What challenges did Super-Pharm face before AI implementation?

Inaccurate demand forecasting and inefficient inventory management leading to revenue loss and poor customer experience. Legacy on-premise systems lacked capacity and agility for large data volumes and complex analytics. Need to modernize IT infrastructure for competitive advantage and recruitment appeal.

What was the impact of the AI solution?

Demand forecasting became 10x more efficient with accuracy improving to 90%. Improved inventory allocation for faster deliveries and better customer satisfaction. Enhanced ecommerce site user experience using AI-driven recommendations. Modernized IT infrastructure attracting new talent and improving operational efficiency.

Rank#2,172of 3,107

Source

GCP

Super-Pharm optimizes ecommerce demand forecasting and merchandising with Vertex AI on Google Cloud

Use case typeDemand forecastingUpdated Jun 13, 2026

Super-Pharm, Israel’s leading pharmacy and beauty retailer, faced challenges in demand forecasting and inventory management due to limitations of on-premise infrastructure. The company migrated to Google Cloud and used Vertex AI for machine learning-powered demand forecasting improving accuracy from 50% to 90%. They automated product categorization using Gemini AI to optimize the ecommerce marketplace website. Improvements include 10x efficiency in demand forecasting, better inventory allocation, enhanced ecommerce user experience, and modernization of IT infrastructure. Partner WideOps supported cloud migration and Intellerts helped with AI system design and implementation.

Organization: Super-Pharm
Industry: Retail
Location: Israel
Published: March 2024

Reported outcomes

10x

accuracyQuality & accuracy

+90%accuracy

Strategic outcomes

Better decisions & insightImproved demand forecasting accuracyCustomer experience & trustImproved inventory allocation for faster deliveriesCustomer experience & trustEnhanced ecommerce user experienceEmployee experienceModernized infrastructure to attract talent

Catalog median for quality & accuracy deployments: +90% across 282 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 4

1Demand Forecasting
2Inventory Optimization
3Ecommerce Personalization

Inaccurate demand forecasting and inefficient inventory management leading to revenue loss and poor customer experience.
Legacy on-premise systems lacked capacity and agility for large data volumes and complex analytics.
Need to modernize IT infrastructure for competitive advantage and recruitment appeal.

Migrated data and systems to Google Cloud.
Leveraged Vertex AI with AutoML for demand forecasting models, improving accuracy and speed.
Used BigQuery for data analytics and Gemini AI for automated product categorization on ecommerce platform.
Partnered with WideOps for cloud migration and Intellerts for technical AI expertise.

Technologies

Vertex AI BigQuery Gemini AI Google Cloud Platform

Demand forecasting became 10x more efficient with accuracy improving to 90%.
Improved inventory allocation for faster deliveries and better customer satisfaction.
Enhanced ecommerce site user experience using AI-driven recommendations.
Modernized IT infrastructure attracting new talent and improving operational efficiency.

Architecture

Uses Vertex AI AutoML for demand forecasting, BigQuery for analytics, Gemini AI for automated product categorization, and Google Cloud infrastructure supporting ecommerce and inventory.

Implementation partners2

Intellerts WideOps

Sources & evidence1

Groundedness: 4/5

Primary source

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Customer evidence