https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Thu, 12 Mar 2026 10:33:15 GMT 2026-03-12T10:33:15Z http://hdl.handle.net/10985/11340 Engineering design memory for design rationale and change management toward innovation ES-SOUFI, Widad; TICHKIEWITCH, Serge; ROUCOULES, Lionel; YAHIA, Esma As 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... Fri, 01 Jan 2016 00:00:00 GMT http://hdl.handle.net/10985/11340 2016-01-01T00:00:00Z ES-SOUFI, Widad TICHKIEWITCH, Serge ROUCOULES, Lionel YAHIA, Esma As 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... http://hdl.handle.net/10985/11339 On the use of Process Mining and Machine Learning to support decision making in systems design ES-SOUFI, Widad; ROUCOULES, Lionel; YAHIA, Esma Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. This paper proposes a double layer framework aiming to identify the most significant process patterns to be executed depending on the design context. Simultaneously, it proposes the most significant parameters for each activity of the considered process pattern. The framework is applied on a specific design example and is partially implemented. Fri, 01 Jan 2016 00:00:00 GMT http://hdl.handle.net/10985/11339 2016-01-01T00:00:00Z ES-SOUFI, Widad ROUCOULES, Lionel YAHIA, Esma Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. This paper proposes a double layer framework aiming to identify the most significant process patterns to be executed depending on the design context. Simultaneously, it proposes the most significant parameters for each activity of the considered process pattern. The framework is applied on a specific design example and is partially implemented. http://hdl.handle.net/10985/11329 Collaborative Design and Supervision Processes Meta-Model for Rationale Capitalization ES-SOUFI, Widad; ROUCOULES, Lionel; YAHIA, Esma Companies act today in a collaborative way, and have to master their product design and supervision processes to remain productive and reactive to the perpetual changes in the industrial context. To achieve this, authors propose a three-layers framework. In the first layer, the design process is modelled. In the second, the traces related to the decisional process are captured. In the third, both the collected traces and the design context model are used to support decision-making. In this paper, authors address the first two issues by proposing a meta-model that allows one to capture the process’ decisional knowledge. The proposal is presented and then illustrated in a case study. Fri, 01 Jan 2016 00:00:00 GMT http://hdl.handle.net/10985/11329 2016-01-01T00:00:00Z ES-SOUFI, Widad ROUCOULES, Lionel YAHIA, Esma Companies act today in a collaborative way, and have to master their product design and supervision processes to remain productive and reactive to the perpetual changes in the industrial context. To achieve this, authors propose a three-layers framework. In the first layer, the design process is modelled. In the second, the traces related to the decisional process are captured. In the third, both the collected traces and the design context model are used to support decision-making. In this paper, authors address the first two issues by proposing a meta-model that allows one to capture the process’ decisional knowledge. The proposal is presented and then illustrated in a case study. http://hdl.handle.net/10985/13895 A Dynamic Contextual Change Management Application for Real Time Decision-Making Support ES-SOUFI, Widad; ROUCOULES, Lionel; YAHIA, Esma Decision making is a fundamental process within organizations for many reasons. It is indeed involved at all levels (new product decisions, management and marketing decisions, etc.) and has a direct impact on companies’ efficiency and effectiveness. Many researches are conducted to enhance the decision-making process by proposing decision support systems where the most frequent challenge is the change management. Indeed, all businesses operate within an environment that is subject to constant changes (like new customers’ needs and requirements, organisational and technological changes, changes in key information used to derive decisions, etc.). These changes have a major impact on the quality and accuracy of the proposed decision if they are not detected and propagated, at the right time, during the decision-making process. The present work attempts to resolve this challenge by proposing a dynamic change management technique that allows three tasks to be automatically performed. First, continuously detect changes and note them. Second, retrieve from the detected changes those that are related to the decision rules. Finally, propagate them by computing the new value of the decision rule. The proposal has been fully implemented and tested in the supervision process of gas network exploitation. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/13895 2018-01-01T00:00:00Z ES-SOUFI, Widad ROUCOULES, Lionel YAHIA, Esma Decision making is a fundamental process within organizations for many reasons. It is indeed involved at all levels (new product decisions, management and marketing decisions, etc.) and has a direct impact on companies’ efficiency and effectiveness. Many researches are conducted to enhance the decision-making process by proposing decision support systems where the most frequent challenge is the change management. Indeed, all businesses operate within an environment that is subject to constant changes (like new customers’ needs and requirements, organisational and technological changes, changes in key information used to derive decisions, etc.). These changes have a major impact on the quality and accuracy of the proposed decision if they are not detected and propagated, at the right time, during the decision-making process. The present work attempts to resolve this challenge by proposing a dynamic change management technique that allows three tasks to be automatically performed. First, continuously detect changes and note them. Second, retrieve from the detected changes those that are related to the decision rules. Finally, propagate them by computing the new value of the decision rule. The proposal has been fully implemented and tested in the supervision process of gas network exploitation. http://hdl.handle.net/10985/12514 A Process Mining Based Approach to Support Decision Making ES-SOUFI, Widad; ROUCOULES, Lionel; YAHIA, Esma 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 Sun, 01 Jan 2017 00:00:00 GMT http://hdl.handle.net/10985/12514 2017-01-01T00:00:00Z ES-SOUFI, Widad ROUCOULES, Lionel YAHIA, Esma 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