User Guide Overview
C3 AI Inventory Optimization is an AI-powered application that helps organizations maintain the right inventory levels across global supply chains. The application uses machine learning and optimization algorithms to reduce excess stock while maintaining enough safety stock to meet demand.
C3 AI Inventory Optimization ingests data from systems such as enterprise resource planning (ERP), material requirements planning (MRP), and supplier networks to create a unified view of inventory and supply chain information. The application models real-world uncertainties such as variable demand, fluctuating lead times, and supply disruptions in real time. It then optimizes reorder parameters dynamically to balance cost, risk, and service performance across all items and facilities.
Using C3 AI Inventory Optimization, you can:
- Receive dynamic recommendations to optimize reorder parameters by item and facility.
- Visualize key inventory metrics to anticipate issues and analyze root causes.
- Track realized savings and potential value with detailed cost and performance dashboards.
- Benchmark KPIs across items, locations, and suppliers to identify improvement opportunities.
- Run scenario analyses to understand the impact of parameter updates before you apply them.
- Scale optimization globally, managing millions of items across large facility networks.
Traditional MRP systems typically calculate reorder quantities based on static inputs like historical demand and lead time averages. They cannot model uncertainty or react to near real-time data changes, which often leads to conservative inventory policies and unnecessary buffer stock.
C3 AI Inventory Optimization applies AI and probabilistic modeling to refine parameters such as safety stock, reorder point, and maximum stock levels. It enables supply chain teams to make proactive, data-driven decisions that minimize holding and shipping costs while maintaining high service levels.
The application also supports stock coordination across suppliers, production sites, and distribution centers. AI-based assortment optimization ensures the right products are stocked at the right locations.
This guide explains how to use each area of the application.
Recommendation System
The Recommendation System presents AI-generated reorder recommendations and an evidence package for each suggestion.
You can:
- Review recommendations and assess potential cost or service-level impact. See more in Recommendations .
- Re-evaluate previous decisions. See more in Reset Recommendations.
- Temporarily delay actions. See more in Snooze Recommendations.
- Override recommendations with custom parameters.
Perform all actions individually or in bulk directly from the Recommendations grid.
Inventory and Item Detail
The Inventory page provides a searchable overview of items and facilities, showing stock positions, consumption classifications, and recommendation status.
From the Inventory grid, you can open the Item Detail page to explore metrics such as demand uncertainty, fill rate, and model confidence. Tabs include:
- Historical and projected inventory trends.
- Model validation and confidence analysis.
- Planning parameters used by the optimization models.
- Comments for collaboration and audit tracking.
Item Groups
The Item Group and Item Group Detail pages allow you to create and manage custom collections of related items. These groups can represent product families, supplier portfolios, or any other grouping your business has defined.
Within these views, you can:
- Analyze key metrics and distribution patterns.
- View open recommendations across grouped items.
- Track actions and leave comments for shared visibility.
Reporting and Configuration
The Reporting page enables you to export accepted and overridden recommendations, generate KPI reports, and review historical parameter configurations. Filter reports by replenishment method, planning parameter, or supplier.
The Configuration allows you to adjust configurable parameters for model calculations. You can define planning parameters, set auto-accept and auto-reject thresholds, and manage APICS-related settings.
Model monitoring
The Model Ops page provides visibility into the machine learning models that drive inventory recommendations. It displays model validation scores, performance metrics, and historical run data to maintain accuracy and trust in model outputs.
Executive Dashboard
Executive Dashboard provides a consolidated view of supply chain performance. It highlights key metrics such as realized savings, recommendation activity, and service-level performance across facilities and product categories.
Executive Dashboard Usage describes how to interpret these metrics and monitor progress toward business goals.
Common workflows
This guide also provides step-by-step workflows for typical tasks, including:
- Calculate Safety Stock with APICS
- Calculate Fill Rate
- Monitor Models
- Group Items
- Open Recommendations
- Create a Report