The objective of evaluating a sourcing model is crucial for businesses to optimize their processes and investments effectively. By focusing on metrics like accuracy of predictions and computational efficiency, companies can ensure reliable outputs and minimize resource consumption. For example, incorporating machine learning algorithms can enhance prediction accuracy, while optimizing algorithm complexity can improve computational efficiency.
User accessibility is also significant, ensuring that the model is intuitive and easy for stakeholders to use. A user-friendly interface and comprehensive training can drastically enhance user interaction. Additionally, integration capability is vital for seamless operation across varying systems, promoting smoother data exchange and collaboration between different IT architectures.
Finally, monitoring return on investment (ROI) ensures that the sourcing model delivers tangible financial benefits within a year. Regularly analyzing the cost-benefit ratio can pinpoint areas for financial improvements, enhancing overall strategic alignment with business goals.
Top 5 metrics for Evaluating a Sourcing Model
1. Accuracy of Predictions
Measures how correctly the sourcing model predicts outcomes compared to actual results
What good looks like for this metric: Typically above 70%
How to improve this metric:- Use more comprehensive datasets
- Incorporate machine learning algorithms
- Regularly update the model with new data
- Conduct extensive testing and validation
- Simplify model assumptions
2. Computational Efficiency
Assesses the time and resources required to produce outputs
What good looks like for this metric: Execution time under 1-2 hours
How to improve this metric:- Optimize algorithm complexity
- Utilise cloud computing resources
- Use efficient data structures
- Parallelize processing tasks
- Employ caching strategies
3. User Accessibility
Evaluates how easily users can interact with the model to obtain necessary insights
What good looks like for this metric: Intuitive with minimal training required
How to improve this metric:- Develop a user-friendly interface
- Provide comprehensive user manuals
- Conduct user training sessions
- Ensure responsive support
- Regularly gather user feedback
4. Integration Capability
Measures how well the sourcing model integrates with other systems and data sources
What good looks like for this metric: Seamlessly integrates with existing systems
How to improve this metric:- Adopt standard data exchange formats
- Ensure API functionalities
- Conduct system compatibility tests
- Facilitate flexible data imports
- Collaborate with IT teams
5. Return on Investment (ROI)
Calculates the financial return generated by implementing the sourcing model
What good looks like for this metric: Positive ROI within one year
How to improve this metric:- Analyse cost-benefit ratios
- Continuous optimisation for cost reduction
- Align model outputs with business goals
- Enhance decision-making accuracy
- Regularly track and report financial impacts
How to track Evaluating a Sourcing Model metrics
It's one thing to have a plan, it's another to stick to it. We hope that the examples above will help you get started with your own strategy, but we also know that it's easy to get lost in the day-to-day effort.
That's why we built Tability: to help you track your progress, keep your team aligned, and make sure you're always moving in the right direction.
Give it a try and see how it can help you bring accountability to your metrics.
