Transforming logistics with AI in supply chain

Supply chains today do not suffer from a lack of systems. They suffer from a lack of adaptability. In a global environment shaped by volatile demand patterns, geopolitical uncertainty, and climate-related disruptions, traditional B2B supply chains, often built on legacy systems, are falling short.

Decision-making cycles are slow. Inventory is either overstocked or unavailable when needed. And operational costs continue to rise without delivering proportional improvements in customer experience. This landscape leaves little room for resilience.

The financial stakes are massive. According to McKinsey, supply chain disruptions wipe out nearly 45% of one year’s profits for companies every decade. The inefficiencies they stem from are estimated to cost businesses over $1.1 trillion annually.

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The state of B2B supply chain

In 2024, supply chain disruption was the top concern for 83% of global CEOs, according to a Gartner CxO Priorities Survey. The same year, BCG reported that more than $2.1 trillion in revenue was at risk globally due to persistent supply chain fragility, driven by climate events, geopolitical shocks, and technology limitations.

A 2025 World Bank study on logistics performance found that average order lead times have increased by 14% since 2019, while inventory carrying costs are up by 21% in North America and Europe. This represents a systemic erosion of efficiency in supply networks once optimized for scale.

In parallel, operational volatility has intensified. For example:

• Climate risk: The 2024 Thailand floods impacted over 120 industrial parks, halting electronics and automotive exports for weeks. Only firms using AI-powered climate analytics were able to reroute supply lines in time to avoid major disruptions.
  
  
• Geopolitical instability: The Red Sea shipping corridor crisis earlier in 2025 added 7–10 days to standard delivery cycles for European importers, disrupting over $100B in trade flows, per UNCTAD. Only a small subset of companies had dynamic supply orchestration models in place.

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Complexity has outgrown legacy systems

Modern B2B supply chains involve hundreds of suppliers, thousands of SKUs, and operations spanning multiple continents. Yet many organizations still run critical functions like demand planning, inventory management, and transportation routing on legacy ERP systems not built for real-time data ingestion or dynamic response.

In a 2025 Deloitte supply chain AI maturity audit, 68% of surveyed B2B firms acknowledged their current systems were inadequate for predictive or real-time operations. Most rely on periodic batch data updates, manual forecasting methods, or disconnected planning tools.

This structural misalignment is not just inefficient. It is expensive. Companies with digitally immature supply chains experience:

• 65% more stockouts
  
  
• 40% higher expedited shipping costs
  
  
• 35% longer lead time variability
  
  

These inefficiencies translate into hard financial outcomes. Ineffective inventory management alone costs global firms $1.1 trillion each year.

Top AI in supply chain B2B usecases

AI in supply chain is already reshaping the way B2B organizations plan, operate, and respond. What distinguishes AI in supply chain optimization from traditional technologies is its capacity to continuously learn from data, detect shifts early, and act at a pace that human decision-making cannot match.

What’s changing is not just the level of efficiency, but the nature of control. AI enables supply chains to operate with anticipatory logic. This allows the technology to adjust sourcing strategies, reroute shipments, or reallocate warehouse resources based on real-time signals.

The next sections examine how this intelligence is being applied across the supply chain optimization AI ecosystem globally. Each presents a distinct opportunity to shift from static process management to intelligent system design.

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From reactive planning to predictive precision

Traditional forecasting often depends on historical sales data and fixed planning cycles, leaving organizations exposed during demand shocks or seasonal volatility. AI changes this by enabling systems that learn continuously and adjust forecasts in real time.

Key enablers include:

• Deep learning models like LSTM and transformers that capture complex time-based patterns across geographies and product categories
  
  
• Real-time data feeds from POS systems, weather forecasts, promotional calendars, and macroeconomic indicators
  
  
• Digital twins that simulate multiple demand scenarios and help stress-test supply strategies
  
  
• Reinforcement learning models that adapt based on actual outcomes and improve forecast reliability over time
  
  

These models integrate directly into ERP and supply chain planning platforms, supporting auto-replenishment, optimized safety stock, and dynamic allocation. The result is greater accuracy, lower working capital, and a supply chain optimization startegy that learns as it operates.

Example:
DHL integrated AI-powered predictive maintenance across its logistics network, using real-time sensor data to forecast equipment failures. This approach reduced maintenance costs by 25% and decreased unplanned downtime by 35%. 

Additionally, DHL achieved a 10x return on its initial investment in the technology. These improvements have optimized operational efficiency and minimized disruptions. The system has significantly enhanced the reliability of DHL’s logistics operations.

Real-time and predictive risk management

Modern supply chains are exposed to a wide range of risks like supplier instability, geopolitical conflict, regulatory shifts, and economic volatility. These disruptions often originate far beyond the first tier of suppliers, making traditional risk management tools inadequate. AI in supply chain enables companies to build a more comprehensive, real-time picture of exposure across the entire value chain.

How it works:

• Graph AI maps multi-tier supplier networks, identifying relationships across ownership structures, geographies, raw materials, and logistics dependencies
  
  
• NLP models monitor global data sources—including news articles, government announcements, regulatory filings, and social sentiment—to detect early signs of disruption such as political unrest, labor strikes, or commodity shortages
  
  
• Predictive analytics evaluate how specific events, such as sanctions or currency fluctuations, may impact lead times, landed costs, or compliance risk
  
  
• Economic indicators and geopolitical risk models are continuously factored in to forecast exposure at a country, region, or supplier level
  
  

Example:
General Mills implemented an AI-powered system for real-time supply chain AI monitoring and procurement optimization. By analyzing data from suppliers, the system proactively mitigates risks related to packaging and delivery.

This AI-driven approach has saved the company approximately $40,000 per day, totaling $14 million annually, and reduced decision-making time from a day to minutes. The system has also contributed to a 30% reduction in manufacturing waste, aligning with sustainability goals.

Intelligent freight optimization 

Freight is still managed by fixed contracts and legacy planning cycles. AI introduces dynamic optimization that considers real-time variables to improve logistics performance.

Key capabilities include:

• Algorithms that evaluate lane volume, market pricing, carrier reliability, and fuel trends to determine the optimal route, mode, and carrier
  
  
• Reinforcement learning models that adjust routing decisions based on actual delivery outcomes
  
  
• Real-time benchmarking tools that detect cost inefficiencies by comparing live rate data across brokers and platforms
  
  
• Digital twins that simulate and recommend shipping scenarios based on delivery windows, cost constraints, and capacity
  
  

Example:
In partnership with a global client, a provider of frontline productivity tools, we developed an Asset Management app that integrates with their existing AirForms workflow system. 

The goal was to enable technicians and managers to access only the most relevant information at the moment of need, whether it’s troubleshooting data for a technician on-site or high-level asset performance trends for a business executive.

By organizing equipment data, documents, parts, and service history into a unified, role-based platform, the app empowers users to act faster and smarter. This reduced downtime, improved service accuracy, and enabled Atheer’s customers to move from reactive to proactive service operations.

Recommendation engines for decision support 

Supply chains generate massive data volumes, but most teams struggle to translate it into timely action. AI-powered recommendation systems surface the most relevant decisions based on context and historical patterns.

In practice:

• Machine learning models identify anomalies, trends, and thresholds to recommend actions like supplier substitution, inventory transfers, or pricing changes
  
  
• Dashboards and planning tools are enhanced with real-time suggestions, reducing manual effort and decision fatigue
  
  
• Recommendations become more accurate over time as the models learn from user responses and feedback
  
  

These systems embed intelligence into daily workflows, allowing faster, more consistent decisions across planning, procurement, and logistics teams.

Efficiency through automation and supply chain optimization

Supply chain AI reduces operational drag by automating repetitive tasks and continuously optimizing key processes. This increases throughput and frees up human capacity for higher-value activities.

Use cases include:

• RPA combined with ML for tasks like invoice matching, customs processing, and order validation
  
  
• AI-based labor planning tools that adjust staffing and scheduling in real time based on order loads and service targets
  
  
• Process mining systems that analyze bottlenecks, simulate process changes, and track performance improvements
  
  

The goal is not workforce reduction, but intelligent orchestration where AI handles the routine, and people focus on oversight, problem-solving, and strategy.

Example:
Kimberly-Clark, a global leader in consumer goods, implemented AI-powered systems to optimize inventory management across its packaging operations. By analyzing sales data, historical trends, and shelf movement, the system automated reordering processes, ensuring optimal stock levels. 

This integration led to a 17% reduction in holding costs and improved order fulfillment rates across regions.

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NL to SQL: Empowering non-technical users with instant insights

Accessing supply chain data often depends on technical fluency or support from analysts and data teams. Business leaders and operations managers may have urgent questions, but must wait in a reporting queue or rely on someone who can write SQL. This slows decision-making and creates bottlenecks across the organization.

AI bridges this gap through natural language interfaces that allow users to interact with complex datasets conversationally. Instead of writing code, users simply ask questions in plain English—and the system responds with structured insights.

Key features include:

• Large language models trained on enterprise schemas, such as supply chain, inventory, or logistics databases. These models understand the underlying relationships between data tables and can accurately translate English questions into optimized SQL queries.
  
  
• Conversational AI interfaces, similar to ChatGPT, that allow for back-and-forth dialogue. Users can clarify, refine, or build on previous questions without starting over.
  
  
• Real-time data integration with BI platforms or internal dashboards, so outputs are presented as live charts, tables, or visual summaries—instantly.
  
  
• Contextual understanding, meaning the AI knows what “on-time delivery rate,” “inventory turns,” or “average delay per route” means in the context of your business data.
  
  

Real-world example: Conversational AI intelligence for maritime supply chain insights
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Insifi, built by Aubergine, is an AI agent-powered platform designed for complex maritime logistics operations. One of its most powerful features is a conversational interface that enables users to ask operational questions in plain language, without writing SQL or relying on analysts.

The agent taps into real-time data on vessel positions, port conditions, order flows, and inventory, and returns insights through clear visualizations or summaries. This eliminates the traditional bottlenecks of data access, making deep operational intelligence accessible to any team member.

By bridging the gap between natural language and structured data, Insifi empowers logistics teams to make faster, smarter decisions, even in one of the world’s most unpredictable supply chain environments.

‍The future of AI in B2B supply chain optimization 

AI is now embedded across every stage of high-performing B2B supply chains. It enables better forecasting, more resilient sourcing, efficient production, smarter logistics, and faster support.

The benefits are already measurable. Organizations are reducing inventory costs, minimizing downtime, improving delivery speed, and raising customer satisfaction, all by integrating AI into core operations.

As volatility and complexity continue to rise, static processes will only fall further behind. AI systems give supply chains the ability to adapt in real time and scale without adding cost or risk.

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Now is the time to act.

If you're looking to improve performance across your supply chain, we can help you get started.

Book a consultation with our team to explore how AI can be applied to your operations.

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