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DELLQUINN: A Machine Learning-Based, Resource Capacity Optimization Solution
Introduction Methodology Key takeaways
Key takeaways
Leveraging historical insights
Utilizing future metrics
Optimizing capacity planning
User feedback Conclusion References

Leveraging historical insights

Leveraging historical insights

  • Historical dispatch data analysis yields fruitful insights on the average duration of each type of dispatch across countries, including the following:

    • Variances in dispatch duration across case types and regions have been accounted for in the capacity planning solution
    • Historical proportion of work carried out by Dell FSEs and amount of work outsourced to Partners have been used to split tasks between Dell and Partner for future workload optimization.
    • The analysis of past dispatches and engineer certification and trainings, along with their dispatch handling performance in conjunction with the future workload demand, is used to tag an engineer as skilled or unskilled across products (LOBs).

    This skilled or unskilled tag is useful in generating training recommendations for FSEs across LOBs and enhancing readiness of FSEs. The metric has been ingested into the ML resource optimization framework of the solution.