Dynamics 365
Dynamics 365 Supply Chain Management
Planning, inventory, warehouse and production execution, with availability that reflects what is physically there.
Manufacturing · Data, AI & Integration
A forecast horizon extended from three months to eighteen
A specialist pipe manufacturer, Europe. Demand forecasting was reactive rather than forward-looking. The planning horizon was short, business units planned separately, and external storage costs reflected imprecise purchasing.
The situation
What the organization was dealing with
Demand forecasting was reactive rather than forward-looking. The planning horizon was short, business units planned separately, and external storage costs reflected imprecise purchasing.
Demand forecasting in manufacturing is frequently reactive: a short horizon, built by planners, revised when reality diverges. The cost appears somewhere else entirely — in external storage, expedited freight and purchasing that is precise about the wrong things.
What was done
The work, and the part that was actually hard
The manufacturer was among the first to adopt demand planning in Dynamics 365 Supply Chain Management, built on Power Platform, taking a low-code approach to demand modelling.
Extending the horizon from three months to eighteen changes what the business can decide. The low-code approach mattered because it let people across the supply chain contribute to the model rather than leaving it with a small planning team, which is what made the longer horizon credible.
Results
What was published
These are the figures exactly as reported in the source. Nothing has been rounded, extrapolated or restated.
Forecast horizon3 mo → 18 mo
Storage cost reductionSignificant
Demand modellingLow-code
What changed
- Forecast horizon extended from three months to a year and a half
- Business units brought together into coordinated planning
- External storage need and associated costs reduced through more precise purchasing
- Forecasting made accessible to people across the supply chain, not only planners
Platforms involved
What each product was doing here
Power Platform
Power Platform
The low-code layer as a whole — apps, automation, portals and agents — governed through managed environments and data policies.
What transfers
If you were to attempt this
Measuring storage cost rather than forecast accuracy is the smarter instrumentation here, because it captures whether anyone changed their behaviour.
Where it usually gets harder than expected: A longer horizon is only useful if the organization actually acts on it. Extending the forecast without changing the purchasing and storage decisions produces a better-looking number and identical costs.
How we would take it on
Our approach to Data, AI & Integration work
1
Agree the definitions first
The least technical and most important step. If two functions cannot agree what a customer is, no architecture will reconcile their reports.
2
Deliver one workload end to end
Ingestion through to a report somebody actually uses, before the second workload starts. Platform builds that produce nothing for nine months lose their sponsor by month six.
3
Prove lineage
Every published figure traceable back to its source transaction. In a regulated setting that is a requirement; everywhere else it is what makes the number trusted.
4
Watch the consumption cost
Capacity-based compute behaves differently from licensed software. Monitoring goes in with the first workload, not after the first invoice.
Recognise the problem?
If any of the above describes your organization, tell us where it hurts most. We will tell you what the same platforms could realistically do in your environment, what we would measure, and whether we think it is worth doing at all.
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