A Probabilistic Approach to Job Shop Manufacturing Estimation and Performance Forecasting

Abstract

In defense job-shop manufacturing, cost and schedule estimates along with performance to those estimates have a heavy impact on overall project budget and schedule for pre-milestone B activities. Variations at this stage of the project tend to directly influence the availability of the system under development, which results in propagating delays through the rest of the Technology Maturation and Risk Reduction phase. Project stakeholders have a need to understand impacts from uncertainty along with anticipated variances to cost and schedule continuously throughout the manufacturing process. Extending earned value management techniques with probabilistic modeling may be able to provide meaningful insight into manufacturing estimates and performance without requiring project stakeholders to have detailed familiarity with the manufacturing process. This thesis explores if probabilistic estimation methods are sufficient to predict actual performance and compare existing methods of project estimation and performance reporting with combined probabilistic forecasting. This work demonstrates that additional insight can be attained with probabilistic forecasting that is useful to program managers during job-shop manufacturing project execution and can serve as an additional leading indicator of cost and schedule variance.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2023
Accession Number
AD1224706

Entities

People

  • Eric V Kline

Organizations

  • Naval Postgraduate School

Tags

Readers

  • Artificial Intelligence
  • Life Cycle Cost Analysis
  • Organizational Process Management (OPM).