Today the U.S. supply chain is full of challenges, including labor shortages, shipping delays and unpredictable demand. That’s why accurate forecasts and flexible forecasts are a must-have. Traditional planning methods can no longer handle the pace and complexity of today’s supply chain.

But artificial intelligence (AI), machine learning (ML), and advanced supply chain management (SCM) software are transforming the way companies plan, adapt, and execute their supply chain strategies. This post will explore AI in the supply chain, how AI and ML are improving forecasting and planning, and how modern SCM software enables organizations to act on those insights.

Supply Chain Best Practices

Download this guide to discover 15 supply chain management best practices that can help your company succeed and keep a competitive advantage.

The Limits of Traditional Forecasting

Traditional methods of demand forecasting rely on historical data and statistical techniques to predict future demand. While these methods can work well in stable environments, they may struggle with rapid change or complex market dynamics.¹ This can result in outdated forecasts that don’t reflect real-world changes in time to react effectively. That’s why leading companies are turning to AI and modern SCM platforms that can predict and respond with agility.

What AI and ML Bring to Forecasting and Planning

AI and machine learning programs are designed to analyze large data sets to detect patterns and continuously improve their predictions over time. Unlike traditional static models, they can adapt to new variables in real-time. ² Here’s how AI/ML enhance supply chain forecasting:

Demand Forecasting

ML algorithms analyze past sales, customer behavior, market trends, and external signals to produce more accurate demand forecasts. Example: A CPG company can predict holiday spikes more accurately by analyzing real-time POS data, influencer trends, and previous seasonal cycles—reducing out-of-stocks and overstocks.

Inventory Optimization

AI helps manage stock levels by accounting for lead times, supplier performance, and service levels, helping companies balance inventory and service risk. Example: A global electronics firm can use AI to determine ideal safety stock levels at regional distribution centers, adjusting for supplier delays or sudden demand shifts.

Scenario Planning

AI models simulate thousands of “what-if” scenarios to help businesses prepare for events like natural disasters, geopolitical disruptions, or supplier risks. Example: A fashion brand might simulate what happens if tariffs increase or if a key factory shuts down for two weeks—then adjust sourcing strategies accordingly.

Where SCM Software Fits In

While AI and ML can generate insights, SCM acts to turn those predictions into decisions and act on them. Today’s modern supply chain management systems include AI functionality, helping companies to take advantage of predictions as well as execution all within the same platform. Leading SCM platforms like SAP, Oracle, Descartes, and Manhattan offer built-in AI/ML capabilities, helping businesses automate planning, increase visibility, and adapt more quickly.

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Benefits of Combining AI with SCM Software

Below are the key benefits organizations are seeing when they integrate AI and SCM software.³

Higher Forecast Accuracy

AI-enhanced SCM systems can reduce forecast error by 20–50%.

End-to-End Visibility

Modern SCM software aggregates data across procurement, production, distribution, and sales, making planning a truly integrated function.

Agile Decision-Making

Scenario modeling and real-time dashboards allow businesses to act fast during disruptions or demand spikes.

Lower Inventory Costs

With AI-optimized replenishment and safety stock planning, businesses can reduce excess inventory without sacrificing service levels.

Cross-Functional Collaboration

Cloud-based SCM platforms connect stakeholders—suppliers, finance, logistics, and operations—within a single, real-time environment.

What to Look for in AI-Enabled SCM Software

If you’re evaluating SCM platforms to enhance your forecasting and planning, here are some features to focus on:⁴

Embedded AI/ML capabilities: Not just plugins, but native intelligence.
Real-time data integration: From ERP, WMS, IoT, and external sources.
Scenario modeling and simulations: What-if planning for risk mitigation.
Cloud architecture: For scale, flexibility, and remote collaboration.
Collaborative tools: Support for supplier portals and internal planning workflows.

Integration Matters

SCM software shouldn’t be another silo—it should connect to your other enterprise software systems to create a unified planning and execution environment.

Ensure your SCM platform integrates with:

ERP systems
CRM tools
Warehouse and transportation systems
IoT sensors and external APIs

The Road Ahead: Where AI + SCM Software Are Going

The evolution of AI in supply chain management is just getting started. Here’s what on the horizon:

Autonomous supply chains: Systems that sense, decide, and act on their own.
Digital twins: Virtual models of the supply chain to test and optimize in real time.
Sustainability modeling: AI optimizing routes and sourcing for lower emissions.
Generative AI: Assisting planners with real-time scenario creation and narrative forecasting.

Final Thoughts

Tomorrow’s supply chains won’t be managed through spreadsheets or instinct. They’ll be data-driven, enhanced with AI, and put to action by advanced software. Businesses that combine AI and ML with robust SCM platforms will see better forecasts, faster response times, and more efficient operations.

If your organization is still relying on outdated tools for demand planning or inventory management, it’s time to rethink. Whether you’re looking to update your SCM software, or explore what AI can bring to your company, the Ultra experts can help you get started. Take the next step by requesting your free discovery call today.