ClearOps in collaboration with TUM launches Inventory Planning 2.0

Next-Level Inventory Planning

ClearOps, working with the Technical University of Munich (TUM), has launched Inventory Planning 2.0, a next-generation algorithm that improves reliability, stability and explainability in spare parts inventory management. The new system helps supportive dealers to ensure parts availability and optimize stock levels while reducing costs.

Project Scope and Goal

The goal of the project was to design and implement an innovative inventory planning algorithm capable of improving accuracy, stability and explainability:

• Enhancing the accuracy and stability of inventory planning results

• Providing explainable recommendations for reorder points and order quantities

• Optimizing inventory across multiple products while achieving individual and segment level target fill rates

Results Achieved

🔶 Released a fully redesigned inventory planning algorithm

🔶 Achieved 10% higher cluster fill rates on average

🔶 Increased the achievement rate of cluster fill rate targets by 27%

🔶 Improved the explainability of inventory recommendations for operational teams

How Inventory Planning 2.0 Works

The algorithm uses historical demand data and key input parameters including SKU-level and cluster-level fill rate targets, ordering costs, and holding costs to calculate optimal reorder points (ROP) and order quantities (OQ). It operates on a periodic review system, where stock levels are checked at regular intervals (review periods). Key features include:

• Part is proposed to be reordered when its inventory position falls at or below the calculated ROP.

• OQs are calculated to minimize total costs while respecting constraints such as safety stock, maximum storage, or budget limits.

• ROPs and OQs are optimized jointly for each product, as well as for the entire segment to achieve the best results.

• Supports both item-level and cluster-level service constraints to ensure weighted fill rate targets are met.

• Models intermittent demand typical for spare parts, recognizing that the likelihood of demand increases with time since the last order.

This new algorithm enables OEMs and dealers to maintain higher service levels, reduce stockouts, and optimize inventory costs while providing full transparency on planning decisions.

Who Was Involved

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The development of Inventory Planning 2.0 brought together experts from both academia and industry, combining deep research knowledge with practical operational experience.

• Technical University of Munich (TUM): Prof. Dr. Stefan Minner and PhD student Patrick Helm contributed research expertise in supply chain, inventory optimization and operations management.  

• ClearOps: Dan Touchette, German Kochnev, Olga Kir, Matthieu Cretier, and Kivanc Dogruyol brought hands-on experience in OEM aftersales operations, inventory management, and SaaS product development.  

This collaboration merged academic rigor with real-world applicability, resulting in an innovative solution that addresses the challenges of modern spare parts inventory planning.

A big thank you to our partners at TUM, our amazing ClearOps team, and all our customers who shared feedback along the way. Your insights and support made Inventory Planning 2.0 possible.
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