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BluePi Optimized EOL Product Markdown for an Electronics Retail Giant
Problem Statement:
A leading Indian electronics retailer came across the challenge of clearing out End-of-Life (EOL) products efficiently while maximizing profits. It was pressing to find the optimal markdown percentage for each product across different stores to
- Optimize Price: Strike a balance between reducing profit margin compression and optimizing sales.
- Expedite Inventory Liquidation: Sell goods rapidly to free up funds and storage space.
- Clear the Stock Profitably: Steer clear of steep discounts that drastically cut into profit.
Solution:
BluePi, a Technology Consulting and Enterprise Modernization Company, implemented a two-stage ML-powered solution: To optimize liquidation, this innovative approach leveraged machine learning in two stages. It used historical sales data in the first stage to accurately forecast the demand. Next, it maximized liquidation within predetermined constraints by applying the knowledge obtained in the first stage.
Demand Forecasting:
- Demand Modeling: Using historical sales data, a LightGBM (gradient boosting machine) model was trained considering the geographic variables, store location, successor products, and product attributes. The weekly sales of every EOL product at various price points were predicted by this model. To determine how demand fluctuated in response to price changes, historical price data was examined. This assisted in figuring out each product's ideal price sensitivity.
Optimal Markdown:
- To determine the optimal markdown percentage for every product-store combination, a linear programming model was developed, taking into account variables like the desired liquidation timeline and cost price. But the machine learning model was where it all came together. It predicted the effects of various markdown levels by analyzing the relationship between price changes and demand. This allows the user to determine the best mix of discounts to clear the specified stock quantity in a given week, reducing losses and complying with business policies such as minimum price thresholds and single pricing. This combined strategy extends a formidable tool to businesses looking for an effective and data-driven liquidation strategy.
Technology Deployed:
-
Machine Learning
1) LightGBM for demand prediction
2) Linear Programming for Optimization - Platform: Python
Business Impact:
- Profitable Clearance: The solution helped the retailer clear the EOL stock while maintaining profitability.
- Improved Efficiency: The potential of the model to forecast demand across different timeframes and geographies sets in motion clearance planning more effectively.
- Data-Driven Decision-Making: The retail business, using this model, had valuable insights into product demand and price sensitivity, empowering it to make well-informed decisions about clearance strategies.
Key Learnings:
- EOL product-clearing procedures can be greatly enhanced by combining optimization techniques like linear programming with machine learning for demand forecasts.
- Setting the optimal markdown prices and maximizing profits require accurate price elasticity modeling.
- The solution can be tailored to diverse clearance scenarios due to its flexibility in managing different timelines and geographical locations.
Future Considerations:
- Integration of the solution with real-time sales data to enable continuous pricing adjustments.
- Adding more variables to the model, such as competitor pricing and promotional activities.
- Inspecting cutting-edge optimization strategies for even more precise pricing management.
Outcome and Key Takeaways:
BluePi’s solution successfully addressed the electronics retailer’s challenges by:
- Optimizing Markdown Percentages: As compared to conventional approaches, the solution produced a significant increase in profit from EOL product clearance.
- Reducing Clearance Time: With this solution, the inventory clearance time was reduced by a significant percentage, allowing a quicker release of capital and storage space.
- Data-driven Decision-making: The retailer was able to formulate informed clearance plans for upcoming products by gaining insightful knowledge about product demand and price sensitivity.
The accomplishment of this project shows the value of a data-driven strategy for EOL product clearance. The retail giant improved operations and made significant financial gains by utilizing machine learning and optimization. The solution’s inherent capacity for ongoing learning and development, however, is what really adds value.
Future considerations are deliberate measures to reinforce and magnify the achievement that has already been accomplished, not just add-ons. Dynamic pricing adjustments are made possible by integrating real-time data, guaranteeing that the optimal price is always provided. The retailer may keep ahead of the curve and modify its strategy by incorporating competitor data and promotions. Exploring cutting-edge optimization strategies can lead to even higher profit margins and more granular control over markdown prices.
Through the use of these forward-looking considerations, the retail giant can now transform its EOL clearance procedure from a reactive undertaking to an anticipatory profit generator, ensuring that it consistently maximizes the inventory value.
