models have been used whenever complex problems arise that cannot be solved
by other means. However, as technology is evolving, modeling is becoming
a standard method for decision support rather than a last ditch effort.
For example, Simulation
studies during design and testing processes may reveal insurmountable
problems that could result in project cancellation, thus saving millions.
It is cheaper and safer to learn from mistakes made with a simulated system,
rather than in reality. Modeling does not only reduce cost and risk, but
also improve understanding of the system. Appropriately, business process
modeling is seeing a lot of industry interest. However, the associated
value has been relatively negated due to lack of implementation guidance.
In case you are not an operations research
professional, it's easy to be misled about the proper implementation and
realistic expectations from modeling solution. To compound the difficulty,
those promoting modeling and optimization software do very little to elucidate
Optimization, simulation and modeling are
terms used interchangeably or without regard to their precise meanings.
When searching for solutions, business houses find it either difficult
or don't know where to go or who to ask.
How do you work through the mystification
and yield from the advantages of these powerful techniques? Let's start
by understanding and chasing away some of the common pitfalls about implementing
Implementing one modeling solution or software
package may not solve the whole range of your problems. Model
need to be seen as the one serving to its precisely defined objective
functions and thus for different scenarios and objectives, different
solutions may be required. This may stand true; as there is a possibility
of having same mathematics for different looking problems, however similar
looking problems may not involve same mathematics.
Unlike other software applications, modeling
solution does require knowledge of business processes. To run
the model it is not sufficient to know about only business rules, constraints
and data inputs. Business domain knowledge and Modeling knowledge are
essential. Modeling is not only a mathematical work out but it also
carries insights of required process re-engineering. Mathematical setup
is usually constrained by customer requirements and current practices
and thus a modeler needs to know about what customer requires keeping
degree of precision and timeframe in mind.
Models need to be considered "suggestive",
as they may not give you cent percent accurate results for given situation.
Models are not reality; they are an extreme simplification of reality.
Deterministic models do not reflect the role of chance and do not provide
confidence intervals on results. Models that incorporate randomness
are harder to analyze than the corresponding deterministic models. Therefore,
Solution package should provide a way to manually override the generated
results before it can be accepted for further processing.
A modeling solution can't work
or run forever. Model is prepared to represent and/or optimize
current business process as per the current situation and is not entirely
futuristic. Models need to be returned to keep it relevant to the
Model may not always produce applicable results
or rather easy to apply results.Depending on the user input a
Model might result in to Sub-optimal solutions. Domain understanding
should be applied to interpret the results and make them more applicable.
Models may not immediately start giving you
the desired results. Like old wines, models give more accurate
and better results as they mature with either automatic feedback mechanism
or continuous upgrades. A gestation period should be considered for
each modeling solution that is being applied.
Models cannot be substitutes for sound business understanding. Models
can only speed up the time required for in-depth analysis but can not
make the decision to choose what level of analysis is necessary to achieve
the objectives. The process of growth and wisdom in model building is
generally accomplished only after a considerable amount of time spent
in actual practice. The limitations of the computer modeling must be well
understood by the analyst. If the computer modeling results are accepted
with complacency and a lack of scrutiny, and with an indifference to performance
correlations, then we are misusing this great tool, and fostering potential
2000-10 DecisionCraft Inc.