Mobility Analysis for the Digitized Brigade

ARMOR — Figure 1 Figure 3 Figure 2 These illustrations are intended to give readers a feel for the screen formats of the mapping system. Many of the fine details visible in the actual color versions are not apparent in these black-and-white renderings.

Even in the black and white versions here, the capability of the system to quickly reveal go and no-go areas can be appreciated. In the lower left illustration, the heavier lines show the optimum routes selected by the system. sates for heterogeneous soil composition as part of the mobility prediction algorithm. Through a series of tests comparing actual movement rates of vehicles and the predicted rate of movement, the differences are factored into the mobility predictions rendered by Risk Based Modeling. In addition, recent precipitation is accounted for in mobility predictions. The model adds further accuracy by allowing the user to consider subjective variables effecting mobility, such as the level of maintenance of the vehicle and the proficiency of the driver. If levels of maintenance and driver training can be generalized to a unit, an adjustment for unit movement times is possible. For example, we know that enemy vehicles are well maintained, but the training of enemy drivers is generally poor. Risk Based Mobility accounts for these conditions and allows differences in movement time based on variance in driver training and vehicle maintenance. Figure 3 identifies two routes from point A to B. The time of travel between these routes is given in minimum, maximum, and average time. A good driver in a well maintained vehicle will take the minimum time, an average driver will take the average time, and a poor driver the maximum time.

The most unique capability of Risk Based Modeling is its ability to predict random movement. This means the model can identify a range of possible routes for vehicles. Using Risk Based Modeling, a start point and an end point are selected for analysis. The model identifies possible routes between selected points for the type of vehicle indicated and specifies the time it will take for each route giving a best case, worst case, and average time. The routes identified in Figure 3 were identified by picking a start point and an end point. The model identifies routes and the minimum, maximum, and average time needed to traverse the routes. To illustrate the utility of a mobility prediction tool at brigade, consider the following scenario. (Borrowed from Virtual Kyrgyzstan III, a JANUS exercise held at Ft. Knox to validate the concepts of ST 71-3, Tactics, Techniques, and Procedures for The Digitized Brigade.) The brigade was to attack an enemy mechanized division. The enemy division defended with two understrength brigades forward and a tank brigade situated to their rear (see Figure 4). The enemy tank brigade had dual missions of division reserve and being lead element of a follow-on force when the enemy division resumed the offensive. The friendly brigade commander’s plan called for destroying the enemy tank brigade and the division artillery group simultaneously. By attacking them simultaneously he could defeat both the enemy defense and the coming offensive operations.

The friendly brigade commander’s scheme of maneuver called for infiltrating subordinate mounted task forces through the enemy main defensive belt, then conducting a near simultaneous attack against the enemy tank brigade and the enemy division artillery group. An air assault task force would be inserted to the north between the enemy tank brigade and an artillery group present to support the future enemy offensive (see Figure 5). Due to the non-linear nature of the battlefield, the friendly brigade commander directed his S2 to develop a graphic showing where and in what time frame the enemy tank brigade could move in any direction. Normally, the short time given to an S2 to develop this product demands that it be done in the TOC and involves the S2 guessing about the trafficability of the terrain, applying normal movement speeds for that type unit, and developing time phase lines to illustrate movement times (see Figure 6). The precision and reliability of this product is low. With a mobility prediction tool, like Risk Based Mobility Modeling, this product is at the S2’s fingertips. By picking a series of start points and end points, as shown in Figure 3, the S2 can develop a product that provides a mobility estimate with much greater precision and speed than any manual product.

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TF 1-41 TF 1-70 TF 2-33 TF 1-327 PD 1 PD 2 PD 3 TANK BRIGADE

Figure 4 Figure 5 The application of mobility prediction software is not limited to intelligence. As part of the same scenario, the scheme of maneuver called for all the subordinate task forces to be in position to attack the enemy tank brigade and division artillery group in a near-simultaneous manner. During execution, the subordinate units crossed multiple points of departure at the same time. Resulting from inaccurate mobility predictions, the unit with the shortest distance to travel, TF 1-41, took the longest to get in position (see Figure 6). This was due to the nature of the terrain along his route. The soil was soft, resulting in slower movement. Because his movement lagged behind the other task forces, the brigade plan had to be altered. Other units had to slow their movement and go into concealed positions to wait for the slow task force to get into position. This gave the enemy commander time to react. He dispersed the tank brigade into battalions and used them to counterattack (see Figure 7).

Using an automated mobility prediction tool with the capabilities of Risk Based Modeling, movement times are indicated on routes selected by the task forces. If a certain route is identified as unsuitable during analysis, an alternate can be chosen. With more precision in planning routes, and a better estimate on movement times, the commander can sequence departure times for subordinate units allowing for the planned near-simultaneous attack against the enemy tank brigade and division artillery group.

This scenario highlights the utility and need for an automated mobility prediction tool, at least at the brigade level. The information requirements and high tempo of Force XXI operations demand a terrain visualization aid that can provide the commander accurate and useful mobility predictions. If resident at brigade level, this capability would provide the commander an invaluable decision-making aid with utility in both operations and intelligence, responsive to his needs before and during the battle.

During the Army’s transition from an industrial age force to an information age force, we are providing brigade commanders the means to gain unprecedented situational awareness of the enemy and his own forces. To complete this, the commander needs a precision tool to help him understand the terrain on which he will fight. An automated mobility prediction application such as Risk Based Mobility Modeling provides this tool.

TF 1-41 TF 1-70 TF 2-33 TF 1-327 PD 1 PD 2 PD 3 TANK BRIGADE

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TF 1-41 TF 1-70 TF 2-33 TF 1-327 PD 1 PD 2 PD 3 Captain Robert S. Mikaloff earned his commission in 1985 through Officer Candidate School. His previous assignments include assistant S3, 311th MI Battalion, 101st Airborne (AASLT); S2, 2/327th Infantry, 101st Airborne (AASLT); G2 ASPS Chief, 6th ID (L); company commander, HHSC, 106th MI Battalion; and instructor, Armor School. Recently, he served as the S2 Trainer at the Virtual Training Program, Ft. Knox. Currently, he is the USAARMC Threat manager and a member of the Advanced Warfighting Working Group. The author would like to thank Dr. Niki C. Deliman of the U. S. Army Corps of Engineers Waterways Experiment Station for technical advice in the writing of this article.