International audienceThe growing complexity of modern organisations poses a series of challenges, among them the cooperation between autonomous and heterogeneous information systems in distributed networks. Actually, Information Systems (IS) are said to be cooperative if they share common goals in their environment and jointly contribute to achieve these common goals. Obviously, this presupposes the ability to exchange information and then to use it in accordance with each information system required. In the literature, these features refer to interoperability. In this context, one of the main issues concerns the evaluation of the lack of interoperability between Cooperative Information Systems (CIS) through the measurement of their semantic gaps. In order to achieve this purpose, this paper proposes a mathematical formalisation of the semantic relationships between CIS conceptual models. The resulting formal model is then analysed for evaluating the lack of interoperability implications to the global information systems shared goals. The proposed approach is illustrated through a case study dealing with a B2M (Business to Manufacturing) interoperability requirement between an Enterprise Resource Planning (ERP) system and a Manufacturing Execution System (MES) application

The grown complexity of the modern enterprise poses a series of challenges, among them keeping competitiveness in the fast changing environment in which the enterprise evolves. Addressing Enterprise Integration is considered as a key to achieve the goal of any enterprise either it is a single or a networked enterprise. Enterprise Modelling is a prerequisite to enable the common understanding of the enterprises and its various interactions in order to "provide the right information, at the right time, at the right place". However, problems often emerge from a lack of understanding of the semantics of the elaborated models resulting from various modelling experience based on different methods and tools. This paper describes the challenges associated to semantics enactment in Information Systems models. To facilitate this enactment, it proposes an approach based on a fact-oriented modelling perspective. Then, it also provides an algorithm to automatically build semantic aggregates that help in highlighting Enterprise Models core embedded semantics. A case study on the field of B2M interoperability is performed in order to illustrate the application of the presented approach.

Enterprise performance is, now more than ever, one of the key points for reaching the market success. In order to increase it, economics paradigms focus on how to better manage knowledge acquiring, sharing and update. Knowledge management can be approached with the possibility offered by the sustainability goals trying to optimize different enterprise strategic domains. The modern architecture of information systems (ISs) is based on distributed networks with a grand challenge of representing and sharing knowledge managed by ISs and consequently, to remove semantics interoperability barriers. First, this paper analyses interoperability issues between Cooperative Enterprise Information Systems (CEIS). Based on this analysis, the authors propose a conceptualisation approach for semantics discovery and management in Enterprise Information Systems models, based on applying fact-oriented transformation rules. The input of the transformation process is a conceptualised UML class model, reverse-engineered from an implemented model, and transformed into a Fact-Oriented Model (FOM), which makes explicit the finest-grained semantics. Semantics aggregates are then computed for structuring the whole semantics embedded in enterprise applications. They define independent set of concepts with their own minimal mandatory semantics. Finally a case study is proposed to validate the practicability of our approach in a real scaled scenario involving an Enterprise Resource Planning (ERP) and a Manufacturing Execution System (MES).

International audienceAs the metaphor of a film, engineering design is a process where stakeholders take decisions from product requirements to the final designed system. Unfortunately, industries lack of long term project memories to go back and forth in order to remember actions and decisions. That generates time consuming retrieval tasks that have definitively no added value since they aim at seeking past information. This paper proposes an extension of a design process meta-model that aims at tracing the project design memory. Instead of seeking past information, industries can look forward innovation and manage changes coming from new technologies, resources, KPI..

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. Currently, organizations tend to reuse their past knowledge to make good decisions quickly and effectively and thus, to improve their business processes performance in terms of time, quality, efficiency, etc. Process mining techniques allow organizations to achieve this objective through process discovery. This paper develops a semi-automated approach that supports decision making by discovering decision rules from the past process executions. It identifies a ranking of the process patterns that satisfy the discovered decision rules and which are the most likely to be executed by a given user in a given context. The approach is applied on a supervision process of the gas network exploitation.

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Manufacturing industry data are distributed, heterogeneous and numerous, resulting in different challenges including the fast, exhaustive and relevant querying of data. In order to provide an innovative answer to this challenge, the authors consider an information retrieval system based on a graph database. In this paper, the authors focus on determining the essential functions to consider in this context. The authors define a three-step methodology using root causes analysis and resolution. This methodology is then applied to a data set and queries representative of an industrial use case. As a result, the authors list four major issues to consider and discuss their potential resolutions.

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. Currently, organizations tend to reuse their past knowledge to make good decisions quickly and effectively and thus, to improve their business processes performance in terms of time, quality, efficiency, etc. Process mining techniques allow organizations to achieve this objective through process discovery. This paper develops a semi-automated approach that supports decision making by discovering decision rules from the past process executions. It identifies a ranking of the process patterns that satisfy the discovered decision rules and which are the most likely to be executed by a given user in a given context. The approach is applied on a supervision process of the gas network exploitation.