A data-driven simulation to support remanufacturing operations

Simulations are a vital component in developing smart manufacturing systems, predicting the behaviour of the manufacturing shop floor operations to support production planning, scheduling and maintenance decisions within manufacturing environments. However, simulations are often limited in their ability to support real-time business decisions in complex fast changing environments due to the cost and time required to build, update and maintain simulation models. Remanufacturing operations in particular could benefit from the use of simulations as a tool to support the assessment of different strategies to real-time scenarios due to the uncertain nature of product returns. This research develops a data-driven simulation approach to predict material flow behaviour within remanufacturing operations, by utilising data from digital manufacturing systems (i.e. databases, traceability systems, process plans) to update and automatically modify the simulation constructs to reflect the real world or planned system. A data-driven simulation is proposed comprising of three elements: (i) an adaptive remanufacturing simulation algorithm to model the complex material flow found within a remanufacturing process in a generic and reusable way, (ii) a remanufacturing information model to structure and highlight the simulation data requirements and (iii) an information service layer to collect and analyse sensor data for use within the simulation. The simulation is implemented to demonstrate how it can automatically reconfigure and adapt to changes within the data inputs (process and factory models) using a case study of operations from a Waste Electrical and Electronic Equipment (WEEE) remanufacturer, utilising data collected from a Radio Frequency Identification (RFID) traceability system installed within the remanufacturing facility. (C) 2018 Published by Elsevier B.V.