Managing global software usage across thousands of users and business units creates significant complexity in cost allocation and chargebacks. Manual chargeback based on actual usage can be precise, but it’s often time-consuming, error-prone, and difficult to sustain. This session explores how shifting from manual, reactive processes to a predictive, data-driven forecast-based model can streamline chargeback, free up SAM resources, and improve planning across business units. The session will highlight key steps, tools, and lessons learned from implementing a more scalable and efficient chargeback model.
- How to create a standard cost model for chargeback using historic usage data and predictive analytics
- How to reduce manual workload by automating license usage reporting and chargeback forecasting
- How to use business intelligence tools to shift from reactive reconciliation to proactive software planning?
- How to balance the trade-offs of improving SAM efficiency while managing the risk of over/undercharging?
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