Defence Preparedness and Economic Rationalists
A System Dynamics Framework for Resource Allocation
Keith Linard Philip Sloper
School of Civil Engineering David Paterson
University of New South Wales Computer Science Corporation (A ustralia)
(Australian Defence Force Academy) Email: psloper@ act.csc.com.au
Email: k-linard@ adfa.oz.au djp@ act.csc.com.au
Abstract
In 1996 the Australian National Audit Office (ANAO) reported critically on the Defence Department's force
capability and preparedness methodology: preparedness objectives did not adequately address interactions
between Army, Navy and Air Force; competing resource implications were not adequately understood;
performance management information systems for preparedness planning were inadequate. The ANAO
directed the Department to develop management systems, which address the interaction between defence
budgets and the operational, logistical, and training dimensions of defence preparedness.
This paper presents the system dynamics framework, developed at the Australian Defence Force Academy
(ADFA), which is seen by senior defence executives as the basis for responding to the ANAO requirements.
ADFA has recently reported on the feasibility of using system dynamics modelling to achieve these goals.
Full implementation of the project would involve integration of the suite of models into the Australian Defence
Headquarters command and control system. The paper outlines the development of a ‘virtual’ management
leaming laboratory, a joint research project between ADFA and Computer Science Corporation (Aust), which
aims to explore the use of these modelling tools in building shared understanding of complex problems.
KEYWORDS: Defence preparedness; system dynamics; learning laboratory; Lotus Notes[] ; Powersim{] .
Background on ADFA Modelling of Defence Preparedness
A System Dynamics stream was introduced in undergraduate and postgraduate teaching programs at ADFA in
1989. In 1993 the Directorate of Army Research and Analysis requested the system dynamics group to explore
the use of system dynamics modelling for Army preparedness planning. Subsequently, formal presentations by
ADFA were made to the Deputy Chief of the General Staff, the Chief of the Air Staff and key corporate
planning staff. In 1994 the Directorate recommended greater use system dynamic modelling tools to assist
management understanding of complex ‘feedback’ areas such as preparedness and mobilisation planning.
In 1997, the Preparedness and Mobilisation Directorate of Australian Defence HQ contracted the ADFA system
dynamics group to advise on the development of a system dynamics based ‘Defence Preparedness Resource
Model (DPRM). DPRM was to be that element of the Defence’s Command & Control System which addressed
the linkages between specified levels of preparedness and the resources required to achieve them, including:
a) identification of resources required to achieve and maintain defined levels of preparedness;
b) identification of the resources required to change between levels of preparedness;
c) the potential impact on ADF preparedness of changes in resource allocation; and
d) development of advice for Government on the resource implications of change in levels of preparedness.
Prototype models were developed (Submarine Squadron, Army Aviation units and some small combat
elements) and a report was presented addressing the project scope, modelling methodology, project
management and risk management. Formal presentations on the proposed concept, including a general
overview system dynamics, have been made to top defence executives. The project has now moved to a detailed
scoping phase.
Feedback and Delay in Defence Planning
Feedback and time dynamics are ubiquitous in defence operations. The functioning of any military unit is
influenced by complex interactions between international politics, national policy, major capital acquisition and
general resourcing decisions, personnel management, logistics management, training doctrine etc.
For example, individual and collective skill levels decay over time if they are not being exercised, necessitating
retraining. (This is most obvious for pilots, where a given number of flying hours per month is required by law
to maintain currency.) But increasing training activity ‘uses up’ equipment life, diverting resources to
maintenance and acquisition, and thereby removing resources from operations which are the very purpose of
Ss
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‘current
Capability Gap
ud Deployinent Time —>
Expansion
Directive
PLoc
Level of Capability
\
\
Normal Peace Time
Assembly
Activity Levels,
Period
Workup
Training
Operational
Viability Period
ma Notice to —e+Deployment™ Time
Persone!
f
Facies &
E Consunpets
{ Taine a
{ suppor } {
Aequtemshs auinmese
Readiness
Sustainability
Figure 1: Conceptual preparedness concepts
driven scenario time frame.
the training. Compounding the
difficulties for management planning
are the lengthy delays, often 10 years
or more, that accompany major
capital re-equipment decisions.
The Defence Organisation's
conceptual preparedness model
contains two dimensions, illustrated
in Figure 1. The vertical axis is a
relationship of required Capability
over time. E.g., the peacetime level
of capability (PLOC) is set at the
minimum value consistent with the
ability to move (through re
equipment, recruiting and training)
to the target operational level of
capability (OLOC) within the policy
The horizontal axis contains two components, Readiness and Sustainability, that are the net result of complex
interactions between personnel, equipment, facilities and consumables with individual and collective training.
Much of the difficulty in developing resource preparedness strategies lies in any rigorous quantified
Personnel Factors
[= Training Time Injury Rate
Percieved Threat —
a+ + +t +
' i
People in Trained | - de
[Cay FS — pasanea |
+ (sees t _2°* Training Factors
- an =A aagad
i STE Target
Equipment Factors a Training —! Training Fly
1 Rate TS intensity ~ 7 Decay
' te lee ‘
Industral f mo
ike! H ' a -| Training
y ' Ws. s. F|_ Recency | +
+ t i Se
Acquisition - +1 Saas
Rate ] ~ | Equipment on Ss
Line
ay. 5 +] b+
Repair Rate Breakdown
+ =
i Equipmentin | =
Repair
Figure 2: Interactions among preparedness sub-systems
understanding of how the
components of preparedness
combine to create a position on the
capability axis. The complex
interactions between the components
of readiness can be described
through the use of influence
diagrams, illustrated in Figure 2,
where the ‘dashed’ arrows represent
some of the interactions between the
different components.
These relationships are all time
dependant, and interact with varying
delays. It is the ability to represent
these that is the strength of systems
simulation modelling, and which
can not be addressed in traditional
resource management tools such as
databases.
Force readiness, however, has a much more significant and more complex dimension of relation-ships. Figure
2, in a sense, represents the ‘vertical’ relationships. That is, the interrelationships within any given
organisational strand from top budgetary and command directives, through doctrine, staffing and training
decisions to day-to-day operating decisions. The prototype modelling at ADFA has concentrated on developing
‘templates’ for this ‘vertical’ dimension of several force elements, for example with the Army Aviation
Regiment:
* Top level resourcing decisions impact on available flying hours and the availability of spare parts.
+ Personnel decisions impact on the availability of skilled pilots for tasking and training.
* Personnel decisions also impact on demand for training flying hours as distinct from operational support
hours.
* Maintenance decisions impact on the number of aircraft available, and hence on their intensity of use.
* Doctrinal, tasking and local management decisions complete the environmental complexity.
“Horizontal” inter-relationships across functions However, few military units operate
Managing resource allocation across competing areas in isolation. There are ‘horizontal’
> interdependencies between force
elements. Thus the submarine fleet
must train with other surface and
air elements to reach full
operational capability. These
‘vertical’ and ‘horizontal’
dimensions of systemic inter-
relationships are depicted in Figure
3
<=
Personnel Costs
Operating & Majftenance Model Aggregation
\craining Congdmables A critical aspect of this modelling is
setting the appropriate level of
aggregation. It is be futile to try to
model the _ interrelationships
Figure 3: Two dimensions of systemic relationships between all personnel, training and
equipment categories. A critical step in modelling is identifying organisation features that are surrogates for
broad classes. For example, if pilot numbers are broadly proportional to other aviation staff (observers,
maintainers etc) modelling the relationships between pilot training and capability may suffice.
“Vertical” inter-relationships within a function.
Policy & operational management
The DPRM is an ambitious project, which is pushing the limits of the technology. There are no “off the shelf”
solutions. Whilst the ADFA research gives grounds for optimism, a multi-phase development process was
advised with clear risk management protocols. Objectives for the initial stage of development included:
* provide a robust ‘vertical’ model of a key force element from each of the Armed Services
* evaluate the different systemic features in modelling capital intensive versus person intensive units, multi-
role versus limited role force elements and short lead time versus long lead time mobilisation units
* analyse the systemic interdependencies between logistics and operational functions
* demonstrate feasibility to address ‘horizontal’ interrelationships between force elements that must operate
in conjunction with each other
Simulation Games and System Dynamics Models and Management Learning Laboratory
An important dimension of the ADFA systems modelling involves building client confidence in the work ... in
addition to ‘technical’ validation. The graphical interfaces for system dynamics modelling packages such as
Powersim{] make it relatively easy to communicate the logic of a model to subject area experts who provide the
initial input and who must validate the model output. Senior executives, certainly, want ‘technical’ confidence
in the model, but they are fundamentally concerned with its use as a tool to develop a shared understanding of
options and consequences. From extensive interactions within a relatively sceptical environment, our work
suggests that, in building a simulation model, careful attention must be given to the following areas:
+ the outcome to be tested - existing organisational performance indicators are often an inappropriate focus
* who are the champions - where simulations challenge corporate policy or where the issues cross
organisational boundaries, top executive participation is critical
* which business rules should be ‘hard wired’ into the model and which should be left to the players
* what are the characteristics of the ‘leamers’ (e.g. their motivation, decision making style, learning style
etc).
As demonstrated in the work of Sterman, Senge and others, addressing systemic problems must go beyond
individual learning to the development of a shared understanding. The concept of the management learning
laboratory is important to this end. A learning laboratory is a ‘training’ workshop where managers experiment
with organisational interrelationships, by cycling back and forth between ‘war gaming’ and debriefing sessions.
The aim is collectively, to understand why the system behaves the way it does, and how they might modify their
behaviour accordingly. The simulation gaming capability of Powersim{] allows the combining of the system
dynamics simulation model with the learning laboratory.
In business and government, specialisation results in ‘walls’ or ‘stovepipes’ that separate different functions
into independent and often warring fiefdoms. A learning laboratory offers the possibility, in a non-threatening
environment, for the respective managers to understand how their performance (on which they are judged) is
impacted by the activities of other units. It enables managers to leam about the impact of delayed feedback
relationships. It challenges the validity of performance indicators and helps find better ones.
Towards a Virtual Learning Laboratory
A shortcoming of traditional workshop learning approaches is the artificiality of the environment. Managers
tend to be on their best co-operative rational behaviour, away from the distractions of telephones, meetings,
deadlines and all the other distractions that characterise their normal decision environment. ADFA, in
conjunction with Computer Science Corporation, is developing a ‘Virtual Learning Laboratory’, where the
simulation game can be played in a
(\ distributed environment, via Lotus Notes
Fan jo on groupware.
“a ~ / \ .
a SS t Lotus Lotus Notes provides the backbone of
S Virtual Learning Laboratory ~\ notes Mail | Australia’s Defence HQ top level Command
-——~ enables simulations across Mea and Control system. It is also widely used
a barriers of distance & time spent
mponent | Within the Army, Navy and Air Force. By
( integrating Powersim]] simulation models
e with Lotus Notes, game players can
\\ Lotus participate in the midst of the everyday
Lotus .
Notes Mail chaos of their normal work environment.
eee Feedback information from the model
simulation to the respective managers can be
provided in familiar corporate report
\ Domino
op oerrene ee feedback, and managerial responses can be
‘component’ fed into the model also in standard corporate
Figure 4: Lotus Notes with Powersim format. Figure 4 illustrates the
A virtual learning laboratory in normal work environments | communications structure of a prototype
simulation game for the Australian
submarine fleet.
Lessons from research thusfar
The flight simulator concept has attracted strong support from Defence top management. The process of
development has highlighted a number of invaluable lessons including:
¢ itis important to involve users in the development and validation of the simulation
* seemingly rigorous data sets are not always what they seem to be - the data may in fact be of limited value
because of lack of clear definition, quality control or both.
* every system has implicit, and often very critical, assumptions based only on ‘professional judgement and
experience’, which are so part of the culture that they are never challenged.
* the design of input and feedback formats is important and should, as far as possible, replicate the formats
used by the players in their normal jobs.
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CC BY-NC-SA 4.0