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30% More Accurate Forecasts, Lower OOS Rates With Planning in a Box: A Fashion Powerhouse’s AI Journey

A prominent luxury fashion brand renowned for its ready-to-wear clothing, shoes, handbags, accessories, and beauty products, serves consumers seeking high-end fashion and lifestyle items.

Solutions Implemented

The core objective of the data analytics initiative was to reduce stockouts using advanced analytics. We unified data from SAP CAR and SAP S/4 HANA into Google BigQuery, creating a central repository for client’s inventory and sales data. This unified data ecosystem was essential for supporting the sophisticated analytics required to understand and predict customer demand patterns.

Our approach leveraged several advanced analytics methodologies to extract actionable insights from the data. Key techniques included:

  • Predictive Modeling (Vertex AI): Machine learning algorithms analyzed historical sales data (BigQuery) to forecast demand.
  • Machine Learning for Stockout Prediction (Vertex AI): A tailored model predicted stockout dates for SKU-Store combinations, using algorithms to process complex data and estimate future stock levels.
  • Historical Data Analysis (BigQuery): Analyzing past sales data uncovered trends to predict future demand, informing inventory management strategies.

Key Results

This luxury fashion brand’s journey with Planning in a Box is a powerful testament to how smart technology can solve traditional problems. Where developing custom solutions often takes months of R&D, this brand was able to address its forecasting and inventory challenges in just four weeks using Planning in a Box. With Planning in a Box, they are now evolved to bring in more data sources, analyze vast datasets in real-time, and address shifiting consumer demands with confidence. 

By embracing advanced analytics and integrating vast datasets, the brand significantly reduced stockouts, streamlined its inventory processes, and improved decision-making. This not only saved time and resources but also reinforced the brand’s commitment to satisfying the high expectations of its customers. As a result, the brand is better positioned than ever to meet demand efficiently and maintain its esteemed place in the luxury market.

Where developing custom solutions often takes months of R&D, this brand was able to address its forecasting and inventory challenges in just four weeks using Planning in a Box. This rapid adaptation not only streamlined their operations but also significantly boosted their ability to meet consumer demands efficiently, reinforcing their position in the competitive luxury market.


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Industry Retail

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  • Persistent Stockouts: A Barrier to Customer Satisfaction and Sales Retention
  • Limited Inventory Visibility: The brand struggles with tracking inventory in real-time across various locations. This issue often results in overstock in some stores and shortages in others, preventing effective stock redistribution based on real-time demand.
  • Inaccurate Demand Forecasting: Difficulty in accurately predicting demand for new and existing products leads to popular items selling out quickly. In contrast, less popular items accumulate, tying up capital that could be better used elsewhere.
  • Fragmented Data Landscape: Crucial data on customer preferences, sales trends, and inventory levels are siloed within different departments. This disjointed information flow hinders cohesive decision-making and strategic planning, ultimately affecting the ability to effectively meet market demand.


  • Streamlined Data Integration with Planning in a Box: Leveraging our proprietary platform, Planning in a Box, we expedited the process of integrating, cleaning, and organizing vast data sources. This approach reduced the time needed to prepare data for analysis—from several months to just two weeks—ensuring that insights could be acted upon more rapidly.
  • Enhanced Predictive Capabilities with Custom Models: Planning in a Box provided the luxury brand with immediate access to predictive models and stockout predictions tailored for their specific needs. This customization sped up the solution deployment, a process that traditionally takes much longer when developing models from scratch.
  • Rapid Improvement in Forecast Accuracy: Planning in a Box enabled us to analyze large datasets and deploy AI models quickly, improving forecast accuracy by 30-40% in just four weeks.
  • Predictive Stockout Prevention: The machine learning model successfully predicted stockout dates for SKU-store combinations over an 8-week horizon, enabling proactive inventory management.

Products Used

  • Google Cloud Cortex Framework
  • Cloud Storage
  • Cloud Dataflow
  • Dataproc
  • BigQuery
  • Vertex AI
  • Cloud Functions
  • Cloud Build
  • Artifact Registry
  • Source Repositories
  • Secret Manager
  • Looker on Google Cloud
  • Looker Embedded Analytics
  • Cloud Identity and Access Management (IAM)
  • Cloud Key Management Service (KMS)
  • Cloud Data Catalog