Question 1

Why do standard demand forecasting models fail in FMCG?

Accepted Answer

Standard models often fail due to 'Signal Latency.' They forecast based on Delivery Date (which includes logistical noise and delays) rather than Order Date (which captures true customer intent). Correcting this latency can improve accuracy from 60% to 95%.

Question 2

How does Survival Analysis apply to Supply Chain?

Accepted Answer

In perishable supply chains, inventory follows a decay curve, not a binary 'good/bad' state. Using Survival Analysis (Time-to-Failure modeling) allows businesses to route inventory based on predicted endurance, reducing spoilage claims by up to 50%.

Question 3

What is the O.L.C.D. Protocol?

Accepted Answer

The O.L.C.D. Protocol is a proprietary framework for AI reliability. It stands for Objective (P&L Metric), Learner (The Model), Controller (The Decision Engine), and Diagnostic (The Feedback Loop). It prioritizes system architecture over model complexity.

Question 4

What is the A.R.T. Matrix in AI Strategy?

Accepted Answer

The A.R.T. (Applicability, Readiness, Transformation) Matrix is Jitin Kapila's proprietary framework for auditing 'Innovation Theater.' It filters AI initiatives by determining technical Applicability (GenAI vs. Predictive), evaluating Data Readiness, and mapping specific Structural Transformations in the P&L.

Question 5

How does the Applicability Filter work?

Accepted Answer

The Applicability Filter diagnoses the 'Physics' of a business problem to prevent tool mismatch. It determines if a problem requires a 'Plier' (Generative AI for content/synthesis) or a 'Foundation' (Predictive AI for logistics/forecasting), identifying 'Logic Leaks' where capital is being lost.

Question 6

What constitutes AI Readiness?

Accepted Answer

In the A.R.T. framework, Readiness is a clinical audit of 'Data Adequacy.' It moves beyond data volume ('Big Data') to assess signal strength and granularity. It prevents enterprises from building expensive models on 'sand' (insufficient or latent data infrastructures).

Categories

The Logic Leak: Why Your AI Investments Are Bleeding Money

AI Strategic Skills: Where Should a CEO Draw the Line?

Why your GenAI strategy is just a pair of pliers in 2026

Why Most AI Strategies Fail

Decision-First AI: Why Data Should Follow, Not Lead

The AI Umbrella: A Simple Guide to What Actually Matters

TrieKNN: Unleashing KNN’s Power on Mixed Data Types

CrossTab Sparsity for Classification

CrossTab Sparsity

A flow to Test Your Hypothesis in Python

Adaptive Regression

The AI Cross Current Briefing