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4
Systems-of-Systems, Distributed Simulations,
and Enterprise Systems
Three topics in modeling and simulation (M&S) receive special
attention in this report: (1 ) the increasing complexity of systems-of-
systems and the corresponding demands on modeling and simulation
capacity; (2) the increasing desire for distributed simulations and their
corresponding technical requirements; and (3) the long-term goal of having
enterprise systems, or M&S systems that include all the aspects of a
business enterprise from product development to manufacturing, to human
resources, cost accounting, marketing, and sales. These three topics cut
across several areas of M&S for defense acquisition and commercial
manufacturing and present particular challenges for research and
development.
SIMULATING COMPLEX SYSTEMS-OF-SYSTEMS
In both the commercial and defense worlds, the need to model
complex systems-of-systems is increasing. Commercially, two examples
are complex, multiunit manufacturing systems, and supply-chain systems
in which interoperability between retailers and suppliers is demanded. One
of the major challenges that DOD faces is the creation and sustainment of
systems-of-systems to satisfy mission needs.
It has been argued that future efforts to modernize DOD's weapons
systems should put more emphasis on novel system concepts (Birkler et
63
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MODELING AND SIMULA TION IN MANUFACTURING
al., 2000). The present acquisition process, however, has difficulty in
accommodating either rapid definition and development of such systems or
implementation of new operational concepts. Accelerated development and
demonstration of new concepts would be needed prior to the commitment
of fu]] funding or fielding. In addition, the simulation-based acquisition
(SBA) process envisioned for DOD requires an assessment of expected
mission effectiveness early in the development of a new system. Mission
effectiveness is a result of a system's ability to gather and share
information and to survive and attack hostile targets. An assessment of
such abilities is extremely difficult to make. Subtle design decisions can
result in significant impacts on mission effectiveness. A modest alteration
in the way a defense system is used or a minor modification to the scenario
in which the system is immersed can also have a marked impact on
effectiveness (Hall et al., 2000; Hall et al., 19991.
It is becoming more accepted within the defense community that
M&S technologies are an essential, and possibly the only, means of
exploring, evaluating, and assessing the complexity of modern warfighting
environments. The establishment of capabilities for representing systems-
of-systems that work together to meet aggregated mission requirements, as
well as integration and interoperability strategies, is therefore important.
An M&S environment to support systems-of-systems evaluation would
assist in evaluating a proposed defense system's mission effectiveness in
the context of a specified set of possible design variations, operational use
patterns, and engagement scenarios. Such an environment would need to
contain a library of sensor, weapon, and command and control
communication platform models that could be composed to model military
systems-of-systems operating in physically realistic environments. It would
need to support discrete event simulations involving large numbers of runs
with different random number seeds and parameter settings for Monte
Carlo sampling and optimization searches (see the subsection "Dealing
with Complexity and Errors," in Chapter 5~. Since it might have to support
a large number of mobile, communicating entities at a significant level of
resolution, this M&S environment would need to be built on a middleware~
layer that efficiently manages interactions among such entities and their
environments.
In addition, such an environment would need to support ergonomic
and informative human-computer interfaces, including visualization
interfaces to display spatially referenced entities. It would also need to
"'Middleware" is software that simplifies the use of network technology in applications by
providing for sending message packets from one node to another. These services would
otherwise have to be programmed from basics. Middleware enables large mainframe
applications to migrate to distributed client/server applications and provides communication
facilities across heterogeneous platforms.
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65
support interactive scenario and experiment definition and analysis,
including data modeling and statistical analysis of simulation output.
Systems-of-systems are complex. One ofthe defining characteristics
of complex systems is emergence, or emergent behavior. A complex
system, such as a system-of-systems, can exhibit behaviors that are
different from those of the separate agents or entities, such as the
individual systems, that compose it (Jervis, 1997~. The aggregation of the
agents' behaviors and the interactions between them can generate large-
scale emergent behavior that is not part of the behavior repertoire of any of
the agents and may be qualitatively different from them. Emergence
manifests itself as the organization of consequential higher-order behavior
from the separate agents. Even if the agents have no specific organizing
behaviors, an overall organization can emerge as a consequence of the
behavior of the individual agents and the interactions between them.
Virtually all organizational behavior in such systems results from agents
adapting to their environments and, in the process of so doing, affecting the
environments ofthe other agents (Sage and Olson, 2001~.
In a complex system, emergent behavior arises in a bottom-up fashion
as the combination of the behavior of many individual agents. However,
knowledge of the behavior of the agents does not allow prediction of the
behavior of the entire system. Therefore, the effectiveness of the
traditional reductionist approach to studying systems is significantly
reduced when applied to complex systems: "It is not sufficient to think of
the system in terms of parts or aspects identified in advance, then to
analyze those parts or aspects separately, and finally to combine those
analyses in an attempt to describe the entire system" (Gall-Mann, 1997~.
What is the significance of emergence for simulating systems-of-
systems? An M&S environment to support systems-of-systems evaluation
must enable emergence to assess systems-of-systems performance
realistically. The modeling methods used in the M&S environment must
include those in which emergence is possible; generally, such methods
explicitly model interagent interactions. Reductionist modeling methods,
as noted, will often not be sufficient.
DISTRIBUTED SIMULATIONS
Advanced distributed simulations, which allow multiple participants
connected by a network to interact simultaneously with each other, are
becoming increasingly important in military and manufacturing
applications. There is steady growth in the number of participants and
amount of information being shared. Two examples of advanced
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MODELING AND SIMULA TION IN MANUFACTURING
distributed simulations are distributed mission training systems and
distributed collaborative engineering environments.
Distributed Mission Training Systems
Distributed simulation training systems have successfully replicated
battlefield conditions for soldiers and pilots. However, as the complexity of
command and control systems continues to grow, an unmet need has arisen
for joint coordinated mission training for participants at higher levels of
military hierarchies. The payoff to the warfighter will be the ability to
conduct various phases of mission execution within one training system,
on demand, with minimal human-in-the-loop coordination. For example, a
distributed simulation system for the U.S. Air Force Theater Battle
Management Core System may include computer-generated forces, terrain,
environment, and cognitive agents such as pilots to replace today's human-
support role-players. The combat scenarios developed can provide training
that differs from individual pilot training in several ways. Initially, skill or
positional training requires small-scale forces and scenario elements. As
the exercise level moves toward team or crew training, the simulation must
support the ability to scale the conflict toward a major theater war by
including many tactical missions, rules of engagement, special instructions,
and pre-mission planning considerations to handle the enormous number of
combat situations that could arise. The wide range of capabilities required
by such training systems demands an architecture that is scalable.
Distributed Collaborative Engineering Environments
With the advent of high-speed networking technology and recent
advances in modeling technology, a distributed and collaborative
engineering environment is closer to reality. Several previous studies and
ongoing efforts have used various terms to describe related or similar
concepts, including simply "collaborative environment" (SBATF, 1998~;
"advanced engineering environment" (NRC, 1 999a); "advanced acquisition
environment" (Hollenbach, 2001~; "collaborative enterprise environment"
(William, McQuay, U.S. Air Force Research Laboratroy, briefing to this
committee); and "collaborative engineering environment" (Crisp, 2002~;
among others. This committee has chosen the term "distributed and
collaborative engineering environment" to capture this general concept,
some aspects of which are described below.
A distributed and collaborative engineering environment would
include realistic, multiscale simulation models of all components of a
system, including human beings and engineered systems such as weapons
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67
systems. Such an environment would also include the ability to define a
complete system by composing models of individual components, thereby
simulating the complete system behavior and performance long before it is
built. Simulation models could be distributed and developed by multiple
organizations in widely scattered locations in such a manner that they
would interoperate. Specific "application services" could be provided for
use by simulation federations such as calculations of electromagnetic
propagation through the atmosphere, as is being done by the Joint Virtual
Battlespace (JVB) federation using simulation services provided by various
U.S. Anmy Research, Development, and Engineering Centers (RDECs)
(Richardson, 2002~. Such simulation models or application services could
be sold or leased on a per-use basis. A distributed and collaborative
engineering environment would be a completely digital, networked
software system in which design and manufacturing engineers and
organizations would collaborate to simulate and design complex new
systems or to upgrade and maintain legacy systems. Such a design
environment should have the ability to store comprehensive design
information in virtual inventories so that one could quickly retrieve
previous design solutions. The infonnation contained in these virtual
inventories would encompass the complete life cycle of products, from
initial design requirements to detailed functional decompositions, from
computer-aided design (CAD) data to process plans. Moreover, this
information would be stored not just as data but rather as semantically
enhanced information, allowing for intelligent retrieval, fixture expansion,
and sharing across interdisciplinary and organizational boundaries.
Software tools would be seamlessly integrated using agent-based
architectures.2 Such integration would occur at the syntactic level
("agentization" of software components) as well as at the semantic level
(ontologies3 for interoperability), so that new tools could be dynamically
incorporated into the system with little or no programming effort. Analysis
of designs and replacement systems would occur completely digitally
through virtual systems prototyping (Sinha et al., 20011. A composable
simulation environment that integrates behavioral models with structural
models would allow designers to quickly evaluate and compare a large
number of design alternatives (Diaz-Calderon et al., 1998~. This design and
simulation environment would accommodate both the fluidity and
2 An "agent-based architecture" is based on the notion of a software agent. A "software agent"
can be thought of as a highly encapsulated piece of software that may be autonomous and also
capable of negotiation.
3 In the context of knowledge sharing, the term "ontology" means a specification of a
conceptualization. That is, an ontology is a description (like a formal specification of a
program) of the concepts and relationships that can exist for an agent or a community of
agents. Researchers have designed ontologies for the purpose of enabling knowledge sharing
and reuse (Gruber 1993).
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MODELING AND SIMULATION IN MANUFACTURING
uncertainty inherent in earlier functional and conceptual design and the
level of complexity involved in detailed design. Beyond the functional
verification of designs, virtual prototypes would also be used for the
analysis of the manufacturing process, maintenance, repair, logistics,
training, and disposal of systems. Therefore, these virtual prototypes would
provide a common infrastructure enabling specialists in different
disciplines to evaluate the system from all life-cycle viewpoints.
Support for the evolutionary aspect of both design information and
the design process would exist. Instead of assuming that static
considerations led to each design decision, design representations in the
virtual inventories would capture the full design rationale not only the
design options that were chosen, but also other possible options, and the
information used to choose among them. In addition, evolution of the
design process would be facilitated through dynamic, extensible
ontologies.
The existence of virtual inventories would significantly reduce the
lead time and the cost of on-demand manufacturing and procurement of
replacement parts and subsystems. This would in turn reduce both
downtime of critical defense systems and the life-cycle cost of deploying
these systems. Virtual inventories would exploit the knowledge residing in
legacy systems that contain designs and process plans by providing ways
to index data for search and retrieval and by adapting the data to fit new
technological capabilities and new design requirements. The potential
benefit is large, as it is widely recognized that practicing engineers spend
large portions of their work time searching through legacy data, catalogs,
and earlier engineering projects. It would permit consideration of a wide
range of conceptual designs through use of tools that could provide fast,
easy solutions with sufficient fidelity for preliminary consideration of
designs. It would minimize errors, especially in long-term programs and
system upgrades, by permitting the capture of design rationale for parts and
systems and identifying ramifications of design changes during the product
life-cycle, preventing many costly and potentially life-threatening errors.
A distributed and collaborative engineering environment would give
designers the ability to assess whether functional design requirements are
met, with need for much less physical prototyping. Perhaps only in the
final design stage might a physical prototype be necessary. Elimination of
physical prototyping would result in considerable savings of both time and
money. Designers would be able to evaluate more design alternatives and
receive immediate feedback on design decisions, thereby developing better
final designs. Virtual prototypes would also allow designers to consider
life-cycle issues, such as evaluating manufacturing and assembly
requirements throughout the design process, thereby avoiding costly and
time-consuming engineering change orders. The prototypes would further
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69
make it easier to generate maintenance and service instructions. The use of
agents and ontologies would provide a way to coordinate the activities of
multiple designers who might be separated in both geography and time. By
combining an agent-based architecture with the use of ontological
translation mechanisms, integration would be provided at both the
syntactic and semantic levels. This would make it easy to integrate a wide
range of heterogeneous tools, including a variety of legacy systems (e.g.,
CAD tools and databases) and tools yet to be developed. Overall, improved
modeling and simulation tools would contribute to the ability to produce
more robust designs and permit their integration into complex systems-of-
systems, as well as improving the ability to support these design over
extended product life-cycles.
Limitations on Advanced Distributed Simulations
The growth in the number of participants and the desire to share
greater amounts of information are placing increasing demands on
bandwidth and computational power. Attempts to overcome bandwidth
limitations have tended to concentrate either on increasing the available
bandwidth or on minimizing the demand for bandwidth made by
applications. Methods of minimizing demand include data compression
and multicast routing systems that incorporate software-based area-of-
interest managers to direct packets across a network to particular groups of
listeners. High level architecture (HLA) supports exploitation of
multicasting hardware at the middleware layer by providing so-called data
distribution management services. These services allow objects to specify
attributes that they will publish and attributes to which they subscribe as
well as associated regions in routing space. When publication and
subscription regions overlap, attribute information flows from publisher to
subscriber. The goal is to send data only where and when it is needed.
To date there has been little interaction between these two approaches
(NRC, 1997), although a solution to the bandwidth problem must come
from a combination of these two to produce efficient use and allocation of
bandwidth in accordance with application requirements. Any form of
information technology combines information generation (computation and
real-world inputs) and information transmission (communication). As is
well known in computer performance modeling, for the best
cost/performance ratio, generation and transmission must be matched to
each other. If transmission capacity exceeds computational capacity,
information overload occurs. Conversely, faster generation is pointless if
transmission is the bottleneck. In today's technology, bandwidth demands
exceed supply so that minimizing application bandwidth requirements and
allocating bandwidth among applications are required to make simulation
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MODELING AND SIMULATION IN MANUFACTURING
possible. However, even with a significant increase in bandwidth supply,
the latter approaches are still needed to match available computational
capacity and maximize effective use of resources.
ENTERPRISE SYSTEMS
An "enterprise system" is a consistent suite of interoperable
application programs that serves all major functions of a business
enterprise, including product design, manufacturing, cost accounting,
human resource management, sales and marketing, and purchasing.
Enterprise systems had their origin in computer-integrated manufacturing
(CIM) systems and evolved to materials requirements planning (MRP),
enterprise-level systems, supply and value chains, and enterprise resource
planning (ERP) systems, using commercial software tools such as
Statistical Analysis System (SAS). The envisioned future defense
acquisition process, SBA, will have to operate within the context of future
commercial enterprise systems. SBA must therefore include consideration
of the enterprise level of system acquisition that integrates concern for
manufacturing with other major enterprise functions, such as cost
accounting, human resource management, sales and marketing, and
purchasing.
Computer networks and the lnternet, in particular, have become a
universal medium for enterprise-level software deployment. The network
operating environment now greatly stretches the range of scalability, from
a few users to millions of simultaneous users. This is true not only of
consumer-oriented retail operations on the Internet, but also of business-to-
business e-commerce and deployment of enterprisewide systems. The
Internet is increasing in connectivity and node capability (Stiles, 2001),
providing a highly interconnected and computationally powerful medium
for companies to increase outsourcing arrangements and self-organize into
v irtua] enterprises (B in stock, 2 0004 .
However, many obstacles to achieving a true enterprise system
remain. The increased connectivity and capability create new complexity
and dynamics (the Internet as a holistic system), which have been neither
fully understood nor addressed. In addition, designers of the architectures
of such extended enterprise systems must consider many factors, including
organizational issues governing the interactions of people and
organizational structures and constraints governing these interactions;
collaborative decision making, including the sharing of collaborative
knowledge and the protection of proprietary knowledge; supply-chain
structures for the integration, coordination, and management of activities in
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71
networked environments; design approaches that facilitate outsourcing or
strategic alliances; and decentralized mechanisms or policies that cause
desirable emergent behaviors. Finally, the design of enterprise systems
demands scalable system architectures.
The committee used information on enterprise-level modeling and
simulation functions developed by IMTI and extended it to form Table 4-1.
The first two columns of the table summarize IMTI conclusions regarding
the current state of practice and the ideal state of enterprise modeling and
simulation functions. The committee developed the material in the
remaining two columns regarding real-world limitations on each function
and the requirements needed to achieve the ideal state.
As indicated in Table 4-1, limitations on the development of
enterprise modeling and simulation functions include the availability of
models at the strategic, industry, and technology level; the availability of
enterprise-level cost and resource models; the availability of supply-chain
models; and limitations on communication and computation resources,
such as bandwidth, computation speed, and memory. Requirements to
reach the ideal state include scalable enterprise systems; integrated model
frameworks with real-time data management; standard frameworks for
model construction; and integration of scalable enterprise systems in the
supply chain.
Generalized Er'`erprise Reference Architecture and Methodology
"Enterprise engineering" is a term used for the set of activities dealing
with designing and redesigning business entities, either industrial systems,
administrative systems, or service systems (Vernadat, 1996~. Enterprise
engineering goes through the following stages: business entity
identification; business entity conceptualization and definition;
requirements definition; design specification; implementation description;
building and testing; and finally, release of the system into operation. Once
the business entity is in operation, such activities may continue with
performance evaluation, change management, and continuous process
improvement. A framework for enterprise engineering recently proposed
by the enterprise integration community the Generalized Enterprise
Reference Architecture and Methodology (GERAM:uses the computer-
integrated manufacturing open system architecture (CIMOSA; see below).
This architecture, shown in Figure 4-1, can be used to understand the
interrelationships among enterprise processes, application domains, and
industries, and it provides an overarching framework/architecture for
integrating different simulation models in different domains.
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Representative terms from entire chapter:
commercial manufacturing
72
MODELING AND SIMULA TION IN MANUFACTURING
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M&S IN COMMERCIAL MANUFACTURING 73
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to ISO WD]5704, "Requirements for enterprise-reference architectures and
methodologies."
GERAM and CIMOSA are important in developing standards for
SBA's dependence on enterprise integration and the role of M&S in
supporting such integration (see Chapter 5~.
CONCLUSIONS
The next-generation M&S capabilities discussed in this chapter are
essential to the achievement of the future defense acquisition vision. The
ability to simulate complex systems-of-systems is essential for evaluating
the mission effectiveness of future weapons systems within SBA.
Advanced distributed simulations are essential for achieving combat-
M&S IN COMMERCIAL MANUFACTURING
75
training simulations that include models of weapons systems and for
achieving the vision of distributed and collaborative engineering for
commercial and defense manufacturing. Finally, enterprise systems are the
ultimate goal for M&S in commercial manufacturing. SEA will have to
function in the context of these systems and will benefit from the
development of the capabilities needed.
In order to achieve the capabilities for simulating complex systems-
of-systems, additional R&D is needed in the following areas: library of
sensor, weapon, and command and control communication platform
models that could be composed to model military systems-of-systems
operating in physically realistic environments; a simulation environment
that supports Monte Carlo and/or optimization simulation involving large
numbers of runs with different random number seeds and parameter
settings; a simulation environment capable of efficiently managing the
interactions among a large number of mobile, communicating entities at a
significant level of resolution; a simulation environment that supports
ergonomic and informative human/computer interfaces; a simulation
environment that supports interactive scenario and experiment definition
and analysis; improved organizational behavior models; and improved
human behavior models.
In order to achieve the necessary capabilities for advanced distributed
simulations, additional R&D is needed in the following areas: scalable
architectures; realistic, multiresolution models; 4 composability; virtual
inventories to include life-cycle information on products and history and
rationale for design; agent-based architectures; and management of
bandwidth and computational power limitations.
In order to achieve the necessary capabilities for enterprise systems,
R&D is needed in the following areas: scalable enterprise systems,
integrated model frameworks with real-time data management, standard
frameworks for model construction, and integration of scalable enterprise
systems in supply chain.
4 The need for research on multiresolution models is explained in Chapter 5.
