=Paper=
{{Paper
|id=Vol-3335/SIoT_short1
|storemode=property
|title=Ontological modeling of ERP for Industry 4.0
|pdfUrl=https://ceur-ws.org/Vol-3335/SIoT_short1.pdf
|volume=Vol-3335
|authors=Fatima Zahra Amara,Mounir Hemam,Tawous Amara,Meriem Djezzar
}}
==Ontological modeling of ERP for Industry 4.0==
https://ceur-ws.org/Vol-3335/SIoT_short1.pdf
Ontological Modeling of ERP for Industry 4.0⋆
Fatima Zahra AMARA1,2,∗,† , Mounir HEMAM1,2,† , Tawous AMARA3,† and
Meriem DJEZZAR1,4,†
1
University of Abbes Laghrour, Khenchela, Algeria
2
ICOSI Laboratory, Khenchela, Algeria
3
Higher National School of Computer Science, Oued-Smar, Algiers, Algeria
4
LIRE Laboratory, Constantine, Algeria
Abstract
Data is a pillar of Industry 4.0 (I4.0), centralized in Enterprise Resource Planning (ERP) for easy access
and collaboration. ERP is the standardized process of collecting and organizing business data via an
integrated suite of ERP software, a collection of applications that automate business functions. Industry
4.0 is a new paradigm for technology-based system automation based on integrating information and
communication technologies and those comprising the concept of intelligent manufacturing. The latter
has provided tremendous opportunities for manufacturing systems, particularly in planning and control,
from resource to supply chain levels. Hence, enterprise resource planning (ERP) based on I4.0 elements
is being researched and developed. Otherwise, ERPs are recognized for autonomy, distribution, and
heterogeneity. As a result, an ERP system’s requirement to deliver requested data in short response
times, exchange data, and process data from multiple systems cannot be met. This limitation is primarily
due to a lack of interoperability and the heterogeneity of data representation formats, which are critical
concerns in such a context. Indeed, interoperability issues arise at various levels, such as the semantic
level. This paper presents a review of the use of ERP in Industry 4.0 and how ontologies offer a relevant
complementary solution to semantic interoperability problems. In addition, an ontological model for
ERPs is developed.
Keywords
Ontology, Semantic Interoperability, ERP, Industry 4.0
1. Introduction
Manufacturers have long relied on a network, machines, and devices. However, these technolo-
gies are insufficient in today’s competitive landscape and rising customer demands. The recent
wave of industry trends is centered on integrating enterprise technologies such as collaboration
and communication technology, business analysis tools, and accounting software that automates
workflows, enhances communication and gives data access to increase productivity. Industry
4.0 (I4.0), or the Fourth Industrial Revolution, is the industrial wave using Artificial Intelligence
SIoT-2022: International Workshop on Semantic IoT (SIoT-2022), Co-located with the KGSWC-2022, November 21-23,
2022, Madrid, Spain.
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open f.amara@univ-khenchela.dz (F. Z. AMARA); hemam.mounir@univ-khenchela.dz (M. HEMAM);
kt_amara@esi.dz (T. AMARA); meriem.djezzar@univ-khenchela.dz (M. DJEZZAR)
Orcid 0000-0001-8463-0330 (F. Z. AMARA); 0000-0002-4410-4528 (M. HEMAM); 0000-0003-0004-1227 (M. DJEZZAR)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
(AI), the Internet of Things (IoT), big data, and cloud computing. Through information and
communication technology, Industry 4.0 concepts have been used to increase horizontal and
vertical integration in manufacturing systems [1]. Thanks to advancements in industry tech-
nologies, employees can always stay connected to the shop floor and their data. Enterprise
Resource Planning is the glue that connects industry solutions and allows for immediate access
from top to bottom.
ERP (Enterprise Resource Planning) is a business management system that combines all
aspects of a business. ERP solutions, designed to integrate into an organization’s environment
over time seamlessly, are increasingly being used by larger businesses and governments [2].
ERPs are defined as computerized management systems that aid in effectively administrating
administrative operations by facilitating data storage and processing [3]. Digital format and
networked technology systems, according to the Industry 4.0 concept, can assign production
planning and control tasks to ”intelligent” objects such as machines, products, and parts [4].
Although Industry 4.0 can help streamline processes across a manufacturing organization, the
full benefits of these advancements can only be realized through an integrated platform, such
as a manufacturing ERP system. This is the point at which Industry 4.0 and ERP meet. Hence,
without ERP, IoT devices may create more data silos, limiting visibility for the industry. ERP
centralizes IoT metrics and others produced by the industry for easy access and collaboration.
ERP interprets the massive amounts of data collected by Industry 4.0 technologies to provide
actionable insights. ERP users benefit from real-time data processing because it provides prompt
insights from the manufacturing plant. Thus, semantic interoperability was discovered to be an
essential aspect of ERP.
Smart manufacturing is powered by the combined strength of ERP software and Industry
4.0 technology. The requirement for diverse modules for various vocabulary businesses causes
numerous problems in the ERP system [5]. ERPs perform in a fast-paced environment; to
thrive in this context, ERP must share information and collaborate effectively. Furthermore,
ERPs are generally characterized by autonomy, dispersion, and heterogeneity. This may cause
semantic interoperability issues. Interoperability is a critical problem in this context for ensuring
that different ERP systems function together toward a mutually beneficial outcome. Indeed,
the semantic web and ontologies offer a complementary answer to semantic interoperability
issues. Ontology can be considered an essential step toward more efficient management of
heterogeneous data[6]. In this work, we will offer an ontological model of ERP in the context of
Industry 4.0, built on semantic web technologies to ensure ERP semantic interoperability.
The remainder of this article is organized as follows: In Section 2, we explore similar works.
In Section 3, we demonstrate the function of ERP in Industry 4.0. Section 4 discusses the
importance of semantic interoperability in ERP systems. In Section 5, we present our suggested
ontological model for ERP. Finally, in Section 6, we conclude and outline future work.
2. Related Work
This section provides an overview of existing knowledge relevant to the current research.
Semantic Web technology can be applied in the context of an ERP system to enable the lack of
automation and overcome deficiencies caused by the heterogeneity of information contents and
semantics provided by multiple sources [5]. The Semantic Web represents a pledge for the next
generation of the Web [7].
Pereira. et al. in [8] suggests an ontological approach to support horizontal and vertical
information integration in smart manufacturing systems to overcome the challenge of semantic
interoperability and horizontal and vertical integration. This research supports the information
and knowledge exchange across various manufacturing systems, assisting the manufacturing
industry’s transition to a semantically interoperable digital environment.
Chuangtao Ma in [9] tackles the problem of inefficiently integrating heterogeneous data
from different legacy ERP systems by suggesting a data integration framework for legacy ERP
systems based on ontology learning from structured query language (SQL) scripts (RDB).
In [10] Badr NM et al. introduce a framework for incorporating semantic process modeling
into existing enterprise applications like ERP. These embedded semantic process models capture
the essential concepts of the process modeling languages through their core reference ontology.
Ma Chuangtao and Bálint Molnár in [11] used an ontology learning technology to solve
integration issues with legacy ERP systems. A general integration framework based on ontology
learning is presented to effectively and efficiently integrate various legacy ERP systems. Data
integration was chosen to demonstrate the legacy ERP system integration process based on
ontology learning, and the critical steps of legacy ERP system integration based on ontology
learning were provided.
The authors in [12] propose the integration of semantics at the level of ERPs using the
techniques proposed by the Multi-Agent System and the use of new technologies to resolve
most semantic conflicts.
3. ERP and Industry 4.0
There can be no Industry 4.0 without an effective ERP that serves as the central core of the In-
dustry 4.0 global information system (Figure 1). This fundamental repository for the company’s
strategic data is at the center of information flows.
Figure 1: ERP in Industry 4.0.
There are numerous concepts and words associated with IIoT and Industry 4.0, and ERP is one
of the fundamental concepts representing a tool for business process management that can be
used to manage information within a company. One of the primary benefits of an ERP in a
modern industry is the ability to manage multiple critical business areas with a single system
and enable them to communicate with one another. The ERP model allows for greater flexibility
and adaptability of the manufacturing system itself [13].
ERP centralizes IoT metrics and other enterprise data for easy access and collaboration, and
it interprets the massive amounts of data collected by Industry 4.0 technologies to deliver
actionable insights. Furthermore, real-time data processing ensures that ERP users receive
instant insights. Furthermore, cloud ERP provides the convenience and reach to assist in the
transformation and tracking of processes across the globe, and scalable ERP solutions enable
companies to implement new technology to build a digital enterprise.
4. Semantic Interoperability for ERP
Interoperability refers to the capacity of systems to provide services to and embrace services
from other systems and to use the services exchanged to operate more efficiently together [14].
Semantic interoperability is a subset of the general definition concerned with comprehending
and interpreting data exchanged between system actors. Semantic interoperability provides a
common understanding of the data using standard nomenclatures and formats. If two systems
are operationally interoperable and share the same semantic reference on the object of the
interaction, they are said to be semantically interoperable. Interoperability is strengthened by
a shared and unambiguous interpretation of the information exchanged between the various
stakeholders. It must be accomplished by enabling applications to share information and view
the meaning and values of transmitted data, allowing it to be reused without errors or data loss
[15].
Semantic Web technology has the potential to significantly contribute to overcoming the
shortcomings caused by the heterogeneity of information contents and semantics generated from
various sources [5]. The detailed capture of activities and process information is challenging in
enterprise information systems. The semantic integration of personal information management
systems is a first step towards modernizing ERP applications. Such management information
systems should be Semantic Web powered to provide quick access to the resources required for
enhanced and effective decision making [16].
Ontologies are the most important way to represent and manage knowledge in the Semantic
Web, allowing for information sharing and shared understanding. They are regarded as an
essential paradigm in human-machine semantic interoperability, data integration, and knowl-
edge exchange [17] to facilitate the search for required configuration objects for a specific data
management configuration task more efficiently. Also, define the configuration required to
create a specific instance of master datasets. Furthermore, compare the structure of enterprise
systems or business processes to user requirements.
5. Approach Ontology Design
Ontologies are essential for the growth of the Semantic Web [18]. Understanding the information
exchanged between systems cooperating in the performance of a global task is a problem with
semantic interoperability. To address this issue, the information exchanged must be structured
in a way that makes it easy to understand. This structuring uses ontologies, which describe
domain knowledge in a formal framework.
The ontology was created in the Web Ontology Language (OWL)1 using the Protege ontology
editor, version 5.5.0 2 ontology editor to represent the knowledge in a formal, structured, and
reusable format. The above ontology (ERPI4.0-Onto) was created to represent an ontology for
ERP systems in Industry 4.0.
5.1. Ontology Modeling and Implementation
Ontology is designed to be flexible in order to solve real-world modeling and knowledge
representation problems. Individuals/objects, classes, attributes, relationships, and axioms are
essential elements of a formal model of a specific domain.
5.1.1. Ontology Domain
In the context of Industry 4.0, the representation of ERP systems was identified as a domain
that can define the following scopes: Supply Chain Management (SCM), inventory, persons,
Customer Relationship Management (CRM), Business Intelligence (BI), etc.
5.1.2. Ontology Terminology Glossary
The main terms of the ontologies In the application domain were mentioned and used in the
class file. This domain model necessitates using specific class terms such as SCM, inventory,
persons, CRM, Business Intelligence, product, stock, purchases, sales, supply chain, etc. Classes
and sub-classes hierarchies are represented in figure 2.
We present a description of some classes and sub-classes in the specific ontology below:
• CRM: Customer relationship management is a technology that enables both large and
small businesses to organize, automate, and synchronize every aspect of customer inter-
action.
• Business Intelligence: BI is a real-time monitoring of business performance from the top
to the bottom of the organization
• Finance: Within a centralized accounting system, record, track, and consolidate business
and operational information. This is made possible by financial ERP software’s centralized
systems, which include general ledger, accounts payable, accounts receivable, and payroll
management.
• Human Resources: manage the various social benefits, staff and talent development, and
track employee working time and company-wide performance reviews.
1
https://www.w3.org/TR/owl-features/
2
https://protege.stanford.edu
Figure 2: Classes and Sub classes hierarchies for ERPI4.0-Onto
• CSM: Supply Chain Management, activities are planned, executed, controlled, and mon-
itored. An ERP solution handles the physical aspects of supply, such as storage and
transportation, and the market aspect of effectively managing demand and supply to
meet customer demands.
5.1.3. Classes object/data properties
OWL was used to define relationships between previously presented classes by defining object
properties of classes. Data properties link individuals and literals to specify the type of data
value. For example, the value of a property has_name is of data type string, and the value of a
property has_value is of data type reel. Figure 3 depicts some of the object/data properties of
the developed ontology model.
Figure 3: The developed ontology’s (ERPI4.0-Onto) object/data properties
5.2. Results and Ontology Evaluation
Ontology verification is the comparison of ontology against the requirements. To test an
ontology and check whether it satisfies its functional requirements, which are ambiguous
and difficult to formalize. Themis3 is a tool that automates the implementation execution of
requirements tests and includes some inference. In addition to the defined test expressions,
an ontology for test requirements is provided. Four outcomes are possible for each test and
ontology: undefined terms, passed, absent relation, and conflict[19].
Figure 4 shows some of the test cases that have been run throughout this tool, along with all
possible outcomes in the results section.
Figure 4: Tests and results of the verification of ERPI4.0-Onto ontology
3
https://themis.linkeddata.es
6. Conclusion
Enterprise Resource Planning (ERP) has a unique role in the Industry 4.0 (I4.0) concept. Thou-
sands of various types of data must be exchanged throughout the process to ensure effective
communication. ERP integration in Industry 4.0 is regarded as a complex problem. The leading
cause of this problem is semantic heterogeneity among various information sources. Real-time
data processing benefits ERP users by providing them with timely insights from the manu-
facturer. As a result, semantic interoperability was discovered to be a critical aspect of ERP,
and it can be addressed by utilizing semantic web technology to achieve software system
interoperability.
In this paper, we have presented the role of ERP for Industry 4.0 and the necessity of semantic
interoperability in this context. Then, we developed an ontological model of the ERP system
built on semantic web technologies to ensure ERP semantic interoperability in the context of
Industry 4.0. In the future, we intend to enrich the business domain ontology by providing an
intuitive approach to suggest new concepts that are missing from the existing ontology.
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