Source: https://daphnislabs.com/services/predictive-analytics

# Predictive Analytics Services

Forecasts and risk scores that support a specific planning decision, with uncertainty, baseline comparisons and feedback from actual outcomes.

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**Demand planning**

Observed · Forecast

Demand Uncertainty interval

## Tools & technologies

-   Python
-   Statistical or ML models
-   SQL
-   Reporting API

## Forecasts that fit a planning decision.

### Demand planning

Estimate future demand at an agreed product, location and time resolution.

### Capacity planning

Translate expected workloads into scenarios for staffing or infrastructure.

### Risk scoring

Prioritize records for review while preserving an accountable decision process.

## A forecast with its assumptions attached.

-   **Forecasting dataset and pipeline**
-   **Backtesting and baseline report**

-   **Scenario dashboard or API**
-   **Monitoring and refresh schedule**

## Plan next week's stock

Capability Example

Historical sales include promotions, stockouts and seasonal variation.

### Prepare

### Backtest

### Compare

Illustrative data. Nothing is sent to an external system.

Example workspace 1 / 3

### Planning history

Illustrative demand series and forecast interval

-   Weekly demand
-   Promotion dates retained
-   Future outcomes excluded

Distinguish observed demand from days when stock was unavailable.

### Backtest

Illustrative demand series and forecast interval

-   Train on earlier weeks
-   Compare seasonal baseline
-   Review stockout periods

Evaluate forecasts on later periods using only earlier information.

### Planning output

Illustrative demand series and forecast interval

-   Next-period estimate
-   Uncertainty interval
-   Planner adjustment retained

Present an expected range alongside a simple seasonal baseline.

A planning forecast with uncertainty and a record of how it performed against actual demand.

Follow the record through the next step

## Agree the horizon and the cost of error.

### Decision horizon

Daily replenishment and quarterly capacity planning require different evaluation windows.

### Error cost

Define the consequences of over- and under-prediction before selecting a metric.

## Questions before we start.

### Are forecasts guaranteed?

No. Predictions carry uncertainty, and unusual events can invalidate historical patterns. The interface should make those limits visible.

### How is this different from a dashboard?

A descriptive dashboard reports past events. Predictive work estimates future or unknown outcomes and needs backtesting against real observations.

[Predictive Analytics & Machine Learning](https://daphnislabs.com/services/predictive-analytics-machine-learning) [Machine Learning Development](https://daphnislabs.com/services/machine-learning) [Big Data Consulting & Development](https://daphnislabs.com/services/big-data) [Data Engineering](https://daphnislabs.com/services/data-engineering)

## Bring your starting point.

Share the decision horizon, historical records and the cost of an inaccurate estimate.

[Discuss your project](https://daphnislabs.com/contact?service=predictive-analytics) [Explore related services](https://daphnislabs.com/services/predictive-analytics-machine-learning)