Aravo Solutions Case Studies Democratizing Data for Supply Chain Optimization at Johnson & Johnson

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	Democratizing Data for Supply Chain Optimization at Johnson & Johnson Aravo Solutions

Democratizing Data for Supply Chain Optimization at Johnson & Johnson

Aravo Solutions

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Consumer Goods Retail
Applicable Functions	Logistics & Transportation Procurement
Use Cases	Inventory Management Supply Chain Visibility
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	Johnson & Johnson, a global consumer goods and pharmaceutical provider, faced significant challenges in managing its supply chain data. The company's growth through acquisitions led to a fragmented data system with disparate priorities and unique configurations. Data was largely being extracted and analyzed manually, limiting opportunities for speed and scalability. The disconnection was negatively impacting customer service and impeding strategic decision-making. The company also faced the challenge of optimizing inventory management and costs on a global scale, which required accurate and abundant data. The inability to understand and control spend and pricing could lead to limited identification of future strategic decisions and initiatives, potentially missing the opportunity to achieve $6MM in upside. Read More
About Customer	Johnson & Johnson is a cornerstone, global consumer goods and pharmaceutical provider that has been serving businesses, patients, doctors, and people around the world for more than 150 years. The company offers a wide range of products, from life-sustaining medical devices to vaccines, over-the-counter and prescription medications, and the tools and resources used to create them. Johnson & Johnson's business strategy is centered on ensuring that items get delivered on time, to the right place, and are sold at a fair price, so that consumers can access and use their products effectively. Read More
Solution	Johnson & Johnson embarked on a journey to democratize its data across the entire organization. The company migrated from Hadoop to a unified approach with Databricks Lakehouse Platform on the Azure cloud. The goal was to create a common data layer that would drive higher performance, allow for more versatility, improve decision making, bring scalability to engineering and supply chain operations, and make it easy to modify queries and insights efficiently in real-time. The company replaced 35+ global data sources with a single view into data that could then be readily available for data scientists, engineers, analysts, and applications. The new data infrastructure can handle an SLA of around 15 minutes for data delivery and accessibility. The Lakehouse approach to data management provided a common data layer to feed a myriad of data pipelines that can scale alongside business needs. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Consumer Goods

Retail

Applicable Functions

Logistics & Transportation

Procurement

Use Cases

Inventory Management

Supply Chain Visibility

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

Johnson & Johnson, a global consumer goods and pharmaceutical provider, faced significant challenges in managing its supply chain data. The company's growth through acquisitions led to a fragmented data system with disparate priorities and unique configurations. Data was largely being extracted and analyzed manually, limiting opportunities for speed and scalability. The disconnection was negatively impacting customer service and impeding strategic decision-making. The company also faced the challenge of optimizing inventory management and costs on a global scale, which required accurate and abundant data. The inability to understand and control spend and pricing could lead to limited identification of future strategic decisions and initiatives, potentially missing the opportunity to achieve $6MM in upside.

About Customer

Johnson & Johnson is a cornerstone, global consumer goods and pharmaceutical provider that has been serving businesses, patients, doctors, and people around the world for more than 150 years. The company offers a wide range of products, from life-sustaining medical devices to vaccines, over-the-counter and prescription medications, and the tools and resources used to create them. Johnson & Johnson's business strategy is centered on ensuring that items get delivered on time, to the right place, and are sold at a fair price, so that consumers can access and use their products effectively.

Solution

Johnson & Johnson embarked on a journey to democratize its data across the entire organization. The company migrated from Hadoop to a unified approach with Databricks Lakehouse Platform on the Azure cloud. The goal was to create a common data layer that would drive higher performance, allow for more versatility, improve decision making, bring scalability to engineering and supply chain operations, and make it easy to modify queries and insights efficiently in real-time. The company replaced 35+ global data sources with a single view into data that could then be readily available for data scientists, engineers, analysts, and applications. The new data infrastructure can handle an SLA of around 15 minutes for data delivery and accessibility. The Lakehouse approach to data management provided a common data layer to feed a myriad of data pipelines that can scale alongside business needs.

Impact #1	The migration to the Databricks Lakehouse Platform has significantly streamlined Johnson & Johnson's data flow. Data teams now use Photon, the next-generation query engine on Databricks, to enable extremely fast query performance at a low cost for SQL workloads. All data pipelines now feed through Delta Lake, which helps to simplify data transformation in Photon. Databricks SQL provides high-performance data warehousing capabilities, feeding data through optimized connectors to various applications and business intelligence (BI) tools for analysts and scientists to consume in near real-time. The company can now track patient therapy products throughout the supply chain, ensuring cost-efficient distribution of drugs around the world. The new system has greatly simplified the operational data infrastructure in the Azure cloud, enabling the company to consistently meet its SLAs, reduce overall costs, and better serve its customers and community.

Impact #1

The migration to the Databricks Lakehouse Platform has significantly streamlined Johnson & Johnson's data flow. Data teams now use Photon, the next-generation query engine on Databricks, to enable extremely fast query performance at a low cost for SQL workloads. All data pipelines now feed through Delta Lake, which helps to simplify data transformation in Photon. Databricks SQL provides high-performance data warehousing capabilities, feeding data through optimized connectors to various applications and business intelligence (BI) tools for analysts and scientists to consume in near real-time. The company can now track patient therapy products throughout the supply chain, ensuring cost-efficient distribution of drugs around the world. The new system has greatly simplified the operational data infrastructure in the Azure cloud, enabling the company to consistently meet its SLAs, reduce overall costs, and better serve its customers and community.

Benefit #1	Achieved a 45-50% reduction in cost for data engineering workloads
Benefit #2	Dropped data delivery lag from around 24 hours to under ten minutes
Benefit #3	Expected to further reduce time and money spent on the demand planning process

Benefit #1

Achieved a 45-50% reduction in cost for data engineering workloads

Benefit #2

Dropped data delivery lag from around 24 hours to under ten minutes

Benefit #3

Expected to further reduce time and money spent on the demand planning process

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Overview

Democratizing Data for Supply Chain Optimization at Johnson & Johnson

Operational Impact

Quantitative Benefit