4.6 Article

Towards design and implementation of Industry 4.0 for food manufacturing

期刊

NEURAL COMPUTING & APPLICATIONS
卷 35, 期 33, 页码 23753-23765

出版社

SPRINGER LONDON LTD
DOI: 10.1007/s00521-021-05726-z

关键词

Industry 4; 0; Smart manufacturing; Food manufacturing; Internet of things; Artificial intelligence; Machine learning; Big data

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This paper presents a case study of introducing Industry 4.0 technologies to a traditional food manufacturer. By utilizing AI, machine learning, Internet of things, big data analytics, cyber-physical systems, and cloud computing technologies, a smart production control system has been developed, resulting in improved efficiency, consistency, and reduced operational cost. The approach outlined in this paper can benefit similar food manufacturing industries and other SME industries.
Today's factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company's existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries.

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