Measured savings on the BOM cost, from the POC onward.
Emmanuel Velasquez
•
Measured savings on the BOM cost, from the POC onward.
Emmanuel Velasquez
•
Measured savings on the BOM cost, from the POC onward.
Emmanuel Velasquez
•
Measured savings on the BOM from the POC stage.
Sector: Industrial robotics — Duration: 12 weeks — IT integration: none
The challenge
This robot manufacturer has to price more than 1,000 components per robot. In their ERP, one data point: the last price used and the material. The offers not selected? Lost. No context, no negotiation history, no market benchmark.
To keep these prices up to date, the procurement team issued price requests to its 40+ suppliers every quarter. A time-consuming process, hard to track, impossible to analyse.
Without consolidated visibility over their BOM, the team did not know which components were single-sourced, which had not been put back out to competition for more than a year, or which prices had gradually drifted. A significant share of the BOM escaped any management.
And once the quotes were received, the trade-off between suppliers was done manually. Each component had its own conditions: different MOQs, varying incoterms, lead times that were incomparable from one supplier to another. Identifying the best allocation (which supplier, for which part, at what quantity, for what total BOM cost) was an exercise impossible to do rigorously without a dedicated tool. Decisions were made in spreadsheets, often on a case-by-case basis, without an overall view.
We never knew whether our BOM cost was accurate or outdated. 1,200+ references, 40+ suppliers every quarter.
The Siembra solution
Siembra was deployed in less than a week, without an IT project, directly on the customer’s existing data.
1. Complete data ingestion
All PDF quotes, emails and ERP data were centralized and automatically standardized. Within a few days, the team had a unified price bible, updated in real time a first.
2. Opportunity detection
Siembra identified obsolete prices, gaps between suppliers and components to renegotiate as a priority. Concrete, actionable alerts, directly in buyers’ hands.
3. Optimisation under constraints
On machined mechanical parts, Siembra simulated multi-supplier allocation scenarios while taking into account MOQ, incoterms and real lead times. Trade-offs that had been impossible to make manually until then.
The results
In 12 weeks, on a single component family:
5% measured savings on machined mechanical parts
€262,500 savings identified on the current order
~8h per buyer per week of automated price research and consolidation tasks
Zero IT integration
And this on a single component family. The manufacturer’s full BOM includes several dozen more, just as many opportunities still unexplored.
Measured savings on the BOM from the POC stage.
Sector: Industrial robotics — Duration: 12 weeks — IT integration: none
The challenge
This robot manufacturer has to price more than 1,000 components per robot. In their ERP, one data point: the last price used and the material. The offers not selected? Lost. No context, no negotiation history, no market benchmark.
To keep these prices up to date, the procurement team issued price requests to its 40+ suppliers every quarter. A time-consuming process, hard to track, impossible to analyse.
Without consolidated visibility over their BOM, the team did not know which components were single-sourced, which had not been put back out to competition for more than a year, or which prices had gradually drifted. A significant share of the BOM escaped any management.
And once the quotes were received, the trade-off between suppliers was done manually. Each component had its own conditions: different MOQs, varying incoterms, lead times that were incomparable from one supplier to another. Identifying the best allocation (which supplier, for which part, at what quantity, for what total BOM cost) was an exercise impossible to do rigorously without a dedicated tool. Decisions were made in spreadsheets, often on a case-by-case basis, without an overall view.
We never knew whether our BOM cost was accurate or outdated. 1,200+ references, 40+ suppliers every quarter.
The Siembra solution
Siembra was deployed in less than a week, without an IT project, directly on the customer’s existing data.
1. Complete data ingestion
All PDF quotes, emails and ERP data were centralized and automatically standardized. Within a few days, the team had a unified price bible, updated in real time a first.
2. Opportunity detection
Siembra identified obsolete prices, gaps between suppliers and components to renegotiate as a priority. Concrete, actionable alerts, directly in buyers’ hands.
3. Optimisation under constraints
On machined mechanical parts, Siembra simulated multi-supplier allocation scenarios while taking into account MOQ, incoterms and real lead times. Trade-offs that had been impossible to make manually until then.
The results
In 12 weeks, on a single component family:
5% measured savings on machined mechanical parts
€262,500 savings identified on the current order
~8h per buyer per week of automated price research and consolidation tasks
Zero IT integration
And this on a single component family. The manufacturer’s full BOM includes several dozen more, just as many opportunities still unexplored.
Measured savings on the BOM from the POC stage.
Sector: Industrial robotics — Duration: 12 weeks — IT integration: none
The challenge
This robot manufacturer has to price more than 1,000 components per robot. In their ERP, one data point: the last price used and the material. The offers not selected? Lost. No context, no negotiation history, no market benchmark.
To keep these prices up to date, the procurement team issued price requests to its 40+ suppliers every quarter. A time-consuming process, hard to track, impossible to analyse.
Without consolidated visibility over their BOM, the team did not know which components were single-sourced, which had not been put back out to competition for more than a year, or which prices had gradually drifted. A significant share of the BOM escaped any management.
And once the quotes were received, the trade-off between suppliers was done manually. Each component had its own conditions: different MOQs, varying incoterms, lead times that were incomparable from one supplier to another. Identifying the best allocation (which supplier, for which part, at what quantity, for what total BOM cost) was an exercise impossible to do rigorously without a dedicated tool. Decisions were made in spreadsheets, often on a case-by-case basis, without an overall view.
We never knew whether our BOM cost was accurate or outdated. 1,200+ references, 40+ suppliers every quarter.
The Siembra solution
Siembra was deployed in less than a week, without an IT project, directly on the customer’s existing data.
1. Complete data ingestion
All PDF quotes, emails and ERP data were centralized and automatically standardized. Within a few days, the team had a unified price bible, updated in real time a first.
2. Opportunity detection
Siembra identified obsolete prices, gaps between suppliers and components to renegotiate as a priority. Concrete, actionable alerts, directly in buyers’ hands.
3. Optimisation under constraints
On machined mechanical parts, Siembra simulated multi-supplier allocation scenarios while taking into account MOQ, incoterms and real lead times. Trade-offs that had been impossible to make manually until then.
The results
In 12 weeks, on a single component family:
5% measured savings on machined mechanical parts
€262,500 savings identified on the current order
~8h per buyer per week of automated price research and consolidation tasks
Zero IT integration
And this on a single component family. The manufacturer’s full BOM includes several dozen more, just as many opportunities still unexplored.
