In brief
Every factory and logistics planning project needs five data categories: objectives and scenarios, production and sales data, product and process data, space and constraints, and material flow and movement data. The goal is not maximum data volume, but granularity appropriate to the planning phase and documented data quality.
1. Objectives and scenarios
Capacity, service level, product portfolio, automation, sustainability and growth define the framework. Without a clear time horizon, historical volumes and future assumptions cannot be combined meaningfully. At least a base, growth and stress scenario create transparency.
2. Production and sales data
Required inputs include volumes by product family and time period, seasonality, variant shares and planned changes. Monthly values may be sufficient for site questions; shift or order data is required when takt, buffers and peak performance are investigated.
3. Product and process data
Bills of material, routings, setup families, times, scrap, equipment and quality requirements connect products to resources. Product families reduce complexity but must not conceal important special processes.
4. Structure and space data
Building plans, columns, heights, doors, utilities, fire protection, escape routes, floor loads and expansion areas define the feasible solution space. Drawing revisions and dimensions must be unambiguous. A 3D scan can supplement uncertain documentation of the existing building.
5. Movement data
Sources, destinations, quantities, frequencies, load carriers, routes, inventory and order profiles describe material flow. Timestamps help reveal peaks and waiting times. Material flow analysis turns these data into decision-relevant relationships.
Data needs by planning phase
| Phase | Required granularity | Most important check |
|---|---|---|
| Feasibility | Product families, annual and peak volumes, rough areas | Completeness and order of magnitude |
| Structural planning | Process chains, space needs, material relationships | Consistent system boundaries |
| Detailed planning | Dimensions, times, connections, workstations | Revision, unit and owner |
| Implementation | Dates, inventory, relocations and acceptance | Currency and change status |
When is planning data ready for a decision?
Completeness and accuracy are not enough. Planning information becomes robust only when its confidence, owner and potential for change are also known. A precise single value of uncertain origin may be a weaker basis for a decision than a documented range.
- 01
Orientation
Order of magnitude, range and open assumptions
- 02
Comparison
Consistent scenarios, sources and change risks
- 03
Release
Owned value, revision and documented scope
Interpretation
Terms and target states are understood differently.
Quantification
Qualitative requirements can only partly be measured.
Capture
Measurement, period or data source is incomplete.
Change
Volumes, products or constraints may shift.
Modelling
Abstraction and data transfer reduce reality.
In practice, every decision-relevant item receives four attributes: source and revision, accountable owner, expected range and an event that triggers reassessment. Factory-planning milestones do not confirm that “all data is complete”; they confirm the maturity needed for the next decision.
Technical basis: Hawer et al., “Klassifizierung unscharfer Planungsdaten” (2015), Krunke, “Reifegradmanagement in der Fabrikplanung” (2017), and Kampker et al., “Planungszeit Halbe” (2015). The decision-gate interpretation follows the still-current VDI 5200 Part 1.
Managing data gaps
Missing values are not estimated silently. Every assumption receives a source, range, owner and sensitivity. The team can then decide which gap must be closed before the next planning stage. Further context is provided by the factory planning process and the brownfield migration stages.