From Insight to Action: Turning LLM-Built KPI Trees into Decision Engines

decision_prompt = """
We have the following KPIs:
1. On_Time_Delivery_Rate
2. Stockout_Rate
3. Support_Tickets_per_Order
4. Order_Cycle_Time_Days
5. CSAT (goal)
For each, state whether it is controllable (decision variable) 
or dependent (outcome), and describe possible business levers 
to change it. Return JSON.
"""
from openai import OpenAI
client = OpenAI()
resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role":"user","content":decision_prompt}]
)
print(resp.choices[0].message.content)

import numpy as np, pandas as pd

# coefficients learned from the earlier validation layer
weights = {
  "On_Time_Delivery_Rate":  0.65,
  "Stockout_Rate":         -0.40,
  "Support_Tickets_per_Order": -0.55,
  "Order_Cycle_Time_Days": -0.25
}

def simulate_csat(on_time_delta=0, stockout_delta=0, 
                  tickets_delta=0, cycle_delta=0):
    base_csat = 4.0
    delta = (
        weights["On_Time_Delivery_Rate"] * on_time_delta +
        weights["Stockout_Rate"] * stockout_delta +
        weights["Support_Tickets_per_Order"] * tickets_delta +
        weights["Order_Cycle_Time_Days"] * cycle_delta
    )
    return round(base_csat + delta, 2)

# Example: improve on-time by +5%, reduce stockouts by -2%
simulate_csat(on_time_delta=0.05, stockout_delta=-0.02)

summary_prompt = f"""
We simulated customer satisfaction under various scenarios:
1️⃣ +5% On_Time_Delivery, −2% Stockouts → CSAT 4.32
2️⃣ +10% On_Time_Delivery, −1% Support_Tickets → CSAT 4.45
Write a concise managerial summary highlighting trade-offs 
and top priorities.
"""
print(client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role":"user","content":summary_prompt}]
).choices[0].message.content)

from scipy.optimize import minimize

# cost per 1% improvement for each KPI
cost = {
    "On_Time_Delivery_Rate":  80_000,
    "Stockout_Rate":          50_000,
    "Support_Tickets_per_Order": 60_000
}

budget = 300_000

def objective(x):
    on_time, stockout, tickets = x
    return -(simulate_csat(on_time, stockout, tickets) - 4.0)

def constraint(x):
    return budget - (
        abs(on_time)*cost["On_Time_Delivery_Rate"] +
        abs(stockout)*cost["Stockout_Rate"] +
        abs(tickets)*cost["Support_Tickets_per_Order"]
    )

cons = {"type":"ineq","fun":constraint}
x0 = [0,0,0]
res = minimize(objective, x0, constraints=cons)
res

opt_summary = f"""
Optimization results:
Improve On_Time_Delivery by 6%
Reduce Stockout_Rate by 3%
Reduce Support_Tickets_per_Order by 2%
Predicted CSAT +0.38
Write a 3-sentence action plan.
"""
print(client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role":"user","content":opt_summary}]
).choices[0].message.content)

from dowhy import CausalModel
model = CausalModel(
    data=df,
    treatment="on_time",
    outcome="csat",
    common_causes=["stockout_flag","support_tickets"]
)
identified_estimand = model.identify_effect()
causal_estimate = model.estimate_effect(
    identified_estimand,
    method_name="backdoor.propensity_score_matching"
)
print(causal_estimate)

cause_prompt = f"""
We found that improving On_Time_Delivery by 10% increases 
CSAT by {round(causal_estimate.value*10,2)}%.
Explain why this relationship is causal and what assumptions 
it depends on.
"""