Discrete Event Simulation Model Performed with Data Analytics for a Call Center Optimization

Optimization models enable organizations to find the best solution and respond demand from an uncertain environment stochastic process promptly with less engineering effort. This study aims optimize number of seasonal agents customer prioritization needed for a call center system using big data analytics discrete event simulations improve satisfaction. The was carried out based on leading heating ventilation company’s center. K-means clustering technique used determine segmentation 6-million-customer data. For prioritization, making Recency-Frequency-Monetary (RFM) analysis applied. modeled ARENA simulation software, performance parameters were measured depending segments obtained. results show that model performed gives better beneficial financial impact numerical values in reducing average waiting time most prioritized customers by more than 90%, least customers, it increased approximately just 40%. However, company segments, increase 300%.