HubSpot (CRM)
Open deals, deal line items and quantities, stage, probability, and expected close date.
Operations
Plan stock on the demand you can already see coming
Your planning system forecasts from history. Your sales team is sitting on a pipeline that says next quarter will not look like last quarter. Those two facts never meet, so you buy too much of what sold before and run out of what is about to sell. Coheed feeds weighted pipeline demand into the planning picture and shows the gap while there is still lead time to close it.
- Systems combined
- HubSpot (CRM)Dynamics 365 SCMExact (ERP)
- Runs
- Every 6 hours
Sound familiar?
Teams bring us this use case when one or more of these is true.
- Planning forecasts from last year's sales while the pipeline says something different.
- A large deal closes and the stock to fulfil it was never ordered.
- Excess stock builds up on articles that stopped selling months ago.
- Sales and planning meet monthly and argue from two different spreadsheets.
How it works
Coheed runs this every six hours, so the projection keeps pace with the pipeline.
- 1
Pipeline demand is converted into units
Open deals are broken down to product level and weighted by stage and probability, so a pipeline in euros becomes a demand signal in articles and quantities.
- 2
Planned supply is read from SCM and ERP
Current stock, open purchase orders, planned receipts, and lead times per article give the supply side of the equation.
- 3
The gap is projected forward
Weighted demand is set against planned supply over the coming months, producing a projected shortage or surplus per article together with the lead time available to act on it.
- 4
Only the gaps that matter are surfaced
Gaps that fall inside the lead-time window and exceed your threshold are raised, so planning acts on the handful of articles where the decision is still open.
Which data it uses
Only the fields this use case needs are read. Nothing is copied that the insight does not use.
Dynamics 365 SCM
Stock positions, planned receipts, lead times, and supplier data per article.
Exact (ERP)
Historical order patterns, open purchase and sales orders, article master data.
What changes
What a team notices once this runs. No invented percentages: the effect depends on your data and your process.
- Purchasing decisions are made with the pipeline in view, not only with history.
- Shortages surface while the lead time is still long enough to do something about them.
- Sales and planning discuss one projection instead of two spreadsheets.
- Excess stock on declining articles is visible before it becomes a write-off.
From insight to action
Inventory Gap Agent
Available as an agent
Promoted from this insight, the agent prepares the purchase proposal: which articles fall short, in which week, at what quantity, and against which supplier lead time. Purchase orders are proposed for approval, never placed automatically.
Mode: Human-in-the-loop
Every insight can stay read-only, run with human approval, or run autonomously. You decide per action type, and you can change it later.
FAQ
Frequently asked questions
Is pipeline data reliable enough to plan on?
On its own, no. Weighted by stage and probability and calibrated against your historical conversion, it is a useful early signal, and it complements your planning baseline rather than replacing it.
Does this replace our forecasting tool?
No. If you run a dedicated demand planning tool such as Slim4, Coheed supplies the pipeline-derived demand signal it cannot see, and the plan stays in your planning system.
How far ahead does it look?
Three months by default, and the horizon is configurable. What matters is that it covers your longest supplier lead time, because that is the window in which a decision still exists.
What if our deals do not carry product lines?
Then the signal is coarser, at product-group level. Getting line items onto deals is usually part of the first configuration, and the lead-to-quote use case puts them there as a side effect.
Built on these maturity levels
Other use cases
Predictive Customer Lifetime Value
Score churn risk and future value per account by combining CRM engagement decline with ERP order frequency and value trends.
Lead-to-Quote Readiness
Surface correct ERP pricing, live stock, discounts, and customer agreements on every new CRM lead so sales can quote immediately.
Order Exception Monitoring
Catch stock gaps, unrealistic delivery dates, pricing errors, and missed agreements after the order, before the customer notices.
See this on your own data
Tell us your ERP, CRM, and SCM stack and we will show what this use case looks like on your systems.