadnicka et al., 2022).

d. **Cloud Computing:** Cloud computing is crucial for enabling manufacturers to fully leverage IoT technologies by providing scalable, secure, and collaborative data management solutions. Embracing cloud-based platforms empowers manufacturers to drive operational excellence, enhance decision-making processes, and unlock new opportunities for growth and innovation in the manufacturing industry. It is a fundamental enabler for manufacturers, allowing them to store, access, and analyze data remotely, thereby enhancing scalability and facilitating collaboration and data sharing across various stakeholders in the manufacturing value chain (Syafrudin et al., 2018). By utilizing cloud-based platforms, manufacturers can overcome limitations related to on-premise data storage and processing, enabling more flexible and cost-effective solutions for managing and utilizing vast amounts of data generated by IoT devices.

The adoption of cloud computing in manufacturing provides numerous benefits, including improved data accessibility, enhanced data security, and increased operational efficiency. Cloud-based platforms empower manufacturers to access data from anywhere, at any time, enabling real-time decision-making and remote monitoring of manufacturing processes (Sun and Ji, 2022). Cloud computing solutions offer scalability, allowing manufacturers to adjust resources based on demand, thereby optimizing operational costs and resource utilization (Yusuf et al., 2022).

Moreover, cloud computing fosters collaboration among stakeholders in the manufacturing value chain by offering a centralized platform for data sharing and communication. This facilitates seamless information exchange, enhances transparency, and streamlines decision-making processes across different departments and organizations involved in the manufacturing ecosystem (Liu et al., 2022). By leveraging cloud-based
technologies, manufacturers can achieve greater agility, innovation, and competitiveness in the rapidly evolving Industry 4.0 landscape.

### 3. Impact on Manufacturing Processes

**a. Predictive Maintenance:** Predictive maintenance empowered by IoT technologies is transforming maintenance practices in manufacturing, enabling proactive and data-driven approaches to equipment upkeep. By leveraging the potential of IoT for predictive maintenance, manufacturers can boost operational efficiency, extend equipment lifespan, and achieve cost savings in their maintenance processes.

Predictive maintenance is a crucial application of theIoT in manufacturing, allowing for continuous monitoring of equipment conditions to minimize downtime, prolong machinery lifespan, and optimize maintenance schedules (Hsu et al., 2020). Through the utilization of IoT technologies, manufacturers can establish predictive maintenance strategies that depend on real-time data collection and analysis to forecast equipment failures and proactively address maintenance needs.

The incorporation of IoT for predictive maintenance has been exemplified in various industrial sectors, including wind turbine fault diagnosis (Hsu et al., 2020), centrifugal pump monitoring (Chen et al., 2022), and building facilities maintenance (Villa et al., 2021). These studies underscore the significance of IoT-enabled predictive maintenance in enhancing equipment reliability, reducing operational costs, and enhancing overall system performance.

Moreover, the amalgamation of IoT with machine learning algorithms and edge computing has facilitated more advanced predictive maintenance capabilities (Zhiyung, 2024). By harnessing artificial intelligence and IoT technologies, manufacturers can scrutinize equipment data, recognize trends, and predict potential failures to optimize maintenance schedules and prevent costly downtime (Zhiyung, 2024).

**b. Supply Chain Optimization:** The application of IoT and data analytics in supply chain optimization has facilitated enhanced inventory management, reduced lead times, and improved overall supply chain efficiency (Malik et al., 2022; Reddy, 2023). By leveraging IoT sensors and data analytics platforms, manufacturers can gain valuable insights into inventory levels, demand forecasting, and production processes, enabling better scheduling and coordination of transportation and delivery (Reddy, 2023).

Supply chain optimization is a crucial element of modern manufacturing, and the incorporation of technologies such as blockchain, IoT, and data analytics has significantly transformed supply chain management practices. Through the utilization of these technologies, manufacturers can achieve real-time tracking and monitoring of inventory, shipments, and production processes, ultimately enhancing supply chain visibility and efficiency (Mondol, 2021; Nanda et al., 2023; Malik et al., 2022).

The integration of blockchain and IoT in supply chain management has enabled secure and transparent tracking of logistics, ensuring the authenticity and integrity of products throughout the supply chain (Mondol, 2021; Nanda et al., 2023). This integration has been effective in eliminating supply chain-related issues, improving traceability, and combating counterfeit products (Nanda et al., 2023). Also, the integration of blockchain, IoT, and data analytics technologies in supply chain optimization has revolutionized traditional supply chain practices, empowering manufacturers to achieve increased visibility, efficiency, and responsiveness in managing their supply chain operations.

**c. Quality Control:** The integration of IoT technologies in manufacturing processes enables real-time monitoring, analysis, and control, leading to improved quality control measures, reduced waste, and enhanced product quality. By leveraging IoT solutions, industries can optimize their operations, enhance efficiency, and ensure the delivery of high-quality products to meet consumer demands. Quality control in manufacturing processes is essential for ensuring product excellence and reducing waste. The IoT indeed plays a significant role in enhancing quality control measures through real-time monitoring and analysis (Nauman et al., 2020). By leveraging IoT technologies, defects can be identified early in the production cycle, leading to minimized waste and enhanced product quality (Lo et al., 2021).

Research has demonstrated that IoT-based systems are crucial for monitoring and controlling various aspects of production processes to ensure high-quality outcomes. For example, IoT technologies have been successfully applied in concrete curing systems to monitor and regulate the moisture content of hardening concrete, thereby ensuring the production of high-quality concrete structures (Lo et al., 2021). Studies has it that IoT has been utilized in the food fermentation process to develop strategies for enhancing product quality and conducting predictive analyses of fermented food products (Adeleke et al., 2023).

The integration of IoT with mixed reality has been explored to understand its impact on manufacturing operations, highlighting the potential for improving efficiency and operational processes in manufacturing settings (Ola, 2023). IoT technologies have also been instrumental in optimizing business processes in evolving manufacturing factories, aligning with the principles of Industry 4.0 or Industrial IoT (IIoT) (Belli et al., 2019). In the context of quality assessment, IoT has been employed to assess and recommend IoT products, demonstrating the effectiveness of IoT-based approaches in evaluating product quality (Naem et al., 2022). IoT has also been utilized in predictive maintenance for textile manufacturing processes, showcasing the potential of
IoT-based analytics modules in enhancing production quality and predictive maintenance in Industry 4.0 settings (Chang et al., 2021; Nwamekwe et al., 2020).

d. **Smart Factories:** The integration of theIoT has revolutionized traditional manufacturing facilities, transforming them into smart factories characterized by automated processes, interconnected machinery, and data-driven decision-making (Shafique et al., 2020). These smart factories leverage IoT technologies to enable real-time monitoring and analysis, leading to increased efficiency and productivity (Nauman et al., 2020). By incorporating IoT solutions, industries can optimize their operations and enhance product quality through proactive defect detection and waste reduction (TaştanandGökozan, 2019).

Research has shown that IoT-based control systems play a crucial role in remotely managing various industrial facilities, such as cold storage units, by facilitating real-time monitoring and system environment control (Mohammed et al., 2022). IoT technologies have been instrumental in enhancing quality control measures in construction processes, such as concrete curing systems, by monitoring and regulating critical parameters for ensuring high-quality outcomes (Uyoata et al., 2021).

The application of IoT in smart water tanks has demonstrated the capability to monitor water levels, detect leakages, and control motors in real-time, providing valuable feedback to end-users through web interfaces or smartphones (Jan et al., 2022). IoT has also been utilized in the agricultural sector to optimize route planning for product distribution, emphasizing the importance of IoT technologies in enhancing supply chain management and product quality control (Zuo et al., 2022).

IoT-based systems have been developed for monitoring and controlling environmental parameters in aquaculture settings, ensuring the quality and safety of pond water for fish farming operations (Sharma and Jangirala, 2022). The implementation of IoT for air quality surveillance has been explored to enable real-time monitoring of air quality parameters, contributing to environmental monitoring and analysis (Saovapakhiran et al., 2022).

### III. Challenges and Considerations

a. **Security Concerns:** The interconnected nature of IoT devices in smart factories raises significant security concerns, including vulnerabilities to data breaches and cyber-attacks (Jeon et al., 2020). As the demand for IoT technology increases, the risks associated with security threats against IoT infrastructure, applications, and devices have also escalated (Jeon et al., 2020). Manufacturers must prioritize robust cybersecurity measures to safeguard sensitive information and protect against potential security breaches in smart factory environments (Jeon et al., 2020).

Studies have highlighted the importance of implementing security measures to address the cybersecurity challenges posed by IoT devices in industrial settings. Studies have explored dynamic analysis for IoT malware detection using convolutional neural network models, emphasizing the need for advanced security solutions to combat evolving threats (Jeon et al., 2020). Classification of botnet attacks in IoT smart factories using honeypots combined with machine learning has been investigated, underscoring the importance of proactive security measures to detect and mitigate potential attacks (Jeon et al., 2020).

The integration of IoT in industries necessitates secure data processing and communication to prevent unauthorized access and ensure data integrity. Studies have proposed secure data parallel processing embedded systems for IoT computer vision applications, highlighting the significance of encryption and secure communication protocols in protecting sensitive data (Jeon et al., 2020). Moreover, the use of physical factory simulation models for business process management research has been suggested as a means to enhance security practices and mitigate potential vulnerabilities in IoT-enabled manufacturing processes (Jeon et al., 2020).

b. **Standardization:** Standardization in the context of IoT devices in manufacturing is crucial to ensure seamless communication and interoperability among diverse devices (Syafrudin et al., 2018). The absence of universal standards can hinder integration efforts and lead to compatibility issues within smart factory environments. To address this challenge, standardization efforts are essential to establish common protocols and frameworks that enable different IoT devices to communicate effectively and work together harmoniously (Syafrudin et al., 2018).

Syafrudin et al., (2018)emphasized the significance of standardization in IoT applications to enhance connectivity and streamline operations in manufacturing settings. For instance, studies have explored the performance analysis of IoT-based sensor systems for real-time monitoring in automotive manufacturing, highlighting the importance of standardized protocols for efficient data processing and machine learning models. By establishing standardized practices, manufacturers can leverage IoT technologies more effectively to optimize production processes and enhance overall efficiency.

The implementation of IoT in smart manufacturing relies on standardized approaches to ensure data accuracy, reliability, and security. Studies have demonstrated the use of IoT platforms for real-time monitoring and control of scientific experiments, emphasizing the need for standardized data processing methods and communication protocols to enable seamless integration of IoT devices (R. et al., 2020). Standardization efforts play a vital role
in promoting data consistency and interoperability, enabling manufacturers to make informed decisions based on accurate and reliable information.

Also, the adoption of standardized IoT frameworks in manufacturing environments can facilitate predictive maintenance, enhance energy efficiency, and improve overall productivity. Research has shown that IoT-enabled condition-based maintenance systems rely on standardized protocols to monitor and analyze equipment degradation, leading to increased operational efficiency and reduced downtime (DewantiandSinggih, 2019). By adhering to established standards, manufacturers can leverage IoT technologies to optimize production processes, reduce costs, and enhance competitiveness in the market.

c. Cost Implications: While the long-term benefits of IoT implementation in manufacturing are substantial, the initial investment can be a barrier for some organizations. Manufacturers need to carefully assess the cost-benefit analysis to justify the adoption of IoT solutions.

Cost implications are a significant consideration for organizations looking to implement IoT solutions in manufacturing. While the long-term benefits of IoT implementation are substantial, the initial investment can be a barrier for some organizations (Celik et al., 2019). Manufacturers need to conduct a thorough cost-benefit analysis to justify the adoption of IoT solutions and assess the return on investment over time.

Research has shown that security and privacy concerns are critical factors influencing the cost implications of IoT adoption in manufacturing. Guidelines and techniques for enhancing security and privacy in IoT systems can help mitigate risks and reduce potential costs associated with data breaches and cyber-attacks (AbdulghaniandKonstantas, 2019). By implementing robust security measures from the outset, manufacturers can minimize the financial impact of security incidents and protect sensitive information.

IoT business model innovation plays a crucial role in shaping the cost implications of IoT adoption in manufacturing. Studies have explored the integration of IoT technologies in production plant and warehouse automation, highlighting the potential for cost savings, increased productivity, and improved efficiency through IoT and Industry 5.0 implementations (Fatima et al., 2022). By leveraging IoT for automation and optimization, manufacturers can streamline processes, reduce operational costs, and enhance overall performance.

Studies have shown that the adoption of IoT technologies in manufacturing can lead to improvements in supply chain management and logistics, impacting cost considerations. IoT-based techniques for online data registration and offline information traceability in digital manufacturing systems can enhance operational efficiency and reduce costs associated with manual processes (Wu et al., 2017), and by leveraging IoT for real-time decision-making and machine collaborations, manufacturers can optimize resource utilization and minimize production costs.

## IV. Future Outlook

It can be vehemently said that the future for the IoT in manufacturing is promising, with advancements expected as technology progresses. Integration with emerging technologies such as artificial intelligence (AI), augmented reality (AR), and blockchain is anticipated to enhance the capabilities of IoT in manufacturing (Ma et al. 2019; GuergovandRadwan, 2021; Ahakonye, 2024; WU et al., 2022). These integrations are projected to lead to more efficient and intelligent manufacturing processes, ultimately improving productivity and decision-making.

The evolution of edge computing is another crucial aspect that will influence the future of IoT in manufacturing. Edge computing is expected to enable faster data processing and decision-making at the device level, reducing the dependence on centralized cloud systems (Fraga- Lamas et al., 2021). This shift towards edge computing is likely to enhance real-time data analytics and enable quicker responses to manufacturing operations, thereby improving overall efficiency and performance.

The convergence of AI, IoT, and blockchain technologies is predicted to revolutionize manufacturing processes by enhancing data security, interoperability, and automation capabilities (WU et al., 2022; Jacob et al., 2021; Ateya et al., 2022). These technologies are expected to help streamline operations, optimize resource allocation, and improve data management practices, leading to more sustainable and efficient manufacturing practices.

Also, the integration of AI and IoT in smart factories is forecasted to drive innovation in manufacturing processes, enabling predictive maintenance, real-time monitoring, and data-driven decision-making. These advancements are poised to transform traditional manufacturing facilities into intelligent and adaptive environments, enhancing operational efficiency and product quality.

## V. Conclusion

The implementation of the Internet of Things in the manufacturing industry is a transformative journey that promises increased efficiency, enhanced productivity, and improved decision-making. While challenges exist, the benefits far outweigh the drawbacks, positioning IoT as a cornerstone of the future of manufacturing. As technology continues to evolve, manufacturers must stay abreast of these developments to remain competitive in an increasingly connected and data-driven industrial landscape.
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