Industry: Food and Beverage

Annual Revenue: $20 Million

Number of Locations: 35 cafes across India

Objective: Enhance inventory management to avoid stockouts and improve customer satisfaction

The client is a burgeoning coffee chain with 35 locations across India, specializing in a variety of coffee and frozen refreshments. Despite a strong revenue stream of $20 million, the chain faced operational challenges due to stock-outs and overstocking, impacting customer satisfaction and sales.

The coffee chain encountered several pressing issues:

Frequent Stock-Outs: Fixed 30-day restocking cycles were inadequate for meeting fluctuating customer demands, leading to frequent stock-outs.

Customer Dissatisfaction: The unavailability of popular items led to a decline in customer satisfaction as patrons left the cafes without making purchases.

Inefficient Inventory Management: Traditional inventory management practices failed to address the seasonality and market trends affecting product demand.

Logistical Concerns: Managing the supply chain efficiently was a challenge due to the unpredictability of sales forecasting and customer behavior.

To tackle these challenges, Onegen AI implemented an Artificial Intelligence (AI) system focusing on Machine Learning (ML) algorithms for predictive analytics:

Advanced-Data Analysis: Utilized AI to analyze historical sales data, customer behavior, and market trends

Machine Learning Models for Sales Forecasting: Deployed ML models to predict future demand accurately, considering factors like seasonality and ongoing market trends.

Integration with Supply Chain Operations: Ensured the AI system was integrated seamlessly with existing supply chain operations to facilitate real-time decision-making.

Training AI Systems: Focused on training AI systems to adapt to the unique needs of each cafe location, enhancing their ability to manage stock levels dynamically.

AI forecasting reduced the time spent on inventory management by 50%, allowing staff to focus on customer service

Data Collection: Integrated with point-of-sale (POS) systems to collect real-time sales data.

AI and ML Deployment: Utilized cloud-based AI tools for analyzing data and generating predictive insights.

User Interface: Developed a user-friendly dashboard that allowed staff to access insights and make informed decisions regarding inventory needs.

Automated Ordering System: Implemented an automated system for placing orders based on AI recommendations, reducing manual errors and improving response times to stock changes.

Reduced excess inventory and minimized wastage

• The AI system demonstrated a strong return on investment with a payback period of just six months due to increased sales and reduced operational costs.

• Yearly Financial Improvement: Following the implementation, the coffee chain saw a 10% increase in overall sales due to better stock availability and customer retention.

Timeline: The AI system was developed and fully integrated within a four-month period.

Training: Staff were trained on the new system to ensure smooth adoption and operation.

System Integration: The AI solution was seamlessly integrated with the client’s existing technology infrastructure, requiring minimal downtime and disruption.

The implementation of AI in inventory management transformed the operational efficiency of the coffee chain. By leveraging Machine Learning for sales forecasting and integrating these insights into the supply chain, the company minimized stock-outs, enhanced customer satisfaction, and achieved its business goals. This case study underscores the transformative potential of AI and Machine Learning in optimizing retail operations, particularly in dynamically changing industries like food and beverage.

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San Jose, CA