Improved efficiency, lower costs, data integrity (DOE)

doe-berkeley-labs

Project Info

Project Description

Objective:

The Department of Energy (DOE) was looking to transform and streamline businesses operations at the Lawrence Berkeley National Laboratory (LBNL)—an award-winning lab performing unclassified research across a wide range of scientific disciplines. Brillient was contracted to help assess LBNL’s current data quality, processes and training for their property management operations and develop improved processes.

Solution:

Brillient partnered with the LBNL to conduct a holistic performance assessment of processes, data quality, data integration, technology, training and people/organization. Based on a detailed gap analysis, the team developed thorough recommendations prioritized by impact and level of effort. The work performed included the following steps:

  • Carefully assessed the current state of LBNL property management operations
  • Defined the ideal future state for LBNL to aim for that ensured compliance and reduced risk
  • Executed a gap analysis to identify problem areas and opportunities for improvement
  • Assembled detailed recommendations based on the gap analysis
  • All of the above were done using quantified as well as qualified Key Performance Indicators (KPIs)
  • Developed training materials
  • Recommended changes to organizational structure
  • Documented roles and responsibilities
  • Performed data cleanup
  • Developed data mapping across different systems and developed a uniform data taxonomy
  • Researched and recommended advanced sensing technologies including budgetary estimates for implementation

Results:

Our work with LBNL delivered significant savings in time, level of effort and cost of doing business. The outcomes of our work included:

  • Revised and highly efficient business processes
  • Automation and semi-automation of previously manual process steps
  • Optimal organization with clearly defined roles and responsibilities
  • Training materials including quick “how to” guides
  • Automated detection of data quality issues
  • Time and cost savings with a ~21% reduction in annual spend
  • Blueprint for incorporating advanced sensing technologies leading to further automation and savings