Kyvos Insights Case Studies Material Forecasting on 650x More Data at a Global Sports Brand

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	Material Forecasting on 650x More Data at a Global Sports Brand Kyvos Insights

Material Forecasting on 650x More Data at a Global Sports Brand

Kyvos Insights

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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 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.

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.

Impact #1	Eliminated multiple points of failure
Impact #2	Reduced costs associated with multiple hops in their BI architecture
Impact #3	High scalability provided the ability to cater to future data growth

Impact #1

Eliminated multiple points of failure

Impact #2

Reduced costs associated with multiple hops in their BI architecture

Impact #3

High scalability provided the ability to cater to future data growth

Benefit #1	Ability to scale analytics from 18 weeks to 104 weeks
Benefit #2	Material forecasting to the lowest level of detail
Benefit #3	Ability to predict color-size level details

Benefit #1

Ability to scale analytics from 18 weeks to 104 weeks

Benefit #2

Material forecasting to the lowest level of detail

Benefit #3

Ability to predict color-size level details

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Overview

Material Forecasting on 650x More Data at a Global Sports Brand

Operational Impact

Quantitative Benefit