Solution Database / Operations
Demand planning workspace
Forecasts hide assumptions and cannot explain revisions. Versioned assumptions and honest error measurement for planners.
01The offer
For commercial planners at specialist distributors, turn historical demand, promotions and customer commitments into demand scenarios and assumption history. Address the recurring problem: forecasts hide assumptions and cannot explain revisions. The value hypothesis is a more complete, reviewable deliverable with less repeated preparation; the pilot must establish whether that benefit is real.
- For
- Commercial planners at specialist distributors
- Takes in
- Historical demand, promotions and customer commitments
- Delivers
- Demand scenarios and assumption history
- Message
- 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.
- Lead magnet
- A demand scenario and backtest report
02How it works
- Validate historical periods
- Model seasonality
- Add promotion assumptions
- Compare demand cases
- Track forecast revisions
- Calculate backtest errors
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 people
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.
Screens
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.
Admin
Scenario versions, baseline reconciliation, constraint checks, assumption ownership, reviewer approvals, plan exports and actual-versus-plan tracking.
03Market gap
Alternatives buyers use today
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.
Where this wins
A validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. For this solution, 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.
04Why now
Operations teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: forecasts hide assumptions and cannot explain revisions.
05Proof & signals
Channels where buyers gather: Distribution planning consultants. Metrics that prove it works: Forecast error, planning adoption.
Paid pilot
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 solution, 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.
06Execution plan
MVP
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.
First 30 days
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. Week 2: prepare this demonstration using authorized or synthetic material: a demand scenario and backtest report. Week 3: present it through distribution planning consultants and seek one narrowly scoped paid pilot. Week 4: review forecast error, planning adoption, total delivery effort and a concrete renewal decision before increasing scope.
After the pilot
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.
Retention
Refresh inputs, compare recorded assumptions with actual outcomes and refine validated constraints. Expand scenario complexity only when the buyer uses it for a decision.
Integrations
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.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case; first modules: validate historical periods; model seasonality. Manual review in the loop. | 3 days | $6,500 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 4 days | $6,500 |
| Full product | Remaining modules: compare demand cases; track forecast revisions; calculate backtest errors. Self-serve onboarding, billing, monitoring and the wider integration set. | 7 days | $9,500 |
| Total | $22,500 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $30–$60 | $50–$100 | $80–$160 |
| Full product (about 50 customers) | $110–$210 | $350–$700 | $460–$910 |
Revenue model to test
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.
Cost drivers
Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance.
Safeguards
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.
Take it further
Concept proposal expanded from the 315-solution conversation. Demand, pricing, differentiation, build scope and integration feasibility are hypotheses, not verified market findings. Category link is inspiration rather than evidence of business viability.