=Paper=
{{Paper
|id=Vol-3647/SemIIM2023_paper_10
|storemode=property
|title=Towards a Knowledge Graph-based Data Mesh for Smart Manufacturing
|pdfUrl=https://ceur-ws.org/Vol-3647/SemIIM2023_paper_10.pdf
|volume=Vol-3647
|authors=Irlán Grangel-González,Marc Rickart,Oliver Rudolph,Rui Dias
|dblpUrl=https://dblp.org/rec/conf/semiim/Grangel-Gonzalez23
}}
==Towards a Knowledge Graph-based Data Mesh for Smart Manufacturing==
https://ceur-ws.org/Vol-3647/SemIIM2023_paper_10.pdf
Towards a Knowledge Graph-based Data Mesh for
Smart Manufacturing
Irlán Grangel-González1,∗ , Marc Rickart2 , Oliver Rudolph2 and Rui Dias3
1
Corporate Research, Robert Bosch GmbH, Renningen, Germany
2
Robert Bosch GmbH, Automotive Electronics, Reutlingen, Germany
3
Bosch Car Multimedia Portugal, S.A., Braga, Portugal
1. Motivation
Manufacturing business competition is driven by efficiency in order to offer the best price for
products. Of paramount importance to achieve this efficiency is to get the right information at
the right time. Manufacturing is a very complex process. To manage such a complex process
a lot of data are required. These data are diversely spread out in different IT systems or silos,
e.g., Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), and Master
Data (MD). These silos comprise no explicit semantics. They also contain differences in the
way real-world concepts are modeled, i.e., Semantic Interoperability Conflicts (SIC) [1], thus
hindering data re-usability. To tackle these problems Knowledge Graph (KG)-based applications
have emerged. For instance, the Line Information System (LIS ) [2] for manufacturing enables
semantic harmonization, i.e., the resolution of SICs of data on production lines. However,
despite this and other previous efforts at Bosch using KGs [3, 4, 5, 6, 7] for handling semantic
harmonization many more data is being generated and consumed (cf. Figure 1). In addition,
there are still no mechanisms to fulfill the FAIR principles [8] in manufacturing scenarios at
Bosch. Of key relevance here is to have the FAIR principles in action, i.e., the data consumers
should be capable of finding, accessing, and reusing data whenever required. Moreover, these
data should be interoperable which remains as a huge challenge. To accelerate the data exchange
and to meet the expectations of data consumers, it is required to move from an application
mindset to a data centric one, where KG-based data products present concrete solutions to the
manufacturing domain. Despite having just one data product in place, i.e. LIS , many more data
from other domains than manufacturing are required by consumers.
2. KG-based Data Mesh for Manufacturing
To tackle the data reusability problems in manufacturing, we propose a KG-based data mesh [9,
10, 11]. Our approach has the KG-based products at its core resolving SICs and exposing
SemIIM’23: 2nd International Workshop on Semantic Industrial Information Modelling, 7th November 2023, Athens,
Greece, co-located with 22nd International Semantic Web Conference (ISWC 2023)
Envelope-Open irlan.grangelgonzalez@de.bosch.com (I. Grangel-González)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Figure 1: Motivating Example. LIS as a KG-based data product resolves the FAIR principles for just a
certain part of the data silos in manufacturing. However, in reality, many more silos are needed to meet
the demand of the smart manufacturing at Bosch.
semantically clean data to consumers (cf. Figure 2). Furthermore, being a source-oriented
solution where users can discover who are the data owners, source origin, sample data sets or
quality metrics. As being understandable means leveraging semantics to explain the syntax
of the datasets, the format on how the data is presented is also important, e.g., serialization,
queries to execute, proper ontologies. Then domain ownership is defined based on the data
products. Furthermore, support functionalities need to be implemented, e.g., policies to be
defined and governed, training, and consultancy of the organization, and provisioning the
data as a self-service. The platform for data as a self-service provides all domains with their
data products as host for all other consumers to integrate them in their applications. This as
a starting point, the decentralized approach accelerates the deployment of the data mesh and
thus faster data sharing and reusability via KGs and described ontologies.
2.1. Line Information System LIS as a data product
serves for instance other domains like product engineering or technology development for reuse
in their respective data products driven by KGs. Common concepts in manufacturing as used
in LIS are defined by the set of ontologies of the Core Information Model for Manufacturing
(CIMM) [12]. The case of the Internal Defect Costs (IDC) as a KG-based solution has to deal
with cost avoidance in a process failure for electronic products in the Surface-Mount Technology
(SMT) area at Bosch. The typical approach for the IDC project would have been to reinvent the
wheel by trying to semantically integrate manufacturing data that is already covered in the LIS
data product. With our approach, several months of man-hours are saved due to the fact that the
IDC project was able to reuse data out of the LIS KG. Having LIS as a data product enables IDC
to reuse related relevant plant manufacturing data, e.g., materials, processes, machines, lines and
even aggregated data by plant. This gives experts across all domains a deeper insight in defect
costs along with reducing the time of decision making with more precise and accurate data for
defining the product cost. Like this getting the edge on manufacturing business competition
improves efficiency and best product price can be offered. Without LIS , there would be a danger
of reverting to siloed data with continuous requests for data in file formats with costly human
Figure 2: KG-based Data Mesh for Manufacturing data. In the center KG-based Data products
like LIS offer semantically harmonized and clean data to other data products and data consumers.
Different domains associated to manufacturing, e.g., Logistics, Engineering, and Sales, offer their data
products which are interlinked with each other. In the side bar the evangelists deal with establishing
best practices, standardization with concrete examples. This approach is only possible with a deep
governance practice, thus, top and bottom bars refer to supporting functions, e.g., FAIR principles, Data
catalog, Access management, Documentation, Data Security, etc. [10]
interaction, increasing data loss and time taken in decision making. Therefore, main driver is
the focus on semantic integration of the data that are still not part of the LIS data product for
further improvement work. For manufacturing domain this means higher performance with
less cost and additionally data available which can be reused by other domains and applications.
2.2. Insights and feedback of the organization
In established market enterprises the competition is tough and use of data will provide an
advantage. The organization in these enterprises is usually more hardware centric than data
oriented and thus data receives a different prioritization as if it was the only source of income.
Roles and responsibilities are equally different in that they are focused on the hardware product
development and assembly, while information technology remains a support function only.
In that setup, a strong lead on the tech stack and its application is missing. This creates the
opportunity for the individual domains to establish their own tech stacks, thus resulting in
a plethora of data storage technologies. As metadata has to be applicable to any and every
source of at least the structured data, a decoupling of the metadata layer from the storage
layer is advisable. Furthermore, implementing the FAIR principles nevertheless allows the
organization to adapt faster to use of the data and metadata offered. At Bosch the data product
LIS is offered to many other domains for reuse by APIs with defined data contracts. The data
product is described by KG-based technologies semantically and provides links between fields
not necessarily on same data source system. In order to avoid SICs link prediction methods
are embedded and by active use of the metadata system the instances themselves offered. Any
data quality concern of mismatch in semantics will be spotted by all of the data consumers and
can be fed back, while the other metadata system can simply be ignored and data consumers
may start having their own description tables put in place. That is exactly what we observe in
our organization. As for future work, we envision to enable further KG-based data products,
e.g., for engineering, logistics, and sales to be able to cover a wider range for manufacturing
applications.
References
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