Description
This paper explores the creation of a Decision Support System (DSS) for the classification and assignment of support tickets within an Information Technology Service (ITS) organization. With a reliance on lower-skilled, outsourced, or temporary workers, many organizations service desks experience issues with processing of incoming support tickets. Beyond this, these workers require extensive training and functional knowledge of the company’s structure, and service offerings in order to effectively process tickets. Training requirements, misassignment, unnecessary escalations, and low confidence all contribute to increased costs in the ITS organization. The DSS leverages optimization methods, machine learning, and historic data in order to match an incoming tickets to a service within a service catalog, and recommends an appropriate assignment to a team or individual within the ITS organization for fulfillment or resolution. With this system, a front-line worker gains confidence in the assignment and classification, with lower training barriers and sunk-costs.
Recommended Citation
Chagnon, Christopher J.; Trapp, Andrew C.; and Djamasbi, Soussan, "Creating a Decision Support System for Service Classification and Assignment through Optimization" (2017). AMCIS 2017 Proceedings. 20.
https://aisel.aisnet.org/amcis2017/DataScience/Presentations/20
Creating a Decision Support System for Service Classification and Assignment through Optimization
This paper explores the creation of a Decision Support System (DSS) for the classification and assignment of support tickets within an Information Technology Service (ITS) organization. With a reliance on lower-skilled, outsourced, or temporary workers, many organizations service desks experience issues with processing of incoming support tickets. Beyond this, these workers require extensive training and functional knowledge of the company’s structure, and service offerings in order to effectively process tickets. Training requirements, misassignment, unnecessary escalations, and low confidence all contribute to increased costs in the ITS organization. The DSS leverages optimization methods, machine learning, and historic data in order to match an incoming tickets to a service within a service catalog, and recommends an appropriate assignment to a team or individual within the ITS organization for fulfillment or resolution. With this system, a front-line worker gains confidence in the assignment and classification, with lower training barriers and sunk-costs.