Keywords: Auto-ID, Information flow, Internet of Things, Maintenance Supply Chain, Material flow, RFID, Rule-based behavior, SCOR, Simchronize, Simulation, Supply Chain, Supply Chain Networks

Affiliation: University of Vienna


One success factor of competitive Supply Chain Networks is their ability to gather, process and distribute information fast and adequately. However, the interactions of Supply Chain Partners, their individual policies and processes, IT-systems, and the resulting information and material flows can cause a highly dynamic interplay of actions and reactions which is due to the occurrence of stochastic events and changing environment, such as demand variations, raw material and resource availability not easily predictable.

SIMchronization supports the improvement and synchronization of information and material flows in supply chain networks by visualizing flows of transferred objects, such as processed physical items and immaterial information objects (messages and calls). An optimized sequencing of information and material flow is hereby considered as a synchronized system.
The method was initially developed to study the benefits of implementing information technologies, such as mobile (smart) devices, Radio Frequency Identification (RFID), and Machine-to-Machine (M2M) communication for e-maintenance supply chains but can be applied to all types of Supply Chains and production processes.

To reveal the dynamics of a supply chain network, a discrete simulation algorithm is applied to the static model and the behavior-describing rules. As a result, during the simulation, messages such as production orders and stock inquiries are created, and items, such as raw material and goods, are correspondingly moved or transformed over time. Simultaneously, the flow of items and information objects through the supply chain is animated to visualize the behavior of the network. After a simulation run, the report generator provides quantitative data, such as lead times and stock levels, which allow for a comparison of different implementation alternatives and supports an informed decision.

The conceptual model of SIMchronization is shown below.



















The metamodel of the method SIMchronization is shown below.




















The implemented metamodel of the static components of the "Supply Chain Network Model" as implemented in the SIMchronization application library v1.0 is shown below.


















An example for State Sequence Models which support the analysis of model dynamics by showing the chronological sequence of control information and resulting material movements is shown bel