Mastering Supply Chain Complexity with AI

Supply Chain Management combines market demand and operational capacity, where sales, procurement, production, and logistics pursue often competing goals. Here, at the crossroads of planning and product handover, companies make one of their most complex and costly decisions. Choices made here shape customer satisfaction, profitability, and resilience.

Many organizations explore AI and GenAI for task automation. While useful, this narrow view misses AI’s greater potential: enabling smarter, faster decisions where they matter most. Beyond automation, AI can unlock insights from unstructured data, support non-linear optimization, and help leaders navigate uncertainty with confidence. A pragmatic approach encompasses two core levers: capacity decision horizons and order book digitalization. Together, they form the foundation for AI-driven decisions that boost operational excellence, profitability, and resilience – in short: delivery performance.

Delivery performance suffers from complex, often intuition-based decisions

Over 90 percent¹ of supply chain leaders cite decision complexity as a major challenge, driven by competing objectives, poor metrics, and lack of transparency. On-time delivery - critical for customer satisfaction and profitability - remains elusive. The main barrier: interdependent decisions across planning and execution. In practice, these choices rely more on experience than on robust, real-time data. Consider the example of capacity procurement during the value chain design or setup phase. Procurement teams often prioritize cost, choosing low-cost suppliers in distant regions, resulting in longer replenishment times. Market priorities differ. While revenue growth demands speed, flexibility, and agility, these goals collide with cost efficiency. The time horizon adds to the complexity: capacity decisions for series products are made years before demand is known, forcing high-stakes commitments under uncertainty.

From crisis management to data-driven steering

This complexity shows why traditional methods fall short. Planners must balance shifting demand, capacity changes, supplier constraints, and production priorities while monitoring global supply chains for risks. However, without systematic, data-based processes, companies often overreact - placing extra material call-offs or overloading production. Intended to protect delivery reliability, these actions instead inflate inventory, extend lead times, and still fail to ensure performance. The result: costly decisions, higher working capital, slower cash conversion. Sustainable delivery performance demands a shift from intuition to transparent, data-supported orchestration. The goal is not replacing human expertise but augmenting it with decision intelligence for clarity, speed, and consistency across the supply chain.

For that matter, AI is pivotal for evaluating trade-offs and guiding resilient capacity. Beyond automating routine tasks, AI and GenAI deliver end-to-end transparency, extract insights from unstructured data, and support decisions in dynamic environments. By combining digital foundations with AI-driven intelligence, organizations can shift from reactive problem-solving to proactive, value-focused operations and turn complexity into a competitive advantage. That said, effective AI adoption requires organizational alignment and strong data governance: without clear roles, standards, and accountability for data quality, even advanced analytics fail to deliver value.

Building transparency across supply chain dependencies

In many organizations, supply chain data is still distributed across functions, leaving planners without a consistent, end-to-end view. As a result, teams often work with different assumptions and partial information, making synchronized decisions difficult and leading to sub-optimized outcomes. What’s missing is a shared logic and a common source of truth that enables fast, confident decision-making across the value chain.

Effective supply chain steering starts with transparency on internal and external dependencies. The horizon model creates this clarity by defining planning horizons that show where flexibility exists and where commitments are fixed. It distinguishes planning layers - from rough planning and smoothing to sequencing and execution - while highlighting flexibility in production and procurement. It also sets freeze points for orders, quantities, or variants. This shared logic aligns decision rights and planning constraints across functions.

The model also clarifies targets such as flexibility ranges, horizon-based accuracy, and stability guidelines. This balance of responsiveness and reliability reduces unnecessary re-planning and escalation-driven decisions. Ultimately, the horizon model enables a common steering logic that drives consistent, predictable delivery performance.

Fast, smart, strategic: driving supply chain intelligence

Instead of building monolithic data lakes, leading firms use agile approaches to quickly connect critical data sources like lead times and capacity flexibility. Advanced analytics platforms integrate heterogeneous data without disrupting existing systems, while visual workflows bridge technical experts and operational teams. Strong governance ensures data quality and accountability. The solution follows a three-step modular approach:

1. End-to-end transparency
   Automated workflows keep the horizon model updated. When demand shifts, the system flags critical impacts, alerting planners in real time.
2. Scenario planning with recommendations
   Optimization models provide weighted options, enabling planners to evaluate scenarios and balance trade-offs between inventory costs and delivery performance.
3. External factor intelligence
   AI agents scan external sources - supplier news, geopolitical risks - and turn insights into proactive recommendations, shifting from reaction to anticipation.

This structure emphasizes speed, agility, and foresight, positioning AI as a driver of decision excellence - not a technical add-on. While GenAI dominates headlines, the AI toolbox for supply chains is broader. Four methods stand out:

• Causal AI: Uncovers patterns and root causes in complex systems.
• Advanced optimization: Finds optimal solutions under given constraints using complex mathematical models, increasingly enhanced by machine learning.
• Foresight analytics: Anticipates patterns to reduce uncertainty, for example by improving demand forecasts.
• Natural language processing (including GenAI): Manages large volumes of unstructured data - a key capability for managing complexity.

These methods go beyond the GenAI trend and form a solid foundation for AI-supported decision-making in supply chain planning.

Fast, transparent, resilient: digital order book 

Once transparency is established via the horizon model, a digital order book consolidates all relevant data - orders, capacities, and supply - into a single source of truth. It tracks every order from placement to handover, including configuration, materials, routing, and delivery deadlines. By integrating data across systems, it provides continuous end-to-end visibility on dependencies, bottlenecks, and priorities. Advanced optimization models process this data to professionalize decision-making.

Turning complexity into actionable options 

Defining an optimal plan under multiple constraints requires advanced optimization models - not intuition. These models simulate thousands of scenarios, quantify trade-offs, and present planners with actionable options rather than isolated assumptions.

• Target dimensions - What do we aim to improve?

Typical objectives for improvement include OTIF (on-time in-full) performance, plan adherence, delivery flexibility and responsiveness, throughput time, warehouse and transportation cost, carbon emissions, and energy consumption.

• Levers – What Can We Influence?

Points at which targeted intervention can influence the above encompass the manufacturing setup, product-to-product allocation, program adjustments, supplier selection and volume commitments, as well as geopolitical exposure.

• Constraints – What Are the System Limits?

Both hard and soft restrictions apply, such as supplier risks and geographical limitations, production capacity and flexibility ranges, material availability and the degree of digitalization of the involved stakeholders.

Decision support, not automation 

AI-enabled optimization thereby strengthens - not replaces - human judgment. It creates a comprehensive decision space, enabling planners to compare options objectively and prioritize based on strategic intent. This shifts roles from firefighting to value-oriented, proactive orchestration: evaluating risks, selecting resilient paths, and aligning stakeholders analytically.

Three core advantages of the digital order book stand out in particular:

1) Efficiency
Centralized data and AI-driven recommendations reduce manual coordination and enable proactive workflows.

2) Responsiveness
Real-time visibility supports agile responses to demand shifts and disruptions while ensuring transparency across stakeholders.

3) Reliability
Harmonized planning logic eliminates contradictions, enabling synchronized execution and improving delivery performance.

The future of supply chain decisions

As supply chains grow in complexity and volatility, timely, well-informed decisions will define competitive advantage. Yet many organizations still rely on intuition when navigating disruptions or allocating scarce resources - often resulting in costly missteps and inefficiencies.

AI-supported decision intelligence will change this paradigm. By combining real-time data, predictive analytics, and self-learning algorithms, companies can anticipate disruptions, quantify trade-offs, and act before problems escalate. This shift from reactive to proactive decision-making unlocks new levels of agility, reliability, and profitability.

AI is not a replacement for human judgment but an enabler to make decisions better and faster. To succeed, organizations must first build strong data foundations and end-to-end visibility that pave the way for advanced analytics and AI solutions. Safeguarded by an effective governance and change management, their integration can take a foothold in the organization. Once established, companies should start with high-impact use cases (e.g. forecasting, inventory optimization) to then scale up gradually.

Those who follow these steps without any further delay will turn decision-making from a risk into a source of sustainable competitive advantage.