To address the challenges of scrap management and detect discrepancies in scrap loading and unloading, the system should include the following features: Operator Login: Implement a secure login system for operators to monitor individual performance and activity. Dashboard: Provide a dashboard for real-time monitoring of both operator performance and overall process efficiency. Data Collection: Gather data using existing weight sensors and the installed module. Separate Console and Server: Set up a dedicated console and server to present and manage the data. The combination of Internet of things (IoT) technology and broadband connectivity presents significant advancements in crane management systems, improving efficiency, safety, and real-time decision-making. This study focuses on designing an IoT-based framework for crane management, utilizing broadband for efficient data transmission and remote supervision. Essential features include load monitoring via sensors, predictive maintenance powered by real-time analytics, and automated safety mechanisms. The research emphasizes the flexibility and scalability of these systems across various industries, showcasing benefits like reduced downtime, better resource allocation, and enhanced safety. By addressing key implementation issues, this work fosters the development of smart industrial solutions leveraging IoT and broadband integration. Process Instance Calculation: Ensure each process instance is calculated and logged separately. Login/Logout Tracking: Track login and logout times for each operator to monitor shifts and activity. User-Friendly Key Panel: Provide an easy-to-use key panel for operators to enter data efficiently. Graphical Review Console: Offer a graphical review console with various visualization options to analyze the data. Quantitative Measurements and Analysis: Support quantitative measurements of inventory and conduct business-related data analysis.

Keywords: Cellular Connectivity, embedded systems, Industry 4.0, manufacturing, transformation

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1. Mittal S, Khan MA, Romero D, Wuest T. Smart manufacturing: Characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: J Eng Manuf. 2019 Apr;233(5):1342–61.
2. Anderl R. Industrie 4.0–advanced engineering of smart products and smart production. In Proceedings Int Seminar on High Technol; 2014 Oct 9;19.
3. Brettel M, Friederichsen N, Keller M, Rosenberg M. How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective. Int J Inf Commun Eng. 2014 Nov 11;8(1):37–44.
4. Crnjac M, Veža I, Banduka N. From concept to the introduction of industry 4.0. Int J Ind Eng Manage. 2017 Mar 1;8(1):21.
5. Frost and Sullivan. From concept to production: A 5–step approach towards successful Industry 4.0 projects. Bosch Software Innovations. 2017.
6. Smart manufacturing: Past research, present findings, and future directions. INT J PR ENG MAN–GT, 2016:3.
7. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp. 2014;16:3–8.
8. Eason G, Noble B, Sneddon IN. “On certain integrals of Lipschitz–Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London; 1955 April;A247:529–551.
9. Xiong G, Helo P, Ekström S, Shen Z. A Service–Oriented Autonomous Crane System. IEEE Trans Comput Soc Syst. 2024 Jun 17.
10. Henríquez R, Xavier Martínez de Osés F, Martínez Marín JE. IoT–driven business model innovation: A case–study on the Port of Barcelona in the context of the Belt and Road Initiative. In Advances in E–Business Engineering for Ubiquitous Computing: Proceedings of the 16th International Conference on e–Business Engineering, 2019. Springer International Publishing; 2020. p.302–314.

To address the challenges of scrap management and detect discrepancies in scrap loading and unloading, the system should include the following features: Operator Login: Implement a secure login system for operators to monitor individual performance and activity. Dashboard: Provide a dashboard for real-time monitoring of both operator performance and overall process efficiency. Data Collection: Gather data using existing weight sensors and the installed module. Separate Console and Server: Set up a dedicated console and server to present and manage the data. The combination of Internet of things (IoT) technology and broadband connectivity presents significant advancements in crane management systems, improving efficiency, safety, and real-time decision-making. This study focuses on designing an IoT-based framework for crane management, utilizing broadband for efficient data transmission and remote supervision. Essential features include load monitoring via sensors, predictive maintenance powered by real-time analytics, and automated safety mechanisms. The research emphasizes the flexibility and scalability of these systems across various industries, showcasing benefits like reduced downtime, better resource allocation, and enhanced safety. By addressing key implementation issues, this work fosters the development of smart industrial solutions leveraging IoT and broadband integration. Process Instance Calculation: Ensure each process instance is calculated and logged separately. Login/Logout Tracking: Track login and logout times for each operator to monitor shifts and activity. User-Friendly Key Panel: Provide an easy-to-use key panel for operators to enter data efficiently. Graphical Review Console: Offer a graphical review console with various visualization options to analyze the data. Quantitative Measurements and Analysis: Support quantitative measurements of inventory and conduct business-related data analysis.

Keywords: Cellular Connectivity, embedded systems, Industry 4.0, manufacturing, transformation

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Mittal S, Khan MA, Romero D, Wuest T. Smart manufacturing: Characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: J Eng Manuf. 2019 Apr;233(5):1342–61.
2. Anderl R. Industrie 4.0–advanced engineering of smart products and smart production. In Proceedings Int Seminar on High Technol; 2014 Oct 9;19.
3. Brettel M, Friederichsen N, Keller M, Rosenberg M. How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective. Int J Inf Commun Eng. 2014 Nov 11;8(1):37–44.
4. Crnjac M, Veža I, Banduka N. From concept to the introduction of industry 4.0. Int J Ind Eng Manage. 2017 Mar 1;8(1):21.
5. Frost and Sullivan. From concept to production: A 5–step approach towards successful Industry 4.0 projects. Bosch Software Innovations. 2017.
6. Smart manufacturing: Past research, present findings, and future directions. INT J PR ENG MAN–GT, 2016:3.
7. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp. 2014;16:3–8.
8. Eason G, Noble B, Sneddon IN. “On certain integrals of Lipschitz–Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London; 1955 April;A247:529–551.
9. Xiong G, Helo P, Ekström S, Shen Z. A Service–Oriented Autonomous Crane System. IEEE Trans Comput Soc Syst. 2024 Jun 17.
10. Henríquez R, Xavier Martínez de Osés F, Martínez Marín JE. IoT–driven business model innovation: A case–study on the Port of Barcelona in the context of the Belt and Road Initiative. In Advances in E–Business Engineering for Ubiquitous Computing: Proceedings of the 16th International Conference on e–Business Engineering, 2019. Springer International Publishing; 2020. p.302–314.