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Optimization & Simulation
A major component of our services is our Quantitative Decision Support
(QDS) Methodology - an approach that includes powerful mathematical
techniques such as simulation and optimization.
These capabilities help our clients identify "what if" scenarios
and to analyze the sensitivity of the environment to changes in constrained
resources. With DACOM's approach, this advanced decision-support process
is as simple as changing the data table associated with an object,
clicking the run button and reviewing the new output report.
Excellent answers... no analytic skills needed!
Although this methodology employs extremely sophisticated tools, you
do not have to be an experienced mathematical analyst to use them.
We capture the domain-specific knowledge base in the model for you.
All you have do is develop your "what if" questions. Our
advanced optimization and simulation algorithms do the rest - operating
in the background to provide solid answers on every scenario.
Total scalability for use throughout the enterprise.
Our QDS model lets you analyze the tactical, operational, and strategic
levels of your enterprise within a single planning and analysis model
supported by a unified data model. This means you can benefit from
a decision support model which is as valuable to your first-level
managers as it is to the Commander or CEO of your enterprise.
Far more than spreadsheet results.
The DACOM methodology gives you far more than a spreadsheet that simply
accounts for cost and lets you change results by changing cells. The
DACOM approach uses modeling techniques such as linear and non-linear
programming, integer and mixed-integer programming, and state-of-the-art
simulation capabilities to go a vital extra step and identify the
optimal solution. This is of value because it:
- Helps us
identify bottlenecks that occur when we reach an upper bound on
one or more resources.
- Identifies
the marginal value of one more unit of resource, which is called
the opportunity cost.
VALUE EXAMPLE:
In a recent logistics model, we included a constraint on the number
of hours available to do scheduled quarterly maintenance on a truck
at two different maintenance activities. The decision-support model
told us not only the number of hours to use at each maintenance activity
which satisfies the quarterly maintenance requirement at the least
cost, but also told us the marginal value of adding additional maintenance
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