Will the month
reach its budget?
A management dashboard that makes the forecast, its assumptions, and the remaining sales gap visible.
The question changed the project.
The project began with backlog levels. The rebuilt tool leads with the question a management team needs to answer this week: are we going to reach this month’s sales budget, and what could change that outcome?
A forecast with visible components.
Sales Outlook separates sales already shipped, expected shipments from open backlog, expected new business, and explicit management adjustments. The dashboard shows a four-month outlook, an unchanged monthly budget, and a planning range alongside the central estimate.
Current shipment values are converted to estimated net sales using overlapping closed accounting months. They are labeled as estimates, rather than presented as reconciled accounting revenue.
What I built.
A monthly management dashboard and financial model with product-level shipment review, equipment scenarios in units or dollars, shipment-month overrides, and saved meeting comparisons. The Python forecast and the portable browser version share the same baseline and historical scenario draws.
Try the management conversation.
- Review September’s forecast and budget gap.
- Open the What-if studio and add equipment sales with a fictional name and reason.
- Move an open shipment to another month and see how the forecast changes.
- Save a meeting snapshot, then add another scenario to see the comparison.
- Use Save a copy to keep a standalone file, or Reset demo to start again.
Where judgment remains essential.
The forecast uses twelve complete calendar months. New business blends the historical average with the corresponding seasonal month. The range comes from historical scenarios; it is not a validated confidence interval. Hypothetical additions do not establish production or customer readiness.
Fictional company, working model. Aster Ridge Fabrication, its customers, products, budgets, and transactions are entirely invented. The dashboard runs locally without an ERP connection or live AI calls. Its scenario results demonstrate the tool, not performance achieved at a real company.
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