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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

Optimizing capacity planning

Optimizing capacity planning

  • Capacity planning solution users leverage historical and future metrics to make capacity planning decisions.

    The solution dashboard provides the workload demand that can be expected for the next two quarters. The optimized workload is based on:

    • Engineers’ availability, skills, and performance
    • The remaining workload demand to be met
    • Overtime needs
    • The efficiency with which engineers across countries are expected to handle dispatches given the future incoming workload.

    The expected efficiency can be thought of as a metric of future utilization of engineers across countries and quarters. These metrics help Regional Services Directors (RSDs) from the business operations team know what to expect in terms of planning the resources to fulfill future demand.

    Further, if an engineer leaves the organization, the capacity planning solution helps with understanding the impact and make replacement decisions accordingly.