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Material Forecasting on 650x More Data at a Global Sports Brand
Technology Category
- Analytics & Modeling - Big Data Analytics
- Infrastructure as a Service (IaaS) - Cloud Computing
- Infrastructure as a Service (IaaS) - Cloud Storage Services
Applicable Industries
- Apparel
- Retail
Applicable Functions
- Procurement
- Product Research & Development
Use Cases
- Demand Planning & Forecasting
- Inventory Management
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
The Challenge
The leading apparel and footwear brand faced challenges in fine-tuning its material forecasting based on consumer demand patterns. With an extensive network of factories servicing global stores, it was difficult to estimate the exact quantity and type of raw materials for different manufacturing locations. The existing BI architecture did not allow them to analyze more than 18 weeks of data. They were pulling source data from Amazon S3 to Snowflake, building aggregates on Azure Analysis Services (AAS), and then performing analysis on Excel. This led to multiple points of failure and each hop had an associated cost. They were hitting the limits of AAS in terms of processing that could be done and missed SLAs due to high data volumes during the holiday season. As data volumes rose, Excel reports would often freeze/crash.
About The Customer
The customer is a leading apparel and footwear brand with an extensive network of factories servicing global stores. They have a wide range of collections planned around seasons or times of the year. The brand wanted to fine-tune its material forecasting based on consumer demand patterns. They wanted to understand past patterns to project future demands. Granular details such as the amount sold by color, size, or style across countries or stores could further help improve the forecasting accuracy. However, their existing BI architecture did not allow them to analyze more than 18 weeks of data, which was a significant challenge.
The Solution
The brand wanted to migrate from AAS to eliminate the data volume limitations. They were looking for a solution that could work directly on S3 while delivering the same functionality as AAS. Kyvos helped them eliminate the inefficiencies in their BI architecture by building an OLAP layer directly on AWS. Advanced algorithms and cloud-native architecture helped build an OLAP cube on two years of data in an hour. They could deal with high-cardinality dimensions such as style or material and build aggregates as needed by the business. With Kyvos, there was no restriction on the amount of data that could be analyzed. They could plug in Excel directly into Kyvos and perform interactive analysis on the entire data without any latency. Since all aggregations were stored in the Kyvos layer, Excel queries became lightweight, and the reports and dashboards refreshed instantly.
Operational Impact
Quantitative Benefit
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