Transform Retail with Data-Driven Decisions

OptiStock combines time-series forecasting and Market Basket Analysis to optimize inventory management and boost sales for SMEs.

Discover Our Solution

The Retail Challenge

Small and medium retailers face critical challenges in inventory management and understanding customer behavior:

Stock Issues

35% of SMEs experience both overstock and stockouts simultaneously due to poor demand forecasting.

Data Blindness

Over 90% make decisions based on intuition rather than data analysis.

Revenue Loss

Inefficient inventory management leads to 15-25% revenue loss annually.

Our Innovative Solution

OptiStock addresses these challenges through two powerful analytical approaches:

Time-Series Forecasting

Predict future product demand with 90%+ accuracy using ARIMA and Prophet models.

Market Basket Analysis

Identify product affinities (e.g., "customers who buy coffee also buy cookies") using Apriori algorithm.

See How It Works

Scientific Methodology

Developed at Universidad Politécnica de Querétaro, OptiStock follows a rigorous 4-phase approach:

Data Exploration & Preparation

Cleaning, transformation, and feature engineering using Python (Pandas, NumPy). Creation of derived variables for enhanced modeling.

Predictive Modeling

Implementation of time-series models (ARIMA, Prophet) for demand forecasting and Apriori algorithm for association rule mining.

Visualization

Interactive dashboards showing sales trends, product associations, and demand curves using Plotly and Matplotlib.

Strategic Recommendations

Actionable insights for inventory replenishment, product bundling, and promotional strategies tailored for SMEs.

Tangible Benefits

Inventory Optimization

Reduce overstock by up to 30% and stockouts by 45% through accurate demand forecasting.

Increased Revenue

Boost sales by 15-20% through data-driven product placement and promotions.

Time Savings

Reduce manual inventory planning time by 60% with automated recommendations.