test_moissonnage_selune
type: Agent

Industrial decision-aid socio-statistical methods : Applied to the capacity management of an IS in the microelectronics industry
type: Dataset
contact point: Organization
  fn: CCSD
keyword: statistiques-et-analyse-de-donnees
keyword: information-system-and-information-technologies
keyword: knowledge-management
keyword: gestion-des-connaissances
keyword: theses
keyword: gestion-des-capacites
keyword: spiotherengineering-sciences-physicsother
keyword: systeme-dinformation-et-technologies-de-linformation
keyword: aide-a-la-decision
keyword: infoeu-reposemanticsdoctoralthesis
keyword: microelectronic-industry
keyword: industrie-microelectronique
keyword: statistics-and-data-analysis
keyword: decision-aiding
keyword: capacity-management
landing page: https://theses.hal.science/tel-00849870
description: A proper analysis of industrial data can provide material for decision making. The research work presented deals with the question: how can one convert raw data into useable information, to contribute to the knowledge management of an organization and improve its dynamic decision making? A decision-aid process is proposed. It implies actors of the organization and use of formal methods. Firstly, it analyses and formalizes decisional problems. Then, it develops an appropriate decision-aid on the basis of statistics. Our methodology is applied to a specific issue: capacity management of IT of a STMicroelectronics plant. This application raises a decision issue: managers have to ensure the right balance between infrastructure cost and service level offered. We demonstrate that the process may provide relevant support. This negates two managerial dilemmas, usually encountered when managing capacity: complexity of IT systems and incorporation of the business activity. Our application has been developed in the scope of the ITIL framework. It will be shown how the process builds predictive models, which link the activity of hardware servers to the industrial activity. Methods are also proposed, to monitor daily the quality of these models, as well as the overall activity of the IS. This work helps at formalizing quantitatively organizational knowledge, facilitating its use in decisional processes, but also ensuring its positive change over time. We hope this research is laying some foundations for a broader exploitation of the data stored in modern manufacturing systems, through future development of decision-support systems and Big Data initiatives.
identifier: NNT: 2013EMSE0688
issued: 2026-05-10T04:12:24.964722
language: fr
modified: 2026-05-10T04:12:24.964726

Industrial decision-aid socio-statistical methods : Applied to the capacity management of an IS in the microelectronics industry
type: Distribution
access URL: https://theses.hal.science/tel-00849870
format: HTML
issued: 2026-05-10T04:12:24.977718
modified: 2026-05-10T04:12:24.950031