Retail | Store Operations
Labor Optimization
Objective
Inefficient labor allocation led to either overstaffing, which increased operational costs, or understaffing, negatively impacting customer service and sales. The goal was to develop a data-driven approach to forecast customer demand and optimize labor schedules accordingly.
Challenges
- Accuracy: Accurately predicting customer footfall and sales patterns.
- Optimization: Balancing labor costs with service quality.
- Adapting to seasonal variations and promotional events.
- Integrating multiple data sources for comprehensive analysis.
- Ensuring compliance with labor laws and employee preferences.
Solution Proposed
Integrated real-time data feeds, utilized ML computations, and incorporated employee feedback for dynamic adjustment of labor schedules, and improved customer satisfaction using:
- Time Series Analysis: Predicts customer footfall and sales based on historical data by understanding trends, seasonality, and cyclic patterns.
- Genetic Algorithms: Handles complex scheduling constraints, such as employee availability, labor laws, and shift preferences, ensuring a feasible and efficient schedule.
Outcome
- Cost Reduction: Achieved a 15% reduction in labor costs through optimized scheduling.
- Enhanced customer satisfaction scores through better staff availability during peak hours.
- Reduced instances of overstaffing and understaffing.
- Increased employee satisfaction through more balanced and preferred shift allocations.
Design & Thinking Wins
- Successful integration of forecasting and optimization algorithms into existing retail management systems.
- Development of a scalable solution adaptable to different store sizes and locations.
- Positive feedback from store managers on the usability and effectiveness of the new scheduling tool.
- Recognition as a best practice within the retail chain, leading to plans for broader implementation.
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.
