What’s your problem statement?

Hyperlocal has taken off in India, but demand prediction remains a challenge. Can AI help with inventory management?

The problem HyperStock solves

Inefficient Demand Prediction and Inventory Management in Hyperlocal Businesses.
Inaccurate Demand Forecasting: Hyperlocal businesses struggle to predict demand accurately, leading to stockouts, overstocking and wasted resources.
Manual Inventory Management: Traditional methods of inventory tracking and management are time-consuming, prone to errors and often result in inadequate stock levels.
Lack of Data-Driven Insights: Hyperlocal businesses often lack access to actionable data and analytics, hindering informed decision-making and business growth.
Supply Chain Inefficiencies: Inefficient supply chain management leads to delayed restocking, lost sales and dissatisfied customers.
Consequences:
Revenue Loss: Inaccurate demand prediction and poor inventory management result in lost sales and revenue.
Waste and Overstocking: Excess stock leads to waste, expired products and unnecessary storage costs.
Customer Dissatisfaction: Stockouts and delayed restocking harm customer satisfaction and loyalty.
Competitive Disadvantage: Hyperlocal businesses struggle to compete with larger retailers and e-commerce platforms that have more sophisticated inventory management systems.

Challenges we ran into

Hurdle:
Data Quality Issues: We encountered difficulties in obtaining high-quality, consistent data from various sources, which hindered the accuracy of our demand forecasting model.
Solution: To address this challenge, we implemented data preprocessing techniques, such as handling missing values, outliers and data normalization, to ensure our model received consistent and good-quality data.
Solution:
Data Preprocessing: Implemented robust data preprocessing techniques to handle inconsistencies and quality issues.
Model Optimization: Optimized the AI model by incorporating techniques such as feature engineering, hyperparameter tuning and ensemble methods to improve its ability to handle seasonality and trends.
Testing and Validation: Conducted thorough testing and validation to ensure the model produced consistent and accurate results.
Overcoming the Hurdle:
Data Aggregation: Developed a data aggregation pipeline to collect and integrate data from multiple sources, ensuring consistency and quality.
Data Validation: Implemented data validation checks to detect and handle inconsistencies, ensuring our model received high-quality data.
Lessons Learned:
Data Quality Importance: Recognized the importance of high-quality data in developing accurate AI models.
Robust Testing: Emphasized the need for thorough testing and validation to ensure model reliability.
Collaboration: Collaborated closely with data scientists and developers to address the issue, demonstrating the value of interdisciplinary teamwork.