Analytical Techniques

Analytical Techniques
Analytical techniques use mathematical formulas to derive an optimal
solution directly or predict a certain result.
Analytical techniques are used mainly for solving structured problem.
Example _ Resource allocation or inventory management.
Algorithms
Analytical techniques may use algorithms to increase the efficiency of the
search.
An algorithm is a step by stem search process for arriving at an optimal
solution.
Blind & Heuristic search approaches
A set of possible steps leading from initial conditions to the goal is called
the search steps.
Two search methods are considered.
1. Blind search
2. Heuristic search
Blind Search
Blind search is arbitrary and not guided.
Complete enumeration, for which all the alternatives are considered, and
therefore an optimal solution are discovered.
Incomplete, partial search, which continues until a good enough solution,
is found.
The method is not practical for solving very large problems because too
many solutions must be examined before an optimal solution is found.
Heuristic search
It is possible to find rules to guide the search process and reduce the
amount of necessary computations. This is done by heuristic search
methods.
Heuristics – derived from the Greek work for discovery
Heuristics are decision rues regarding how a problem should be solved.
In contrast, guidelines are usually developed as a result of a trial & error
experience.
In practice, such a search is much faster and cheaper than a blind search
and the solutions can be very close to the best once.

Evaluation: Multiple goals, sensitivity analysis, what if and Goal seeking

1) Multiple Goals 4) Trial & Error
2) Sensitivity Analysis 5) What if Analysis
3) Automatic sensitivity Analysis 6) Goal Seeking
Evaluation is the final step that leads to a recommended solution.
Multiple Goals
Today’s management systems have become more complex, and a single
goal is rare. Instead, managers want to attain simultaneous goals, some
of which conflict with each other.
Following difficulties occur when analyzing multiple goals. (Page 28 – 29)
Several methods of handling multiple goals can be used when working
with DSS.
1. Utility theory
2. Goal programming
3. Expression of goals as constrains, using linear programming
4. A point system
Sensitivity analysis
A model builder makes predictions & assumptions regarding the input
data, many of which deal with the assessment of uncertain futures.
Sensitivity analysis attempts to check the impact of a change in the input
data or parameters on the proposed solution.
Sensitivity analysis checks relationships such as...
o The impact of changes in external variables & parameters on the
outcome variables
o The impact of changes in the decision variables on the outcome
variables
o The effect of different interactions among variables
o The robustness of decisions under changing conditions
Sensitivity analysis are used for
o Revising models to eliminate too large sensitivities.
o Adding details about sensitive variables or scenarios
o Obtaining better estimates of sensitive external variables
o Altering the real world system to reduce actual sensitivities
o Living with a sensitive real world, monitoring actual results
continuously and closely.