What challenges did Cainz face before AI implementation?

Accurate demand forecasting was challenging due to diverse product categories and varying sales patterns, resulting in inefficient inventory management. Preprocessing sales data for 209 stores was time-consuming and sequential processing would not scale as stores increased. Existing forecasting methods lacked sophistication and scalability needed for multi-store, multi-category retail demand prediction.

What was the impact of the AI solution?

Preprocessing time for sales data reduced to 50 minutes regardless of number of stores, enabling scalable AI forecasting. Demand forecasting accuracy improved, aiding better inventory control and supply chain planning. Enabled deployment of an AI solution across 209 stores with plans for further expansion. Provided a foundation for optimizing broader supply chain and product-planning operations in retail.

Source

GCPProductionEvidence: Medium65/100

Cainz AI-powered Demand Forecasting

Use case typeDemand forecastingUpdated Jun 13, 2026

Cainz, a leading Japanese home improvement retailer, implemented an AI-powered demand forecasting solution using Google Cloud Vertex AI Forecast and Cloud Run jobs to improve accuracy and reduce preprocessing time across 209 stores. The solution uses multi-horizon prediction models and explainable AI features from Vertex AI Forecast to enhance forecast precision and transparency. By employing parallel Cloud Run jobs for data preprocessing, Cainz reduced preprocessing time to a consistent 50 minutes regardless of store count, significantly improving processing scalability. Google Cloud Tech Acceleration Program (TAP) provided engineering support that helped design and refine the scalable forecasting architecture twice. The improved AI forecasting enables better inventory planning, stock replenishment, and product-demand management across Cainz's large retail network.

Organization: Cainz
Industry: Retail
Location: Japan
Published: June 2024

Reported outcomes

50 minutes

timeTime & speed

Strategic outcomes

Scale & capacityEnabled scalable multi-store forecastingBetter decisions & insightImproved demand forecasting accuracyNew product / capabilityDeployed explainable AI forecastingNew product / capabilityAutomated stock replenishment optimization

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 50 minutes decrease

Google Cloud Customer StoriesJun 1, 2024Customer storyInferred claimMedium evidence strength

Preprocessing time for sales data reduced to 50 minutes regardless of number of stores, enabling scalable AI forecasting.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Cainz
Provider: GCP
Maturity: Production
Linked source: Google Cloud Customer Stories

Deployed weekly training and feedback cycles into Cainz’s core retail systems to refine forecasts and optimize stock replenishment automatically

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 2 of 2

1Demand Forecasting
2Inventory Optimization

Accurate demand forecasting was challenging due to diverse product categories and varying sales patterns, resulting in inefficient inventory management.
Preprocessing sales data for 209 stores was time-consuming and sequential processing would not scale as stores increased.
Existing forecasting methods lacked sophistication and scalability needed for multi-store, multi-category retail demand prediction.

Built an AI-powered demand forecasting model leveraging Vertex AI Forecast for advanced multi-horizon predictions and explainable AI.
Implemented parallel data preprocessing using Cloud Run jobs, drastically reducing time and accommodating data volume growth from expanding stores.
Received architecture design and technical support from Google Cloud Tech Acceleration Program (TAP) for scalability and model improvements.
Deployed weekly training and feedback cycles into Cainz’s core retail systems to refine forecasts and optimize stock replenishment automatically.

Technologies

Vertex AI Forecast Cloud Run jobs BigQuery Google Cloud Tech Acceleration Program (TAP)

Preprocessing time for sales data reduced to 50 minutes regardless of number of stores, enabling scalable AI forecasting.
Demand forecasting accuracy improved, aiding better inventory control and supply chain planning.
Enabled deployment of an AI solution across 209 stores with plans for further expansion.
Provided a foundation for optimizing broader supply chain and product-planning operations in retail.

Architecture

The architecture leverages Vertex AI Forecast for demand prediction and Cloud Run jobs to parallelize preprocessing data pipelines, supported by ongoing Google Cloud Tech Acceleration Program (TAP) consulting. Forecasts are integrated with core retail systems for inventory optimization.

Sources & evidence1

Evidence: Medium65/100Evidence strength