Supply chain management ontology from an ontology engineering perspective
Introduction
The premise of supply chain management (SCM) is that the performance of a single company depends more and more on its ability to maintain effective and efficient relationships with its suppliers and customers [1], [2]. Therefore, managerial tasks are moving from an organizational scale to a supply chain scale [3] and thus encompass the inter-organizational integration and coordination of dispersed supply chain activities. Empirical research suggests that knowledge sharing and reuse between supply chain participants are important determinants of supply chain performance at both the strategic and operational level [4], [5]. The role of information systems to support this task is subject of much research [6], [7], [8].
Knowledge sharing and reuse between supply chain participants face many organizational obstacles such as confidentiality, trust, and norms. However, fundamental prerequisites for knowledge sharing are means for exchanging, processing, and interpreting the relevant domain knowledge by using one or more representations of this knowledge. Since such representations may be diverse and serve different objectives, formal ontology has been proposed to represent domain knowledge, enhance communication between participants, and support interoperability of systems [9]. A formal ontology formally captures knowledge through concepts, relationships and axioms, and can be regarded as the conceptual model of a knowledge base [10]. The application of ontology in SCM has led to a large number of ontologies for various SCM tasks, e.g., planning [11] as well as more generally representing arbitrary supply chains [12].
Although researchers make use of ontology specifically for SCM, this stream of research seems to be less connected with the ontology engineering (OE) field as it could be. Over the past 20 years, the OE field made significant advances with regard to its constructs, models, methods, and tools, and contributes specific techniques that assist ontology developers [13], [14]. However, the extant literature does not inform us sufficiently about the concrete linkages between OE and SCM ontology. In particular, little is known to what extent the development of these ontologies is informed by the techniques available from this field. The first steps to increasing our knowledge about these ties were taken by Grubic and Fan [15], who review six supply chain ontologies: Two out of five evaluation criteria used in their review concern the methodological foundation as follows. “Scientific paradigm” studies the epistemological stance of the ontology researcher. “Methodological approach” studies the adoption of five general approaches to ontology design that were proposed in Ref. [16]. Our review complements and extends this research by (1) studying the adoption of concrete techniques from the OE literature and (2) reviewing a larger set of in total 16 SCM ontologies of which three are also found in the study by Grubic and Fan [15].
While empirical research has contributed to understanding the applicability and usefulness of OE techniques [17], [18], assessing their adoption in concrete ontologies has received little attention. Therefore, the objective of this article is to review and analyze current SCM ontologies with regard to their methodological foundation, i.e., the adoption of OE techniques. This study concerns the concrete linkages between OE techniques and SCM ontology as a particular type of application ontology. The study contributes to understanding these linkages and motivates avenues of future research.
This article proceeds as follows. The theoretical background to the review is described in Section 2. The review process and the relevant SCM ontologies are presented in Section 3. The review results can be found in Section 4. The discussion of the findings and their implications for future research are part of Section 5. A summary of the research is given in Section 6.
Section snippets
Formal ontology
Originally, the term ontology has its roots in philosophy. As a discipline of philosophy, ontology denotes “the science of what is, of the kinds and structures of objects, properties events, processes, and relations in every area of reality” [19]. Starting in the late 1980s and early 1990s, ontology gained increasing awareness in Computer Science and Artificial Intelligence (AI). AI requires formal representations of real world phenomena in order to reason about these phenomena. In a literal
Review process
A structured approach was employed to identify the relevant SCM ontologies in the literature. A systematic search was used to retrieve publications that describe SCM ontologies. We used citation count as a proxy measure to identify probable core publications. Since filtering based on citation count may exclude some relevant ontologies, an exploratory approach was used to find additional publications on supply chain ontologies. The latter approach allowed us to identify and include some very
Analysis of SCM ontologies
This section presents the review results. We refer to the model of ontology engineering (constructs, measurements) that was discussed in Section 2. For each measurement, we check if the publication contains a statement or indication about how the measurement materializes in the ontology, respectively the reported development process. If no reference is found, we code the measurement as “not reported”. Due to the wide time range (2000 to 2013), not all measurements are applicable to all
Discussion
In this section, we discuss the findings by revisiting each construct of the research model and draw implications from our review.
Conclusion
This article provides an in-depth review of the existing literature on SCM ontology. To assess the extent of linkages between OE techniques and this type of ontology, a systematic process was used to classify the literature along salient OE constructs. We identified 16 SCM ontologies, which we analyzed for six constructs and 14 measurements. The review enables us to succinctly describe the proposed ontologies, assess their adoption of OE techniques, and outline an agenda for future research by
Andreas Scheuermann: Andreas Scheuermann received his MSc degree in Business Administration and Education from the University of Mannheim. From 2009 to 2013, he was a Research Assistant in the Department of Information Systems 2 at the University of Hohenheim. His research interests focus on Knowledge Management, Ontology Engineering, and Empirical Knowledge Representation. He is currently an IT consultant in the finance industry and an external PhD candidate at the University of Hohenheim.
References (69)
- et al.
Towards a theory of supply chain management: the constructs and measurements
Journal of Operations Management
(2004) - et al.
Supply chain management: an analytical framework for critical literature review
European Journal of Purchasing & Supply Management
(2000) - et al.
Issues in supply chain management
Industrial Marketing Management
(2000) - et al.
Knowledge as a strategic resource in supply chains
Journal of Operations Management
(2006) - et al.
Inter-organizational communication as a relational competency: antecedents and performance outcomes in collaborative buyer-supplier relationships
Journal of Operations Management
(2008) - et al.
Information systems in supply chain integration and management
European Journal of Operational Research
(2004) - et al.
Organization and problem ontology for supply chain information support system
Data & Knowledge Engineering
(2007) - et al.
Supply chain ontology: review, analysis and synthesis
Computers in Industry
(2010) - et al.
Knowledge engineering: principles and methods
Data & Knowledge Engineering
(1998) - et al.
A semiotic metrics suite for assessing the quality of ontologies
Data & Knowledge Engineering
(2005)
User evaluations of IS as surrogates for objective performance
Information & Management
Evaluating quality of conceptual modelling scripts based on user perceptions
Data & Knowledge Engineering
Methodologies, tools and languages for building ontologies. Where is their meeting point?
Data & Knowledge Engineering
An overview of knowledge acquisition and transfer
International Journal of Man–Machine Studies
GenCLOn: an ontology for city logistics
Expert Systems with Applications
A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration
Computers in Industry
Rule-based ontological knowledge base for monitoring partners across supply networks
Expert Systems with Applications
Ontological approach for products-centric information system interoperability in networked manufacturing enterprises
IFAC Annual Reviews in Control
A Software engineering approach to ontology building
Information Systems
What do the pictures mean? Guidelines for experimental evaluation of representation fidelity in diagrammatical conceptual modeling techniques
Data & Knowledge Engineering
Where to publish and find ontologies? A survey of ontology libraries
Journal of Web Semantics: Science, Services and Agents on the World Wide Web
Coordinating for flexibility in e-business supply chains
Journal of Management Information Systems
Firm performance impacts of digitally enabled supply chain integration capabilities
MIS Quarterly
Computational ontologies and information systems I: foundations
Communications of the Association for Information Systems
Formal ontology and information systems
An approach for formalising the supply chain operations
Enterprise Information Systems
Methodologies for the development of knowledge-based systems, 1982–2002
The Knowledge Engineering Review
A collaborative approach to ontology design
Communications of the ACM
The Semantic Web vision: Where are we?
IEEE Intelligent Systems
Achieving maturity: the State of Practice in Ontology Engineering in 2009
International Journal of Computer Science and Applications
Ontology
Task Ontology for supply chain planning—a literature review
International Journal of Computer Integrated Manufacturing
Ontology engineering methodology
Cited by (35)
Survey on robotic systems for internal logistics
2022, Journal of Manufacturing SystemsCitation Excerpt :However, these have still not met a wide recognition as a standardised representation of the production elements within the manufacturing domain for the production elements. Moreover, the need for an even higher modularity level has made it necessary to represent in a shared ontological model not only strictly manufacturing elements but also aspects of internal logistics (i.e. warehousing) that have not been considered enough in previous works [132]. As such, there is a need to have fully flexible and modular systems [133].
Feature-based ontological framework for semantic interoperability in product development
2021, Advanced Engineering InformaticsCitation Excerpt :Ontologies are defined for semantic product models. These semantic product models are specific to a specific application such as PRoduct ONTOlogy [14] for logistic planning activities, Garcia-Crespo et al. [13] for industrial manufacturing processes to enable exchanges from/to the manufacturing application, Scheuermann and Leukel [69] for supply chain management, papers [58,46] for facility management, OntoQualitas [65] for quality assessment in information interchanges between heterogeneous systems, Wang and Yu [77] for volumetric shape feature recognition, Jeon et al. [30] for CAD model retrieval and Palmer et al. [55] for development of production network systems. A comparative review of product feature classifications based on the requirement, domain knowledge, specification and design is presented by Romero et al. [66].
Ontology-based systems engineering: A state-of-the-art review
2019, Computers in IndustryCitation Excerpt :This section reports the results of the analysis of the ontologies from an ontology engineering perspective. We followed the framework proposed by Scheuermann and Leukel (2014) and took languages, methods, and tools into consideration in our analysis. The results are presented in Table 6.
An ontology-based approach to knowledge representation for Computer-Aided Control System Design
2018, Data and Knowledge EngineeringSemantic multi-agent system to assist business integration: An application on supplier selection for shipbuilding yards
2018, Computers in IndustryCitation Excerpt :By this way, pre-communication time between agents is reduced and the traceability of changes is ensured for either the global ontology or the regional ones. For the purpose of finding accessible and reusable ontologies, this research conducted a comparison of related ontologies in terms of several aspects defined in [35]. The results are listed in Table 1, with an extension of the reused ontology source.
Andreas Scheuermann: Andreas Scheuermann received his MSc degree in Business Administration and Education from the University of Mannheim. From 2009 to 2013, he was a Research Assistant in the Department of Information Systems 2 at the University of Hohenheim. His research interests focus on Knowledge Management, Ontology Engineering, and Empirical Knowledge Representation. He is currently an IT consultant in the finance industry and an external PhD candidate at the University of Hohenheim.
Joerg Leukel: Joerg Leukel is a Senior Researcher and Lecturer in the Department of Information Systems 2 at the University of Hohenheim, Stuttgart, Germany. He obtained a PhD in Information Systems from the University of Duisburg—Essen, Germany. His research interests focus on inter-organizational information systems, supply chain management, ontologies, and service-oriented computing. Joerg has published in journals including Decision Support Systems, IEEE Systems Journal, Knowledge and Information Systems, and International Journal of IT Standards and Standardization Research.