Suchergebnisse

The following article is taken as an excerpt from the proceedings of the annual Acquisition Research Program. This annual event showcases the research projects funded through the Acquisition Research Program at the Graduate School of Business and Public Policy at the Naval Postgraduate School. Featuring keynote speakers, plenary panels, multiple panel sessions, a student research poster show and social events, the Annual Acquisition Research Symposium offers a candid environment where high-ranking Department of Defense (DoD) officials, industry officials, accomplished faculty and military students are encouraged to collaborate on finding applicable solutions to the challenges facing acquisition policies and processes within the DoD today. By jointly and publicly questioning the norms of industry and academia, the resulting research benefits from myriad perspectives and collaborations which can identify better solutions and practices in acquisition, contract, financial, logistics and program management. For further information regarding the Acquisition Research Program, electronic copies of additional research, or to learn more about becoming a sponsor, please visit our program website at: www.acquisitionresearch.org. For further information on or to register for the next Acquisition Research Symposium during the third week of May, please visit our conference website at: www.researchsymposium.org.;The Federal Acquisition Regulation (FAR) mandates the use of contractor past performance information (PPI) as an evaluation factor in all source selections involving negotiated procurements above $1,000,000. Different agencies within Federal Government have lowered the dollar threshold to as little as $100,000 depending on the type of contract action. Using PPI as a factor in all source selections was a tactic modeled after industry best practices. The Office of Federal Procurement Policy (OFPP) envisioned that industry PPI collected by Federal agencies would eventually be automated, maintained on secure databases, and available to all Government agencies for source-selection purposes. This practice would eventually lead to efficiencies similar to those in the private sector. However, along the way, Government and private-sector industry have begun to disagree about how PPI is collected and how PPI is used. Industry prefers a passive system of collecting delivery and quality data during contract performance, while Federal government uses both a passive system (similar to industry) as well as an active system of pulling PPI during contract performance. Industry uses PPI to establish and maintain a preferred vendor list from which to solicit bids, quotes or proposals, while government uses PPI to assess risk and establish vendor responsibility in a full and open competition environment. Contract award cycle-time within the Federal Acquisition process is more than double that of the private sector due to an evaluation process that is cumbersome, time-consuming, and lacking the efficiencies enjoyed by private industry. Government (the DoD in particular) has recently become more curious regarding industry best practices and how those practices can be implemented in the government--in particular, as a possibility of diminishing the Government Contracting Officer's and the Source Selection team's added burden. This paper will explore through field research the current PPI collection and evaluation process used by the DoD and by those employed in industry. The goal behind such research is, again, industry best practices and improving the DoD's use of PPI as a tool in the acquisition processes. ; Second Annual Acquisition Research Symposium ; Approved for public release; distribution is unlimited.

Acquisition research (Graduate School of Business & Public Policy) ; The Federal Acquisition Regulation (FAR) mandates the use of contractor past performance information (PPI) as an evaluation factor in all source selections involving negotiated procurements above $1,000,000. Different agencies within Federal Government have lowered the dollar threshold to as little as $100,000 depending on the type of contract action. Using PPI as a factor in all source selections was a tactic modeled after industry best practices. The Office of Federal Procurement Policy (OFPP) envisioned that industry PPI collected by Federal agencies would eventually be automated, maintained on secure databases, and available to all Government agencies for source-selection purposes. This practice would eventually lead to efficiencies similar to those in the private sector. However, along the way, Government and private sector industry have begun to disagree about how PPI is collected and how PPI is used. Industry prefers a passive system of collecting delivery and quality data during contract performance, while Federal government uses both a passive system (similar to industry) as well as an active system of pulling PPI during contract performance.--p. i. ; Approved for public release; distribution is unlimited.

Using an Euler discretisation to simulate a mean-reverting CEV process gives rise to the problem that while the process itself is guaranteed to be nonnegative, the discretisation is not. Although an exact and efficient simulation algorithm exists for this process, at present this is not the case for the CEV-SV stochastic volatility model, with the Heston model as a special case, where the variance is modelled as a mean-reverting CEV process. Consequently, when using an Euler discretisation, one must carefully think about how to fix negative variances. Our contribution is threefold. Firstly, we unify all Euler fixes into a single general framework. Secondly, we introduce the new full truncation scheme, tailored to minimise the positive bias found when pricing European options. Thirdly and finally, we numerically compare all Euler fixes to recent quasi-second order schemes of Kahl and Jäckel and Ninomiya and Victoir, as well as to the exact scheme of Broadie and Kaya. The choice of fix is found to be extremely important. The full truncation scheme outperforms all considered biased schemes in terms of bias and root-mean-squared error.