Retail | Supply Chain, Logistics & Transportation
Workload Optimization
Objective
The client faced inefficiencies in workload management due to inaccurate demand forecasting. This led to overstaffing or understaffing, which impacted customer satisfaction and operational costs.
Challenges
- High variability in customer demand due to seasonal trends and market fluctuations.
- Limited historical data on new product lines and promotions.
- Complexity in integrating multiple data sources for accurate forecasting.
Solution Proposed
- ARIMA was chosen for its effectiveness in handling linear data trends, while LSTM was used to capture long-term dependencies and non-linear patterns.
- Implemented Business Intelligence (BI) tools to visualize data trends and provide actionable insights.
- Engineering efforts were utilized to integrate data from POS systems, inventory databases, and external market indicators.
Outcome
- Improved forecast accuracy by 25%, reducing instances of overstaffing and understaffing.
- Enhanced customer satisfaction scores due to better service levels.
- Operational cost savings through optimized workforce allocation.
Design & Thinking Wins
- Seamless integration of AI/ML models with existing IT infrastructure.
- Real-time data processing and visualization capabilities.
- Scalable solution adaptable to future business needs and market changes.
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.
