cpm-lib 1.0.4

Tool for calculating risk of change propagation in a system.

Change Propagation Library

This library is intended to assist in the calculation of change propagation (Clarkson et al., 2004) in a system. The library takes to DSMs as an input: one for likelihoods and one for impacts. These DSMs should be in a CSV format. Once the DSMs are loaded, a ChangePropagationTree can be constructed for each possible interaction. The ChangePropagationTree can then be polled for the risk and probability of changes.

Install

pip install cpm-lib

Use

First, create the DSMs using CSV-files as inputs. This is done using cpm.parse.parse_csv(), which returns a DSM created from the input CSV. Then, use the DSMs to form a ChangePropagationTree. This tree will calculate the risk of change propagating from one part of the system, to another.

import cpm.parse as parse
from cpm.models import ChangePropagationTree
dsm_likelihood = parse.parse_csv('./dsm-likelihood.csv')
dsm_impact = parse.parse_csv('./dsm-impact.csv')

# Calculate the risk of change propagating 
# from sub-system 0, to sub-system 3
start_index = 3
target_index = 0
cpt = ChangePropagationTree(start_index, target_index, dsm_impact, dsm_likelihood)
risk = cpt.get_risk()
probability = cpt.get_probability()

In this example, assuming a 4x4 DSM with the columns: A, B, C, and D, with the respective indices 0, 1, 2, and 3, the code above will calculate the risk of propagation from sub-system D to sub-system A.

References

Clarkson, P. J., Caroline, S., & Claudia, E. (2004). Predicting Change Propagation in Complex Design. Journal of Mechanical Design, 126(5), 788–797. https://doi.org/10.1115/1.1765117