Portfolio Planning

    Government Agency Uses Predictive Portfolio Simulation to Reduce Project Approval Cycle Time and Improve Resource Capacity Planning

    Situation

    A government agency whose responsibility involves overseeing and approving very long cycle time (years) and high risk construction projects was struggling with the complexity of managing multiple projects of this magnitude at the same time.  They were facing resource constraints as well as pressure to shorten the approval time

    Objectives

    • Shorten the contract approval cycle time

    • Develop a simplified, yet accurate way, of analyzing long term resource requirements

    Results

    ProModel worked with the agency to develop a re-usable predictive project portfolio planning capability to help them achieve their objectives both now and in the future.  To date they have been able to achieve the following:

    • Understand more accurately their actual approval process and where the bottlenecks are

    • Test ‘What-If” scenarios around potential different courses of action without interrupting their live process

    • Improve the process for forecasting their resource requirements over the next 5-10 years

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    Process Improvement

     

    Lean Six Sigma Analysis for Army Depot Paint Shop Capacity and Labor Resources

    United States Army

    Situation

    One of the critical functions for which the Army repair depot is responsible is the Paint Shop reset of mechanical items through staging, steam clean, prep, blast and paint. Approximately 11 different items run through the depot's Paint Shop, taking varying times to reset and repaint.

    The depot was anticipating increased demand due to the continued high level of troop deployment throughout the world, and needed to know the maximum capacity of items it was capable of repairing for a given month. Anticipating a demand of resetting a minimum of 200 units per month, the depot engaged ProModel to help develop a simulation solution which would give them the capability to analyze their paint shop reset operations.

    Objective

    • Identify the actual maximum capacity of items the Paint Shop can reset given the current state of equipment and resources.

    • If the current throughput did not meet the demand of 200 units per month, identify the primary and secondary constraints.

    Results

    Lean Six Sigma principles were used to develop current and future state predictive analytic simulation models. The current state model indicated a maximum monthly output of 93 mechanical items reset with resources scheduled for 2 shifts, four days a week, validating data reflective of the actual process.

    A new model was created to simulate the future state scenario to determine the system's maximum capacity with the current staffing while running two blast booths. The maximum capacity with two blast booths and current staffing is 107 items per month.

    Then the future state model was used to determine that once the blast booth process was no longer the constraint, labor was the new constraint of operating a system with two blast booths.

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