Systems Informatics and Analysis - Engineering and Science of Biomass Feedstock Production and Provision - page 194

while accounting for form, quantity, and quality criteria. The scenarios must be
consistent with the model being used for prediction.

• Performance improvement and optimization : For performance improvement, the

decision or control variables are fi rst identifi ed, and the feasible space of these
variables is systematically explored to determine the optimal set of variable val-
ues. Some of the approaches used for exploring parameter space include mathe-
matical/heuristics-based optimization, control theory, and rule-based systems.

• Recommendations : The fi nal step in systems analysis is to use scenario study

results to provide recommendations, which represent a shift from information
space to physical space. For BFPP systems, these recommendations could
include the crop management strategies, equipment selection, storage facility
location and sizing, or the transportation logistics.

The systems analysis work must also consider the system targets (criticality),
scenarios (boundary conditions), and the technology readiness levels (available
options and their readiness) to provide a sound design or systems confi guration
(see Fig. 8.2 ).
A system-level study can be based on two modeling approaches. One can develop
a case-specifi c, data-driven model, which is specifi c to the scenario being analyzed
and uses data related to that scenario [ 12 - 15 ]. It is also possible to develop a generic
model that is potentially applicable to multiple scenarios [ 9 , 10 , 16 , 17 ].
Simultaneously, a database pertaining to different scenarios may also be developed
and connected with the model. In the fi rst approach, the model is not extensible and
therefore the analysis results are case specifi c. The second approach is more desir-
able as the generic model and the accompanying database allow us to readily incor-
porate new scientifi c information generated through concurrent research. This
enables a near-real-time analysis to study the implications of the new scientifi c and
technological developments. Moreover, comparison of different scenarios is possi-
ble since the same set of assumptions is used. A generic model, however, is more
diffi cult to develop and is computationally more challenging. We have primarily
focused on such generic models and have discussed specifi c applications of those
models. However, at selected places, we have also presented discussion on case-
specifi c data-driven studies.

8.2.3 Decision Support Systems

Decision support systems (DSS) are information technology solutions that can be
used in complex decision making [ 18 ]. Specifi cally, a decision-making system can be
characterized as an integrated, interactive, and fl exible computer system that supports
all phases of the decision-making process [ 19 ]. Classic DSS include elements such as
sophisticated database management capabilities with access to a range of data, pow-
erful modeling functions accessed by a model management system, and powerful
and simple user interface designs that enable interactive queries, reporting, and
graphing functions [ 18 ]. The knowledge-based management subsystem is one of the
core elements of the DSS. Examples of DSS in agriculture are I-FARM [ 20 ] and