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3 Logistics Trade-off Analysis The Army depends on a broad array of analytical techniques to assist in decision making. For several reasons, these techniques will play an even larger role in designing the AAN. First, systems for the AAN will be developed and fielded in an environment of increasing resource constraints, and trade-offs, particularly trade-offs that result in logistical savings, will become increasingly important. Second, an AAN battle force that can meet operational performance objectives and be self-sustaining for up to 14 days will require revolutionary advances in mobility and reliability. The conflicting technical requirements associated with these objectives will require many trade-offs in materiel capability and force structure. Making these trade-offs in a reasoned way will require focused tra~e-off analysis. In this chapter, the committee argues that AAN trade-off analysis must be supported by Army-developed modeling and simulation (M&S) tools. FACTORS IN TRADE-OFF ANALYSES To assess the potential impact of various technologies on logistics burdens, the committee was divided into three panels that focused on mobility, engagement, and sustainment functions. The panels quickly found that many of the technologies and system concepts being considered for the AAN battle force would have pervasive effects on both AAN operational capabilities and logistics burdens. However, the effects of one technology or system concept often conflicted with the effects of another. The panels found that all of them had both advantages and disadvantages. In short, there were many potential solutions but no simple or easy answers to meeting the needs of the AAN. At first glance, the target date of 2025 for an AAN operational capability appears to be far away. But existing system concepts, such as the FCS (future combat system), the FSCS (future scout and cavalry system), and the Army tactical missile system, do not address AAN battle force requirements directly. The new AAN systems will have to be built in prototype, tested, refined, manufactured, and distributed to troops for a substantial period of time before they can be integrated into the force. These steps to fielding a mission-ready AAN battle force will require at least 15 years after the major design has been comp1tetedl. A fully engineered design, ready for prototyping and subsequent engineering and manufacturing development must, therefore, be completed by 2010. Thus, critical decisions on the materiel and technologies in the system designs will have to be made in the next dlecadle. Making the difficult trade-offs to arrive at optimal designs for all AAN objectives, including reducing logistics burdens, will be time-critical. Given the complexity of the decisions, the range of possibilities, and the tight constraints on 29

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30 REDUCING THE LOGISTICS BURDEN FOR THE ,4RMYAFTER NEXT resources and schedules, analytical approaches fully supported by M&S tools offer the best chance of meeting the target date without sacrificing AAN program goals in general and the goal of reducing logistics burdens in particular. Capabilities for AAN Performance and Reducing Logistics Burdens Box 3-l is a list of representative performance capabilities that have significant logistics consequences and are also dependent on choices of technology and design concepts. These capabilities must be addressed in the trade-off analyses that support system design decisions. Many of these capabilities represent performance and logistics characteristics that are in direct conflict with each other and will require in-depth, quantitative analyses to make appropriate trade-offs. For example, if the objectives of high-speed cross- country mobility, improved fuel economy, and AAN mission reliability were pursued independently, the resulting design would have serious flaws. Achieving high-speed cross-country mobility may be at cross purposes with fuel economy and 14-day sustainment goals. Conversely, improving durability and mission reliability with heavy structural designs could decrease high-speed mobility and increase fuel consumption (the major logistics burden for the AAN battle force). Using M&S to analyze design options for all capabilities interactively and selecting the options with the best overall capabilities for AAN operations is the only affordable approach to deciding on trade-offs in time to proceed with development by 2010. Almost all concept and technology trade-offs will have significant consequences for the self-sustainment goal of 14-days. To meet this goal, logistics must be a primary objective, if not the highest priority ob . ~ jective, for trade-offs in design and technology options. This will require reversing the conventional approach of first designing and fielding materiel and then turning to the logisticians to deter- mine how to support it (Box 3-2). Mate- riel for the AAN will have to be de BOX 3-1 Technology-Dependent AAN Capabilities with Significant Effects on Logistics Demands Mobility High cross-country speed Low vehicle weight Fuel economy Engine duty cycle Engagement Situational awareness Communications Precision-guided firepower Protection from projectiles and directed-energy weapons Stealth Sustainment Mission reliability Fuel and ammunition resupply Energy management Subsystem durability _ signed to meet sustainment require- ments as well as other performance requirements and affordability con- straints (including life-cycle cost, not just acquisition cost). Trade-off analyses, using M&S tools to capture the complex interac- tions among design options and to quantify their relative values in meeting AAN performance goals, will be critical for reversing the conventional process. But reducing logistics burdens can only become a primary factor in trade-off analyses if the M&S tools can simulate all of the logistics-relevant aspects of the operational context for the system

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LOGISTICS TRADE-OFF~4NALYSIS being designed. Unfortunately, many of the revolutionary new concepts and technologies being considered for AAN are beyond the capability of current M&S tools in two essential respects. First, because AAN materiel and operational con- cepts are outside the box of conven- tional materiel and force structures, they are also outside the box of existing M&S tools, which were de- signed for conventional systems and forces. Second, because logistics considerations have never before been a primary factor in design analysis, or even in engineering de- velopment and testing, current M&S tools do not necessarily mode! the elements of system operation that determine logistics demands. The extraordinary requirements for high-speed cross-country mobility, drastically reduced fuel consumption, and 14-day self-sustainment will challenge the Army to use advanced technologies in areas of system performance that have not been emphasized in the past. Enormously complex trade-offs in performance, logistics support, affordability (including life-cycle-costs), and schedules of AAN development are well beyond the capabilities of current analysis tools. In other words, existing M&S tools cannot support rational and timely trade-off decisions for AAN planners and materiel developers. If AAN planners want to field "out of the box" systems by 2025, they must support and even demand that M&S tools and technologies required for the analysis and design of AAN systems be developed in the next decade. 3 BOX 3-2 14-Day Self-Sustainment Requirement Must Dictate Materiel Design Traditional Model Design-driven logistics Accept existing system reliability standard "Just in case logistics" (take everything you might need) AAN Model Logistics-driven design Design systems for AAN reliability standard "Just enough logistics" (take only what you need) 1 Requirements for AAN Trade-off Analysis The committee identified three areas in which analysis capabilities, particularly M&S capabilities, will be critical to AAN trade-offs and optimization for reducing logistics demand: high-speed cross-country mobility; materiel reliability; and small unit and force-on-force engagement. Box 3-3 lists the M&S capabilities required in each critical area. M&S for high-speed cross-country mobility must include reducing vehicle system weight and fuel consumption. Accurate M&S of (1) high-speed mobility across soft soils and moderate to rugged terrain, (2) Toads that influence mission capability, and (3) fuel consumption and the life-cycle system cost for radical new vehicle designs will require significant advances. Simulation tools and capabilities will have to be greatly improved for mission rehearsal, for determining the logistics requirements of specific tactical operations (including operations with logistical constraints, such as fuel shortages, etc.), and for training drivers and operators (particularly for cross-country mobility).

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32 REDUCING THE LOGISTICS BURDEN FOR THE ARMY AFTER NEXT BOX 3-3 Critical M&S Needs for AAN Trade-off Analyses in Support of Reducing Logistics Demand High-Speed Cross-Country Mobility Vehicle system/subsystem trade-offs Mission rehearsal for logistics requirements Driver training to achieve mobility objectives Materiel Reliability Mechanisms of failure modeling Uncertainty modeling Human factors modeling Small Unit and Force-on-Force Engagement Integration with engineering models Sort out trade-offs The committee identified ma- teriel reliability as a critical capability for meeting the AAN operational ob- jective of 14-day self-sustainment. Significant improvements in M&S ca- pabilities, including additions to ex- isting models and the implementation of new models, will be required to design systems and make the in- evitable system trade-offs. A critical M&S capability will be the incorpora- tion of reliability engineering into subsystem and component M&S to model mechanisms of failure.' Equally important will be improved represen- tations of variable environmental con- ditions and human factors, two uncer- tainties in real-worId performance that should be simulated as realistically as possible during design analysis. The importance of linking en- gineering analysis tools to small-unit and force-on-force engagement mod- els became clear as the committee discussed alternative system concepts and technology options with representatives of the Anny's materiel development and war-f~ghting com- munities. Revolutionary tactical concepts being considered by AAN planners will have stringent performance and support requirements, which planners are assuming can be met by new systems based on advanced technologies. At the same time, novel and emerging technologies suggest all kinds of possibilities for new tactics and doctrines. These "requirement pulls" and "technology pushes" have created an overwhelming number of operational, technological, and materiel design alternatives the Army will have to evaluate in terms of performance, affordability, and logistics. But trade-offs can only be studied effectively if the engagement models and the engineering analysis tools are linked. Requirement pulls must lead to specific, quantifiable performance constraints and objectives at all levels, down to the models used for engineering design studies. Promising technology-push opportunities must be weighed against each other and competing performance requirements using reliable, realistic parameters of system performance and limitations in force-on-force engagement models. The engagement models must constrain tactics and outcomes to parameters Mechanisms offailure, as used in this report, are causal links between physical properties at one level and performance characteristics at the next higher level of system structure or integration. The designer's knowledge of mechanisms of failure may come from statistical analysis of past performance of a material or structure in similar applications (often called statistics of failure), from a theoretical understanding of how certain properties relate to performance, or from engineering experience with the material or structure. Mechanisms of failure at the microscale in materials are commonly referred to as the physics offailure for that material in the application of interest. See Chapter 7 for a further discussion of mechanisms of failure as related to designing AAN systems for reliability.

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LOGISTICS TRA DE-OFF ANALYSTS 33 based on solid engineering constructs rather than on wishful thinking. The committee found little evidence that M&S environments for engagement and engineering analyses are being linked. Nor has the Army made a concerted effort to implement linkages in the time frame relevant to AAN design decisions. This problem is analogous to the difficulties faced by manufacturers in effectively linking computer-aided design tools with manufacturing tools, an area that is receiving a great deal of attention in the commercial manufacturing sector. Comparison with the STAR 21 Study As a "reality check" on its finding that M&S capabilities will be critical to system trade-off analyses, the committee reviewed the results of an earlier, much larger study of future Army technologies. For that study, the NRC committee wrote a series of reports called STAR 21: Strategic Technologies for the Army of the Twenty-First Century. The STAR 21 committee considered a broad spectrum of Army needs, although not the specific needs of an AAN battle force with the operational characteristics described in Chapter 2. However, the air-transportable "middle tier" force that was central to the STAR 21 recommendations on force structure and strategy is roughly analogous to an AAN battle force (NRC, 1992, 1993a). In fact, most of the technology opportunities and AAN systems concepts now being discussed by the Army for the AAN were addressed in one form or another in the STAR 21 reports. Table 3-1 lists 14 high-priority technologies that were discussed in the STAR 21 Long-Term Forecast (a 30-year technology forecast from 1990, the date of the study, TABLE 3-1 Rank Orderinga of Technologies Identified in the STAR 21 Technology Relevance Matrix Advanced Technology Importance to Army Systems Computer simulation/visualization Complex systems design Materials design by computation Hybrid materials Information explosion Human-machine interfaces Battlefield robotics Battle zone electric power Terrain-related technologies Weather modeling and forecasting Metals Surface mobility propulsion Ceramics 64 61 52 51 49 33 28 28 28 21 14 13 12 aNumerical values are the sum of (1) 3 times the number of systems for which this technology is critical, (2) 2 times the number of systems for which this technology is important, and (3) the number of systems for which the technology is relevant. Source: NRC, 1993a.

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34 REDUCING THE LOGISTICS BURDEN FOR THE WAFTER NEXT to 2020) and were included in the summary table of advanced technologies for 32 representative systems concepts. The rankings, which are based on ratings by the STAR 21 committee, indicate whether advances in a technology area are required, im- portant, relevant, or irrelevant to a class of systems. The STAR 21 ranking shows that computer simulation is the highest priority technology for the analysis and design of complex systems. Despite this strong endorsement more than six years ago of M&S technology for designing complex systems, the committee found little evidence that creating and implementing the M&S capability that is needed now for AAN analyses and designs has been given a high priority. The remainder of this chapter spells out in general terms the kind of M&S environment that will be necessary for analyses of ANN systems. Examples of M&S tools and needs are provided primarily to illustrate the general approach. Readers will find suggestions for specific M&S applications and unmet needs in Chapters 4 through 7 and Appendices C through F. MODELING AND SIMULATION ENVIRONMENT TO SUPPORT LOGISTICS TRADE-OFF ANALYSIS Figure 3-1 illustrates the linkage of M&S tools at multiple structural and functional levels using vehicle system design as a focus. (The same hierarchical concept - ~D ~5 - ._ En cot E V - Strategy 1 r Force-on-force go) - Force performance and cost Multiple vehicles with operators ~ Tactics I ~ System volume, weight, range, cost, performance Single vehicle with on~r~tiorA~rr1w~r" Icon - Operational | history ' System architecture Average loads I. Subsystem architecture Vehicle volume, weight, off ciency, cost, performance - Subsystem volume, weight, efficiency, cost, performance Peak I t Component volume, weight loads ~efficiency, cost, performance Component design o ._ ce ._ An u' 0 .~ co Q o .= AD ~5 ~ U) Q ._ - ._ CO FIGURE 3-1 Hierarchical system of modeling and simulation for AAN trade-off analyses for a vehicle system.

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LOGISTICS TRADE-OFF ANALYSIS I''. See Figure 3-1 (tress Analysi:) - t Dynamic I----------I Analysis (Communications);' - ( Component Design a'. ' . . ". 2 . . . . .. :: 1 ~ : : Sensor ~ ~ PerformanceJ ---- - Dataflows FIGURE 3-2 Component design considerations. 35 >a (serials Select ~ Analysis J Reliability Analysis J applies to non-AAN systems and systems for different battlefield functions, such as lethal systems, energy systems, and communications systems, as well as mobility platforms.) Figure 3-1 is adapted from an in-depth virtual prototyping plan for hybrid electric vehicles (IDA, 1996a). Force-on-force simulations at the highest operational level of the drawing involve strategic and force-level performance and affordability issues. At the other extreme, tools for engineering analysis and design use peak Toad information from higher level simulations to drive detailed component designs toward meeting or undershooting constraints on volume, weight, and cost, while achieving or exceeding performance objectives. Just as the force-on-force analysis at the top of Figure 3-l involves a complex military system, the component analysis and design at the bottom of the figure involves complex engineering considerations. Figure 3-2 shows a few of the considerations in- volved in component design. Each design consideration requires information on duty cycle requirements from the next higher level analyses in Figure 3-1 to optimize rational designs, which in turn provide performance information for higher level analyses. The Army has excellent computing facilities that can support the recommended spectrum of M&S tools. Many of the simulations require only workstations, which are available to virtually every scientist and engineer in the Army. In addition, the Army has invested heavily in shared-use supercomputers, which are readily available via high- speed networks. These prior investments in computing assets, as well as the availability of most software required to support the recommended hierarchy of M&S tools, will significantly enhance the cost-effectiveness of the recommended approach. One might consider the M&S tools and the underlying enabling technologies depicted in Figures 3-1 and 3-2 as a "system of modeling and simulation systems." In keeping with the DoD high-level architecture (HLA) for M&S systems now being de- veloped, a more accurate description would be a "federation of models and simulations," that is, a distributed set of models that can be either loosely or tightly coupled when used

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36 REDUCING THE LOGISTICS BURDEN FOR THE ARMY AFTER NEXT to support trade-off analyses, depending on the needs of the engineering and war- fighting communities. The concept and practice of using analysis tools like these have been well established in the commercial sector by Boeing, Ford, Chrysler, and other in- dustry leaders. In fact, almost all competitive manufacturers of complex, technology- dependent products use this approach for developing new products to meet customer needs and stay ahead of competitors. To illustrate the use of the M&S hierarchy shown in Figures 3-1 and 3-2, con- sider the conflicting AAN objectives of vehicles with high cross-country speed, high mission reliability, and low fuel consumption. A traditional approach to achieving high reliability, despite the extreme Toads encountered in high-speed cross-country mobility, would be to incorporate more material into the vehicle structure, which would make the vehicle heavier and would increase fuel consumption. To overcome the fundamental conflicting trade-offs inherent in conventional technology, advanced technology con- cepts, such as active suspension and traction control, could be simulated in a virtual proving ground with a soldier driving the vehicle (the midIeve! simulation in Figure 3- ~ i. This simulation exercise could determine the loads on vehicle subsystems and the duty cycles required for the power train. Once these are determined, detailed subsystem and component designs can be carried out (the engineering simulations at the Tower levels in Figure 3-~) to minimize weight and fuel consumption, subject to AAN mission con- straints, especially reliability. Once technically feasible designs have been created, they can be simulated in an operational environment (the higher level simulations in Figure 3- 1) to assess their performance against AAN mission requirements. If performance levels are inadequate, either another round of subsystem and component-level designs can be initiated to address the specific inadequacies or trade-offs can be identified and made. in practice, a combination of these two approaches iterating the analysis cycle and making performance trade-offs is usually used. O ~ Using the M&S Hierarchy for Exploratory Development and Defining Research Needs The distributed M&S environment described above is directly applicable to ex- ploratory development at all of the levels shown in Figure 3-! . Exploratory development and engineering development involve using any of the constituent models for design studies, for modeling test scenarios for testing prototypes, or for testing proposed design changes prior to production. Exploratory development also involves different levels for system design analyses and trade-off decisions. The committee believes that linking existing M&S tools to improve existing models or developing new ones to complete a distributed hierarchy for a broad applica- tion area (such as combat vehicle or projectile weapon systems), will involve more ex- ploratory and engineering development than research. These linkages can be imple- mented using established, proven approaches that have been developed by the commercial manufacturing sector. Although distributed M&S environments consistent with Figure 3-! could be used for AAN design studies in broad application areas and are well within the Army's budget for technology development, the use of these environments will have serious im- plications for defining new research and technology development needs. In general, engineering design studies and system trade-off analyses for designing the first generation of AAN systems in the near terra will rely on current knowledge in the

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LOGISTICS TRADE-OFF ANALYSIS 37 component-design considerations shown in Figure 3-2, which defines the existing technological options. if existing options have been exhausted and the performance goals defined at the higher levels in the hierarchy have still not been met, the systems designer will probably have to make performance trade-offs to complete a design for further development by 2010. The models used for logistics trade-off analyses must be accurate. Simulations can only include what their creators build into them; they cannot generate basic new knowledge. Credible simulations can organize and present the trade-off information the Army will need to make rational decisions about AAN systems. In the longer term, the absence of technology options that can meet the combined criteria for AAN operational goals will help to determine the direction of applied research. if applied research cannot meet these well defined needs, either because of a lack of fundamental knowledge or a lack of basic tools for solving the problem, the resulting knowledge gap can then be used to guide basic research. The committee believes it would be imprudent for the Arrny to rely on either applied research or basic research to produce a proven new technology option by 2010 to solve a presently unsolvable system design problem. Nevertheless, some research breakthroughs could be proven in time to provide a new design approach or (more likely) to improve on one already formulated with existing technology options. Once a significant research breakthrough has been made, M&S can provide the engineering basis for incorporating it into design alternatives. Near-term breakthroughs aside, the systematic approach to defining research needs based on hierarchically linked M&S environments will be vital to ensuring the long-term technological dominance (beyond 2025) of both AAN-styTe Army forces and the Army as a whole. The likely continuation of budgetary constraints and the impor- tance of leveraging joint research programs and other DoD-leve! research to meet long- term Army needs are strong incentives for the Army to use this systematic approach. Appendix C includes an example of how the hierarchy shown in Figure 3-1 can be ex- tended to a particular technological discipline (materials selection and design) to enable basic research. To return to the central problem of developing a capability for near-term trade- off analyses that will achieve logistics reduction goals, the remainder of this section will use three kinds of design problems to illustrate the general concept of hierarchically linked, distributed M&S systems. The examples are drawn from the three focal areas listed in Box 3-3: mobility trade-off analyses for AAN combat vehicle concepts, trade- off analyses at the level of small-unit and force-on-force engagements, and AAN mission reliability trade-offs for AAN vehicle design. Mobility Trade-off Analyses Three mobility analysis capabilities start out as high-priority requirements for making AAN logistics trade-offs: comparative analysis of vehicle performance and logistics requirements; mission rehearsal to determine logistics requirements; and driver training for optimum mobility. The strenuous vehicle system performance requirements implied by the AAN tactical concept, combined with the wide array of system and technology alternatives, dictates that trade-offs in vehicle performance objectives should be made early to

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38 REDUCING THE LOGISTICS BURDEN FOR THE ARMYAFTER NEXT achieve the goal of a 14-day mission without logistics support. Only high-fidelity simulations of vehicle operations in the anticipated operating conditions, including realistic modeling of the logistics requirements for those operations, can support the design decisions. Once a system that can mode] vehicle performance and relevant logistics has been implemented, that capability can be linked with other tools to support simulation of mission rehearsal for determining logistics requirements and for designing training simulators for drivers. Modeling Vehicle Performance, Including Fuel Consumption A starting point for M&S of the required vehicle performance is the North Atlantic Treaty Organization (NATO) Reference Mobility Mode! (NRMM), which was developed in the late 1 960s and 1970s and has been validated and used constructively for the past two decades. The NRMM characterizes a vehicle's mobility in terms of speed and tractability. When values for these parameters are assigned, either from empirical knowledge or arbitrarily for a "what if' analysis, and a terrain database characterizing the field of operation has been chosen, the NRMM can predict the time required for a vehicle to move between two specified positions in a tactical environment. However, the values for the speed, tractability, and fuel consumption parameters for NRMM are problematic at best for the advanced vehicle system and subsystem concepts being considered for the AAN. Engineering models can not yet accurately predict speed and tractability for vehicles with active suspension, all-wheel traction control, electric drive or a power source other than an internal combustion engine, and a host of related advanced technologies. Some combination of these technologies will undoubtedly be required to approach the AAN off-road mobility requirements. For example, current tractional force models that represent fundamental physical interactions ~ known as "first principles" models) between the traction surface and a soil or similar soft surface can not model the speed and acceleration/deceleration conditions relevant to high-speed cross-country travel. Thus, basic vehicle mobility processes, such as the distribution of sprocket power (energy transferred at time, t) between dissipative interactions (soil deformations, heat of friction, etc.) and changes in vehicle momentum, cannot be realistically modeled. Another major weakness of the NRMM (and most other existing models) is the lack of parameters for relating operating performance under variable operating conditions to logistics requirements. As a consequence, logistics demands for fuel cannot be modeled as a function of operating performance and operating environment. Because fuel will be the dominant logistics burden for the AAN, the capability of simulating fuel consumption for alternative vehicle designs in highly mobile AAN tactical scenarios will be critical. The committee found no evidence that serious work is under way, or even contemplated, to develop models of high-speed, off-road vehicles, at either the vehicle system level or the mobility subsystem level, that would mode! the underlying physical processes to enable rational trade-offs based on fuel consumption. Army models for this purpose must account for diverse combinations of vehicle characteristics, tactical alternatives, and force structure options. In short, the models should treat the reduction of the largest logistics burden as a primary design criterion. Since the NRMM was developed, significant advances in simulation methods have rendered the NRMM vehicle dynamics subsystem (VEHDYN) obsolete. For exam- ple, commercially available simulation software for mechanical system dynamics,

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L O GIS TI CS TRY DE- OFF A NA L YSIS 39 software that is used extensively by the Tank-Automotive Research and Development Center (TARDEC) and the vehicle manufacturing community, can more accurately model the vehicle dynamics of the advanced vehicle concepts being considered for the AAN than VEHDYN can. This technology should be incorporated into extensions to the NRMM for AAN trade-off analyses (specific developments are discussed in Chapter 5~. Virtual Proving Grounds for Vehicles and Drivers High-fidelity vehicle simulators with hardware (i.e., a vehicle subsystem prototype or production unit), including a driver in the simulation loop, have recently been created as "virtual proving grounds" for testing advanced mobility concepts. These simulators can be used for relatively inexpensive experiments involving human drivers, concept vehicles, alternative vehicle technologies, terrains and soils, and tactics in a realistic test environment. With these tools, uncertainties associated with vehicle and driver performance, especially at the high cross-country speeds being considered for AAN, could be quanti- fied and used for concept development and materiel optimization. A critical need now is to interface the results from vehicle performance models with the parameters in the vir- tual proving ground simulations to represent interactions between vehicle and terrain. That is, results from mobility subsystem modeling (e.g., an improved NRMM model) must be able to flow up to the soldier-and-system interactive level provided by the vir- tual proving ground. The capability of modeling the interactive effects of driver behavior, tactics, and variations in system configuration on fuel consumption and mobility performance measures is particularly important. Moving down the M&S hierarchy of Figure 3-l, results from the virtual proving ground experiments could be used to identify critical elements of vehicle-terrain interaction in the mobility subsystem (and critical elements in models for other subsystems, such as the situational awareness subsystems for both driver and vehicle) that require design changes to meet mobility per- formance goals without sacrificing fuel economy. (Vehicle modeling capabilities that would have to be extended to bring these new tools to bear on AAN logistics are discussed in Chapter 5.) Linking System-Leve! Modeling with Engagement Simulations During the past decade, major advances have been made in distributed interac- tive simulation (DIS), a revolutionary new capability that enables soldiers to operate vehicles in a realistic battlefield environment. The use of DIS technology to simulate op- erations involving unit-level AAN forces would provide a rational basis for assessing the military value of advanced mobility concepts and tactics that take advantage of revolu- tionary new mobility capabilities. Of course, the conceptual vehicles in a DIS experiment should realistically reflect the vehicle handling and performance characteris- tics (including realistic fuel consumption!) determined in the virtual proving ground (linkage upward in the M&S hierarchy of Figure 3-~. Tactics explored and developed in the DIS environment should be analyzed for sensitivity to performance of the vehicle- driver-terrain system to identify systems-level conditions and criteria that should be

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40 REDUCING THE LOGISTICS BURDEN FOR THE ARMYAFTER NEXT further explored, or even redesigned, in the virtual proving grounds (linkage to models further down the M&S hierarchy). General Implications for Implementing an M&S Environment Many opportunities and unmet needs remain for each of the three "mobility performance" modeling levels discussed above. However, to meet the larger need for an M&S environment to support AAN systems design with effective logistics trade-off analyses, the M&S tools at different levels should be used to pass information up and down the structure-performance hierarchy shown in Figure 3-~. The performance results for specific designs modeled at one level must be incorporated into initializing conditions and physical relationships represented in the models at the next higher level. The outcomes of simulation runs at one level must be analyzed into critical elements of underlying structures or functions that become the objectives or the outcomes to be avoided in designs at the next lower level. Note that coupling between levels, in the sense that the models at two (or more levels) are run together, is neither essential nor, for many design issues, even desirable. Running a series of optimizing runs with a mode! at a given level, then feeding the lessons learned into subsequent simulations and design analyses at levels above and below in the hierarchy is usually more efficient. Iterative passes up and down the hierarchy of distributed M&S tools that can be run independently of one another are more practical than an integrated "system of models" that run simultaneously. Mission Rehearsal, Mission Logistics Planning, and Training Applications A hierarchical M&S environment well suited to system trade-off analyses in which logistics requirements are a primary design objective is not just a design tool. The same M&S capabilities can be used to support mission rehearsal, detailed logistics planning for specific missions, and troop training exercises. The committee identified M&S capabilities for all three of these additional functions as highly desirable for reducing AAN logistics burdens on an operational basis (beyond the reductions achievable by rational systems design). Indeed, if a hierarchical M&S environment were available for system trade-off analyses in a broad area such as vehicle mobility, system lethality, or communications for situational awareness-the major problem for managers would be allocating sufficient computer time. Trade-off Analyses for Small-Unit and Force-on-Force Engagements A typical AAN mission scenario involves operational units employing numerous diverse systems. Unless wider performance goals (that is, the collaborative, coordinated perfo~ance of these units as functional components of a military operation) are brought to bear in a systematic way during design, field training, and other activities, systems could be optimized only for their mission profiles, without regard for optimizing the combined outcome of all systems participating in an operation. An axiom of systems engineering is that optimizing the performance of components does not necessarily optimize the performance of the system as a whole. In the context of an M&S hierarchy

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LOGISTICS TRADE-OFF ANALYSIS analogous to the one in Figure 3-1, engagement M&S would ensure that the performance characteristics for which individual systems are optimized derive rationally from the performance requirements for military operations. Each system must be considered as a component in a larger overall system. The trade-off analysis strategy should address an entire AAN mission, including the pre-injection logistics of assembling the force, transportation from the continental United States to the staging area, deployment to the battle area, mobility and engagement in the battle area, and extraction. Models in each phase of the mission should be coupled for simulations of the integrated system. Existing models must be extended to represent the revolutionary tactics and materiel capabilities being considered for the AAN at both the small-unit and force-on- force engagement levels. Box 3-4 is a preliminary list of the major characteristics of an AAN operational unit and the interactions that must be represented in M&S tools to support AAN logistics trade-off analyses at this level. Although some characteristics of an AAN unit may be only indirectly related to logistics, every characteristic influences AAN logistics trade-offs: everything depends on everything else. Existing operational M&S tools at the unit level and higher appear to be adequate to support AAN logistics and system capability trade-offs, but these tools are not linked to the engineering-level analytical tools used to assess technological alternatives. To correct this situation, the logistics and performance modeling capabilities at the Army Materiel Systems Analysis Activity (AMSAA) should be linked, via the DoD HLA, with M&S tools at the systems modeling level and lower. The hierarchy of M&S tools in Box 3-5, shown with their proponent organ~za- lions, suggests the scope of analytical methods that would support AAN logistics trade- offs up and down the six levels shown in Figure 3-~. According to representatives of AMSAA and the ARL, the first three categories of M&S tools (the engagement levels) are generally capable of supporting AAN mobility analyses. They observed, however, that the engineering analysis tools, which are the responsibility of the various research, development, and engineering centers (RDECs), are in need of substantial development and would have to be linked with the higher level tools to provide a distributed M&S environment capable of supporting AAN logistics trade-off analyses in the vehicle mobility area. To the committee's knowledge, similar needs exist at system, subsystem, and component levels in other significant technology application areas. Capabilities to support uncertainty and sensitivity analyses will be essential for interactions between the engagement level of analysis and the system/subsystem levels below it to ensure that realistic trade-offs are made and that decisions are based on sound data. For example, errors in a mobility performance simulation could amplify errors in the results at the engagement level. If the soil data provided to an NRMM-like mode! are invalid, vehicle speed predictions will be incorrect. This error, which would affect the arrival times of units at an operational area, could not only have serious tactical consequences but could also make it impossible for the unit to sustain itself for the duration of its mission. 41 BOX 3-4 Characteristics of an AAN Unit for Small-Unit and Force-on-Force Engagement Analyses Lethal force inserted and extracted by air or see Logistically self-contained force Complete situational awareness Stand-offprecisionf~repower Self-protection, both active and passive Rapid ground maneuvering

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42 REDUCING THE LOGISTICS BURDEN FOR THE ARMY AFTER NEXT BOX 3-5 M&S Tools to be Linked for AAN Logistics Trade-off Analysis Simulation of Force-on-Force Engagement (TRADOC Analysis Center) Battlefield effectiveness Weapons mix analysis Support/logistics requirements Requirements trade-offs Simulation of Fighting Unit (AMSAA) Small unit effectiveness Supply consumption rates Costlperformance trade-offs Simulation of One-on-One Engagement (AMSAA) Weapon system performance Expendable consumption rate Technology trade-offs Simulation of Components and Subsystems (ARL/ARO/RDECs) First principles performance/resource Consumption modeling Hardware/so ldier- in-the-loop simulation Durability/reliability assessment Componentlsubsystem optimization Constraint enforcement -- Weight and volume -- Fuel efficiency -- Personnel Speed and agility The fidelity of results from modeling at one level in the M&S hier- archy will not necessarily scale linearly with the analyses conducted at the next level up or down. Because of the non- linear, dynamic nature of the complex systems being modeled at several levels in a typical M&S hierarchy, even van- ishingly small errors in one model may result in significant errors in apparently unrelated models, making the entire mission simulation inaccurate. Mathe- matical methods of analyzing coupled models that are vulnerable to nonlinear "breakdowns" are an area that will re- quire applied (or even basic) research. At a minimum, sensitivity analysis will be required to ensure that trade-offs are based on compatible levels of mode! fi- delity and that they account for uncer- tainties in mode! data and physical representation. Trade-off Analyses to Support AAN Mission Reliability The extraordinarily high levels of operational performance desired by AAN planners must be traded off against the equally fundamental logisti- cal objective of 14-day self-sustain- ment. For example, traveling over rough terrain will create enormous loads and stresses on vehicle subsystems. Unless the subsystems are designed for reli- ability under these operating conditions, the vehicles might not be able to remain operational for the full period of time. Analytical tools will have to be developed for designing for this extremely high level of reliability. M&S requirements to support this essential capability ("AAN mission reliability") are detailed in Chapter 7. The discussion is focused on the role of a hierarchical M&S environment in achieving materiel reliability objectives. In the hierarchy of tools shown in Figures 3-1 and 3-2, detailed consideration of design for reliability occurs at the lower levels of the modeling chain, during component and subsystem design. The properties (and quantitative measures) to achieve durability and reliability are dictated by the load and stress histories for extended operation of the entire system. For new systems with unconventional designs that are required to operate in wholly novel performance regimes, these factors must be formulated as quantifiable

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L O GIS TI CS TRY DE- OFF ~ NA L YSIS 43 structural requirements in the higher levels of M&S analysis shown in Figure 3-l, beginning with the engagement and DIS levels, at which operational performance begins, and translated into realistic structural requirements. As shown in Figure 3-1, operational use and load histories cascade down the left communication channel (shown as arrows between levels in the distributed M&S environment), providing data for designing for reliability at successive levels, down to the lowest level.2 Traditional methods of system design for military vehicles, for example, do not include the reverse communication channel (shown as the right arrows between levels in Figure 3-~. This channel feeds information on achievable reliability and performance at the component and subsystem levels back up the hierarchy to the operational level. System designers and war-fighters can only work together effectively at the higher levels to dete~ine acceptable trade-offs when the results of iterated analyses indicate that all performance objectives cannot be met. The capability to iterate down and up the hierarchy is, therefore, essential to meeting reliability objectives, but this capability does not exist in current military system modeling, simulation, and design technology. AMSAA has made a considerable effort to bring M&S to bear on designing electronic systems for reliability using physics-of-failure methods. Although this ap- proach has been useful in the electronic systems domain, it is not widely accepted or used in the mechanical systems design arena. An in-depth study completed under the auspices of the National Security Industrial Association and the Computer-Aided Acquisition and Logistics System initiative, recommended a "simulation-based" ap- proach to designing for reliability (CALS, 1989~. The blueprint for technology d~evelop- ment and implementation to support design for military mechanical system reliability was, however, never acted upon. This blueprint is still an excellent guide for technology development and implementation to achieve AAN reliability goals. AMSAA has also identified a need for the Army to use methods based on what the committee refers to as "mechanisms of failure" to design mechanical systems and subsystems for reliability. The conceptual relation of physics-of-faiTure modeling in electronics to the more general use of mechanisms of failure for reliability engineering of AAN systems is discussed in Chapter 7. An adequate systems design environment to meet the goal of 14-day self- sustainment will require significant development and integration of engineering-level M&S tools, as well as technology developments of dynamic-system simulation tools for loads prediction and of stress analysis tools for failure prediction. The Army's most urgent need, however, is for integration of these tools into an interactive environment that enables rational trable-o~s among reliability, operational capability, and sustain- ment. In some instances, integration will mean tight coupling, in which the coupled models are run together. In other instances, the integration will mean allowing for the easy and consistent transference of results (for initializing and bounding conditions, establishing parameters and other data) from one module! to another. This point was illustrated in the section on mobility trade-off analyses. Transforming load histories ranidlv and accurately into reliable suh~v~tem and sv.stem designs, will require a concerted ellort to link state-of-the-art computer-aided 2Appendix C and Chapter 7 describe how this flow of structural and performance requirements (for reliability or other fundamental system objectives) can cascade to levels of materials selection and, in longer- term projects to achieve difficult performance requirements, even to M&S applications for designing materials with novel microstructures and nanostructures. Conversely, information on materials alternatives developed at lower levels will be fed up to the materials choices used in the design of components, at the lowest level shown in Figure 3-1.

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44 REDUCING THE LOGISTICS BURDEN FOR THE ARMYAFTER NEXT engineering M&S software with virtual proving ground simulators. These designs can then be communicated upward to the operational simulation arena (engagement M&S), where performance of the designs can be assessed. Loads based on realistic projections of operational use can then be evaluated and communicated back down the hierarchy to assess and improve reliability at the engineering levels of design analysis. The capability for designing reliability into a system using hierarchical, linked models of structure-function relationships has already been implemented and used effectively in designing electronic systems. Success in modeling and simulating the physical processes and causal structures that underlie functional relations and capabilities at all levels of the design hierarchy for electronic systems is in part a reflection of the relative simplicity of the structure-function relationships, compared with the far more complex relationships underlying mechanical reliability in military platforms (ground and air vehicles, lethal systems, etc.~. Furthermore, much more investment has been made in developing M&S tools to design highly reliable electronic systems, especially computer systems, than for tools in designing the mechanical aspects of military systems. FACILITATING A MODELING AND SIMULATION ENVIRONMENT TO SUPPORT SYSTEM TRADE-OFF ANALYSES The Army will have to establish the foundation for trade-off analyses and vali- date it through constant application to problems relevant to the AAN over the next four years to have any chance of completing systematic exploratory engineering of the feasi- ble subsystem and component-level design options before 2010. Therefore, development and integration of the M&S capabilities that can support trade-off analyses must be initi- ated immediately. In the face of competing interests and program inertia, the Army may have to establish a programmatic mechanism to coordinate and realign existing activities and maintain a strong focus on the overall environment. One way to accomplish this would be to establish a strategic technology objective that had strong support and con- tinuing oversight from senior war-fighters committed to the AAN process. In this section, the committee suggests how the Army can facilitate integration, improvement, and development of M&S tools. While Army operational and engineering details differ from those encountered in the commer- cial manufacturing sector, the consider- able experience gained there can be exploited to integrate M&S tools. Box 3-6 lists key elements for implementation. At the outset, the Army set its priorities for the M&S tools needed to meet AAN requirements. I he Army should adopt an evolutionary approach to developing and applying the M&S technology base. Many of the elements required for AAN logistics trade-off analyses already exist, but past attempts "to start with a clean sheet of paper" and create the "mother of all models" have failed. BOX 3-6 Facilitating AAN Logistics Trade-off Analyses Define priorities for M&S development to meet AAN needs Secure buy-in and commitment from others Focus on AAN logistics trade-offs Continuously develop tools and technology based on need Validate models based on rapid prototype testing

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LOGISTICS TRADE-OFF ANALYSTS Setting Priorities The Anny should focus its limited resources on the following tasks: 45 I. Define the modeling environment and tools necessary for the AAN systems de- signs to be developed first (i.e., tools for engineering and manufacturing devel- opment to begin around 2010~. Identify useful existing tools. Integrate existing tools using the DoD HLA. 4. Apply the M&S tools to specific high-priority logistics trade-offs. As shortfalls in existing tools are identified, develop the missing capabilities. The architecture of the modeling system details how various parts of the models share information on all simulation elements, including data and representations related to logistics burdens and benefits. Information on logistics consequences, as well as other AAN performance objectives, must be able to travel up and down the modeling hierar- chy of Figures 3-1 and 3-2 in order to fully integrate those objectives into the decision- making process. The M&S systems must be designed to be upgraded incrementally as new knowledge is gained regarding performance and logistics trade-offs. Managing this process will require a dedicated and capable program manager. Securing Buy-in and Commitment from Others The Army should collaborate whenever possible with organizations that have similar interests. Although the analysis technology for high-speed cross-country mobility may be beyond the interests of the sport utility vehicle and conventional on-highway vehicle manufacturing community, for example it may interest construction, agricultural, forestry, and mining equipment manufacturers. This common interest could be the basis for a joint DoD-industry technology development program to meet the needs of both the AAN and the development goals of the commercial sector. Agricultural and construction manufacturers have been leaders in the development and application of computer-aided engineering (CAE) tools and have shown great interest in virtual proving grounds. Because of budget constraints, they have shown a remarkable willingness to work with their own major competitors to create the basic M&S capabilities they all need to remain competitive in the global market. Focusing on Logistics Trade-offs The Army must focus squarely on AAN logistics trade-off analysis. For the recommended exploratory development program to bear fruit, the necessary capabilities can be built on the extensive existing infrastructure, which includes models developed by the defense community and M&S tools developed by commercial enterprises, existing and developmental virtual proving ground simulators, and the DoD DIS (distributed interactive simulation) environment. These assets represent an investment of many millions of dollars. With a moderate, well managed development program focused on the integration of available tools, the Army can take advantage of past investments to meet AAN design and analysis needs.

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46 REDUCING THE LOGISTICS BURDEN FOR THE ARMYAFTER NEXT The best way for the Army to focus on AAN needs is to apply these tools to the most pressing trade-off challenges from the very beginning. A considerable effort will be required to integrate engineering and operational simulation tools using the DoD HLA. Missing capabilities can be identified through a systematic process of logistics-driven analyses. In the creation of new models and analysis tools, the Army should only invest its limited resources when deficiencies become apparent. New tools must be continually applied, tested, and validated via rapid prototyping for technology demonstrations. The involvement of soldiers in AAN war games, exercises, and interactive testing of proto- types of advanced systems will be critical for focusing M&S development on evolving AAN materiel and logistics support needs. The integrated idea teams and AAN war games that are part of the AAN process are excellent mechanisms for guiding the im- provement and use of analysis tools. The committee's emphasis on focused management of M&S in support of AAN was reinforced by a recent finding and recommendation by the Army Science Board (ASB, 1997), which pointed out the discrepancy between the Anny's numerous, but un- coordinated, attempts to develop and use M&S to support Army programs, and the strong focused management necessary for these attempts to realize their full potential. The committee strongly encourages Army leadership to make focused management of the recommended exploratory development program a high priority. At the same time, the Army should pursue basic research in several areas with the potential for significantly affecting AAN logistics. These high-priority research areas are discussed in Chapters 4 through 7. SCIENCE AND TECHNOLOGY INITIATIVES TO REDUCE LOGISTICS BURDENS THROUGH TRADE-OFF ANALYSES The committee concluded that the Army must make use of system trade-off analyses, beginning with the conceptual design phase, to ensure that AAN systems fielded in 2025 meet the objectives for reducing logistics burdens. The committee recommends that the following areas of scientific research and technology development be pursued to ensure that the trade-off analyses are both efficient and rational. The order of the numbered items reflects a rough order of priority. 1. Strategic Technology Objective for the Development of Distributed Modeling and Simulation Environments to Support Logistics Burden Reductions. An effective operational AAN force by 2025 will depend on critical decisions being made on materiel concepts and technologies to be used in system designs. The trade-offs in design and technology options must consider logistics burden reductions as a primary objective. Given the complexity of the decisions, the range of possibilities, and the tight constraints on resources and schedules, the best chance of meeting the target date without sacrificing AAN program goals will be analytical approaches that are fully supported by M&S tools.

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LOGISTICS TRADE-OFF ANALYSTS 47 Many components of this M&S capability already exist, especially in the areas of engagement simulation and mobility performance. But these components are not linked. In a coherent M&S environment, results at one level must flow upward and downward iteratively in a hierarchy from force-on-force engagement simulation at the top to engineering analyses that support the design of components and structures at the bottom (see Figures 3-! and 3-2~. Important components of this hierarchy are missing, and a few of the existing pieces can not incorporate key logistics sustainment issues in their simulations or interact effectively with the tools above or below them. Validation of models for use in AAN trade-off analyses, both those that exist and those will have to be developed, will require continuous attention. Test data on military and commercial equipment similar to the equipment envisioned for the AAN should be used as much as possible to help validate AAN models. As prototypes of systems and subsystems targeted for AAN use are fabricated and tested, systematic experimentation should be planned specifically to validate models. Establishing and maintaining a strong strategic focus on the overall M&S envi- ronment will require that the Army establish a programmatic mechanism, perhaps a strategic technology objective, to manage a vigorous program for AAN M&S develop- ment, applications, and validation. The committee identified three focus areas where the development of M&S capabilities will be critical to system decisions in the next decade: high-speed cross-country mobility, materiel mission reliability, and small unit and force- on-force engagement. These and other applications are described in Chapters 4 through 7 and Appendices C through F. 2. Basic and Applied Research to Improve M&S Capabilities. Although a distributed M&S environment for key near-term decisions on AAN systems can be implemented largely through exploratory development, the implementation and application of these M&S capabilities should provide the basis for prioritizing the Army's research needs. Applied research should be directed toward filling gaps in the M&S-supported analyses. If applied research cannot fill these gaps, basic research initiatives should be supported. Although most of the payoffs in terms of technology insertion into AAN systems and materiel, either from applied or basic research, are likely to materialize after 2010, this research will be vital for the Army's technological dominance beyond 2025. In addition, there will probably be some research breakthroughs that will either affect design decisions before 2010 or can be engineered into systems undergoing development between 2010 and 2025. Mathematical tools for assessing the propagation of uncertainty and errors in distributed modeling environments, particularly in nonlinear dynamic relationships, is one example of a research need. Others are identified in Chapters 4 through 7 and the appendices.