Schedule Risk Data Decision Methodology (SRDDM)

Abstract

One of the top priorities of the U.S. Army is to make decisions regarding acquisition programs that will best serve the Warfighter. Providing an accurate and precise schedule risk assessment for a set of alternatives is a key input to the decision making process. Weapon System Acquisition Reform Act of 2009 is driving more analysis to support the Analysis of Alternative (AoA). AMSAA conducts independent schedule risk assessments to support AoAs and other major Army acquisition studies. A probability is assessed for completing a given phase within the schedule developed by the Program Manager (PM). The probabilities are based upon historical data for analogous programs. AMSAA developed a Schedule Risk Data Decision Methodology (SRDDM) that determines if enough historical data exists to utilize quantitative techniques to conduct the schedule risk assessment. This methodology lays the mathematical and decision-making foundation for all future schedule risk assessments. SRDDM uses Monte Carlo simulations and mathematical models to build a confidence interval (CI) for the probability of meeting the PM s schedule. If this CI width is within tolerance then enough analogous programs exist to build risk distributions. AMSAA has applied SRDDM to the Indirect Fire Protection Capability and the Armored Multi-Purpose Vehicle AoAs. Future work includes risk mitigation, trade space analysis, and developing event-driven models.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2012
Accession Number
ADA579646

Entities

People

  • John Nierwinski Jr.

Organizations

  • United States Army Materiel Systems Analysis Activity

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Army Procurement
  • Computational Science
  • Data Science
  • Fire Protection
  • Indirect Fire
  • Information Science
  • Intervals
  • Military Acquisition
  • Models
  • Monte Carlo Method
  • Normal Distribution
  • Probability
  • Risk Analysis
  • Sampling
  • Simulations
  • Standards

Readers

  • Life Cycle Cost Analysis

Technology Areas

  • Space