Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

Olumide Emmanuel Oluyisola (), Swapnil Bhalla (), Fabio Sgarbossa () and Jan Ola Strandhagen ()
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Olumide Emmanuel Oluyisola: Norwegian University of Science and Technology
Swapnil Bhalla: Norwegian University of Science and Technology
Fabio Sgarbossa: Norwegian University of Science and Technology
Jan Ola Strandhagen: Norwegian University of Science and Technology

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 1, No 17, 332 pages

Abstract: Abstract In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.

Keywords: Production planning and control; Smart manufacturing; Internet of things; Machine learning; Industry 4.0; Decision support systems (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (14)

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DOI: 10.1007/s10845-021-01808-w

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