Todd C Runyon

Mdmp for All Seasons: Modifying the Mdmp for Success Todd C Runyon

Publisher Marketing: The U. S. Army 's Military Decision-Making (MDMP) has been criticized repeatedly during the past decade for being untimely, unrealistic, dogmatic, and stifling creativity. U. S. Army Field Manual, FM 5.0, Army Planning and Orders Production, is currently in final draft. FM 5.0 identifies the theory behind decision-making, the decision-making environment, and describes key planning concepts that answer the criticism surrounding U. S. Army decision-making. It is the author 's assertion that the MDMP has not been revised to capitalize on what is published in doctrine therefore it is not the optimum decision-making process. However, alternative decision-making models exist that can enable the Army to modify the MDMP to meet the demands of the military decision-making environment. The purpose of this monograph is to prove that the MDMP is not the optimum decision-making process and provide an alternative that meets the requirements of the military decision-making environment. In order to determine if the MDMP provides the optimum decision-making method, this monograph reviews decision-making theory; determines requirements of the military decision-making environment; describes and analyzes the current U. S. military decision-making doctrine and MDMP; and describes and analyzes alternative military decision-making models. The following requirements from the military decision-making environment are used as analytical criteria to evaluate the MDMP: uncertainty, time, complexity, joint, commander 's focus, flexible plans, and bold plans. The alternative decision-making models are: Canadian Operational Planning Process; Major John Schmitt and Gary Klein 's Recognitional Planning Model; Colonel Kevin Benson 's Collaborative Decision-making Model; and the Naval War College Commander 's Estimate of the Situation (CES). A comparative analysis shows that the MDMP is equal to or better than the alternative decision-making processes with respect to uncertainty and complexity. The Colla