The RFQ Process Has a Cost Nobody Tracks
Preparing a bid for a complex contract (defense, aerospace, industrial plant engineering) requires a significant investment. Yet this cost is rarely tracked systematically: it gets absorbed into departmental overheads, loosely attributed to commercial activities, and almost never correlated with the outcome of the bid. This is not a matter of perception: the U.S. Defense Contract Audit Agency (DCAA) dedicates an entire accounting category, the Bid & Proposal costs, to the formal recognition of these costs as a structural and legitimate line item in defense contracts. The existence of specific regulation confirms that the cost of participating in a bid is real, material, and (in most companies) still poorly managed.
The result: ETO companies compete in dozens of bids each year without knowing precisely what participation costs, what their real conversion rate by segment is, or which requirements and conditions make a bid systematically unprofitable.
Win Rates and Margins: What the Numbers Show
Available data on the public sector and defense market tells a consistent story. The RWCO Public Sector Procurement Barometer (conducted on a sample of federal contractors with a specific focus on defense) found that 7 out of 10 companies have a win rate of 30% or lower, and that more than half of defense-sector contractors work with win rates below 25%. The Loopio 2026 RFP Response Trends report confirms a European average of 39% across all sectors , with industrial and manufacturing markets typically below this threshold due to the high competitiveness of tenders.
On margins, the pattern is equally well-documented. The Standish Group estimates that 71% of complex projects exceed their initial budget, with an average overrun of 43%. The Project Management Institute (PMI) identifies poor-quality initial estimates, not unforeseeable events, as the primary cause of deviation. While these figures reference IT and infrastructure projects, they reflect dynamics identical to those of ETO contracts: partially interpreted requirements, underestimated subcontracting costs, raw material volatility not incorporated into pricing. The problem is structural, not occasional.
Where Value Is Lost: From Analysis to Quotation
The RFQ response process involves very different phases, each with specific failure points. The document reading and structuring phase concentrates the most errors: unidentified technical requirements, missed contractual clauses, partially read quantitative specifications. In complex bids, documentation can exceed 2,000 pages and reading is almost always manual.
The quotation building phase faces a different problem: reliance on unstructured historical data. Costs from previous bids, pre-negotiated supplies, subcontractor performance, this data exists, but it's scattered across different systems, spreadsheets, emails, and individual memory. The result: each new offer essentially starts from scratch.
The Hidden Value in Lost Bids
There's a dimension of the problem that's often overlooked: every lost bid contains valuable information that is almost never capitalized on. Why did we lose? Was it price, technical requirements, timing? Answering this requires systematic analysis of outcomes, something very few ETO companies do in a structured way.
Organizations that have introduced structured debriefing and outcome archiving report significant improvements in the medium term: better bid/no-bid calibration, a reduced number of bids pursued with sharper focus on high-probability opportunities, and progressive refinement of pricing models.
From Subjective Assessment to Data-Driven Decision Support
The bid/no-bid decision is where the most value, and risk, is concentrated. In ETO companies, this decision is typically made based on the experience of the technical or commercial lead, with little objective data to support it.
The decision intelligence systems emerging in the industrial sector make it possible to structure this assessment: by integrating internal data (bid history, costs, performance) with external data (raw material trends, competitive context), it becomes possible to build an attractiveness and feasibility score for each bid. Not to replace human judgment, but to support it with quantitative evidence.
The starting point is not a large transformation project: it's a single real bid, analyzed with a structured approach, generating the first benchmark from which to build a proprietary decision asset over time.
