Simulating a Contract Closeout Process

Simulating a Contract Closeout Process

Clayton Jerrett Capizzi (University of Tennessee-Knoxville, Knoxville, TN, USA), Joseph Wilck (East Carolina University, Greenville, NC, USA) and Xueping Li (University of Tennessee-Knoxville, Knoxville, TN, USA)
DOI: 10.4018/jssmet.2012100103
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Government defense contractors are burdened by contracts which have ended, but have not been finalized and closed. In order to keep good relations with organizations regulating government contracts, contractors have been forced to devise a strategy to address contract closeouts. Data was collected about a defense contractor’s contract closeout process, and a simulation model of the system was developed to aid in completing the contract closeout process. Using simulation software, the closeout process was successfully modeled under varying resource levels. The simulation model included true worker process times with integrated schedules, including holidays, over the expected period of performance. The simulation produced a realistic model which allows an organization to plan their resources to accomplish their contract closeout process under specified conditions and deadlines. The results are relevant to government (public sector) contracts as well as industrial (private sector) contracts where similar regulations are applicable.
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Literature Review

Moser and Arviso (2007) emphasize the importance of closing complete contracts in a contracting organization. They outline many of the important FAR clauses, which regulate closeout procedure, and what contributes to a successful contract closeout process. The message that is being emphasized in this article is that an effective contract closeout process requires extensive knowledge about FAR clauses and procedures, solid communication between the contractor and the government, and a strong contract closeout team dynamic.

Forrester (1961) centralized many of the theories and principles underlying the modeling of industrial systems in his book Industrial Dynamics. When describing models of industrial systems, Forrester expressed the importance of how mathematical models must be dynamic, address business fluctuations, and uncertainty in the system. Mathematical models are only useful when the model fully explains the real system and is able to predict future conditions. Any vagueness must be eliminated, or else the model cannot be validated. The true value of a mathematical model is derived from the precision of the model, not the accuracy. Forrester outlines the development of a scientific method for process improvement for which he refers to as the “Steps in Enterprise Design.” The steps are as follows:

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