10 research outputs found
How to Discover Knowledge for Improving Availability in the Manufacturing Domain?
This paper presents a specific process model for Knowledge Discovery in Databases (KDD) projects aiming at availability improvement in manufacturing. For this purpose, Overall Equipment Efficiency (OEE) is analyzed and used, since it is an approved approach to monitor and improve the degree of availability in manufacturing. To define the specific process model, we use the generic CRISPDM reference model and conduct a mapping for availability improvement. We prove the applicability of our model in the context of a specific KDD project in a large enterprise in the manufacturing industry
Business Intelligence in Industry 4.0: State of the art and research opportunities
Data collection and analysis have been at the core of business intelligence (BI) for many years, but traditional BI must be adapted for the large volume of data coming from Industry 4.0 (I4.0) technologies. They generate large amounts of data that need to be processed and used in decision-making to generate value for the companies. Value generation of I4.0 through data analysis and integration into strategic and operational activities is still a new research topic. This study uses a systematic literature review with two objectives in mind: understanding value creation through BI in the context of I4.0 and identifying the main research contributions and gaps. Results show most studies focus on real-time applications and integration of voluminous and unstructured data. For business research, more is needed on business model transformation, methodologies to manage the technological implementation, and frameworks to guide human resources training
A review of data mining applications in semiconductor manufacturing
The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.publishersversionpublishe
Advanced Process Monitoring for Industry 4.0
This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and âextreme dataâ conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes
La crĂ©ation de valeur des donnĂ©es de lâIndustrie 4.0 : une Ă©tude empirique dans les manufacturiers quĂ©bĂ©cois
Abstract : Manufacturing companies in developed countries face a digital transformation that is meant to improve their productivity, but also produces a large volume of data. This data will go to waste if it is not valorized by using it to gain actionable insights, for example with business intelligence and analytics. This masterâs thesis presents a systematic literature review and a multiple case-study on the subject of Business Intelligence in manufacturing companies. The first article, âBusiness Intelligence in Industry 4.0: Research opportunitiesâ, present a literature review. Results show a lack of studies on the impacts of business intelligence activities on manufacturing small and medium enterprises. The strategic impacts should be studied, since they are often neglected in favor of the operational impacts such as quality improvement and operating costs reductions. The second article, âBusiness intelligence value creation: A multiple case study in manufacturing SMEsâ, presents an exploration of the factors influencing strategic and operational business values of business intelligence. Results show the limit of the traditional models based on the Resource-Based View of the firm, which overlooks organizational factors that might be more important in smaller organizations. Contingency factors, such as organisational learning, leadership style, and the role of the owner, should be included when studying small and medium enterprises, as in these smaller organizations the lack of resources and the simpler structure affect business value of business intelligence and analytics systems differently than in larger firms. There is an interesting potential for the model suggested in this masterâs thesis to understand the factors linked to business value creation in smaller organization, which should be empirically tested with a larger and more diverse sample in a future study.Ce mĂ©moire prĂ©sente les travaux rĂ©alisĂ©s dans le cadre de ma maĂźtrise en
StratĂ©gie de lâintelligence dâaffaires, de lâĂcole de Gestion de lâUniversitĂ© de
Sherbrooke. Il consiste en deux articles. Le premier est une revue de littérature
systématique ayant été soumise et acceptées à la 51e
Ă©dition de Hawaii International
Conference on System Sciences, qui a eu lieu du 3 au 6 janvier 2018. Il est présenté
intégralement au chapitre deux. Le second article, présenté dans sa version longue au
chapitre trois, a été soumis à la 7e
Ă©dition de International Conference on Information
Systems, Logistics and Supply Chain qui aura lieu du 8 au 10 juillet 2018. Les notices
dâacceptation seront envoyĂ©es aprĂšs la date de dĂ©pĂŽt de ce mĂ©moire. Toutes les preuves
de soumissions sont présentées dans les annexes de ce mémoire. Les articles ont tous
Ă©tĂ© rĂ©digĂ© par moi, Fanny-Ăve Bordeleau, qui a Ă©galement rĂ©alisĂ© toutes les prises de
données et les analyses, assistée de mes co-directeurs, les professeurs Elaine Mosconi
et Luis Antonio De Santa-Eulalia
FIN-DM: finantsteenuste andmekaeve protsessi mudel
Andmekaeve hĂ”lmab reeglite kogumit, protsesse ja algoritme, mis vĂ”imaldavad ettevĂ”tetel iga pĂ€ev kogutud andmetest rakendatavaid teadmisi ammutades suurendada tulusid, vĂ€hendada kulusid, optimeerida tooteid ja kliendisuhteid ning saavutada teisi eesmĂ€rke. Andmekaeves ja -analĂŒĂŒtikas on vaja hĂ€sti mÀÀratletud metoodikat ja protsesse. Saadaval on mitu andmekaeve ja -analĂŒĂŒtika standardset protsessimudelit. KĂ”ige mĂ€rkimisvÀÀrsem ja laialdaselt kasutusele vĂ”etud standardmudel on CRISP-DM. Tegu on tegevusalast sĂ”ltumatu protsessimudeliga, mida kohandatakse sageli sektorite erinĂ”uetega. CRISP-DMi tegevusalast lĂ€htuvaid kohandusi on pakutud mitmes valdkonnas, kaasa arvatud meditsiini-, haridus-, tööstus-, tarkvaraarendus- ja logistikavaldkonnas. Seni pole aga mudelit kohandatud finantsteenuste sektoris, millel on omad valdkonnapĂ”hised erinĂ”uded.
Doktoritöös kĂ€sitletakse seda lĂŒnka finantsteenuste sektoripĂ”hise andmekaeveprotsessi (FIN-DM) kavandamise, arendamise ja hindamise kaudu. Samuti uuritakse, kuidas kasutatakse andmekaeve standardprotsesse eri tegevussektorites ja finantsteenustes. Uurimise kĂ€igus tuvastati mitu tavapĂ€rase raamistiku kohandamise stsenaariumit. Lisaks ilmnes, et need meetodid ei keskendu piisavalt sellele, kuidas muuta andmekaevemudelid tarkvaratoodeteks, mida saab integreerida organisatsioonide IT-arhitektuuri ja Ă€riprotsessi. Peamised finantsteenuste valdkonnas tuvastatud kohandamisstsenaariumid olid seotud andmekaeve tehnoloogiakesksete (skaleeritavus), Ă€rikesksete (tegutsemisvĂ”ime) ja inimkesksete (diskrimineeriva mĂ”ju leevendus) aspektidega. SeejĂ€rel korraldati tegelikus finantsteenuste organisatsioonis juhtumiuuring, mis paljastas 18 tajutavat puudujÀÀki CRISP- DMi protsessis.
Uuringu andmete ja tulemuste abil esitatakse doktoritöös finantsvaldkonnale kohandatud CRISP-DM nimega FIN-DM ehk finantssektori andmekaeve protsess (Financial Industry Process for Data Mining). FIN-DM laiendab CRISP-DMi nii, et see toetab privaatsust sĂ€ilitavat andmekaevet, ohjab tehisintellekti eetilisi ohte, tĂ€idab riskijuhtimisnĂ”udeid ja hĂ”lmab kvaliteedi tagamist kui osa andmekaeve elutsĂŒklisData mining is a set of rules, processes, and algorithms that allow companies to increase revenues, reduce costs, optimize products and customer relationships, and achieve other business goals, by extracting actionable insights from the data they collect on a day-to-day basis. Data mining and analytics projects require well-defined methodology and processes. Several standard process models for conducting data mining and analytics projects are available. Among them, the most notable and widely adopted standard model is CRISP-DM. It is industry-agnostic and often is adapted to meet sector-specific requirements. Industry- specific adaptations of CRISP-DM have been proposed across several domains, including healthcare, education, industrial and software engineering, logistics, etc. However, until now, there is no existing adaptation of CRISP-DM for the financial services industry, which has its own set of domain-specific requirements.
This PhD Thesis addresses this gap by designing, developing, and evaluating a sector-specific data mining process for financial services (FIN-DM). The PhD thesis investigates how standard data mining processes are used across various industry sectors and in financial services. The examination identified number of adaptations scenarios of traditional frameworks. It also suggested that these approaches do not pay sufficient attention to turning data mining models into software products integrated into the organizations' IT architectures and business processes. In the financial services domain, the main discovered adaptation scenarios concerned technology-centric aspects (scalability), business-centric aspects (actionability), and human-centric aspects (mitigating discriminatory effects) of data mining. Next, an examination by means of a case study in the actual financial services organization revealed 18 perceived gaps in the CRISP-DM process.
Using the data and results from these studies, the PhD thesis outlines an adaptation of
CRISP-DM for the financial sector, named the Financial Industry Process for Data Mining
(FIN-DM). FIN-DM extends CRISP-DM to support privacy-compliant data mining, to tackle AI ethics risks, to fulfill risk management requirements, and to embed quality assurance as part of the data mining life-cyclehttps://www.ester.ee/record=b547227