Engineering design memory for design rationale and change management toward innovation

IFIP Advances in Information and Communication Technology

2016

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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.

Section: Discussionmentioning

confidence: 99%

Section: Decision and Trace Variants Mining Based Approachmentioning

confidence: 99%

On the Use of Process Mining and Machine Learning to Support Decision Making in Systems Design

IFIP Advances in Information and Communication Technology

2016

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“…The execution of this module generates the "statically filtered ranked patterns" file (step5) that associates to each pattern, of the "ranked patterns" file, a decision rule flag whose value is either 1 or 0. 1 means that the pattern satisfies the decision rules in the current context's conditions and is, thus, ready to be proposed to the user (step 6) via the IDSS graphical user interface presented in [18]. The user chooses one of the proposed patterns and starts to execute its first activity (step 7).…”

Section: Fig 2 Workflow Of the Proposal (Expressed In Bpmn 1 )mentioning

confidence: 99%

A Dynamic Contextual Change Management Application for Real Time Decision-Making Support

Product Lifecycle Management to Support Industry 4.0

2018

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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.

“…In order to master these complex processes, authors propose a three-layers framework [2]. The first layer uses a process meta-model, that captures the knowledge of the design and supervision processes, in order to model them and thus it helps companies to understand them.…”

Section: Introduction and Research Backgroundmentioning

confidence: 99%

Collaborative Design and Supervision Processes Meta-Model for Rationale Capitalization

Lecture Notes in Mechanical Engineering

2016

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International audienceCompanies 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