IMPROVED EFFICIENCY, LOWER COSTS, DATA INTEGRITY (DOE)

doe-berkeley-labs

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
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