Quick Overview
SPAR ICS partnered with Microsoft to develop AI-enabled demand forecasting and supply chain optimization tools, improving inventory accuracy, reducing unsold goods, and accelerating delivery times to stores.
SPAR ICS
Company Size
50-999 employees
Revenue Range
Part of SPAR Austria Group, €8.3B+ annual revenue (SPAR Austria)
Primary Challenge
Inefficient inventory forecasting leading to food waste and delayed deliveries
Key Metrics
\- 90%+ inventory prediction accuracy\- Reduced unsold goods to 1%\- Delivered fruit and vegetables to stores 3 days earlier\- Handled 8 million daily app requests with peak 400 requests/sec
The Problem
Legacy forecasting systems and manual ordering processes resulted in overstocking, food waste, and slower delivery cycles across 1,500+ stores.
The Solution
Created an AI-enabled demand forecasting system using Azure Synapse Analytics and Microsoft Fabric, integrating weather, marketing, seasonality, and sales data; developed a scalable, secure SPAR app for customers; automated supply chain and ordering processes.
Results
\- Inventory prediction accuracy improved to over 90%\- Unsold groceries reduced to 1%\- Faster delivery times, especially for perishables\- Enhanced shopping experience via a customer-facing app\- Scalable system handling millions of daily requests with high reliability
“With our new advanced data and analytics capabilities, we are able to accelerate time to market, proactively anticipate market demands and really drive innovation.”
Andreas Kranabitl, Managing Director, SPAR ICS
Details
Industry
Retail & E-commerce
Departments
Product Development & Innovation
Use Cases
Predictive Modeling
Tags
Predictive Modeling
NLP
Client Satisfaction
AI Tools Used
No items found.
Sources
https://www.microsoft.com/en/customers/story/1724583469377234521-spar-ics-retailer-cloud-for-retailhttps://www.vktr.com/ai-disruption/5-ai-case-studies-in-retail/https://spar-international.com/news/spar-austria-minimises-food-waste-through-digitalisation/
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