Purpose -To develop a methodology to augment enterprise resource planning (ERP) systems with the discrete event simulation's inherent ability to handle the uncertainties. Design/methodology/approach -The ERP system still contains and uses the material requirements planning (MRP) logic as its central planning function. As a result, the ERP system inherits a number of shortcomings associated with the MRP system, including unrealistic lead-time determination. The developed methodology employs bi-directional feedback between the non-stochastic ERP system and the discrete event simulation model until a set of converged lead times is determined. Findings -An example of determining realistic production lead-time data in the ERP system is presented to illustrate how such a marriage can be achieved. Research limitations/implications -The research demonstrates that the limited planning functionality of the ERP system can be complemented by external system such as discrete event simulation models. The specific steps developed for this research can be adopted for other enhancements in different but comparable situations. Practical implications -The organizations who have been using the discrete event simulation in their planning and decision-making processes can integrate their simulation models and the ERP system following the steps presented in this paper. The ideas in this paper can be used to look for automatic data collection process to update or build the simulation models. Originality/value -The ERP implementation is a significant investment for any corporation. Once the ERP implementation is completed successfully, the corporations must look for ways to maximally return on their investment. The research results may be used to enhance the implemented ERP systems or to fully utilize the capabilities in a corporation.
Enhancing ERP system's functionality with discrete event simulation REF .
Enhancing ERP system's functionality with discrete event simulation
Industrial Management and Data Systems
Industrial Management and Data Systems
Engineering Computer Science