methodologies: ai-native operational consulting
subatix encodes top-tier consulting rigor into software.
diagnostic → sized gaps → validated fixes → in-house execution. same playbook as top firms; faster, auditable, fraction of cost.
core methodology principles
1. evidence-based decision making
- •zero speculation: every claim backed by data.
- •statistical rigor: p < 0.05, sanity checks, reality tests.
- •conservative finance: understate upside, prove it later.
- •audit-ready: full trace from raw data → executive line.
2. consulting frameworks integration
- •scr for structure + exec comms.
- •mece for coverage without overlap.
- •theory of constraints to surface bottlenecks.
- •six sigma methods for performance optimization.
3. human-in-the-loop validation
- •ai does the heavy lift: processing, patterns, correlations.
- •ex-mbb experts confirm plant reality and add context.
- •stakeholders co-validate root causes → ownership.
- •iterative loops: findings refine with every pass.
phase 1: diagnostic (independent data analysis)
build a bulletproof fact base, size improvement potential, and produce an executive-ready diagnostic that justifies investment.
phase 2: opportunity building (collaborative validation)
turn diagnostic findings into validated, stakeholder-owned initiatives with clear root causes and implementable plans.
data handling & quality standards
calculation preservation
save functions with docstrings; document methods; keep full audit trail.
statistical validation
test assumptions (normality, n, independence); apply significance tests; cross-check against business logic.
units consistency
declare uoms on every output; compare like-for-like at identical levels; document conversions.