Information Technologies and Computer Engineering

This paper focuses on optimizing the task distribution process in distributed computing systems. By applying statistical analysis methods, a strategy has been developed to automate the selection of task performers, improving the efficiency of task distribution, daily productivity, and employee satisfaction. The research shows that the optimized approach reduced the average processing time for specific user requests from 34 to 31 minutes, which is 7% more effective compared to random task allocation, thereby enhancing service quality and overall productivity. 
The proposed unified model for optimized task distribution considers key factors such as internal user profiles, their workload levels, task priority, interaction among performers, and other available system resources. This model balances employee competencies with the speed of task processing, significantly improving the system's overall performance. 
Particular attention is given to the methodology based on Salesforce CRM tools, which allows for the effective use of historical data on employee performance to identify the most suitable task performers. Combined with statistical data analysis methods, this approach not only optimizes task distribution but also enables accurate time prediction for task completion, identification of process anomalies, and the development of flexible distribution strategies. Considering both competencies and productivity ensures high-quality task execution, reduces processing time, and minimizes workload, which is critical for the efficient operation of distributed systems. 
In overall, the proposed study confirms that the use of statistical analysis and CRM tools enhances the efficiency of distributed computing systems. This opens opportunities for the implementation of optimized task distribution strategies across various sectors, especially in the context of the growing volume of data and the complexity of business processes

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