A leading omnichannel retail chain faced challenges in forecasting demand across 800+ stores and multiple digital channels. Despite historical sales data and ERP-based forecasting models, the client struggled with stockouts, overstocking, and slow reaction to market trends, especially during promotions, holidays, and new product launches. Static, rule-based models failed to account for unstructured signals like social media buzz, weather conditions, and regional events, leading to inaccurate demand planning. The client needed a smarter forecasting engine to read the market in real-time and adapt faster than traditional models.  

The Challenge: Forecasting in a Volatile Retail Environment

The retailer faced significant limitations in its demand planning and inventory forecasting processes, resulting in both lost sales and excess stock.

Key pain points included:

• Limited External Signal Integration: Forecasts did not account for weather, promotions, holidays, or competitor activity.

• Poor Short-Term & SKU-Level Visibility: Lack of granular insights hindered quick decision-making.

• Missed Sales During Demand Spikes: Frequent stockouts occurred during high-demand periods, leading to lost revenue.

• Overstock of Seasonal Items: Excess inventory resulted in markdowns and profit erosion.

• Inflexible Forecasting Models: Existing models struggled to adapt across regions, store formats, and changing market conditions.

The client required a dynamic, data-driven demand sensing solution that could provide accurate, real-time forecasts and actionable recommendations for inventory optimization.

 

The Amantra Solution: AI-Powered Demand Forecasting Model 

Amantra deployed an AI-powered Multi-Agent Demand Sensing Model designed to process and correlate both structured and unstructured data, delivering highly accurate, SKU-level demand forecasts across stores and regions.

Key Capabilities:

• Historical & Channel Data Analysis: Pulled and analyzed sales, promotions, and distributor data to identify trends.

• Incorporation of External Signals: Integrated unstructured data such as news, weather, events, and social media activity to capture demand drivers.

• Regional Buying Pattern Analysis: Considered local customer preferences and behavior to refine predictions.

• SKU-Store-Week Level Forecasting: Adaptive learning models predicted demand at granular levels, adjusting dynamically to changing patterns.

• Prescriptive Recommendations: Provided category planners with actionable insights for inventory planning, promotions, and replenishment.

This intelligent framework enabled data-driven decision-making, reducing stockouts, overstocking, and lost sales while improving operational efficiency across retail chains.

Solution Highlights

• Multi-Source Data Fusion (ERP, social, weather, events)
• Semantic Interpretation of Demand Influencers Using LLMs
• SKU & Channel-Level Forecasting granularity
• Self-Updating Models with feedback loops
• Forecasting-as-a-Service APIs integrated into the client’s retail planning system

Business Outcomes

The implementation of Amantra’s Multi-Agent Demand Sensing solution delivered significant improvements:

• Forecast Accuracy: Increased from approximately 72% to 94%, enabling more reliable inventory planning.

• Stockouts: Reduced by 60%, minimizing lost sales and improving customer satisfaction.

• Overstock Inventory: Cut by 45%, lowering holding costs and excess stock risks.

• Planning Cycle Time: Reduced from weeks to real-time, allowing agile decision-making.

• Markdown Losses: Decreased by 35%, protecting margins on seasonal and promotional items.

Client Testimonial

“With Amantra LLM-based forecasting, we’ve gained the agility to predict demand spikes even before they happen. Our planners now act proactively, not reactively.” — Chief Supply Chain Officer, National Retail Chain

Ready to Predict the Future of Demand?

Discover how Amantra LLM-powered forecasting agents can drive inventory accuracy, reduce losses, and make faster decisions in your retail enterprise. Request a Forecasting Demo