A Framework for Smart Production-Logistics Systems Based on CPS and Industrial IoT

TLDR

Industrial Internet of Things (IIoT) has attracted growing academic and industrial interest, yet it still suffers from long waiting times and significant energy waste in job‑shop production‑logistics integration. The study proposes a framework for smart production‑logistics systems that models key manufacturing resources and explores self‑organizing configuration mechanisms to overcome IIoT integration challenges. The framework employs a data‑driven analytical target cascading model, validated through a case study of a Chinese engine manufacturer to assess feasibility and performance. Results demonstrate reduced manufacturing time and energy consumption with reasonable computing time, indicating that the framework can help manufacturers deploy IIoT applications and enhance production‑logistics efficiency.

Abstract

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. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems.