Potential of industrial internet of things (IIoT) to improve inefficiencies in food manufacturing

• Main Authors: Hasnan N.Z.N., Yusoff Y.M., Lim S.A.H., Kamarudin K.
• Other Authors: 57209140758
• Format: Conference Paper
• Published: American Institute of Physics Inc. 2024

The aims of this review were twofold, namely 1) to analyze the main operational inefficiencies in food manufacturing and 2) to identify the main IIoT-related technologies with their potential operational improvement for the food manufacturing sector. An analytical literature review was performed using the main scientific literature databases as the secondary data source. The review has found nine major operational issues that are most frequently reported in the food manufacturing sector namely 1) too long manufacturing lead time, 2) low productivity, 3) absence of systematic quality management, 4) low compliance to food safety requirements, 5) lack of innovations in product development, 6) lack of training, 7) unsustainable marketing strategies, 8) poor traceability and 9) lack of documentation along the supply chain. While IIoT is relatively new, it is important to embrace that food manufacturing can have many of these operational issues solved when incorporating digital technologies. The key starting point is the identification of the correct and effective application that suits the industry's requirements in their pursuit of an improved level of operational efficiencies, productivity and a higher level of quality. In this regard, this review intended to clarify the identified seven groups of IIoT technologies that could improve the above-identified operational issues, whereby these are 1) smart manufacturing technologies, 2) Big Data, Analytics and Artificial Intelligence, 3) robotics, 4) additive manufacturing, 5) augmented reality, 6) manufacturing simulation, and lastly 7) the cloud. The study concluded that food manufacturers could only benefit from the IIoT advantages when the purpose of the technology fulfils their operational objectives and requirement as well as fits within their constraints. � 2023 Author(s).