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Leveraging Real-Time Process Data to Predict Delays and Improve Deployment Time to Value
Introduction
Summary
Methodology Solution and architecture Key findings Initial reactions and feedback Conclusion Summary of key facts and takeaways References

Summary

Summary

  • The usually lengthy time taken for an end-to-end process from initiation to deployment and customer value realization for a deployment project is called time to value. This paper showcases a solution to decrease the overall time to value for deployment projects.

    Empowering customers to have their newly purchased IT equipment installed and providing value as quickly as possible is our core objective. To support this goal, we combined deployment process mining with machine learning (ML) and developed a model to predict which deployment projects will likely need multiple, extensive planning sessions. This prediction model is integrated into our Deployment Project Management and Customer Relationship Management (CRM) systems to both alert project managers of potential delays and intelligently recommends mitigating action. The new, comprehensive approach results in the elimination of nonvalue-added steps and improved customers’ time to value by about seven to 10 days. We will explain our original process as well as our new framework, which is called Multi-Planning Predictor & Automation (MPPA).