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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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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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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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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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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.
Representative terms from entire chapter:
logistics burden
