AI in the Chain

Introduction

Forecasting is no longer a back-office task. In today’s volatile supply chain environment—marked by shifting consumer behavior, persistent disruption, and tighter margins—forecasting must be fast, explainable, and aligned with decision-making.

Artificial intelligence (AI) and machine learning (ML) are transforming forecasting into a dynamic, iterative process. This playbook provides a step-by-step guide to building modern forecasting workflows, combining statistical models, machine learning, and generative AI to improve accuracy, interpretability, and agility.

Why AI Forecasting Must Evolve

Traditional “set it and forget it” models no longer work. Static parameters, manual overrides, and quarterly updates can’t keep up with the pace of change. Modern forecasting must respond to:

• Market shocks and demand variability
• Shortages and supply-side constraints
• Promotions and pricing strategies
• The need for faster, cross-functional planning

AI enables planners to detect emerging trends earlier, reduce bias and overfitting, simulate what-if scenarios, and make better trade-offs.

Forecasting Models: From Classic to Generative

A well-rounded forecasting stack blends time-tested statistical tools with newer machine learning and LLM-based support systems. Each model category serves a different purpose.

1. Time Series Models
Useful for stable demand patterns, with built-in support for trend, seasonality, and simple error correction. Examples include ARIMA, ETS, Holt-Winters, and TSB.

2. Machine Learning Models
Best for SKUs influenced by external drivers (e.g., pricing, promotions, events). Tree-based models like XGBoost or LightGBM learn from many features and adapt to nonlinearity.

3. Deep Learning Models
LSTM, GRU, and N-BEATS are ideal for complex or multivariate series but require large datasets and tuning. They’re best used for fast-moving, data-rich portfolios.

4. Generative AI / LLMs
Large Language Models (like GPT-4 or Claude) assist with interpretation, summarization, scenario generation, and planning communication—not numeric prediction.

Model Comparison Table

Enhancing Forecasting with Generative AI

Generative AI adds value across multiple steps in the forecasting process, especially in narrative interpretation, scenario planning, and collaborative workflows.

Prompt Examples

• “Compare forecast vs. actual for weeks 4–10 and explain the deviation trends.”
• “Simulate three Q4 demand scenarios with changing price and promo parameters.”
• “Suggest features to add to my ML model based on this dataset with sales, price, holidays, and promotions.”
• “Draft a short executive summary explaining this month’s forecast risk areas.”

These prompts help bridge technical analysis with stakeholder decision-making.

Clean Data Is Strategic: Detect and Correct Outliers

Outliers distort models, increase error variance, and reduce forecast trust. Instead of relying on rigid thresholds or simple filters, use predictive modeling approaches that compare actuals to expected values and evaluate error distributions to flag anomalies.

This process:

• Retains real demand signals
• Differentiates true anomalies from business-driven spikes
• Enhances forecast stability across retraining cycles

Avoid default z-scores or hardcoded limits. Always factor in business drivers and recent event context when labeling and correcting.

Don’t Rely on Legacy Segmentation

Volume or variability-based segmentation (like ABC/XYZ) is too simplistic. High-volume items aren’t always forecastable, and low-volume SKUs can be strategically critical.

Instead, segment based on:

• Forecastability (historical MAE, bias, error volatility)
• Promo dependency
• Supply and lead time risks
• Margin and strategic value

Use model performance to drive assignment. For example:

• Consistent, low-error SKUs → AutoETS
• Event-driven SKUs → XGBoost or LightGBM
• New launches → Analog forecast + manual review
• High volatility → Ensemble or hybrid models

Planning Needs to Be Dynamic, Not Static

Forecasting models must evolve with changing signals. Don’t treat forecasts as fixed for a quarter or rely solely on past data. Adaptive planning requires:

• Regular model updates (weekly/monthly cadence)
• Real-time risk signals (e.g., delays, shortages, POS spikes)
• Scenario simulations and fast overrides

Models should incorporate feedback loops and constantly compare expected vs. actual. This supports trust and responsiveness.

Implementation Blueprint

1. Data Preparation
   • Clean historical sales, filter out invalid transactions
   • Engineer lags, rolling averages, holidays, and promo flags
   • Add external variables (weather, marketing, pricing)
2. Model Testing
   • Evaluate multiple models with rolling cross-validation
   • Measure MAE, RMSE, bias—don’t rely on MAPE
   • Visualize forecast fit per product family
3. Segmentation + Assignment
   • Group products by forecast performance, not historical sales
   • Assign models per group and track ongoing performance
4. Forecast Generation
   • Run automated models per cadence
   • Layer in override rules for constrained SKUs
5. LLM-Augmented Communication
   • Use LLMs to generate summaries, flag anomalies, and write “forecast at a glance” narratives
6. Continuous Monitoring
   • Set alerts for high deviation
   • Refresh models as inputs evolve

Case Snapshot: Hybrid Forecasting in Action

A European consumer goods firm deployed a forecasting transformation across 4,500 SKUs. Their approach:

• Stable SKUs: AutoETS
• Promo-influenced: XGBoost + engineered features
• Launch SKUs: GPT-supported analog selection
• GPT-4 used for summary reports and exception handling

Results:

• 20% improvement in MAE
• 35% less manual rework
• 5 days faster consensus on S&OP forecasts

They adopted dynamic modeling and outlier prediction techniques, and transitioned from ABC-style grouping to performance-first segmentation.

Closing Reflections

AI-enabled forecasting is not about automating guesswork—it’s about bringing structure and speed to human judgment. With the right blend of models, prompts, and monitoring, forecasting becomes a dynamic business function rather than a retrospective spreadsheet.

Start small: pilot new models on high-impact SKUs, embed error analysis into your weekly rhythm, and let AI support your decision-making—not replace it.

References

1. Vandeput, N. (2024) A forecasting model for Animalcare, a veterinary pharmaceutical manufacturer and distributor
2. Cecere, L (2025) Driving Value from Supply Chain Planning