Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review

The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. Industrial Internet of Things (IIoT) intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It is time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this article provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then, we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in functions, operations, deployments, and application. Afterward, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. The ethical implications and environmental impacts of adopting IIoT intelligence in manufacturing are analyzed as well. Finally, we explore the open challenges and development trends from four aspects to inspire future researches.

I. Introduction

Manufacturing holds an important position in the global economy. It accounts for 16% of global GDP, and its research and development (R&D) spend accounts for 64% of global R&D expenditure [1]. However, manufacturing is suffering from many troubles. The stagnant productivity, increasingly personalized customization requirements, long overdue innovation, rapidly shrinking humanpower, and expectation for one-stop comfortable service, all of these are pushing manufacturing to upgrade and transform. At the same time, the rapid development of intelligent technologies in recent year also inspires governments and enterprises to carry out digitization transformation. Under the circumstance, governments issued a series of policies [2], [3], [4], [5], such as New Robot Strategy, Industrial Strategy 2030, etc. Inspired by the policy dividend, enterprises begin to take the path of industrial revolution and innovation [6], [7].