Methodology
Our methodology.
Recommendation engines in this industry are usually pay-to-rank. Ours is a deterministic scoring model: the same answers always produce the same scores, every number traces back to specific answers you gave, and no vendor pays for placement in your results. AI writes narrative - it never computes a score.
Our approach
Evidence before advocacy.
Independent by design
No vendor pays for placement in your assessment results. Your requirements determine your shortlist.
Explainable by default
Scores follow a consistent methodology. Understand the inputs, the tradeoffs, and the limits.
Built for the decision
Research should help you ask better questions and make a more informed choice.
System fit scoring
Each system is scored 0–100 against your profile, weighted like this:
Industry match
Is your industry one this system demonstrably serves well?
Company size
Does your revenue band sit inside the system's proven sweet spot? Being far outside it overrides everything else - a system that's too small stays 'likely too small' no matter how well it scores elsewhere.
Complexity alignment
Your operational complexity (entities, currencies, operations) vs. the complexity range the system is built for - too much system is a real failure mode, not a safe default.
Capability coverage
Of the capabilities you actually need (from your function checklist and answers), how many does the system cover natively? Gaps are shown, not hidden.
Your priorities
Stated preferences - cost sensitivity, Microsoft-stack affinity, industry depth, scalability headroom - nudge but never dominate.
Why you may see fewer questions than someone else
Not every question applies to every company, so the questionnaire prunes itself. Each conditional question carries a versioned relevance rule that decides, from your earlier answers, whether it gets asked at all. A pure services firm never sees the warehouse management question, because that rule looks for inventory signals: an inventory or manufacturing selection on the needs checklist, a physical-goods industry, or a WMS in your current systems. A distributor sees the full physical-goods path.
Skipped questions are not guessed at. They are scored exactly as if you had answered no, and your report lists every question we skipped along with the rule that skipped it. The rules are data, not judgment calls made at scoring time. Same answers, same questions, same scores.
Readiness scoring
The readiness track scores the factors that decide whether implementations succeed - executive sponsorship, documented requirements, budget clarity, data quality, and change risk - alongside implementation complexity, data migration risk, and integration scope. Each is a 0–100 score built from explicit rules, so you can see exactly what would move it.
Test the shortlist with your own evidence
The fit score helps choose what to investigate. A vendor demo should then show how the proposed product handles your actual requirements. Use our free worksheet to record subscription changes, intercompany mismatches, partial shipments and returns, and integration recovery. Adapt the illustrative inputs to your business.
Keep one copy per vendor, record what was demonstrated, and assign an owner to each unresolved gap. Review critical requirements individually before deciding.
Download the ERP demo evidence worksheet (.xlsx)Editable Excel file. No contact details required.
Who maintains this
The scoring model, system research, and pricing anchors are built and maintained by Brady Justice, an independent ERP consultant. The about page explains his background and how to submit a factual correction.
What we don't do
- ▪No vendor pays to rank or be recommended in your assessment results.
- ▪No single "the answer" - output is always a tiered shortlist with reasoning.
- ▪No score is generated by an LLM - AI only narrates results the scoring engine computed.
See it on your own company:
Start the Assessment