Understanding Your Data

How PLAIO uses data

PLAIO’s planning intelligence is built on your structured data — a representation of how your supply chain behaves across time, products, and resources. Every plan is a direct result of the data provided, the relationships between that data, and the assumptions defined in the planning models.

Getting data in

Review and import data from the left-hand navigation under Data Management → Overview, then click the relevant upload button for a guide to the required fields. PLAIO asks you to map your Excel format on the first import and remembers it for every future import.

For integration support, contact your Customer Success Manager. See Manually Uploading Data for the full upload reference.

Core data categories

Master data defines the supply chain structure: products and SKUs, bills of material, resources, and hierarchies. PLAIO uses this to understand relationships — for example, how demand for one item generates demand for its components.

Demand data represents expected market needs over time: forecast demand, firm orders, and derived demand from BOMs or production requirements. Demand is the driver that initiates planning and propagates through the supply and production models.

Supply data defines how demand is fulfilled: planned receipts, lead times, ordering policies, delivery dates, and constraints such as MOQ and IOQ. This is where planning decisions become concrete.

Forecasting and demand propagation

PLAIO generates forecast types using machine learning models that analyse historical demand patterns — trend, seasonality, variability. These provide a baseline planners can trust, adjust, and build on.

Forecast performance is evaluated across horizons, products, and time periods, so you can see where forecasts are reliable, where bias exists, and where human judgement adds value. See Forecasting Approach & Performance Evaluation.

Constraints, assumptions, and data quality

PLAIO’s planning models are constraint-aware by design. Lead times, capacity limits, inventory policies, and ordering rules shape plans from the beginning — they are not applied after the fact.

Where explicit data is not available, PLAIO applies reasonable assumptions that are transparent, adjustable, and reflected in planning outcomes.