From One-Time Planning to Continuous Optimization: The AI Revolution in Supply Chain Network Design

Supply chain network design (SCND) is a strategic process aiming to optimize the structure and flow of an organization’s supply chain while supporting long-term strategic goals It determines multiple factors such as the number, location and roles of different suppliers, factories, warehouses and distribution centers, to ensure efficient flows of goods.

Historically, SCND was treated as a one-time optimization exercise. However, it has gained renewed importance due to evolving challenges. Increasing customer requirements, geopolitical tensions, disruptions but also technological advancements are reshaping how companies need to set up their networks.

Artificial Intelligence (AI), once a futuristic concept, is now transforming SCND. It brings dynamic, data-driven, and scalable capabilities that were previously unavailable. This enables SCND to evolve from a static process into a continuous cycle of review and refinement.

Focusing on a tactical and strategic level, in this article we will go through new capabilities that a refined SCND-process can offer along what has changed over the last years.

In recent years, agility and resilience have become strategic differentiators as the latest disruptions exposed the fragility of traditional supply chains. Therefore, it is crucial for companies to navigate two dominant challenges: unprecedented volatility and escalating complexity.

To begin with, supply chains worldwide are facing unprecedented volatility. Geopolitical risks, deglobalization, policy and trade disputes, market shifts and frequent disruptions expose vulnerable points across a supply chain. Companies are being pushed to reevaluate their global footprint and consider options such as near shoring, regionalization and multi-regional strategies to avoid the risks coming with the current political uncertainty.

At the same time, the goals that supply chains must support, sustainability, cost efficiency, customer experience, and responsiveness, are more complex than ever. Networks must align with these evolving objectives.

In this unstable environment, fact-based, real-time decision-making is essential. Therefore, organizations should not and cannot rely solely on intuition or outdated spreadsheets anymore. Advanced analytics and AI can model multiple scenarios, simulate upcoming disruptions and provide the user with insights in making informed decisions on trade-offs between service, costs and risk.

While traditional SCND methods laid the foundation for structured supply chains, they are no longer sufficient in a world defined by constant change and volatile environments. Common limitations include:

One-Time Effort:
Due to the necessary time to create static designs, they become often outdated until implementation.

Inflexibility:
Traditional tools don’t easily adapt to changes in demand, supply, or macroeconomic conditions, forcing the project team to restart the design process from scratch.

Siloed Decision-Making:
Disconnected decisions across departments lead to suboptimal outcomes, unaligned with the company’s strategic direction.

Lack of Scalability:
As data grows, traditional models struggle to cope with complexity, failing to account for all relevant factors.

AI is transforming SCND from a static exercise into a dynamic, continuous process (see figure 1).

Align
The first phase, ‘Align’, ensures that the simulation targets and goals are fully aligned with the business’s overall strategic goals, while ensuring the supply chain can support those priorities. Artificial Intelligence (AI) plays a role here by analysing large volumes of business data to uncover patterns in demand, margins, or supply bottlenecks. This enables companies to define the right KPIs and performance goals based on data rather than assumptions. AI can also support stakeholder analysis, identifying which functions or regions should be involved in the decision-making process.

Create
Once alignment is clear, advanced modelling tools and AI are used to create digital versions of potential supply chain configurations. This is where AI really shines: using machine learning algorithms, it can evaluate millions of scenarios, such as the impact of relocating a distribution center, a supplier shutdown, or geopolitical disruptions. With Gen-AI planners can quickly define the simulation requirements and describe what-if scenarios without programming knowledge. This helps to optimize trade-offs between cost, service levels, and resilience, something traditional models struggle to do at scale and speed.

Implement
After selecting the optimal design based on simulations and business targets, the implementation unfolds on two horizons. Below are just a few examples of possible adaptations:

• Short-term tactical levers (1 week – 12 months)
  • Reroute transport lanes across the network, use cross-docks or last-mile partners to bypass bottlenecks.
  • Re-allocate inventory across the existing network with AI-guided stock moves
  • Up- or downscale Third Party Logistics warehouse capacity as demand fluctuates

• Mid- to long-term structural moves (1 – 5 years)
  • Open, close, relocate distribution centers, warehouses or plants
  • Adopt near-shoring or dual sourcing strategies
  • Redesign SKU portfolio to reduce complexity in the Supply Chain, hence network requirements (see upcoming article)
  • Address network challenges in collaboration with suppliers and / or customers (e.g. Vendor Managed Inventory)

Change management is key here, especially if the redesign impacts multiple regions, business units, or third-party providers AI tools can run impact analyses to identify which departments, regions, or partners will be most affected and even help tailor communication plans using insights from internal communication behaviours.

Validate
During the validation phase, AI is key in monitoring performance and detecting deviations from the expected results. If discrepancies occur, AI-supported systems can automatically refine model assumptions and recommend targeted adjustments. Through predictive analytics, the network becomes proactive, anticipating shifts in demand, cost, or regulations, and adapting continuously, keeping the supply chain agile and resilient over time.

When executed well, this process delivers four critical benefits:

• Scalability: As companies grow, expand into new regions, or launch new products, a well-designed network can scale without breaking.
• Cost efficiency: Optimized flows and reduced complexity enable improved service at a lower total cost-to-serve.
• Resilience: Risk exposure is reduced through dual sourcing, redundant inventory, and other strategic safeguards.
• Continuous improvement: Digital twins and scenario modeling allow to test changes virtually before implementation and continuously improve their network strategy over time.

The ideal environment to support the new SCND-process is the digital twin of a physical supply chain. By creating a digital twin of a company’s supply chain, you have a virtual replica that mirrors the behavior, processes, flow of goods and performance of your supply chain in real time.[1]

The capabilities of digital twin tools vary by provider and application. Using River Logic as an example, a digital twin solution can offer:

• End-to-end modeling: Captures the complete value chain, from suppliers to customers, allowing for comprehensive analysis and optimization.
• Real-time data integration: Incorporates data from various sources, including Internet of Things devices and Enterprise Resource Planning systems, to provide up-to-date insights into supply chain performance.
• Advanced scenario planning: Enables users to model “what-if” scenarios, evaluating the impact of decisions on cost, service levels, and risk.
• Financial alignment: Integrates financial metrics into supply chain planning, ensuring that operational decisions support overall business objectives.[2]

This is only one from many use cases of a digital twin. If set up correctly, digital twins can be leveraged to optimize both long-term strategic goals and daily operations of a company.

At TenglerConsulting, we believe that AI-powered supply chain network design is not just the future, it is the present. We help organizations transform their networks by combining deep supply chain expertise with leading simulation technologies. Whether companies aim to improve resilience, reduce costs, or enable sustainable growth, we provide a structured yet flexible approach to redesign their supply chain networks.

 

 

Sources:
[1] McKinsey, 2024: “Digital twins: The key to unlocking end-to-end supply chain growth” [accessed: 06 May 2025].
[2] RiverLogic, 2025: “Network Strategy & Design” [accessed: 06 May 2025].

In our series “AI in Supply Chain Planning”, we describe our top use cases of Artificial Intelligence in the supply chain. Previous use cases and other supply chain trends can be found in our insights.