ISO 8000-119

ISO/DISPRF 8000-119:2025(en)
ISO/TC 184/SC 04/WG 13 4
Secretariat: ANSI
Date: 2025-07-1811-14
Data quality — —
Part 119:
Application of ISO 8000-115 to transport unit identifiers
PROOF
ISO/DISPRF 8000-119:2025(Een)
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ISO/DISPRF 8000-119:2025(en)
Contents
Foreword . iv
Introduction . v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Fundamental principles and assumptions . 2
5 Encoding a transport unit identifier . 2
5.1 Prefix . 2
5.2 Date and time . 2
5.3 Natural location identifier for transport unit origin. 2
5.4 Natural location identifier for transport unit destination . 2
5.5 Purchase order number formatted as an identifier . 2
6 Requirements for transport unit identifiers . 3
7 Conformance . 3
Annex A (informative) Document identification . 4
Bibliography . 5

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Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out through
ISO technical committees. Each member body interested in a subject for which a technical committee has been
established has the right to be represented on that committee. International organizations, governmental and
non-governmental, in liaison with ISO, also take part in the work. ISO collaborates closely with the
International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types of
ISO documentdocuments should be noted. This document was drafted in accordance with the editorial rules
of the ISO/IEC Directives, Part 2 (see www.iso.org/directives).
ISO draws attention to the possibility that the implementation of this document may involve the use of (a)
patent(s). ISO takes no position concerning the evidence, validity or applicability of any claimed patent rights
in respect thereof. As of the date of publication of this document, ISO had not received notice of (a) patent(s)
which may be required to implement this document. However, implementers are cautioned that this may not
represent the latest information, which may be obtained from the patent database available at
www.iso.org/patents. ISO shall not be held responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO'sISO’s adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www.iso.org/iso/foreword.html.
This document was prepared by Technical Committee ISO/TC 184, Automation systems and integration,
Subcommittee SC 4, Industrial data.
A list of all parts in the ISO 8000 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html.
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Introduction
0.1 0.1  Foundations of the ISO 8000 series
Digital data deliver value by enhancing all aspects of organizational performance including:
— — operational effectiveness and efficiency;
— — safety and security;
— — reputation with customers and the wider public;
— — compliance with statutory regulations;
— — innovation;
— — consumer costs, revenues and stock prices.
In addition, many organizations are now addressing these considerations with reference to the United Nations
1 1)
Sustainable Development Goals . .
2 2)
The influence on performance originates from data being the formalized representation of information . .
This information enables organizations to make reliable decisions. This decision making can be performed by
human beings directly and also by automated data processing including artificial intelligence systems.
Through widespread adoption of digital computing and associated communication technologies,
organizations become dependent on digital data. This dependency amplifies the negative consequences of lack
of quality in these data. These consequences are the decrease of organizational performance.
The biggest impact of digital data comes from two key factors:
— — the data having a structure that reflects the nature of the subject matter;
EXAMPLE 1 A research scientist writes a report using a software application for word processing. This report includes
a table that uses a clear, logical layout to show results from an experiment. These results indicate how material properties
vary with temperature. The report is read by a designer, who uses the results to create a product that works in a range
of different operating temperatures.
— — the data being computer processable (machine readable) rather than just being for a person to read
and understand.
EXAMPLE 2 A research scientist uses a database system to store the results of experiments on a material. This system
controls the format of different values in the data set. The system generates an output file of digital data. This file is
processed by a software application for engineering analysis. The application determines the optimum geometry when
using the material to make a product.
ISO 9000 explains that quality is not an abstract concept of absolute perfection. Quality is the conformance of
characteristics to requirements. This means that any item of data can be of high quality for one purpose but

https://sdgs.un.org/goals
1)
https://sdgs.un.org/goals
ISO 8000-2 defines information as “knowledge concerning objects, such as facts, events, things, processes, or ideas,
including concepts, that within a certain context has a particular meaning”.
2)
ISO 8000-2 defines information as “knowledge concerning objects, such as facts, events, things, processes, or ideas,
including concepts, that within a certain context has a particular meaning”.
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not for a different purpose. The quality is different because the requirements are different between the two
purposes.
EXAMPLE 3 Time data are processed by calendar applications and also by control systems for propulsion units on
spacecraft. These data include start times for meetings in a calendar application and activation times in a control system.
These start times require less precision than the activation times.
The nature of digital data is fundamental to establishing requirements that are relevant to the specific
decisions that are made by each organization.
EXAMPLE 4 ISO 8000--1 identifies that data have syntactic (format), semantic (meaning) and pragmatic (usefulness)
characteristics.
To support the delivery of high--quality data, the ISO 8000 series addresses:
— — data governance, data quality management and maturity assessment;
EXAMPLE 5 ISO 8000--61 specifies a process reference model for data quality management.
— — creating and applying requirements for data and information;
EXAMPLE 6 ISO 8000--110 specifies how to exchange characteristic data that are master data.
— — monitoring and measuring information and data quality;
EXAMPLE 7 ISO 8000--8 specifies approaches to measuring information and data quality.
— — improving data and, consequently, information quality;
EXAMPLE 8 ISO/TS 8000--81 specifies an approach to data profiling, which identifies opportunities to improve data
quality.
— — issues that are specific to the type of content in a data set.
EXAMPLE 9 ISO/TS 8000--311 specifies how to address quality considerations for product shape data.
Data quality management covers all aspects of data processing, including creating, collecting, storing,
maintaining, transferring, exploiting and presenting data to deliver information.
Effective data quality management is systemic and systematic, requiring an understanding of the root causes
of data quality issues. This understanding is the basis for not just correcting existing nonconformities but for
also implementing solutions that prevent future reoccurrence of those nonconformities.
EXAMPLE 10 If a data set includes dates in multiple formats including “yyyy--mm--dd”, “mm--dd--yy” and
“dd--mm--yy”, then data cleansing can correct the consistency of the values. Such cleansing requires additional
information, however, to resolve ambiguous entries (such as, “04--05--20”). The cleansing also cannot address any
process issues and people issues, including training, that have caused the inconsistency.
0.2 0.2  Role of this document
As a contribution to this overall capability of the ISO 8000 series and a specific application of ISO 8000-115 to
solve a current problem in the transport exchange, this document specifies requirements for a transport unit
identifier (TUID) also referred to as a load identifier. These requirements support the data to represent
unambiguously the information that organizations use. Transport unit identifiers are important to
organizations because these identifiers allow them to unambiguously identify a load throughout its shipment
phases. The transport unit identifier allows systems and so
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