Abstract
Digital twin testbed and practical applications in production logistics with real-time location data
Highlights
To reduce the often high costs of logistics operations with low added-value, [1], [2] stakeholders try to implement technologies to offer the same or better services at a lower cost [3]–[5]
In their review, Negri et al [33] state that their analysis has shown that the understanding of digital twin (DT) is diverging and that the research is in its infancy
The literature review related to DT in manufacturing and logistics revealed that the research is in its infancy and that there is a significant difference in the maturity of these DT, in the logistics domain
Summary
To reduce the often high costs of logistics operations with low added-value, [1], [2] stakeholders try to implement technologies to offer the same or better services at a lower cost [3]–[5]. The usage of safe environments like testbeds or pure simulation environments can, with some limitations, contribute to overcoming these challenges [8]. Such a limitation is related to the decentralised decision-making process carried out by the autonomous robots (like automated guided vehicles (AGVs) or intelligent cargo) [1], [3], [9], which is hard to simulate since we know little about the used algorithm. The usage of digital twins can help overcome this challenge since it can visualise the differences between the optimal path based on algorithms and the optimal route based on the inbuilt decision-system in the AGV. (RQ1) When do we need digital twins and real-time location data in production logistics?. (RQ2) What kind of practical applications of digital twins and real-time location data can benefit production logistics operation?. (RQ3) What is the actual benefit of having digital twins instead of digital shadow in production logistics?
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