**What does the Solution Space look like**

– Problem solvers and analysts use the term “Solutions Space” to describe the graphical

relationship between the inputs (levers) they experiment with in their analysis and the

outputs (results) achieved .

– Originally, people thought of the solution space as being line ar .

– Then, our thinking evolved to understand that the solution space is no-linear .

– Lately, our thinking evolved to understand that the solutions space is not just

non-linear, but actually has multiple “local optimals”

– Add to that , the understanding that the solution space is not two dimensional. There

are numerous factors (operational levers) that impact output, resulting in a

multi-dimensional solution space. Picture the solution space as a “Mountain Range”

(pretty intimidating to think of it that way).

Reasonable Expectations of the Solution Space

– With any kind of analysis, you can never know the “Optimal” optimal solution, we can

only discover (through analysis or experience) a number of “local” optimal solutions.

– The number of “local” optimal solutions we discover is limited by our perception of

reality, and our understanding of the operational levers (dimensions) impacting the

solution space.

Solution Space Challenges

– First, to identify which of the “controllable” operational levers (solution space

dimensions) have the greatest impact on operational performance (output).

– Second, Narrow this list of operational levers to a manageable number. This is optimally

two to three (at most five) levers.

– Third, experiment with this manageable number of levers to identify the “Best Practical”

solution.

Solution Space Strategy

– If cost effective, first simulate the operations to 1) understand process behavioral patterns, so

as to 2) identify Best Practical Solution(s).

– If cost effective, follow up the simulation with an optimization-based analysis, so as to

identify as many Local Optimals as possible.

* Note:* when I use the term “simulation”, I am talking about what would be considered a

“statistically accurate” simulation. This implies that:

1) you utilize input Distributions,

2) run the simulation model for multiple replications,

3) you eliminate non-steady state statistics from the output results,

and 4) you develop Confidence Intervals for all critical output statistics.