How PLAIO Uses Data
PLAIO's planning intelligence is built on your structured data, representing 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 planning models. PLAIO does not require perfect data, but it does rely on data being structurally consistent and logically connected.
How to upload data
You can review and import data from the left-hand navigation under Data Management > Overview. From there, click the relevant upload button for a guide on required fields. The system will ask you to map your Excel format on the first import, but then it will remember it for all future imports.
For integrations support, please contact your Customer Success Manager.
Core Data Categories
Master data defines the supply chain structure: products and SKUs, bills of material (BOMs), resources and hierarchies. PLAIO uses this to understand relationships—for example, how demand for one item generates demand for components.
Demand data represents expected market needs over time: forecasted demand, firm orders, and derived demand from BOMs or production requirements. PLAIO treats demand as a driver that initiates the planning process and propagates through 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 that use Machine Learning models to analyze historical demand patterns (trend, seasonality, variability). These forecasts provide a baseline that planners can trust, adjust, and build upon. Forecast performance is evaluated across different horizons, products, and time periods to understand where forecasts are reliable, where bias exists, and where human judgment adds value.
Constraints, Assumptions, and Data Quality
PLAIO planning models are constraint-aware by design. Constraints such as lead times, capacity limits, inventory policies, and ordering rules shape plans from the beginning—not applied after the fact. Where explicit data is not available, PLAIO applies reasonable assumptions that are transparent, adjustable, and reflected in planning outcomes.
PLAIO does not require exhaustive data to deliver value. What matters most is consistent structure, logical relationships, and clear intent behind parameters. Well-structured data enables stable, interpretable plans even when inputs are imperfect.
💡 Data Maturity Matters
As data maturity increases, PLAIO naturally produces more refined and reliable outcomes without requiring a redesign of the planning model. Focus on getting the structure right first, then refine data quality over time.