Use case type

Inventory planning

AI that forecasts demand and recommends stock levels, replenishment timing, and allocation. It helps organizations avoid shortages, reduce excess inventory, and improve supply planning.

Use cases

4

Examples

4

Industries

3

Timeline

4 mo

Data updated 1 day ago

Adoption over time

Documented cases per month

By case publish month · completed months only

3 cases documented across 37 months (Jul 23 – Jul 26), peaking at 1 in April 2025.

1 earlier case before Jul 23 not shown

Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.

Company examples

Use cases of this type

4 shown from 4 use cases

GCPMay 25, 2026

Orica case study | Google Cloud

Orica modernized its SAP and analytics environment on Google Cloud to improve performance, resilience, and data-driven planning across its global operations.The company migrated SAP workloads with Google Cloud Professional Services Organization support, used BigQuery and Cortex Framework for data transformation, and used Vertex AI for proprietary machine-learning-based demand planning and sales forecasting.Orica reported faster SAP response times, shorter backup windows, and improved monthly planning and forecast accuracy.

OricaEnergy & Utilities
MicrosoftMay 1, 2025

Walmart innovates holiday inventory with predictive AI

Walmart deployed AI-powered predictive analytics for its inventory systems, using historical data and trends like customer demographics to improve product placement and supply chain logistics. This AI system streamlined inventory processes and reduced inefficiencies during the holiday season.

WalmartRetail
MicrosoftApr 25, 2025

Retalon Enhances Retail Processes with Dynamics 365 and Analytics

Retalon's Retail Inventory Optimization 365 uses Dynamics 365 alongside award-winning predictive analytics to revolutionize inventory handling for multi-channel retailers. Key capabilities include demand forecasting, seasonal planning, and returns management, which allow businesses to optimize supply chain operations, reduce inventory costs, and balance assortments effectively. With built-in integration into the Dynamics 365 ecosystem, it provides a unified operational framework.

RetalonRetail
MicrosoftFeb 7, 2023

Nestle boosts sales with Azure-based AI by Deloitte

Nestle, in partnership with Deloitte, revolutionized its sales operations by utilizing Microsoft Azure to establish a modern, AI-driven analytics and recommendations platform. This infrastructure includes a Sales Recommendation Engine that is actively used by over 1,500 sales representatives weekly, enhancing sales efficiency and accuracy. The integration also unified data from previously siloed systems, providing actionable insights for better decision-making.

NestleManufacturing

Common questions

Inventory planning at a glance

How many inventory planning use cases are documented?
The AI Use Case Hub documents 4 real inventory planning deployments across 3 industries, with 4 detailed company examples you can browse.
Which industries adopt inventory planning the most?
Inventory planning is most common in Retail (50%), Manufacturing (25%) and Energy & Utilities (25%).
Which countries lead in inventory planning?
United States leads documented inventory planning deployments, followed by Australia.
What technologies are used for inventory planning?
Teams most often build inventory planning with Azure, Dynamics 365 and Azure AI.
What AI capabilities power inventory planning?
Across the documented deployments, the most common capability patterns are Sustainability (25%).
What results do companies report from inventory planning?
Across the 4 deployments reporting outcomes, companies most often cite better decisions & insight (100%), new product / capability (75%) and speed & agility (50%). Where impact is quantified, the strongest evidence is in time & speed: a median −4.5% across 1 reported metric.