Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. The presented work potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems.

Automated guided vehicles (AGVs) have been widely used in manufacturing and supply chain management for material handling. The efficiency of the material handling process has been the bottleneck of the production manufacturing. By applying maturing technologies such as sensing, cloud computing, and wireless communication, the efficiency and the reliability of the material delivery could be enhanced. In this paper, a Cyber-Physical System-based smart control model for shopfloor material handling is designed. In contrast to the traditional vehicle control methods, AGVs and base stations at intersections can communicate and interact with each other and share the real-time information online. Then, the smart control model, which consists of car following model, overtaking model, and collision warning and avoidance model, are designed and developed. The presented model is demonstrated by a set of simulations and an experiment, which proved that the overall task completion efficiency and the utilization of the road are improved.

Purpose: This paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process. Design/methodology/approach: Four components, namely, big data application, big data sensing and acquisition, big data processing and storage, model establishing, data mining and process optimization were presented to comprise the framework. Key technologies including the big data acquisition and integration, big data mining and knowledge sharing mechanism were developed for the big data analytics for AM. Findings: The presented framework was demonstrated by an application scenario from a company of three-dimensional printing solutions. The results show that the proposed framework benefited customers, manufacturers, environment and even all aspects of manufacturing phase. Research limitations/implications: This study only proposed a framework, and did not include the realization of the algorithm for data analysis, such as association, classification and clustering. Practical implications: The proposed framework can be used to optimize the quality, energy consumption and production efficiency of the AM process. Originality/value: This paper introduces the concept of big data in the field of AM. The proposed framework can be used to make better decisions based on the big data during manufacturing process.