How AI Supports Supply Chain Planning and Forecasting

Spreadsheets and gut instinct can’t keep up with today’s supply chains.

Not when demand swings unpredictably, suppliers face constant disruption, and customer expectations continue to rise.

Gartner analysts forecast that, by 2030, 70% of large orgs will adopt AI-based forecasting to predict future demand. This marks a significant shift from manual processes to automated demand planning.

When AI supports forecasting and planning in this way, the payoff extends beyond improved predictions.

Organizations build supply chains that can flex under pressure. They absorb volatility, respond faster, and, most of all, turn uncertainty into a competitive advantage.

McKinsey’s 2025 AI survey found that 90% of organizations now regularly use AI in at least one business function — underscoring the growing prevalence of AI in operational roles.

This article explores how AI enhances forecasting and planning. And how these intelligent capabilities drive improvements across the entire supply chain.

The Planning Gap: Why Traditional Methods Can’t Keep Up

Most planning still runs on disconnected systems that can’t handle rapid change.

Planners waste hours reconciling conflicting data from ERP, CRM, and IoT systems instead of making strategic decisions.

Finance leaders face misaligned operational and financial models, discovering margin erosion too late to course-correct.

Legacy tools weren’t built for conditions that shift hourly. Traditional planning works in cycles: forecast quarterly, plan monthly, react when things break.

This approach fails when supplier lead times creep up unexpectedly, regional demand shifts overnight, or transportation delays threaten delivery commitments.

AI closes these gaps by unifying data and showing how decisions impact the entire business in real time.

AI analyzes millions of data points —sales trends, IoT signals, supplier performance insights, economic indicators, etc.

Then, it uses that information to generate accurate, continuously updating forecasts.

In functional terms, AI models have been shown to reduce forecasting errors by 30–50% and translate that improvement into up to 65% fewer lost sales due to stockouts.

AI makes planning continuous.

It detects demand shifts weeks earlier, simulates thousands of scenarios in real time. Then, it takes action—automatically adjusting production, inventory, and purchasing plans as conditions evolve.

The Foundation: Three Layers That Power Modern Planning

AI-powered supply chain planning depends on three interconnected layers: a unified data foundation, continuous intelligence, and human-guided action.

Here’s how they work together:

Unified Data Foundation

A unified data foundation eliminates silos by connecting ERP, warehouse management, transportation systems, and IoT sensors into a single source of truth.

Instead of reconciling conflicting spreadsheets across disconnected platforms, planners see demand, inventory, capacity, and constraints in one place—continuously updated in real time.

This shared view is critical.

Gartner consistently identifies data fragmentation and poor data integration as the main barriers preventing organizations from realizing ROI from AI-driven planning and decision intelligence investments. Without a unified foundation, even advanced AI models fail to scale effectively.

Continuous Intelligence

AI continuously monitors this unified data stream, detecting patterns that humans would miss.

When supplier lead times creep up, regional demand shifts unexpectedly, or transportation delays threaten delivery commitments, machine learning flags these risks before they appear in traditional monthly reports.

McKinsey research shows that organizations using AI-driven forecasting and demand sensing can reduce forecast error by 20–50%, enabling earlier interventions to prevent stockouts, excess inventory, and service failures.

Human-Guided Action

Human-guided action is the final—and most critical—layer. Here, human expertise and judgment are augmented, not replaced, by AI.

The platform translates real-time intelligence into actionable decision models.

Planners can run scenario analyses using natural language, asking questions such as, “What happens if we shift 20% of production to our Texas facility?” Within seconds, the system returns scenarios that show cost, service-level, and risk trade-offs.

Humans retain ownership of strategy and final decisions, while AI handles the heavy lifting—modeling thousands of scenarios, simulating outcomes, and updating recommendations as conditions change.

Organizations using Dynamics 365 Supply Chain Management with Fabric and Power BI gain these layers natively—no custom integrations or fragmented tools required.

As a result, teams move from data analysis to decision-making in minutes, not days.

AI Capabilities in Action

Continuous Forecasting That Adapts

Traditional forecasting relies on historical sales data and struggles to account for sudden market shifts.

AI captures and analyzes structured and unstructured data to detect demand signals earlier and more accurately. Now, forecasting occurs continuously, iteratively, and in real time—not as a static annual exercise.

Dynamics 365 Supply Chain Management integrates enterprise-wide data—ERP, CRM, warehouse automation, IoT sensors, supplier portals, and external feeds such as POS data and weather alerts. The platform’s AI-driven forecasting models automatically adjust predictions as new insights arrive.

Multivariate analysis

AI analyzes weather patterns, economic indicators, social media sentiment, and geopolitical events.

For example, Unilever uses AI to correlate weather patterns with ice cream sales to enable hyperlocal forecasting and detect emerging consumer trends weeks earlier than traditional analytics. This level of precision enables them to position inventory exactly where demand will spike.

Advanced modeling

Dynamics 365 Demand Planning analyzes historical data alongside real-time market trends, automatically handling outliers and continuously improving forecast accuracy.

According to MIT research, LLM-driven scenario tools can compress a week’s worth of planning analysis into minutes—freeing planners to focus on strategy rather than spreadsheet manipulation.

Continuous recalibration

Unlike periodic manual forecasting, AI updates predictions based on real-time data streams. A sudden demand spike, a late supplier shipment, or a weather alert instantly triggers new predictions and updated planning recommendations.

According to KPMG, this continuous approach enables supply chains to anticipate disruptions and develop virtual models to test strategies before committing resources.

As an example, a consumer electronics manufacturer using D365, Microsoft Fabric, and Azure IoT can detect declining supplier performance through IoT-enabled quality metrics. The platform’s built-in AI might recommend shifting production to another plant.

Once the decision is made, it adjusts reorder points in real time—preventing a stockout could’ve cost the company millions in lost sales and damaged customer relationships.

Test Decisions Before You Commit

Digital twins are virtual replicas of end-to-end supply chains that allow planners to simulate decisions before implementation.

According to IDC, enterprise AI spend is projected to rise from $307 billion in 2025 to $632 billion by 2028, with significant investments going toward simulation and digital twin solutions.

Azure Digital Twins enables “simulate-then-act” capabilities. Planners run thousands of “what-if” scenarios to evaluate how disruptions would affect service, costs, and inventory. Think port closures, tariff hikes, supplier failures, and so on.

This identifies capacity “breakpoints” without placing actual strain on facilities or employees.

For example, a pharmaceutical distributor mitigating hurricane risk, a digital twin can model the cost of relocating 30% of coastal inventory inland. AI would optimize facility locations and transportation, predicting service-level impacts. After reviewing simulation options, the distributor might select a plan that maintains 99% service levels while reducing weather-related disruption risk by 60%.

Plan in Plain English

Where predictive AI shows what’s likely to happen, generative AI shows what to do about it.

Demand Planning in Dynamics 365 offers a no-code approach, allowing planners to perform what-if analyses in minutes without data scientist skills.

It facilitates collaboration through Microsoft Teams, enabling stakeholders to reach consensus on demand plans without endless email chains or meeting cycles.

With Dynamics 365 and Copilot, planners ask questions in natural language. For example:

“What’s our best option if West Coast demand jumps 15% next quarter and we want to protect margin while keeping service above 98%?”

Copilot analyzes demand plans with cursor prompts that explore specific data points in forecasts. It provides insights into notable shifts, trends, and anomalies across multiple dimensions, returning natural-language summaries and visuals that make information digestible for executives and operational teams alike.

Copilot generates scenario options—cost, service, risk implications, etc. Then, it recommends a path forward in seconds.

Planners spend less time building scenarios and more time making judgment calls, aligning stakeholders, and preparing the organization for what comes next.

What once required a week of analyst time now happens during the planning meeting itself.

AI That Acts, Not Just Advises

Agentic AI is a hot topic right now – poised to dominate the supply chain in 2026 and beyond.

The term refers to systems that reason, plan, and execute tasks autonomously, going beyond simple analysis.

Instead of just offering insights, agentic systems:

• Reason, plan, and execute routine operations without human intervention.

• Orchestrate cross-departmental processes (e.g., a Sales Order Agent automating order processing).

• Coordinate across functions (e.g., demand forecasting, procurement, and logistics) to address bottlenecks, including rerouting shipments or adjusting production schedules when delays are anticipated. If a delay is predicted, an agent might autonomously reroute shipments or adjust production schedules without waiting for human approval on routine decisions.

According to the World Economic Forum, agentic AI will likely transform the supply planner role. With powerful, data-driven tools, human planners will shift from tactical execution to strategic orchestration.

Let’s say you’re a food distributor. An AI agent can detect an imminent cold-storage failure using IoT sensor data and instantly reroute perishable shipments, adjust schedules, and notify customers—without human intervention.

Here, what could have been a major crisis was a seamlessly managed event.

Meanwhile, supply chain teams were able to stay focused on more important work – handling exceptions, outliers, and strategic value delivery.

Business Impact: From Operational Excellence to Strategic Advantage

Align Planning With Financial Goals

In many organizations, supply chain planning and financial planning still run in parallel.

AI provides finance, operations, and supply chain leaders with a standardized, data-driven view of how plans affect the P&L and balance sheet—eliminating the disconnect that leads to missed targets and last-minute surprises.

When Dynamics 365 Supply Chain Management syncs with Dynamics 365 Finance, CFOs see real-time P&L implications of inventory and sourcing decisions.

A procurement manager’s choice to expedite a shipment or shift to a lower-cost supplier updates margin projections and cash-flow forecasts in real time, not weeks later, during the close.

For example, a CFO using Dynamics 365 and Power BI models the impact of tightening safety stock on slow movers. Within seconds, they see cash freed, potential stockout risk, and OTIF impact by customer tier. They decide which customers justify higher safety stock and which SKUs to lean out.

This then unlocks significant working capital without jeopardizing key relationships. The analysis that once required a finance analyst two weeks of spreadsheet work now takes two minutes.

Integrated Business Planning Becomes Reality

Modern integrated business planning unifies operational and financial planning on a single data and modeling layer.

AI sits on top of this foundation to automatically align demand, supply, and financial plans, so teams work from a single version of truth rather than reconciling three different departmental forecasts.

Microsoft Fabric and Power BI provide the shared visibility layer. A VP of Operations and a CFO review the same scenario, ask questions in their own language through Copilot, and instantly understand OTIF, cost, margin, and cash-flow implications—no translation layer required.

Decision intelligence in action:

• Sales leader: “What’s the service-level impact if we commit to this 10-day delivery SLA?” Copilot shows whether production capacity, inventory, and transportation can support it—and at what cost—before the contract is signed.
• Supply chain director: “What’s our risk exposure if this supplier is disrupted?” Copilot highlights alternatives, cost deltas, and expected lead times, enabling proactive dual-sourcing decisions rather than reactive scrambling.
• CFO: “How does shifting 20% of production to Mexico affect working capital and margin?” Copilot reveals full P&L and balance-sheet implications before decisions are made, preventing costly mistakes.

This clarity reduces debate, eliminates redundant review cycles, and builds confidence across the organization. Decisions that once took multiple meetings and conflicting analyses now happen with shared understanding in a single session.

Deliver Better Customer Outcomes

AI planning shifts focus beyond internal efficiency to customer outcomes.

By forecasting at granular levels—regional, customer, and channel—organizations allocate inventory and capacity based on profitability, priorities, and service commitments rather than relying on simple averages.

DHL’s AI-powered forecasting platform has improved prediction accuracy to 95% across 220 countries. Their “Smart Trucks” use machine learning to dynamically reroute deliveries based on traffic, weather, and new pickup requests—demonstrating how AI translates forecasting accuracy into operational excellence.

AI integrates sustainability considerations, such as emissions targets and sourcing rules, into plan generation.

A retailer prioritizes inventory for key regions and e-commerce while choosing lower-emission routes. A construction firm plans material deliveries and crew schedules to reduce on-site waste, truck rolls, and idle time while balancing operational efficiency with environmental responsibility.

Final Thoughts

AI and real-time data have redefined supply chain forecasting and planning.

Organizations that adopt unified platforms and integrated intelligence adapt faster, plan more confidently, and align more closely with financial and customer goals.

Velosio helps enterprises implement and optimize Microsoft’s intelligent supply chain tools—from Dynamics 365 Supply Chain Management and Demand Planning to Microsoft Fabric, Copilot, and Azure Digital Twins—to strengthen planning, resilience, and competitiveness. Contact us to support your strategy.