Demand forecasting

SPAR ICS, the IT arm of SPAR Austria Group, implemented advanced Microsoft technologies to enhance the retail shopping experience and optimize its supply chain.A scalable and secure SPAR app was developed using Microsoft Cloud for Retail, ensuring a seamless shopping experience and robust performance during peak times.The company created an AI-enabled demand forecasting system using Azure Synapse Analytics, achieving 90 percent inventory prediction accuracy and continued excellence in performance.SPAR ICS is migrating its demand forecasting solution to Microsoft Fabric to leverage unified analytics and further boost efficiency.The success of these solutions is fueling additional plans to deploy AI productivity tools, such as Copilot for Microsoft 365, for employee enhancement.The optimized supply chain enables better inventory management, more accurate demand planning, and robust support for the organization's digital transformation initiatives.These technology upgrades position SPAR ICS as a modern retail leader in Austria with a strong foundation for future AI and analytics-driven innovations.

SPAR ICSRetail

Niveus Solutions, a Google Cloud Premier Partner, deployed Google Cloud Vertex AI and generative AI models (PaLM, Gemini) to address retail challenges in Singapore and Malaysia.The solution enabled hyper-personalized customer experiences, predictive analytics for inventory management, and automated marketing content generation.Uses of Gen AI include automated product tagging, conversational agents, and marketing content automation across digital and physical retail channels.

Unnamed retail clients in Singapore and MalaysiaRetail

LTIMindtree's innovative Demand Forecasting Accelerator leverages Microsoft's Azure platform to enable Consumer Packaged Goods (CPG) businesses to predict product demand at the Distributor SKU level. By automating the framework with advanced machine learning and AI, the solution identifies optimal time-series models, improves inventory planning, manages safety stocks, and enhances supply chain coordination. This scalable and accurate forecasting solution empowers businesses to tailor demand prediction across product categories.

LTIMindtreeConsumer & Food

Tesco, a leading UK grocery retailer, has integrated Microsoft Azure AI with in-house and third-party AI systems to transform multiple facets of its business. In supply chain management, AI models forecast demand by analyzing sales data, seasonality, and external factors—enabling optimal stock levels, reduced stockouts, and faster product replenishment. For customer engagement, Tesco analyzes Clubcard loyalty data to deliver deeply personalized digital and in-store promotions, improving both engagement and sales. AI also powers self-service checkouts with computer vision, cutting down errors and wait times. Together, these technologies optimize inventory, streamline operations, and personalize customer experience, driving cost savings and boosting satisfaction across Tesco’s network of stores.

TescoRetail

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.

CainzRetail

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.

Super-PharmRetail

Leading German retailer Tchibo built an automated forecasting service on Google Cloud to predict customer demand for its online sales channel and support warehouse replenishment.The solution helps Tchibo reduce overstock and handling effort while improving product availability and reducing stock-outs.

TchiboRetail

paiqo GmbH developed the AI.S² Demand Forecasting Solution leveraging Microsoft Azure. The platform uses AI and machine learning to provide precise sales forecasts for production and supply chain planning. It automates data importing, enrichment with external factors (e.g., weather, market conditions), analysis, and prediction, reducing dependence on data-scientists. With streamlined integration of data from ERP, CRM, and other business platforms, the tool helps businesses mitigate risks of over- and understocking and optimizes workflows throughout the value chain. Databricks is used for advanced analytics. Available in Austria, Germany, and Switzerland, the platform demonstrates value for companies wanting to shift from traditional, slow, or inaccurate planning to modern, data-driven approaches.

paiqo GmbHManufacturing

o9 Solutions integrates Google Cloud Vertex AI Forecast within its Digital Brain platform to improve demand forecasting accuracy for Consumer Packaged Goods (CPG) and retail companies.The solution leverages Vertex AI Forecast's multivariate data analysis and hierarchical modeling to generate accurate, scenario-based demand predictions using both internal and external data, including for cold start products without historical data.This integration targets key challenges of reducing lost sales from stock-outs, optimizing inventory levels, and improving supply chain efficiency to directly boost financial performance and brand loyalty.

o9 SolutionsRetail

Ernst & Young (EY) developed an AI-powered inventory and demand forecasting platform tailored for retail clients, leveraging Microsoft Cloud for Retail and Microsoft Azure. The solution enables trusted data pipelines for standardizing, modeling, and scaling customer and inventory data with predictive analytics and machine learning.Multiple advanced forecasting models, including eight different techniques, are deployed to generate SKU- and location-level demand predictions, accommodating both repeat and new orders.Customer-specific histories and demographic data are incorporated to optimize stock levels and better anticipate demand variability, supporting scenario analysis and strategy simulation before real-world deployment.The solution uses a simulation engine for inventory improvement, reduces supply gaps, and helps prevent overstock or lost sales. Machine learning models are distributed across multiple clusters for scalable computation, supporting hundreds of thousands of SKUs and locations.By reducing human bias through automation and including external/internal factors, the platform streamlines inventory management, cuts unnecessary stock, increases forecast accuracy, and enhances overall customer experience by ensuring the right product availability.The approach is flexible and can be specialized to client needs with configurable add-ons, seamlessly integrating into existing inventory management workflows and systems.

OTTO implemented Google Cloud AI tools including Vertex AI and BigQuery to improve demand forecasting accuracy dramatically for better inventory management and customer satisfaction.Using the Time-series Dense Encoder (TiDE) model on Vertex AI, OTTO analyzes complex multivariate time-series data to capture both short- and long-term demand dependencies.Google Kubernetes Engine (GKE) is used to manage large-scale model deployment and enable rapid model experimentation and adjustments based on real-time data.OTTO benefits from up to 30% improvement in forecasting accuracy which reduces inventory costs, minimizes waste, and boosts revenues.The AI-driven forecasting capability allows OTTO to optimize stock levels and pricing strategies, resulting in better product availability and satisfaction.

OTTORetail