System dynamics analysis for an Internet-of-Things-enabled production logistics system

• Published in: International journal of production research Vol. 55; no. 9; pp. 2622 - 2649
• Main Authors: Qu, Ting, Thürer, Matthias, Wang, Junhao, Wang, Zongzhong, Fu, Huan, Li, Congdong, Huang, George Q.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 03.05.2017; Taylor & Francis LLC
• Subjects: Control systems; Design analysis; Design engineering; Dynamical systems; Dynamics; Internet of Things; Logistics; production logistics; Simulation; Studies; System dynamics; Systems analysis
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2016.1173738

A production logistics system is often subject to high operational dynamics due to large working areas, frequent resource interactions, long operation periods and intensive human involvement. Researchers have applied system dynamics to design the structure of statistically robust systems which accommodate common dynamics. Yet this approach begins to lose its feasibility because dynamics anticipation and statistics are becoming more difficult in ever more competitive markets and adjustments to system structure typically incur high costs. In response, this study explores how a robust information structure can be designed and real-time control schemes for controlling the dynamics inherent to real-life systems applied. Motivated by the wide application of industrial Internet-of-Things (IoT) systems, this paper investigates the typical production logistic execution processes and adopts system dynamics to design cost-effective IoT solutions. The internal and external production logistic processes are first investigated separately. Using sensitivity analysis, the optimal IoT solutions are evaluated and analysed to provide guidance on IoT implementation. Internal and external production logistic processes are then combined into an integrated structure to offer a generic system dynamics approach. This research does not only enhance the use of system dynamics, but also presents a quantitative IoT system analysis approach.