Democratizing Data for Supply Chain Optimization at Johnson & Johnson
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
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
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