Demand Planner Features & Definitions

Main features

Multiple demand lines

Each SKU can carry several demand lines representing different forecast types: market forecasts, actual orders, tenders, budget, and machine learning forecasts. Comparing different demand indicators for a single SKU improves planning accuracy.

Demand lines are versioned, so planners track changes over time without overwriting prior values. You can also create a customised, new plan type.

Machine learning and benchmarking

PLAIO generates forecasts using ML models trained on historical sales data, which automatically capture trends, seasonality, and demand shifts. In parallel it produces a benchmark forecast from a rolling average as a reference point. See Machine Learning Forecasting in PLAIO.

Flexible forecast granularity

Plans can be created and maintained at different dimensions — product, market, or customer group — and then aggregated or broken down depending on the configuration.

Performance and analysis

Forecast KPIs

PLAIO evaluates forecast quality with two complementary metrics:

• Error — quantifies the overall magnitude of inaccuracy.
• Bias — identifies systematic tendencies to over-forecast or under-forecast.

Both are visualised on each demand line, so planners can assess reliability and adjust.

Forecast accuracy tracking

PLAIO measures forecast accuracy by comparing forecast market quantities to actual sales over a 12-month period:

See Forecast Accuracy for the dedicated worksheet.

Planning support

On-demand updates

Trigger an update at any time to recalculate both forecast types on the latest available data. Planners work with current information while keeping control over when new calculations run.

Exception alerts

Standard exception alerts include:

• Missing Demand — non-discontinued items with no demand forecast.
• Forecast Reliability — items with high forecast error and bias.

Volume and value visibility

Demand Planning supports viewing the demand plan in both quantity (volume) and value. Value is a derived view: planned quantities multiplied by the applicable unit price.

Integration and data management

Sales and inventory history

Sales history is the basis for statistical forecast calculations. Inventory history is informational, showing how stock has evolved over time.

Make-to-Stock vs Make-to-Order

PLAIO accommodates both MTS and MTO strategies, defined down to individual SKU level.

Column definitions

Click the … on any column to choose your columns. Custom columns can be added if needed.

Column	Meaning
SKU No	Unique identifier for the Stock Keeping Unit
Name	Full name of the product variation, including format and dosage
Demand Segment	SKU + Market ID, allowing high granularity when planning
Product Family	Grouping of products by family or product line
Series Type	Forecast type: Market, DemandML, Benchmark, Customer Orders, or a custom demand segment
Location	Physical location in the supply network
Start Date	The item’s lifecycle start date
Market	Geographic or commercial market where the product is sold (e.g. Spain, Sweden)