Achieve responsive demand-supply matching with AI support

Matching demand with supply is a continuous challenge for companies. The complexity arises from various factors – poor demand visibility, low transparency and flexibility in supply capacity, extensive firefighting that drains organizational resources, long lead times, and rigid frozen zones. Companies often struggle to react efficiently to changing market conditions, leading to poor customer service, lost revenue opportunities and inefficiencies across the supply chain.

To overcome these challenges, companies develop advanced supply chain capabilities that enable responsive demand-supply matching at both tactical and strategic levels. We see that companies leverage AI-driven tools and methodologies to anticipate demand fluctuations, optimize resource planning, and allocate supply in a way that maximizes business objectives (such as service excellence, growth, cost efficiency, etc.).

This article is part of the series where we discuss the most important use cases of AI in supply chain planning.

Achieving a dynamic balance between demand and supply requires three core capabilities:[1, 2]

Sensing demand exceptions
The ability to detect anomalies in demand patterns quickly, leveraging AI-driven analytics. It builds on the foundations laid out in our previous article on AI in demand planning.

Targeting revenue opportunities
Identifying high-value demand that aligns with strategic priorities, ensuring that resources are allocated to the most profitable opportunities.Great example of mastering such capability is Zara – this international fast fashion retailer adopted AI agents in various fields of supply chain management to predict customer behavior and reduce idea-to-market time to just one week![3, 4]

Resolving supply challenges

• Planning for constrained and realistic resources: Optimizing the use of materials, labor, and equipment to balance demand fluctuations with available capacity.
• Strategic supply allocation: Distributing aggregated supply across the network in alignment with business strategy, ensuring that key markets and customers are prioritized effectively.

Companies operate at different levels of digital maturity when it comes to demand-supply matching. The journey toward AI-driven optimization typically follows these stages:

AI-driven solutions such as digital twins are becoming increasingly relevant, enabling companies to simulate and optimize demand-supply scenarios dynamically. By mirroring real-world conditions in a virtual model, in a risk-free environment and make data-driven decisions to enhance real-world supply chain responsiveness and resilience that drive customer satisfaction.

Leading companies have successfully leveraged AI-driven digital twins to enhance demand-supply matching and conduct what-if scenario simulations:

• Unilever employs digital twins in its factories to simulate various production scenarios, optimize processes, reduce waste, and improv overall equipment effectiveness (OEE).[5]
• BMW uses digital twins to simulate and optimize assembly lines, ensuring precision, reducing errors, and enhancing manufacturing quality.[6]
• Walmart simulated different store layouts in over 1.700 locations with digital twins, to identify the optimum before actual investment was made.[7]
• Airbus integrates digital twin technology into aircraft design and production, improving performance while reducing rework by 20%.[8]

The software market for demand-supply matching has evolved significantly, offering a broad range of digital solutions. Companies must navigate a highly differentiated landscape, balancing between flexibility and complexity when selecting the right tools.

Key trends influencing software selection include:

• Shift toward modular and specialized solutions: many organizations prefer smaller, targeted applications that seamlessly integrate into the available digital landscape over large monolithic systems, prioritizing flexibility and faster ROI (return on investment).
• Integration of AI-agents: from simple process automation (e.g., order handling, automated data cleansing) to complex solutions (e.g., enhanced demand sensing through pattern recognition in the big data, reduced reaction times to supply chain disruptions through dynamic scenario analysis and real-time decision-making) – AI is an essential element of the advanced demand-supply matching.
• Adoption of cloud-based solutions: ensuring scalability, accessibility, and seamless data integration across the network.

At TenglerConsulting, we have deep expertise in evaluating and implementing AI-powered demand-supply matching solutions. We support our clients with:

• Assessing their current digital maturity and defining a roadmap for improvement.
• Leveling-up their demand-supply matching strategies and processes to drive profitability and resilience.
• Identifying the best-fit software solutions tailored to their business needs.
• Ensuring smooth transition to the target state.

Achieving a responsive demand-supply matching capability is not just about implementing technology – it requires a strategic approach that aligns with business goals. With our extensive knowledge of market solutions and transformation strategies, TenglerConsulting helps you to unlock the full potential of AI in supply chain management.

Sources:
[1] Deloitte, 2025: “Responsive Demand-Supply Matching (RDSM)” [Accessed: 27 February 2025].
[2] Gartner, 2023: “Market Guide for Analytics and Decision Intelligence Platforms in Supply Chain”  [Accessed: 27 February 2025].
[3] AI Expert Network, 2023: “Case Study: Zara’s Comprehensive Approach to AI and Supply Chain Management” [Accessed: 05 March 2025].
[4] DigitalDefynd, 2025: “5 Ways Zara is Using AI” [Accessed: 05 March 2025].
[5] Technology Magazine, 2025: “How Unilever Uses AI & Digital Twins For Sustainability” [Accessed: 05 March 2025].
[6] BMW group, 2024: “Innovative “3D human simulation”: BMW Group Plant Regensburg uses virtual tools to plan assembly processes years ahead of NEUE KLASSE series launch” [Accessed: 05 March 2025].
[7] The U.S. Sun, 2025: “RETAIL REVIVAL Walmart CEO reveals how chain is using ‘digital twin’ to plan store changes – and it’s been used for over 1700 locations” [Accessed: 05 March 2025].
[8] Lantec, n.d.: “The digital transformation of Airbus” [Accessed: 05 March 2025].

Our series “AI in SC planning” focuses on how to create value in your supply chain, therefore stay tuned for the upcoming articles! In the meantime, check our insights for more information on supply chain optimization.