Retail | Financial Planning & Analysis
Planning and Budgeting
Objective
The clients faced significant challenges in accurately planning and budgeting due to fluctuating consumer demand, seasonal trends, and market competition. Ineffective forecasting led to overstocking or stockouts, impacting revenue and customer satisfaction.
Challenges
- Integrating data from multiple sources such as sales, marketing, and macroeconomic factors.
- Handling the complexity of various forecasting models and selecting the most accurate one.
- Generating actionable insights from large datasets.
- Ensuring the scalability and robustness of the forecasting model.
Solution Proposed
The solution proposed can handle complex, non-linear relationships in data while ensuring robustness and scale, simultaneously using:
- Data Integration: Collate data from sales, marketing, promotions, and macroeconomic factors.
- Forecasting Models: Implement multiple techniques such as ARIMA/ARIMAX, Random Forest, and Neural Network Models to develop a robust forecasting framework.
- Scenario Evaluation: Use tools to arrive at optimal scenarios.
- Automated Anomaly Detection: Incorporate automated triggers.
Outcome
- Improved forecast accuracy by 20%, leading to reduced stockouts and better inventory management.
- Boosted sales through personalized recommendations.
- Enhanced customer engagement with increased repeat visits.
Design & Thinking Wins
- Flexibility to evaluate multiple scenarios and choose the most optimal one.
- Focus on meeting strategic objectives that vary across geographies.
- Error-free forecasting through continuous monitoring and CICD.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
