A new open access research article authored by Rodrigo Caporali de Andrade, Paul Grogan, and Somayeh Moazeni appears in IEEE Open Journal of Systems Engineering today as Early Access / Open Access.
Data-driven operations management methods can transform company operations, respond rapidly to customer demands, and enable new business models. However, companies face the challenge of measuring and evaluating how new technology will impact operational processes. This paper takes a systems engineering approach to assess the tradeoffs of adopting data-driven mechanisms to improve operational processes in a multichannel customer support system. We investigate potential cost savings from two technology applications: classification methods to direct customers to efficient self-service communication channels and routing methods to match customers with agents based on the query type and available skill set. Discrete event simulation evaluates how new technology adoption affects system-level performance. What-if scenarios combine distinct configurations of customer classification mechanisms and available communication channels to evaluate the reduction in the total number of agents required to meet a target service quality level. Discussion includes practical examples of how operational managers could use experimental information to make strategic operational decisions when adopting data-driven technologies.