Enhancing Helpdesk Efficiency through Management Information System – A Resource Allocation Study in Technology Firms

Abstract

Purpose: This paper aims to enhance helpdesk efficiency by integrating deep reinforcement learning for resource allocation and customer satisfaction improvement. Methodology/Approach: The study implemented a deep Q-learning algorithm within a helpdesk management system. The dataset was divided into training and testing sets in an 80:20 ratio. The architectural and computational parameters of the model were optimised, focusing on resource utilisation and workload distribution. Findings: The proposed method reduced the normal resolution time from 3.5 hours to 2.65 hours, representing a 24.3% improvement. Customer satisfaction improved, averaging a score of 3.85. The allocation of support staff workloads was enhanced, leading to a more balanced distribution across different locations. Research Limitation/Implication: Various parameter patterns for the proposed method were tested, revealing the approach's computational expense. Originality/Value of paper: The study proposes a novel use of deep Q-learning for helpdesk management, significantly improving classification accuracy and workload distribution over conventional methods.