Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities, Vol.2

Purpose: This study aims to develop an intelligent teleinformatics system to alleviate bottlenecks and optimize sales efficiency during recurring demand peaks at points of sale. The system redistributes workload intensity by shifting selected tasks to periods of lower demand, thereby improving resource utilization and increasing overall transaction throughput. Need for the Study: Recurring demand peaks lead to order congestion, long wait times, and lost sales. Traditional Order Management Systems (OMS) often fail to manage such surges effectively, resulting in customer dissatisfaction and operational inefficiencies. This study addresses these challenges through an ICT-based approach designed to distribute workload more evenly, reduce bottlenecks, and enhance customer experience. Methodology: The research utilizes Business Process Model and Notation (BPMN) for process simulation and optimization. A comparative analysis was conducted between a conventional order processing model and an ICT-enhanced version, focusing on execution time and system efficiency. Data from a high-transaction sales environment were collected to identify bottlenecks, assess process durations, and evaluate the impact of automation. Findings: The ICT-optimized model reduced total process execution time by automating some process steps. This led to an time savings and enabled an increase in processed orders during peak periods. Workload redistribution to off-peak periods further reduced pressure during high-demand intervals. Practical Implications: The system is applicable in retail, hospitality, and event settings where demand spikes affect service delivery. Shifting tasks to off-peak hours improves throughput, reduces congestion, and increases revenue.