Retail | Financial Planning & Analysis
P&L Target Planning
Objective
The FP&A department wanted to scientifically set the P&L targets for the entire retail chain at granular levels. The existing process was heuristic and gut-feel-driven.
Challenges
- High variability in consumer demand and preferences.
- Complexity in integrating data from multiple sources (sales, inventory, market trends).
- Difficulty in forecasting due to seasonal fluctuations and promotional impacts.
- Limited visibility into real-time performance metrics.
Solution Proposed
- Time Series Forecasting was used because it can handle seasonal patterns and trends, while Regression Analysis helped understand the impact of various factors on P&L.
- Implemented Business Intelligence (PBI) tools for real-time data visualization and performance tracking.
- Leveraged Data Engineering to integrate and clean data from various sources for accurate analysis.
Outcome
- Improved accuracy in P&L target setting by 20%.
- Enhanced decision-making capabilities with real-time data insights.
- Increased operational efficiency through better resource allocation.
- Achieved significant increase in overall profitability.
Design & Thinking Wins
- Seamless integration of ML models with BI tools for comprehensive analysis.
- Scalable data architecture supporting large volumes of retail data.
- User-friendly dashboards enabling quick access to Key Performance Indicators (KPIs).
- Automated data pipelines ensure up-to-date and accurate information.
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.
