Author: Korey Cheung, Certified Master Anaplanner and Senior Associate at Spaulding Ridge.
The challenge
A leading consumer goods company faced challenges in aligning their statistical forecast model with their disaggregation and demand planning models. The statistical forecast and disaggregation models were primarily calculation-based, with minimal user interaction. Monthly updates to the statistical forecast process led to fluctuating values across the models. This complexity made it difficult for end-users to trace discrepancies, leading to inefficiencies in the demand planning process.
The solution
Leveraging the power of Anaplan's Connected Planning platform, the company implemented an innovative solution to address these challenges. They created a new Anaplan demand audit model that seamlessly integrated data from the statistical forecast, disaggregation, and demand planning models.
The demand audit model featured user-friendly dashboards that presented data from each model using the familiar hierarchy employed in the demand planning model. This intuitive approach allowed users to easily navigate and compare data across all three models in a single, unified view.
The new model significantly simplified the process of identifying discrepancies, dramatically reducing the time required for investigations. Users could now quickly pinpoint areas of misalignment and focus their efforts on resolving these issues efficiently.
Furthermore, the team conducted in-depth analyses on various SKUs and scenarios, refining the logic in each model to address edge cases. This proactive approach led to continuous improvements in the accuracy and reliability of the forecasting process.
Results
The implementation of the Anaplan demand audit model yielded impressive results:
- Enhanced visibility: Users gained a comprehensive view of data across all three models, improving overall understanding of the forecasting process.
- Increased efficiency: The time required to investigate discrepancies was significantly reduced, allowing planners to focus on value-added activities.
- Improved accuracy: Refinements to model logic resulted in more precise forecasts and better alignment across all models.
- Greater user engagement: The intuitive dashboards encouraged more active participation from end-users in the forecasting process.
- Scalability: The solution provided a framework for ongoing improvements and adaptations to changing business needs.
Key takeaways
- Stakeholder engagement: The success of the solution was largely attributed to the active involvement and buy-in from stakeholders and planners. By shifting the focus from blaming data quality or the Anaplan platform to gaining a deeper understanding and control of the forecast, the team fostered a collaborative problem-solving environment.
- Leveraging Anaplan's flexibility: While it might seem counterintuitive to address an Anaplan-related issue by creating another Anaplan model, this approach showcased the platform's versatility. The additional layer of visibility provided by the new model was crucial in cutting through the data fog and providing clarity to users.
- Focused model development: Although building a new model may appear daunting, the team demonstrated that a lightweight, purpose-driven model could be highly effective. By focusing on a single objective — in this case, audit and visibility — they created a powerful tool without unnecessary complexity.
- Continuous improvement: The solution went beyond just creating a new model. By conducting further analyses and refining the logic in existing models, the team demonstrated commitment to ongoing improvement and optimization of their forecasting processes.
By leveraging Anaplan's flexible and powerful platform, the company successfully transformed its demand forecasting process, achieving greater accuracy, efficiency, and user satisfaction. This case study demonstrates the potential of connected planning solutions to drive significant improvements in complex business processes.
