Introduction

Sector: Hardware deeptech; Duration: 3 weeks; IT integration: none

The challenge

Our client develops clip-on energy monitoring sensors, installable in 2 to 3 hours without a power cut. A compact hardware product, but one whose BOM brings together more than 120 references: microcontrollers, connectivity modules, passive components, enclosures, connectors, power supplies.

When launching their new sensor, the team faced two related problems.

First, a sourcing problem. The team knew only about a dozen suppliers - mainly generalist distributors and a few direct contacts in Asia. No benchmark, no structured geographical coverage, no visibility on possible alternatives for each component family. In a context of persistent strain on electronic supply, this lack of diversification represented a real risk to lead times and costs.

Then, a modelling problem. The team wanted to simulate different BOM scenarios - depending on volumes, supplier choices, trade-offs between cost and risk - to make informed decisions before committing. But without a dedicated tool, building these scenarios by hand in spreadsheets was too long, too fragile, and impossible to compare rigorously.

"We wanted to know how much this sensor would really cost us at 10,000 units vs 50,000. And above all, with which suppliers."

The Siembra solution

Siembra was deployed in just a few days, without an IT project, directly on the client's existing data.

1. Structured sourcing On each component family of the BOM, Siembra identified and qualified 40+ alternative suppliers - in Eastern Europe, Southeast Asia, and beyond the usual channels. For each supplier: capacity, MOQ, lead times, terms, geographical areas. A complete sourcing base where only a scattered contact list had existed.

2. BOM scenario modelling Siembra simulated 6 distinct allocation scenarios: low volume vs high volume, single-source vs multi-source strategy, cost optimisation vs reduction of stockout risk. Each scenario incorporated the real constraints: MOQ, incoterms, delivery times, geographical risks.

3. Decision support For each scenario, the client had a clear view of the total BOM cost, supplier dependencies, and associated risks. Trade-offs that would have taken months to build manually.

The results

In 3 weeks, on a BOM of 120+ references:

• 40+ suppliers identified and qualified (vs ~10 known initially)

• 3 new geographical areas sourced, reducing dependence on traditional channels

• 6 BOM scenarios modelled and comparable in real time

• -22% on target BOM cost identified between the initial scenario and the optimised scenario

• Equivalent to 2-3 months of a senior buyer's work, delivered in 3 weeks

• Zero IT integration