ScenarioSafe
Faster planning decisions, without contaminating baseline truth.
ScenarioSafe is a decision layer for planning under uncertainty. It keeps facts clean, forces assumptions and constraints into the open, and makes trade-offs explicit before you commit capital. It is built for operators who need speed, not noise.
Forecast accuracy: ScenarioSafe does not claim to improve forecast accuracy. It preserves forecast integrity and improves decision quality around that forecast.
What we do
Most planning slowdowns are not a tooling problem. They are a decision-quality problem: assumptions get buried, constraints get ignored until it is too late, and scenarios quietly become “the new truth.” ScenarioSafe prevents that drift and keeps your planning loop repeatable.
Shorter decision cycles
Run clean what-ifs without rebuilding models or re-litigating definitions in every meeting.
Trade-offs in plain view
Make the real tension explicit: working capital vs service, speed vs cost, buffers vs exposure.
Constraint-first planning
Treat lead times, MOQs, materials, capacity, and policy thresholds as first-class inputs.
How it works
A repeatable loop you can apply to planning conversations, spreadsheets, BI dashboards, and S&OP rituals. The goal is simple: reduce ambiguity fast, then record decisions in a way you can defend later.
The workflow
- Policies & thresholds: encode buffers, service targets, and risk boundaries before scenario work.
- Baseline: establish what is verifiable right now.
- Scenario: inject a what-if with explicit inputs and time bounds (volume, timing, lead times, constraints).
- Implications: surface first failure points and trade-offs (what breaks first, and when).
- Decision note: record what was chosen and what made it reasonable at the time.
- Replayability: re-run later against updated facts to see what changed and why the prior decision no longer holds.
Common use cases
- Inventory coverage and stockout risk
- Lead time and receipt timing
- Capacity and staffing planning
- Vendor constraints and service risk
- Forecast volatility containment
- Capital exposure
- Cashflow planning
- Demand pressure analysis
If you can name the constraint and the time window, it can be modeled cleanly.
Governed LLM exploration
ScenarioSafe uses LLMs to accelerate planning exploration and meeting prep, but outputs are constrained by explicit rules. The model is not allowed to invent inputs, guess facts, or silently promote hypotheticals.
What the LLM can do
- Prepare S&OP agendas and pre-reads from the current baseline.
- Summarize what changed since the last cycle and which constraints tightened.
- Run scenario variants by adjusting multiple assumption or constraints at a time.
- Surface trade-offs and where risk concentrates across time.
What it cannot do
- Invent missing inputs or “fill gaps.”
- Recommend “best” choices, optimize, or claim sufficiency.
- Overwrite baseline truth with hypothetical edits.
- Ignore baseline constraints to force a preferred outcome.
The deliverable is not a chatbot. The deliverable is a governed planning loop that keeps facts, assumptions, constraints, implications, and decisions cleanly separated. This requires custom deployment aligned to your data contracts, policies, and operating cadence.
Outcomes
The result is faster cycles with higher integrity: fewer rebuilds, fewer debates about definitions, and decisions that remain replayable and defensible.
Observed impact in practice
- Clarified seven-figure working capital exposure before purchase commitment.
- Applied inside a ~$300M health and wellness brand operating under real constraints.
- Reduced planning churn by enforcing clean inputs and replayable decision notes.
- Made overrides constraint-aware and auditable instead of opinion-driven baseline edits.
What teams typically see
- Fewer model rebuilds and fewer “version wars” in planning cycles.
- Cleaner discussions: trade-offs instead of debates about what the numbers mean.
- Earlier constraint visibility, so ceiling effects show up before execution breaks.
- Better continuity across cycles, because decisions remain tied to what was true at the time.
What working together looks like
Fast, practical, and tied to your cadence. The objective is to lock a decision loop your team can repeat.
- Explore: map constraints, thresholds, policies, objectives, and real trade-offs.
- Contract: define the minimal data contract that makes scenarios deterministic and auditable.
- Deploy: implement on top of what you already run: ERP / MRP / DRP / WMS / OMS outputs, spreadsheets, BI, or a cloud warehouse (Google, Snowflake, etc.).
- Operate: embed the loop into S&OP and execution reviews with replayability and decision records.
What you are not buying
This is not a rip-and-replace implementation. It is governance and decision hygiene layered on top of your existing stack.
- No baseline contamination from hypothetical edits.
- No “optimize” language without a declared objective and constraint boundary.
- No black-box outputs without traceable inputs and decision context.
- No adoption theater: the loop works inside the tools your teams already use.
Contact
Schedule a call, or email directly.
Schedule
Book 30 minutes. We’ll define the baseline, name constraints, set scenario inputs, and identify the decision and trade-offs.
Schedule a callPrefer to start hands-on? Use the Lite version to see the separation and loop structure in action.
Prefer async? Email works. Include baseline (what’s true), scenario (what changes), constraints (what cannot be ignored), and the decision you need to make.
Email: scenariosafe@gmail.comSend a message
Founder
ScenarioSafe is built by Rui Goncalves, an operations and supply chain leader focused on decision systems that hold up under real constraints: incomplete information, volatile demand, compliance pressure, and distributed accountability. Rui is open to executive leadership roles or fractional deployment through ScenarioSafe LLC, depending on scope and organizational needs.
Background
- Operations leadership across regulated logistics and manufacturing environments.
- Inventory-intensive planning across multi-site networks and multiple sales channels.
- Practical decision frameworks designed to survive executive scrutiny and real-world constraints.