Avocado_Sales_Optimization

🥑 Avocado Sales Optimization: ROI Analysis for Quality Control Technology

📌 Executive Summary

This project demonstrates how data-driven decisions solve critical logistics and sales bottlenecks in the Colombian retail sector. By analyzing avocado maturity levels, I identified an annual revenue leakage of $123,321,900 COP. Implementing an automated grading solution yields a Project ROI of 29.27%, with an investment recovery period of 41 months.

💼 The Business Problem

In Colombia, the Hass avocado market is expanding, but retailers struggle with quality consistency at the point of sale.

• The Issue: Customers reject fruit that is too firm (immature) or overripe, leading to “purchase frustration.”
• The Goal: Quantify the financial impact of “unripe fruit” and evaluate the profitability of a $150M COP investment in grading technology.

📈 Key Insights from Data

1. The “Rejection Threshold”

Statistical analysis reveals that sales rejection spikes dramatically when average maturity drops below 2.5 on a 1-5 scale. This identifies the exact “danger zone” for inventory management.

Figure 1: Correlation between fruit immaturity and lost sales units.

2. Recovering “Ghost Demand”

Retailers typically only track physical waste (shrinkage). This project tracks Lost Demand: the revenue from customers who intended to buy but walked away. By reducing rejection from 15% to 3%, we unlock a previously invisible revenue stream.

Figure 2: Annual Revenue comparison: Current State vs. Optimized Scenario.

💰 Financial Results & ROI

Through Python-based simulations, the following annual metrics were established: | Metric | Value (COP) | | :— | :— | | Annual Avoided Loss (Gross) | $98,657,520 | | Annual Operational Cost | $54,750,000 | | Net Annual Profit | $43,907,520 | | Initial Investment | $150,000,000 | | Payback Period | 41 Months |

🛠️ Tech Stack & Methodology

• Python: Advanced data simulation (numpy), processing (pandas), and visualization (seaborn/matplotlib).
• Statistical Modeling: Normal distribution modeling for ripening cycles.
• Business Intelligence: Financial modeling for ROI and Payback Period projections.

🏁 Conclusion & Recommendation

The investment is strategically sound. While the payback period is ~3.4 years, the implementation transforms a recurring quality issue into a measurable competitive advantage. It ensures product-market fit at the shelf level, significantly increasing net profit and customer loyalty.

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Contact: Helian Fierro LinkedIn Profile