Generative AI and Predictive Planning: New Horizons in Forecasting

Generative AI and predictive planning have become two of the key talking points when discussing the next generation of forecasting in supply chains. Until recently, forecasting was a discipline grounded in statistical models and machine learning algorithms that learned from historical data to anticipate future trends. However, in an environment defined by volatile demand, customisation, and growing business complexity, operations leaders now need to go a step further. They no longer want just a number; they also need to understand the why and how behind a prediction, create alternative scenarios, and make decisions quickly and collaboratively.

In this article, we will explore how combining generative AI with predictive planning adds a new layer of value to the forecasting process. We will answer key questions such as: what does generative AI really add to predictive models? What data and architecture requirements should be considered? How can its impact on business KPIs be measured? And what steps should organisations follow to integrate these capabilities into their supply chain management solutions?

Why Combine Generative AI and Predictive Planning

Traditional predictive models provide a solid foundation for anticipating demand. Thanks to techniques such as exponential smoothing, neural networks and hierarchical models, we can now estimate sales with greater precision. However, these models rarely explain why they predict a particular figure or help orchestrate decision-making. Generative AI complements this foundation by turning data into useful, actionable narratives. Instead of a list of numbers, decision-makers receive explanations, arguments and alternative plans.

From a business perspective, combining generative AI with predictive planning makes it possible to:

• Accelerate decision-making: generative algorithms can create thousands of what-if scenarios in minutes, making it easier to compare outcomes without lengthy meetings or manual analysis.
• Explain the forecast: generative AI interprets model outputs and translates them into natural language, highlighting the key influencing factors (promotions, weather, consumer signals) and the reliability of the prediction.
• Connect business areas: by presenting conclusions in a format that everyone can understand, it facilitates coordination between operations, finance and management.

This does not mean abandoning existing methodologies. In fact, not everything requires generative technology, and classical machine learning methods remain valid in many cases. Moreover, generative AI has a high computational cost and should only be applied where it provides clear added value. It is best viewed as a complementary layer that amplifies the capabilities of predictive planning.

From Predictive Models to Explainable Decisions

The outputs of a predictive model are often presented in tables and charts that require interpretation. Generative AI acts as an interface that explains forecasts in a reasoned and contextualised way. For example, if an algorithm predicts a 5% increase in demand for a product, generative AI can produce a text summarising the key factors (such as an upcoming heatwave or a promotional campaign), the level of confidence, and suggested inventory or production actions.

This level of explanation is essential for planners and management alike, as it supports decision-making in S&OP committees and eliminates the “black box” perception often associated with advanced models. The result is a forecast with context and a plan supported by a clear narrative that drives action.

From Operational Exceptions to Actionable Insights

Supply chains generate vast amounts of data that can hide important anomalies, items with unusual peaks, channels with atypical behaviour, or customers whose buying habits change suddenly. Generative AI can prioritise exceptions and present only those that impact KPIs.

For example, instead of reviewing 500 records with demand variations, the system could generate a summary highlighting the 10 SKUs most likely to cause stockouts, explaining why (weather changes, promotions, supplier issues) and proposing the necessary purchasing, production or replenishment actions. This ability to move from data to actionable insight increases efficiency and reduces team stress.

Reference Architecture for Integrating Generative AI and Predictive Planning

For generative AI to deliver value rather than become an isolated element, a robust architecture is required. This framework integrates data, models, processes and validation flows. The main components are outlined below.

Data Sources and Quality: Masters, Events and External Signals

The first step is to build a consistent, high-quality data foundation. Master data (SKUs, product hierarchies, calendars, units of measure, etc.) must be complete and standardised. It is crucial to integrate internal sources such as ERP, CRM, APS, WMS and OMS with external signals, real-time point-of-sale data, weather conditions, search trends and social media mentions.

A recent report from Georgetown University emphasises that organisations can process large volumes of data in real time to improve demand forecast accuracy and optimise production and inventory. The quality, completeness and synchronisation of this data are key for the generative layer to produce reliable outputs.

Feature Store, Classical Models and LLM-Orchestration

Once data quality is assured, the next step is to build a feature store, a repository that stores derived variables, transforms time series, and normalises external signals. This store enables the reuse of the same features across multiple models, version control and consistent evolution.

Traditional predictive models (time series, regression, Random Forests, gradient boosting, neural networks) are trained with these features and serve as the numerical foundation. Generative AI then steps in as the orchestrator: an LLM (Large Language Model) queries an internal corpus (documentation, manuals, model results) and generates explanations and scenarios.

Research by McKinsey has shown that, in some cases, generative AI can reduce the time needed to create documentation by up to 60% and cut administrative work by 10–20%. These gains come from automating report generation, summaries and narratives based on model results.

Integration with APS/ERP and “Human-in-the-Loop” Approval Flows

Integration with planning systems (APS), ERP or demand planning tools is essential. Only then can recommendations be converted into purchasing, production or distribution actions. Generative AI should expose its results through APIs and receive user feedback.

Within workflows, the human-in-the-loop (HITL) concept plays a crucial role. It means a human expert reviews and validates the AI’s suggestions before execution (for example, verifying purchase orders or production plan adjustments). This approach recognises that humans and AI do not compete but complement each other: the model provides speed and analytical coverage, while the expert contributes business judgement, tacit knowledge and ethical oversight. Combining human supervision with algorithms therefore reduces errors and accelerates system learning.

Governance, Security and Compliance in Generative AI and Predictive Planning

The power of LLMs comes with significant responsibilities. To integrate them safely into industrial environments, governance and security mechanisms must cover the entire data chain.

Traceability, Prompt Auditing and Version Control

Each time generative AI produces a recommendation, it is advisable to record:

• The prompt used (the full request with context) and its version.
• The data sources consulted (dates, tags, models) and the state of the feature store.
• The generated response and who reviewed it.

Keeping a decision history is not only necessary for regulatory compliance but also for understanding which prompts work best and for identifying potential biases or misinterpretations.

Privacy, Environment Isolation and Access Policies

LLMs process large volumes of data and text, making data protection essential. This can be achieved through:

• Environment isolation: separating training and production data, and maintaining different access layers (for example, encrypted data versus anonymised data).
• Access policies: applying the principle of least privilege so each user only sees what is necessary for their role.
• Anonymisation and masking of sensitive data.

A Data Loss Prevention (DLP) system should also be implemented to prevent the model from disclosing confidential information. When combining generative AI with predictive planning, configurations must ensure that privacy and supply chain security are preserved.

Measuring Impact: KPIs that Matter to the Business

Adopting generative AI in planning only makes sense if its impact on operations and financial performance is measurable. Let’s look at the key metrics used to assess its effectiveness.

Forecast Accuracy by Segment (ABC-XYZ) and Plan Stability

The first and most traditional metric remains accuracy. Instead of a single aggregated percentage, it is better to segment by economic value and variability per SKU (ABC-XYZ method). Thanks to generative AI, explanations can be produced by segment, allowing inventory strategies to be fine-tuned.

Plan stability is also critical: a highly volatile forecast forces frequent order reviews and increases re-planning costs.

Impact on OTIF, Average Inventory and Service Cost

The most relevant business indicators for a COO or CFO include:

• OTIF (On Time In Full): measures the percentage of orders delivered on time and in full.
• Average inventory: refers to the value of stock available over a given period. A more accurate forecast should reduce this without increasing stockouts.
• Service cost: includes transport, storage and last-minute actions to avoid stockouts.

The Georgetown Journal also reported that early adopters of AI in supply chains cut logistics costs by 15%, improved inventory levels by 35% and boosted service levels by 65%. While these figures refer to AI in general rather than generative AI specifically, they demonstrate that the technology can have a direct impact on operational KPIs.

MAPE/MAE by Cluster, Bias and Horizon Stability

MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) remain essential metrics. It is recommended to calculate them by cluster, as an A-class product can behave very differently from a C-class one.

Bias should also be monitored, when a model consistently over- or under-estimates. If generative AI combines models with opposing biases, it is important to detect and correct these deviations.

Horizon stability refers to how forecasts vary as consumption dates approach. A stable forecast enables smoother planning, while a volatile one forces constant re-planning and harms plant efficiency.

Drift Metrics and Inter-Model Consistency

Over time, consumption patterns and sales channels change, a phenomenon known as drift. There are two main types:

• Data drift: when input data distribution changes (for example, more products are sold online than in physical stores).
• Concept drift: when the relationship between input variables and outputs changes (for example, weather conditions have less impact due to widespread air conditioning).

Generative AI should monitor these shifts and alert users when a model needs retraining. It can also compare consistency across models and explain why one performs better than another.

Planner Productivity and Response Time to Exceptions

A less-measured but highly relevant indicator is planner productivity. Integrating generative AI into forecasting can reduce the hours teams spend gathering data, preparing reports and justifying changes. The time once spent on administrative tasks can instead be devoted to strategic analysis.

It is also important to measure response time to exceptions, from the detection of a major change to the execution of an action. Generative AI helps detect and prioritise exceptions, generate explanations and recommendations, and streamline approvals. This shortens the decision cycle and prevents potential losses.

Working Capital and Decision Cost

The final group of metrics concerns working capital. A more accurate and explainable forecast can reduce excess inventory, free up capital and improve cash flow.

At the same time, integrating generative AI reduces decision costs by automating tasks and minimising errors. Savings come not only from lower inventory levels but also from fewer urgent actions caused by inaccurate or poorly explained forecasts.

How to Adopt Generative AI in Predictive Planning

Integrating generative AI into forecasting is not a simple plug-and-play process. It requires a phased approach, always grounded in quality data and a solid governance framework. The following roadmap provides a practical guide.

1. Define the Scope and Objectives

Start with a pilot in a specific area, for example, a group of SKUs with high variability or a particular region. Set clear objectives such as improving MAPE by 5% or reducing inventory by 10%. This will help determine whether investing in generative AI makes sense.

2. Ensure Data Quality and Formalise Data Agreements

Make sure internal (ERP, WMS, TMS, CRM) and external (POS, weather, social media) sources are synchronised, share common definitions and are updated on a consistent schedule. Define responsibility agreements for data quality and exclusion criteria (for instance, removing outliers caused by one-off promotions).

3. Build the Feature Store and Train Baseline Models

Before applying generative AI, train and validate predictive planning models using best-practice statistical and machine learning techniques. Segment by clusters and fine-tune hyperparameters until you achieve a solid baseline.

4. Design the Generative Layer and Prompts

Create a knowledge corpus with internal manuals, inventory policies, catalogues, historical series data and model outputs. Write structured prompts that specify the context, objective and expected KPIs. Implement guardrails to prevent off-topic responses.

5. Integrate with Your APS/ERP and Define Review Workflows

Use APIs or connectors so that generative AI recommendations automatically update the master production plan, purchasing module or distribution plan. Define who validates each type of recommendation and within what timeframe. This step is especially critical in regulated environments.

6. Implement MLOps/LLMOps and Monitor Performance

Manage versions of models, data and prompts. Measure response latency, inference costs and changes in forecast metrics. Set up alerts to detect drift and downgrade or retrain models when necessary.

7. Measure and Communicate Results

Document the impact on KPIs (accuracy, inventory, OTIF, productivity, capital) and communicate the results across the organisation. Where possible, create dashboards showing the evolution of each indicator and comparisons between pre- and post-adoption periods.

8. Scale and Optimise

Once the pilot is validated, extend generative AI to other categories, regions or processes (for example, production planning, demand planning, replenishment or capacity allocation). Improve prompt engineering and model efficiency to reduce costs and response times.

A Planning Software Solution as a Key Success Factor

Throughout all these steps, it is advisable to rely on a supply chain management platform that already includes forecasting, capacity planning, procurement and distribution modules. Adding a generative AI layer can enhance explanation capabilities and enable “what-if” scenario generation directly within the planning environment.

Integrating Predictive AI into Forecasting Is the Next Step to Optimise Your Operations

The evolution of generative AI and predictive planning goes beyond improving forecast accuracy. It’s about elevating decision-making quality. By combining the robustness of traditional models with the synthesis and scenario-generation abilities of generative AI, organisations gain a tool that not only predicts but also explains and drives action.

In markets where customers demand speed and personalisation, transparency and agility are essential. Generative AI offers natural-language explanations, generates alternatives and alerts teams to potential risks. This means operating with less uncertainty, reducing manual work and focusing on strategic analysis, while freeing up working capital, improving liquidity and optimising costs.

That said, adoption should be thoughtful. It requires quality data, a strong governance model, well-defined performance indicators and human validation in decision-making. Only then will generative AI complement, rather than replace, existing forecasting techniques.

The opportunity is clear: harness this intelligent layer to anticipate volatility and make forecasting a more transparent, collaborative and efficient process.

If you’d like to explore how to digitalise your supply chain, contact our experts to learn how our software can evolve towards generative forecasting, combining the best of statistics, machine learning and generative intelligence. We’d love to hear from you!