Learn how the world’s largest pizzeria chain partnered with Nihilent to deploy Resense, an AI/ML-based demand forecasting and inventory planning platform that automated ingredient indenting across 1,675 stores and improved availability, efficiency, and customer experience.
Service
AI/ML Demand Forecasting and Inventory Planning
Vertical
Retail (Quick Service Restaurant)
Region
India
Tech Stack
Resense, SAP, POS, Cloud Data Pipelines, ML forecasting engine, Inventory Planning Dashboards
The client operates a large network of stores across India in malls, highways, and high streets, serving freshly prepared menu items with ingredients that have short shelf lives. To maintain service quality and profitability, each store must plan daily ingredient requirements carefully to maximize availability while minimizing wastage.
01
Manual ingredient indenting consumed 8-10 hours per week for store managers and planners.
02
Demand was influenced by holidays, festivals, local events, and sports, making manual planning inaccurate.
03
Frequent stockouts, wastage, and inter-store transfers increased operating cost and affected customer experience.
04
The business needed an automated way to forecast demand and generate optimal store-to-warehouse indents.
Nihilent deployed Resense across world's largest pizza store supply chain network on the cloud, integrating data from SAP, POS, and other enterprise sources.
Daily Demand Forecasting
Resense predicts ingredient demand for each store and SKU using a library of ML algorithms.
Usable Inventory Forecasting
The platform estimates usable inventory by factoring in ingredient shelf life.
Safety Stock Optimization
Resense calculates safety stock based on demand drivers and replenishment cycles.
Automated Indent Generation
The platform computes indent quantities and pushes them into the system for approval and execution.
End-to-End Inventory Visibility
Dashboards track stock availability, stockouts, wastage, and inter-store transfers at store level.
Continuous Learning
Prediction models improve over time by learning from actual consumption and forecast deviations.
now use Resense for automated ingredient forecasting and indenting.
per week have been freed up for store managers to focus more on customer experience.
in daily forecast accuracy has strengthened ingredient planning.
in stockouts has improved stock availability and supported sales.
in inter-store stock transfers has lowered unplanned operational activity.
was the time to realize ROI after deployment.
For a fast-moving food service business, demand forecasting must respond to multiple changing variables while protecting both freshness and availability. By implementing ReSense, world's largest pizza store supply chain network replaced manual planning with an intelligent forecasting and inventory platform that improves accuracy, reduces disruption, and gives store teams more time to focus on the customer experience.
