For most engineer-to-order (ETO) manufacturers, the Bill of Materials is not just a document. It is a deadline. Before procurement can place a single purchase order, before production can schedule a single work center, and before a long-lead component can even be identified, the BoM has to exist. And in most ETO shops, creating it falls to the engineers who are already stretched thin.
The result is a recurring frustration felt by operations and engineering leaders: sales closes a deal, and then everything stalls while one or two senior engineers reverse-engineer a BoM from a similar past job, rebuild sub-assemblies from memory, manually assign routing operations work center by work center, and release the whole structure two to four days after the sale. In a high-mix, low-volume environment, that delay is not a minor inconvenience. It is the constraint that determines how fast the business can grow.
The Real Cost of Manual BoM Creation
The bottleneck created by manual BoM workflows carries measurable consequences at every stage of an ETO operation. Long-lead purchase orders cannot be placed until the BoM is finalized, which means procurement is perpetually starting late. According to 2025 manufacturing industry reporting, raw material delivery lead times now average 81 days — about 25% longer than pre-pandemic levels — while electronic component lead times run from 12 to 40 weeks. A two-to-four-day delay in BoM creation does not just push out the first PO; it compounds across every downstream lead time.
George Labovitz and Yu Sang Chang created the 1–10–100 rule, which explains that errors become more costly the longer they go unnoticed.
What costs $1 to fix in design can balloon to $10 in production and $100 after delivery. In ETO manufacturing, a significant share of that variance traces back to the gap between the quoted estimate and the BoM that was actually built. In manual workflows, these two documents are created separately and rarely reconciled, which is how quote-to-actual cost variance climbs into double digits.
Beyond schedule and cost, inconsistency compounds quietly over time. When different engineers build BoMs from different reference jobs using different naming conventions, cross-project reporting becomes unreliable, traceability deteriorates, and errors from outdated components or wrong part numbers get carried forward from one job to the next.
Why the Standard Approach Falls Short
The instinct at most ETO shops is to standardize templates and to train engineers to use their long-standing internal processes. This helps at the margins but does not address the structural problem: the engineering knowledge required to build an accurate BoM for a custom order has never been written down in a machine-readable format. It lives in the heads of two or three senior people who have been doing this for years.
That knowledge gap is what makes ETO BoM automation genuinely difficult. Generic configurators built without a deliberate rules-capture phase produce BoMs that engineers still have to fix manually, defeating the purpose. Novobi’s approach begins with that acknowledgment. Before any code is written, a rules-capture phase is conducted with senior engineers, reverse-engineering the decision logic from 20 to 30 historical BoMs into business-readable rules. Component master data is cleaned and standardized, covering naming conventions, lead times, and attribute tagging. Only then is a structured configurator built to replace free-text customer specifications with constrained fields that map directly to BoM logic.
How Automation Changes the Workflow
Novobi’s approach is to create a configurator layer and custom Python automation within Odoo, which shifts BoM creation from a days-long manual effort to a triggered, system-driven event. The event begins when an engineer enters the customer specifications through the structured configurator. Then, a custom server action creates a customer-specific product, generates a complete multilevel BoM, assigns routing operations across the appropriate work centers, and establishes the correct procurement routes for every sub-assembly.
What the automation produces is a draft, not a finalized record. Engineering review and approval remain the final step before release. This design choice is intentional. It preserves the engineering judgment that makes ETO products accurate while eliminating the clerical work that made BoM creation a bottleneck. The goal is 80% automation with a clean engineer handoff, not 100% automation that bypasses the people who understand the product.
The downstream effects are significant. Because the BoM is generated on the day the sales order is confirmed, long-lead purchase orders can be placed immediately. The make-to-order cascade, which fans out from a single sales order confirmation into a full tree of purchase and manufacturing orders, can be designed deliberately per product family so that procurement and production move in step from the moment a deal closes.
Measurable Operational Results
ETO manufacturers who successfully automate BoM and routing generation can expect improvements across the metrics that matter most to operations and engineering leaders.
The ranges below are Novobi’s projections, drawn from its ETO implementation experience, for well-executed projects targeting repeatable configurations; actual results vary with product complexity and the state of a shop’s component data.
- BoM creation time can fall by an estimated 70% to 90%.
- Engineering throughput can increase by roughly 1.5 to 2 times with the same team size, as senior engineers are redirected from clerical BoM assembly to genuine design work.
With the BoM generated on the day of sales order confirmation, long-lead purchase orders can be placed immediately, reducing the expedite fees that accumulate when procurement starts late. And because the estimate and the BoM are generated from the same structured inputs rather than built independently, quote-to-actual cost variance can shift from double-digit to single-digit percentages.
The structural benefit is equally compelling. When every BoM flows through the same configurator logic with consistent naming conventions and component selection rules, cross-project reporting becomes reliable, and traceability holds across every job. The tribal knowledge that previously lived in individual engineers’ heads becomes encoded in the configurator rules, where it is visible, auditable, and available to anyone building the next order. These are not incidental improvements. They are the compounding returns of replacing ad hoc processes with a disciplined, repeatable system.
What This Looks Like in Practice
Inline Cleaning Systems (ICS), an Indiana-based custom machine builder serving automotive and aerospace customers, illustrates what this transformation produces. Before working with Novobi, ICS operated on a 20-year-old legacy system with no integration between its CAD software and manufacturing operations. As ICS President Andy Wilcox described it, the company had been hesitant to bid on large, complex projects because it lacked confidence in its cost data — and in an ETO shop, that cost data is only as accurate as the BoM it’s built from.
After Novobi implemented a customized Odoo solution with ETO-specific features, cost data was captured accurately. Wilcox put it plainly: “I can accurately quote the next project. I’m not going to quote it too high or too low. That’s critical for us as a custom machine builder.” ICS now pursues large, multi-machine bids with the confidence it previously lacked.
The Right Platform for ETO Automation
Odoo provides the manufacturing data model that makes this level of automation feasible without replacing or heavily modifying the underlying platform. Multilevel BoMs, routing, work centers, and procurement routes are native. PLM and engineering change order workflows are built in, meaning any post-release modification is governed by versioned, approval-gated processes. The make-to-order cascade through multilevel BoMs means that one confirmed customer order automatically fans out into the full tree of purchase and manufacturing orders. Because Odoo’s architecture is open, the custom configurator layer and Python server actions Novobi develops plug into standard modules without breaking upgrade paths or standard platform behavior.
For ETO operations leaders who have watched their most experienced engineers spend their days reassembling BoMs from prior jobs instead of solving actual engineering problems, the answer is not more engineers. It is better systems that preserve their judgment while eliminating the repetitive work surrounding it.
To learn how Novobi’s ETO automation approach can be applied to your operation, visit the Novobi ETO Blueprint page.
DISCLAIMER: The information in this article reflects the views and opinions of Novobi, based on publicly available information, and is intended for informational purposes only. It is not legal or financial advice. All trademarks are the property of their respective owners.
