Prepare meaningful history
Check calendar consistency, missing periods, outliers and changes in product or organizational structure. Identify which explanatory inputs would actually be available at prediction time to avoid misleading tests.
Data & AI / Overview
DIGITAL CLOUD / Data & AI
AI Planning & Forecasting
Explore forecasts that support planning decisions. Review historical data, compare outputs with a baseline and keep accountable people in control.
Plan your solution01
AI Planning & Forecasting
Understanding the solution
Digital Cloud designs planning and forecasting implementations around your own data and decision process. A forecast is an estimate to evaluate, not an approved plan. We establish the target measure, time horizon and planning level before exploring Azure Machine Learning or a suitable Fabric-based analytical workflow.
Business AI should begin with a bounded task and a way to judge the output. Good results depend on suitable information, defined access and clear handling of uncertainty or mistakes.
Capabilities and scope
Compare with a baseline
Reserve later periods for evaluation and compare candidate models with a simple reference forecast. Review errors by product, location or period, including sparse demand and unusual business events.
Keep planners accountable
Separate model output from manual adjustments and approved versions. Record assumptions, review thresholds and reasons for overrides; decide when new data should trigger a model review.
A practical example
An operations team evaluates weekly demand estimates for selected product groups and reviews exceptions before adjusting its purchasing plan.
What your project can deliver
- A data-readiness assessment and agreed forecast specification.
- A reproducible comparison with baseline results and limitations.
- A planning review workflow with version and approval rules.
The final deliverables, licensing and responsibilities are agreed for your environment before implementation.
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Requirements and considerations
Identify permitted sources, sensitive data, expected outputs and human review points. Evaluate quality, costs and failure cases before allowing broader access or connected actions.
AI Planning & Forecasting
Project priorities
Historical data
Forecast baseline
Decision oversight
We begin with a conversation about the task, the people involved and the systems already in place. Together we identify what a useful result would look like and which dependencies need attention first. The agreed proposal sets the delivery boundaries, responsibilities and acceptance criteria.
Your engagement
How Digital Cloud can help
| Project phase | What happens |
|---|---|
| 01Define the design | Translate the requirements into a practical design. Confirm product choices, interfaces, permissions and the responsibilities needed to operate the solution. |
| 02Deliver in stages | Configure or implement the agreed scope, test representative workflows and resolve material issues. Plan user communication and any controlled transition from existing systems. |
| 03Prepare for ongoing operation | Confirm acceptance, document the relevant configuration and prepare the people responsible for daily use. Define maintenance and support arrangements before handover. |
AI Planning & Forecasting
Questions before you start
Will AI always outperform our existing planning method?
No. The pilot tests that question using historical periods and agreed error measures. If the evidence is weak, improving data or keeping the simpler method may be the appropriate outcome.
What determines the cost and schedule?
The starting environment, integrations, user groups and agreed outputs determine the effort. We confirm scope and commercial terms before work begins. Software licenses, infrastructure consumption and ongoing support may be separate items.
What will we receive?
The proposal identifies the deliverables: these may include findings, a prioritized roadmap, a tested configuration, a prototype, documentation or training. We agree what is included and how completion will be assessed.
Can this work with our existing systems?
We review the actual applications, data sources and access requirements before recommending an integration. Dependencies and compatibility limits are recorded so the delivery plan reflects your environment.
What happens after the initial work?
You can use the findings to guide your own team or discuss a follow-on phase. Any maintenance, monitoring or support includes separately agreed service hours, responsibilities and response targets.
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