Environmental Data Flow Six Sigma Process Improvement Savings Overview
Company Size
1,000+
Region
- America
Country
- United States
Product
- Cloud Computing
- Google Maps
- Automatic Electronic Validation
Tech Stack
- Cloud Computing
- Data Validation
- Google Maps Integration
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Process Analytics
- Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
- National Security & Defense
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
The customer in this case study is the Los Alamos National Laboratory (LANL), a multidisciplinary research institution engaged in strategic science on behalf of national security. LANL enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.
The Challenge
The Environmental Data Flow Six Sigma improvement project was initiated in September 2009, driven by a cost-benefit analysis on data validation conducted earlier that year. The project aimed to improve LANL’s environmental data processing following receipt from the analytical laboratories. The project identified thirty-three process improvements, broken into seven subgroups. However, six of the improvements were never implemented, and two of the seven improvement subgroups did not lead to any cost savings but did lead to more accurate sample planning and increased transparency into the system.
The Solution
The solution involved implementing a series of process improvements. These included implementing cloud computing, restructuring the data stewards’ jobs, eliminating redundant data reviews, implementing change control on the system, incorporating google maps, implementing automatic electronic validation (auto-validation) of the analytical data, and mapping the data process. The implementation of cloud-based computing was associated with sixteen individual improvements. The improvements reduced the time to deliver data to clients to 1 day (98% decrease) with an uncertainty of 1 day.
Operational Impact
Quantitative Benefit
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