ISO 15531-44:2017

Overview

ISO 15531-44:2017 - part of the ISO 15531 series on industrial manufacturing management data - defines an information model for shop floor data acquisition. It covers the modelling of quantitative and qualitative data captured by data acquisition systems at the control level (level 2) that are stored and used at the manufacturing management level (level 3). The standard dates from 2017 (second edition) and provides a reusable, shareable model intended for consistent storage and subsequent processing of shop-floor measurements, events and time references.

Key technical topics and requirements

• Scope: Modelling of level‑3 data derived from level‑2 acquisition (data translation and handling remain outside the scope). Included are time stamping and time measurement used for manufacturing management.
• Data model: A formal EXPRESS schema for shop floor captured data is provided (see Clause 6 and informative annexes). Entity types defined include Stock, Manufactured_product, Orders, Manufacturing_batch, Work_order, material_consumption, Productivity_and_maintenance, Quality, Resource, and Time stamping/time reference.
• Time handling: Explicit treatment of time stamping and time measurement for captured data; each time measure is related to a resource-specific time reference (links to ISO 15531-42 time concepts and IEC 62264 functional levels are noted).
• Data characteristics: Coverage of quantitative and qualitative measurements, traceability (genealogy), productivity, maintenance and quality events, and size optimization strategies for captured data.
• Modelling principles: Emphasis on unique, well-defined models for information that may be captured repeatedly by different resources or devices to ensure interoperable management-level data.

Practical applications

• Standardizes the structure of shop-floor data for use in MES (Manufacturing Execution Systems), analytics platforms, maintenance systems, quality management and traceability solutions.
• Enables consistent storage of sensor/PLC-derived measurements and events so they can be aggregated, queried and used for rescheduling, KPI calculation, root-cause analysis and compliance reporting.