Demand planning workspace

Buyer

Commercial planners at specialist distributors

Problem

Forecasts hide assumptions and cannot explain revisions.

Format

Assumption-driven planning and decision workspace

Also fits

Finance; Management

USP

Versioned assumptions and honest error measurement for planners.

The product

Key screens: Demand drivers, scenario view, backtest results. Place editable drivers and constraints beside a clearly labeled scenario output. Include a baseline view, comparison chart or schedule, and an assumptions history. Let users trace a proposed quantity or date back to its inputs. Keep forecasts distinct from actual results. In this product, the first view is demand drivers, followed by scenario view and backtest results.

Core functionality

1. Validate historical periods.
2. Model seasonality.
3. Add promotion assumptions.
4. Compare demand cases.
5. Track forecast revisions.
6. Calculate backtest errors.

Customer workflow

Validate baseline inputs, confirm definitions and constraints, select editable assumptions, calculate feasible alternatives, inspect sensitivities, let the responsible person approve a plan, and compare later actuals with the recorded assumptions. Start with historical demand, promotions and customer commitments and finish with demand scenarios and assumption history.

AI and human review

Extract input context and explain scenario differences. Use deterministic calculations or explicit optimization for quantities, compatibility, dates and prices. Show uncertain assumptions. Never let generated prose silently change the calculation rules.

What the customer puts in

Historical demand, promotions and customer commitments

What the customer gets

Demand scenarios and assumption history

Accounts and administration

Scenario versions, baseline reconciliation, constraint checks, assumption ownership, reviewer approvals, plan exports and actual-versus-plan tracking.

MVP scope

Begin with commercial planners at specialist distributors and one recurring use case. Build the first two modules: validate historical periods; model seasonality. Provide operator assistance for the third module: add promotion assumptions. Deliver demand scenarios and assumption history through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.

After the MVP is validated

After paid pilots establish value, automate the remaining modules: compare demand cases; track forecast revisions; calculate backtest errors. Add one validated source integration, reusable customer configuration and recurring delivery. Expand to additional teams, document formats or languages only after testing the new scope.

Build dependencies

A defensible calculation model, explicit units, constraint validation and representative boundary tests. Advanced forecasting or optimization needs adequate historical data.

Integrations and data access

Orders, inventory, supplier files, process documents and workflow records. Read-only operational exports, calendars and finance or inventory records as relevant. Start with plan exports and retain human approval for execution. These are candidate integration categories, not verified supported connectors.

Defensibility

A validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. For this idea, build around versioned assumptions and honest error measurement for planners. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.

Alternatives and positioning

Spreadsheets, planners, specialist forecasting tools and existing scheduling or configuration software. Differentiate on this specific proposed advantage: versioned assumptions and honest error measurement for planners. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.

Revenue model and test pricing

Test USD 750-3,000 for a scoped planning setup and review, then USD 200-900 monthly for refreshes within agreed complexity. Data integration and optimization are separately scoped. All ranges are hypotheses.

Main delivery costs

Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance.

Marketing message to test

Demand planning workspace for commercial planners at specialist distributors. Versioned assumptions and honest error measurement for planners. Demonstrate the claim through a demand scenario and backtest report.

Acquisition channels

Distribution planning consultants

Lead magnet

A demand scenario and backtest report

The first 30 days of marketing

1. Week 1: interview five prospective buyers in this segment: commercial planners at specialist distributors. Ask to see a recent example of the problem and their current process.
2. Week 2: prepare this demonstration using authorized or synthetic material: a demand scenario and backtest report.
3. Week 3: present it through distribution planning consultants and seek one narrowly scoped paid pilot.
4. Week 4: review forecast error, planning adoption, total delivery effort and a concrete renewal decision before increasing scope.

Paid pilot and validation

Reproduce a known historical plan, test missing inputs and boundary constraints, then run a new scenario. Compare feasibility, reconciliation and observed error rather than judging the quality of the explanation alone. For this idea, use historical demand, promotions and customer commitments and evaluate demand scenarios and assumption history. Agree success thresholds with the buyer before starting; collect a baseline for forecast error, planning adoption. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.

Success metrics

Forecast error, planning adoption

Retention and expansion

Refresh inputs, compare recorded assumptions with actual outcomes and refine validated constraints. Expand scenario complexity only when the buyer uses it for a decision.

Operating controls and limitations

Make operational states and ownership explicit. Validate data and require appropriate approval before purchases, scheduling commitments or external system writes. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.

Investment indication

What it would take to build, from a first MVP to the full product. A planning range to start the conversation, not a quote. Running costs (model usage, hosting, reviewer hours) come on top.

Brand style (concept)