Managing Performance Based Logistics Success for a Multibillion-Dollar Aeronautics Company

Global sustainment – PBL portfolio management

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This aeronautics company, specializing in products of military aircraft, wanted to improve its current performance based logistics (PBL) strategies. Long–term objectives of this ongoing project included:

  1. Change from issue management to risk mitigation  
  2. Reduce analysts’ non value-added time
  3. Better understand what’s driving PBL performance
  4. Improve strategic decision making

The Challenge

Key PBL challenges included:

  1. Lack of common and consistent PBL metrics across the entire portfolio of contracts
  2. Lack of understanding of the correlation between contract performance and related cost
  3. Limited visibility into drivers of poor performance
  4. Profitability buried in the programs
  5. Inefficient use of analyst time

How We Helped

To tackle the PBL challenges, we deployed an ongoing approach. The process included five key steps: 

  1. Isolate key performance indicators (KPIs) –  Determine which KPIs are critical to understanding PBL financial and operational performance. Define a common set of KPIs across the programs.
  2. Define correlation between operational and financial metrics – Determine which integrated logistics support elements contribute to supporting the overall contractual metrics (e.g., mission capable status) and how they are weighted.
  3. Build a financial hierarchy to capture the data – Provide a financial and operational hierarchy for each PBL program such that complete drill-down to the event level can be achieved when analyzing PBL performance drivers. This involves extending the current financial work breakdown structure from three to seven levels.
  4. Provide portal access to the results – Design and implement a presentation layer, with appropriate security, to provide access to the results and drill-down capability to various levels in the organization.
  5. Change the reporting philosophy of the programs – Today, the programs use a combination of earned value model and data extracts to create their combined metrics. The end users will need to be coached to understand the need to shift their efforts from data mining and manipulation to have more focus on analysis.


Instead of analysts using non-value added time for mining data, they are analyzing the data and developing creative solutions to business problems.  The new paradigm is more effective because:

  • Valuable analyst time is freed up, allowing for companies to understand key financial correlations
  • Analysts analyze the results looking for the best risk mitigation approach
  • A single set of metrics and KPIs are implemented across the programs, which includes an integrated view with drill-down to identify source of risk
  • Data is trend-based and indicates potential risk