Go/No-Go Decision Framework

A go/no-go decision framework is a structured evaluation methodology that government contractors use to assess whether to commit proposal resources to a specific opportunity, based on objective criteria including win probability, strategic alignment, competitive intelligence, and organizational capacity.

What is a Go/No-Go Decision Framework?

One of the most consequential decisions in GovCon is choosing which opportunities to pursue. Bid and proposal costs on a competitive federal contract can run from $10,000 for a simple small business award to millions of dollars for a major defense program. A go/no-go framework applies consistent, objective criteria to that investment decision.

A well-structured framework evaluates both hard filters, mandatory criteria that disqualify a bid if not met (wrong NAICS, missing certifications, geographic restrictions), and scored factors that produce a weighted win probability assessment. Common scored factors include customer relationship strength (Do we know the COR? The program manager?), competitive position (Are we the incumbent? Do we know who else is bidding?), solution fit (Can we staff and deliver this scope?), price-to-win assessment (Can we be competitive?), and strategic value (Does this win open doors to follow-on work?).

The FAR Part 6 set-aside type is also a critical go/no-go data point: if a solicitation is set aside for 8(a) only and the firm lacks that certification, no amount of competitive analysis can overcome the eligibility barrier. Similarly, a sole-source award already decided means the opportunity is closed regardless of capability.

See the blog on go/no-go decision frameworks for scoring rubrics, weighting examples, and templates.

Why the go/no-go framework matters for government contractors

Without a disciplined framework, business development teams feel pressure to bid everything and win nothing. A scoring methodology creates accountability, surfaces problems early, and builds institutional memory about the types of opportunities the company wins. Over time, analyzing go decisions against win outcomes allows continuous improvement of the scoring criteria.

Example

A cybersecurity firm scores a $12M DHS task order against its go/no-go framework. The firm scores high on technical fit (9/10) and past performance alignment (8/10) but low on customer relationship (3/10, they have never worked for this program office) and competitive position (4/10, the incumbent is well-regarded). The weighted score falls below the firm's go threshold of 65, and leadership decides not to bid, instead investing capture time in developing a relationship with the program office for the anticipated recompete in 18 months.

Frequently Asked Questions

What is a minimum go/no-go score for most government contractors?

There is no universal standard. Most companies set go thresholds between 60% and 75% on their weighted scoring systems. The right threshold depends on the company's win rate targets, B&P budget, and capacity to pursue multiple simultaneous bids. A lower threshold means more bids but often lower win rates.

How early in the capture cycle should a go/no-go decision be made?

The first go/no-go decision should occur at initial opportunity identification, before any significant resources are committed. A second decision should occur at solicitation release, when actual requirements are known. Many companies conduct a final go/no-go at the draft RFP stage, where the full competitive landscape and final requirements are clearer.

Should every bid go through a formal go/no-go review?

Larger companies apply formal reviews to bids above a dollar threshold (e.g., $5M or $10M) and use abbreviated checklists for smaller awards. Micro-purchases and very small task orders may not warrant a full review. Small businesses should at minimum apply a mental checklist even for small bids, as proposal effort costs real time and money.

Can a go/no-go framework be automated?

Partially. Platforms like Bidovate can score opportunities against criteria such as NAICS fit, set-aside eligibility, and historical win rates automatically. Judgment-dependent factors such as customer relationships and competitive intelligence require human input, but a tool can surface and organize the data that feeds those judgments.