Marelec

Diving deep into existing systems to uncover the truth

Machine builder Marelec stood at a tipping point. For years, their methodology was based on engineering-to-order: every machine was only designed after the order was placed. This led to long lead times and a high dependency on undocumented knowledge.

The ambition was to evolve toward an assemble-to-order model, where machines can be delivered faster by pre-building components. To make this transformation possible, the reliability of the data and systems first had to be investigated. Humaniti was brought in to determine how existing systems could support this new way of working.

Data analysis as a foundation: master data, transactional data, and system capabilities

Our mission began with a full analysis of the existing system. We examined the availability and consistency of both master data and transactional data. This was crucial to determine which parts and assemblies could be standardized for the new assemble-to-order model.

Additionally, we explored whether Marelec could utilize the current system in different ways. In close consultation with the software vendor, we analyzed alternative workflows and configurations. The central question was: can we simulate the future way of working in the current system, even before a new platform or ERP is purchased?

The core of the change: from engineering-to-order to assemble-to-order

Over the years, Marelec realized that their custom machines could be reduced to a series of reusable building blocks. This meant they no longer had to engineer every device entirely from scratch. Humaniti investigated how the current system could support this new reality.

The goal for Marelec was ambitious and clear:

• Reduce lead time: From 16 weeks to approximately 3 weeks.
• Inventory management: Pre-stocking standard building blocks (semi-finished goods).
• Speed: Assembling orders faster by combining the right components.

Our analysis demonstrated whether the system possessed the necessary data and flexibility to make this shift possible and which adjustments were essential.

Prototyping with PowerBI: creating insight before new systems arrive

One of the most important results of our assignment was a PowerBI-driven analysis layer that served as a prototyping tool for future processes. This tool brought together three crucial data streams:

1. Expected sales (how many orders are in the pipeline?)
2. Current inventory (which parts are ready?)
3. Lead times of missing items

By combining this information, we could simulate a Material Requirements Planning (MRP) before a new system was even operational. The tool provided insight into scenarios such as:

• "If we want to build 10 machines in December, which parts do we need to order today?"
• "Which parts are at risk of a shortage?"
• "What happens to our planning if a component has a longer delivery time?"

When a part was at risk of not being available on time, the system flagged it red in the timeline, allowing users to see immediately when orders needed to be placed.

Defining clear master data

In addition to the insight tool, Humaniti helped Marelec set up their master data structure. This created a solid foundation for future digitalization projects, such as an ERP renewal or MRP implementation.

We mapped out, among other things:

• How parts and bills of materials (BOMs) were defined.
• Which product flows actually moved through the company.
• What data was still missing to correctly support assemble-to-order.
• Which flows were usable for future automation.

This ensured the organization had a complete overview of their supply chain data for the first time and knew exactly how it needed to be reworked for the systems of tomorrow.

From data to advice: what was possible in the old system?

Following the analysis, it became clear that the existing system was capable of more than suspected. Together with the vendor, we investigated alternative work methods that could partially prepare the company for assemble-to-order. These insights were bundled and transferred to the implementation partner, who could proceed smoothly thanks to the clarity of the preparatory work.

Although Humaniti did not remain involved in the final implementation, our role was crucial: we provided the analytical foundation and the prototyping insights for the strategic decisions that followed.

A clear basis for a future transformation

After completing the data analysis, Humaniti was engaged for additional business analysis before the implementation started. Thanks to the data analysis, prototyping, and business analysis, the implementation partner could perform their work more efficiently.

Delivered deliverables:

• Master data analysis
• Data preparation
• MRP prototyping tool to simulate scenarios
• Fit-Gap analysis of the current systems