Hierarchical Sales Target Cascading using Directed Acyclic Graphs (DAGs) in Python

To implement a pipeline-aware quota redistribution system that ensures fairness and operational health while adhering to strict constraints, we need to follow these steps:

1. Diagnose Pipeline Health: This involves calculating the coverage ratio (Current Pipeline / Cascaded Quota) for each node in the hierarchy.
2. Classify Nodes Based on Coverage Ratio: Determine if a node is healthy, at risk, or unhealthy based on predefined thresholds.
3. Redistribute Quotas Within Managers' Teams: Adjust quotas of individual contributors (ICs) within the same manager's team to balance coverage ratios while adhering to constraints.

Let's break down these steps with code snippets and explanations:

Step 1: Diagnose Pipeline Health

First, we need a function that traverses the DAG and calculates pipeline health for each node. This involves aggregating pipelines bottom-up from leaf nodes (ICs) to their managers.

python
 
1import networkx as nx
2
3def diagnose_pipeline(graph, cascaded_quotas, coverage_thresholds):
4    diagnostics = []
5
6    def resolve_ancestor_threshold(node, thresholds):
7        while True:
8            if node in thresholds:
9                return thresholds[node]
10            parent = next((p for p in graph.predecessors(node
11
12[Read the full article at Towards AI - Medium](https://pub.towardsai.net/hierarchical-sales-target-cascading-using-directed-acyclic-graphs-dags-in-python-1426c7980b87?source=rss----98111c9905da---4)
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