ClearOps has been named a finalist for the IP | Innovationspreis by CNA e.V. The nomination recognizes ClearOps new Inventory Planning algorithm, a next gen spare parts planning algorithm developed with the Technical University of Munich (TUM). It was developed to address a persistent challenge in spare parts planning: Keeping the right parts available while minimizing inventory costs.
Why Service Parts Planning Is Different
Spare parts inventory is a balancing act. OEMs need parts on hand to keep vehicles and equipment running, but demand for any individual service part is often sparse, irregular, and hard to forecast. Overstock ties up capital and warehouse space; understock leads to downtime and dissatisfied customers. Many conventional planning methods assume more regular demand patterns, and can produce recommendations that are either too conservative or too aggressive when applied to spare parts, and difficult for planners to trust either way.
Rethinking Spare-Parts Planning
ClearOps’ next gen Inventory Planning addresses this through three linked design choices.
- It is built specifically for the unique intermittent demand patterns of service parts, rather than adapted from methods designed for steady, high-volume sales.
- It optimizes reorder points and order quantities not just part by part, but across customizable product segments, helping planners hit fill-rate targets for whole categories of parts while avoiding unnecessary inventory elsewhere.
- It makes its recommendations explainable. Rather than returning blackbox recommendations without rationale, the algorithm makes it easy for planners to understand why a given reorder point or order quantity was chosen; based on demand history, cost inputs, service-level targets, just to name a few.
What the New Approach Delivers
In evaluation, the ClearOps new Inventory Planning capabilities delivered >10% higher cluster fill rates on average compared with the previous approach, and a 27% higher rate of achieving cluster fill-rate targets. Additionally, it’s led to >15% decrease in changes to parts orders resulting in higher satisfaction for both OEMs and their dealers.
It also improved the clarity of the reasoning behind each recommendation. Together, these results point to a planning process that is more reliable and more transparent, and can support better service parts availability, lower inventory costs, and stronger After Sales performance.
ClearOps’ next gen Inventory Planning was developed with Prof. Dr. Stefan Minner and PhD student Patrick Helm at TUM, together with the ClearOps team, pairing academic expertise in inventory optimization with operational experience from OEM After Sales.
We congratulate the other finalists and winners at this year's IP | InnovationsPreis, and thank CNA e.V., our TUM partners, and the customers whose feedback shaped this work.