“We translate the planned production volume into our own items: how much can be produced with one of our tools, and therefore, how many tools are needed over a specific period.”
— Esmee Bos, Logistics Engineer at MAG45
At one of its European production sites, a leading medical equipment manufacturer had long struggled to strike the right balance between material availability and inventory cost. It wasn’t just about avoiding downtime—overstock and excess working capital also needed to be addressed. Within a Kaizen-driven mindset and leveraging our integrated approach, MAG45 introduced a new demand forecasting method that enables the site to proactively plan its tooling needs, purchase more strategically, and reduce stock levels—while improving control, visibility, and supply reliability.
The situation before the new demand forecasting approach
Before the introduction of MAG45’s new demand forecasting model, the manufacturer did not take into account future information in the prediction of indirect material requirements. The team relied on historical consumption data from the past year, assuming future demand would follow the same trend. This method led to delays, hesitance in decision-making, and manual adjustments based on incomplete or outdated insights.
Changes in production planning were difficult to anticipate, and every deviation triggered time-consuming discussions and validation steps. The process was not only inefficient but also error-prone and stressful for the local teams. In 2023, this lack of predictability contributed to 14 stock-outs, which – although not all equally disruptive – posed serious risks for production continuity and required emergency actions.
The solution implemented by MAG45
To ensure tool availability without overstocking, MAG45 implemented a custom demand forecasting model. The forecasting method combines historical consumption and past production volumes to calculate the expected tool life. This data is linked directly to the client’s production forecast to calculate tooling needs per item group. Once the reorder levels are set, orders are triggered automatically. This enables restocking just in time—without excess.
While the model was initially developed for indirect materials such as cutting and milling tools, its structure and logic can be applied to any item category where demand follows production planning, making it scalable across a wide range of materials and sites.
Results and impact
Since the implementation of demand forecasting at this production site, the improvements have been both quantifiable and strategic:
- Stock-outs reduced by 57%
- Better cost avoidance related to production risks and emergency orders
- Inventory value reduced by 35%
- Improved negotiation position by enabling larger, confident volume purchases
- Less stress and manual work for the site team due to clearer insight and predictability
These results demonstrate how demand forecasting contributes not only to supply chain performance, but also to peace of mind on the work floor.
Closing perspective
What makes this case stand out is the combination of precision and partnership. By aligning production forecasts with tool usage, the manufacturer can now make sharper decisions, avoid waste, and reduce complexity. While setting up such a system requires time and collaboration, the long-term benefits far outweigh the effort. As this site has shown, demand forecasting is not just a planning tool, it’s a strategic capability.