115 lines
2.6 KiB
Markdown
115 lines
2.6 KiB
Markdown
---
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id:
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aliases: []
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tags:
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- destiny/uncertain
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- status/draft
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- topic/estimating
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- type/philosophy
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- authorship/original
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title: Estimating Methodologies
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dg-publish: true
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---
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# Estimating Methodologies
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## The 4 Traditional Methodologies
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> [!quote] Defense Acquisition University
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> There are four principal cost estimating methodologies:
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> 1. Comparison/analogy,
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> 2. Parametric,
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> 3. Detailed engineering/bottom up, and
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> 4. Extrapolation from actual costs.
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### Comparison / Analogy
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**Pros:**
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* simple to implement
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* quick in practice
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**Cons:**
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* unsatisfying input
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* unsatisfying output
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### Parametric
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**Pros:**
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* quick in practice
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* nuanced input
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* accounts for incomplete input
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* effectively accurate through the entire estimate process
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* well-suited for automation
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**Cons:**
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* complex to implement
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### Detailed Engineering / "Bottom-Up"
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**Pros:**
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* simple to implement
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* satisfying output
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**Cons:**
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* slow in practice
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* unsatisfying input
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* accuracy and risk depend solely on estimator
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* output dependent on complete input
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* ill-suited to automation
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### Extrapolation from Actual Costs
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Depending on the application this is one is either
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* better described as the previous 3
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* or not estimating at all.
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## In Other Industries
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There exist articles from software development project management resources
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stating essentially what I believe to be true,
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that parametric estimating is _more_ accurate than bottom-up,
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in contrast to common assumption.
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I suspect that industry tolerance for cost-modeling
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is related to the cost of estimation.
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Software is largely impractical to estimate bottom-up,
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whereas anyone can take off construction scope.
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Alternate theory:
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tolerance is related to the industry's ability to understand the model.
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Regardless, a continuum emerges:
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```
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^
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| Software
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| Defense
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| Construction
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v
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```
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Industries that would benefit the most from cost-modeling
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are least likely to consider it a valid method.
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These methods are actually the same, just different levels of abstraction.
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This can be observed in the _very_ subtle distinction
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between parametric and analogous estimating.
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```
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# of Parameters
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^
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| Bottom-Up (100+)
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| Parametric (10)
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| Analogous (1)
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| Extrapolation (0)
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```
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These different methods are just a response
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to different levels of requirement specificity.
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Parametric is obviously more accurate and precise than analogous,
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however the question not being asked
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is if there are diminishing returns to estimate specificity.
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Suppose there are: at some point then,
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there must then be a point at which **risk of human error**
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outweighs the value of more perfect information.
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