From the Chairman and Executive Editor


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Dr. Larrie D. Ferreiro, Executive Editor, Defense ARJ

Chairman Larrie FerreiroThis issue’s theme, “The Visible Hand of Defense Systems Management,” reflects the fact that the development and fielding of military systems and services has never been directed solely by the “invisible hand” of market mechanisms, but rather has always relied on strong guidance by the “visible hand” of deliberate, well-considered management of the acquisition process. The origins of this process are engagingly described in Alfred Chandler’s The Visible Hand: The Managerial Revolution in American Business, this issue’s selection for the Defense Acquisition Professional Reading List and reviewed by Dr. Nayantara Hensel, a member of the Defense ARJ’s Research Advisory Board.

The first article, “Relieving Joint Pain” by Anthony Wicht and Edward Crawley, describes how strategic planning for commonality among acquisition programs can translate directly into life-cycle savings. The next article, “Inserting Agility in System Development” by Matthew Kennedy and Dan Ward, argues that a hands-on approach to change management during the system development cycle can avoid system obsolescence before initial fielding. Melissa Thomas’ article, “Identifying Organizational Conflict of Interest,” suggests the need for a robust means of collecting information and monitoring government contractors to guard against organizational conflict of interest during the acquisition process. Adedeji Badiru, in “Half-Life Learning Curves in the Defense Acquisition Life Cycle,” introduces the concept of half-life analysis of learning curves (i.e., reflecting the decay of some learning even as other learning increases) as a potential tool for planning career and training strategies in the defense acquisition process.

Finally, Victor Apodaca, in the online-only article “Decision Cost Model for Contractor Selection,” introduces an alternative technical evaluation methodology to the current government contractor selection process using a statistical model to identify the contractor with the lowest expected total cost.


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