Hervé Panetto

Enterprise integration and interoperability in manufacturing systems: Trends and issues

Recent advances in information and communication technologies have allowed manufacturing enterprise to move from highly data-driven environments to a more cooperative information/knowledge-driven environment. Enterprise knowledge sharing (know-how), common best practices use, and open source/web based applications are enabling to achieve the concept of integrated enterprise and hence the implementation and interoperability of networked enterprises. Enterprise integration and interoperability in manufacturing systems is a key concept to face the challenges of these new environments. This paper describes challenges, trends and issues that must be addressed in order to support the generation of new technological solutions.

ONTO-PDM: Product-driven ONTOlogy for Product Data Management interoperability within manufacturing process environment

This paper proposes an approach for facilitating systems interoperability in a manufacturing environment. It is based on the postulate that an ontological model of a product may be considered as a facilitator for interoperating all application software that share information during the physical product lifecycle. The number of applications involved in manufacturing enterprises may in fact refer to the knowledge that must be embedded in it, appropriately storing all its technical data based on a common model. Standardisation initiatives (ISO and IEC) try to answer the problem of managing heterogeneous information scattered within organizations, by formalising the knowledge related to product technical data. The matter of this approach is to formalise all those technical data and concepts contributing to the definition of a Product Ontology, embedded into the product itself and making it interoperable with applications, thus minimising loss of semantics.

Towards a classification framework for interoperability of enterprise applications

Software applications interoperability is a challenge for modern enterprises. It needs establishing standards and protocols for data exchange between different enterprise systems. Nevertheless, since there is no methodology for collecting data, exchanged information is generally incomplete. Business process modelling aims at specifying object flows and processes inside enterprise levels and among networked enterprises. Enterprise-control systems aim at driving and scheduling the manufacturing resources based on information coming from the whole enterprise. However, the increased complexity of these models does not help to ensure coherent relationships between its components. In the current paper, the author will analyse how enterprise applications, models and standards used at different levels of the enterprise could be classified to come to a framework of many interoperability types.

Manufacturing Enterprise Control and Management System Engineering: paradigms and open issues

Abstract Over several decades, control theory has developed its own set of more or less formal modelling techniques designed to automatically control the dynamic behaviour of complicated manufacturing systems and processes. The emerging Internet society is addressing new enterprise control and management integration (ECMI) challenges for agile business to manufacturing (B2M) purposes which enlarge the traditional setting of Automation Engineering to the systems engineering (SE) approach. In order to cope with the increasing complexity of integrating intelligence/information-intensive manufacturing automation within the networked manufacturing enterprise, Automation Engineering should be integrated into the systems engineering approach to achieve a holistic approach that treats in fine the technical operational manufacturing system emerging from the deployment of an ad hoc combination of formal and informal partial models. This paper emphasises that a Holonic Manufacturing Execution System Engineering ( HMESE ) approach should be a relevant B2M SE approach along with other relevant scientific, industrial and educational areas dealing with information and intelligence control and management issues in agile automation.

A multi-agents based E-maintenance system with case-based reasoning decision support

Abstract Today, one challenge of a manufacturer is to maintain with the consumer, the expected service of the supplied product during the whole product life cycle, no matter where the product and the consumer are located. The combination of modern information processing and communication tools, commonly referred to as Tele-service, offers the technical support required to implement this remote service maintenance. However, this technical support is insufficient to face new remote maintenance decision-makings which requires not only informational exchanges between customers and suppliers but also co-operation and negotiation based on the sharing of different complementary and/or contradictory knowledge. It requires an evolution from Tele-service to E-service and e-Maintenance in particular where the maintenance decision-making results from collaboration of maintenance processes and experts to form a Distributed Artificial Intelligence environment. For this purpose, a Problem-Oriented Multi-Agent-Based E-Service System (POMAESS) is introduced in this paper. The protocol of negotiation for multi-agents and the “Case-Based Reasoning”-based decision support function within this system are discussed, emphasised at the service maintenance problem solving. A prototype system based on these methodologies is developed to demonstrate the feasibility.

An approach for formalising the supply chain operations

Reference models play an important role in the knowledge management of the various complex collaboration domains (such as supply chain networks). However, they often show a lack of semantic precision and, they are sometimes incomplete. In this article, we present an approach to overcome semantic inconsistencies and incompleteness of the Supply Chain Operations Reference (SCOR) model and hence improve its usefulness and expand the application domain. First, we describe a literal web ontology language (OWL) specification of SCOR concepts (and related tools) built with the intention to preserve the original approach in the classification of process reference model entities, and hence enable the effectiveness of usage in original contexts. Next, we demonstrate the system for its exploitation, in specific – tools for SCOR framework browsing and rapid supply chain process configuration. Then, we describe the SCOR-Full ontology, its relations with relevant domain ontology and show how it can be exploited for improvement of SCOR ontological framework competence. Finally, we elaborate the potential impact of the presented approach, to interoperability of systems in supply chain networks.

Information systems for enterprise integration, interoperability and networking: theory and applications

Today, enterprises can be characterized by various key facets: globalization, distributed manufacturing, data and knowledge management, advanced automation and robotics, virtual engineering, rapid response to market and more. In todays competitive economy, enterprises need collaborating using Information Technology (IT) and other tools to succeed in this dynamic and heterogeneous business environment. Enterprise integration, interoperability and networking are some of the major disciplines that are enabling companies to improve collaboration and communication in the most effective way. In this direction, the enterprise information systems engineering process aims to develop information systems to respond to increasingly complex objectives, to align these information systems with business goals and processes of the company, or simply to adapt and improve them when facing given requirements or rapidly changing opportunities. As enterprise information systems models become more ubiquitous, the sharing of best-in-class models becomes more desirable. Interoperability between dissimilar systems in sharing information is important, but other aspects are also required in the sharing of enterprise systems knowledge. First, this process is based on the need for collaboration, sharing and mutual understanding of the needs of each stakeholder i.e. each persons involved or affected by the future information system, at each stage of its development. Second, this process follows principles which highlight the need for formal semantics definition of these models to facilitate this work, at various abstraction levels ranging from specification to implementation on site. There is a need to also couple new theoretical results with applied methods and tools supporting existing business reconfiguration and transformation both locally and globally. In this editorial, we reflect on the current and future theory and applications that would further empower networked enterprises by means of collaborative information systems.

Enterprise Integration, Interoperability and Networking, Proceedings of the 5th IFAC/IFIP EI2N'2010 workshop, Lecture Notes in Computer Sciences, On the Move to Meaningful Internet Systems: OTM 2010: International Workshops, Hersonissou, Crete, Greece

The position paper aims at discussing the potential of exploiting linked data best practice to provide metadata documenting domain specific resources created through verbose acquisition-processing pipelines. It argues that resource selection, namely the process engaged to choose a set of resources suitable for a given analysis/design purpose, must be supported by a deep comparison of their metadata. The semantic similarity proposed in our previous works is discussed for this purpose and the main issues to make it scale up to the web of data are introduced. Discussed issues contribute beyond the reengineering of our similarity since they largely apply to every tool which is going to exploit information made available as linked data. A research plan and an exploratory phase facing the presented issues are described remarking the lessons we have learnt so far. 1 Selecting Complex Resources Effective sharing and reuse of data are still desiderata of many scientific and industrial domains, e.g., environmental monitoring and analysis, medicine and bioinformatics, CAD/CAE virtual product modelling and professional multimedia, where the selection of tailored and high-quality data is a necessary condition to provide successful and competitive services. For example, in the domain of environmental data, many data resources are usually obtained through complex acquisition-processing pipelines, which typically involve distinct specialized fields of competency. Oceanographers, biologists, geologists may provide heterogeneous data resources, which are encoded differently in text, tables, images, 2D and 3D digital terrain models. Semantic web and in particular the emerging linked data best practice [1] provide a promising framework to encode, publish and share complex metadata of resources in these scientific and industrial domains. Enabling factors for establishing the web of data as preferred selling point for complex resources are: (i) linked data best practice relies on light-weighed ontologies encoded in Resource Description Framework (RDF) which can be exploited to provide ontology driven metadata. Such a kind of metadata takes advantage from the Open Word Assumption, enabling the adoption of complex, domain specialized and independently developed metadata vocabularies, which are pivotal to document resources produced in complex and loosely coupled pipelines; (ii) linked data best practice relies on content negotiation exploiting the standard HTTP protocol, it is not proposing a brand new platform replacing the existing technologies. Rather, it can be placed side by side to domain specific protocol and standards (e.g., Open Geospatial Consortium specification for the geographic domain) making metadata available in human and machine consumable format; (iii) technological headways have brought to mature prototypes in order to expose resource as linked data (e.g., D2R and Pubby), to query them by appropriate query language (i.e., SPARQL), to retrieve their pertaining RDF fragments published around the web (e.g., Sindice), to reason, store and manipulate these fragments once there are retrieved (e.g., JENA API). However, even supposing the linked data was massively adopted to share the metadata of complex resources, the selection of the most suitable datasets for complex domains like environmental analysis would still be an enervating task. A huge amount of resource features and their complex relations must be considered during the selection process. Especially for assisting in this process, semantic similarity algorithms supporting a deep comparison of resource features are pivotal. The term “semantic similarity” has been used with different meanings in the literature. It sometimes refers to ontology alignment, where it enables the matching of distinct ontologies by comparing the names of the classes, attributes, relations, and instances [2]. Semantic similarity can also refer to concept similarity where it assesses the similarity among terms by considering their distinguishing features [3, 4]; their encoding in lexicographic databases [5,6,7,8]; their encoding in conceptual spaces [9]. In this position paper, however, semantic similarity is meant as instance similarity since this similarity is fundamental to support detailed comparison, ranking and selection of multidimensional data through its ontology driven metadata. Different methods to assess instance similarity have been proposed. Some rely on description logics [10]; some have been applied in the context of web services [11]; some others have been applied to cluster ontology driven metadata [12, 13]. Surprisingly, none of these methods supports recognition in the case of those instances, albeit different, have effectively the same informative content: they lack of an explicit formalization of the role of context in the entity comparison, and they fail identifying and measuring if the informative content of one overlaps or is contained in the other. Thus, the similarity results are not easily interpretable in terms of gain and loss the users get adopting a resource in place of another. To address these problems, we have recently proposed an asymmetric and context dependent semantic similarity among ontology instances, which meets the aforementioned requirements. The results are shown to be very promising for fine-grained resource selection when operating on a local repository of resources [14]. Unfortunately, there are still many issues that have to be addressed to scale the instance similarity up to the web of data. In this position paper, we are going to discuss these issues.

ONTOLOGICAL APPROACH FOR PRODUCTS-CENTRIC INFORMATION SYSTEM INTEROPERABILITY IN NETWORKED MANUFACTURING ENTERPRISES

Abstract Standardisation initiatives (ISO and IEC) try to answer the problem of managing heterogeneous information, scattered within organizations, by formalising the knowledge related to products technical data. While the product is the centred object from which, along its lifecycle, all enterprise systems, either inside a single enterprise or between cooperating networked enterprises, have a specific view, we may consider it as active as far as it participates to the decisions making by providing knowledge about itself. This paper proposes a novel approach, postulating that the product, represented by its technical data, may be considered as interoperable per se with the many applications involved in manufacturing enterprises as far as it embeds knowledge about itself, as it stores all its technical data, provided that these are embedded on a common model. The matter of this approach is to formalise of all technical data and concepts contributing to the definition of a Product Ontology, embedded into the product itself and making it interoperable with applications, minimising loss of semantics.

THE MISSING LINK: CULTURE AND LANGUAGE BARRIERS TO INTEROPERABILITY

Interoperability is key to ensuring that a global supply chain operates as seamlessly as a vertically integrated organization. Much research has been accomplished and is on-going related to the technical, organisational and scientific issues concerning interoperating dissimilar enterprise systems and languages. However, there are significant issues concerning interoperating information across the barriers of cultures and national languages. This paper presents the key drawbacks regarding the cultural and language barriers to true exchange of knowledge. The paper then presents an example enterprise model in light of these identified issues. Copyright