Manufacturing process improvement through technical solutions : a case study
Tekijät
Päivämäärä
2021Tekijänoikeudet
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
The purpose of this exploratory thesis study is to observe a manufacturing process area in its current state and the opportunities to improve the process area by implementing Artificial Intelligence (AI), Machine Learning (ML) and other technical solutions in industrial manufacturing companies. The theoretical baseline is based on process management, process improvement solutions, AI, and ML. The research is centred around studying industry research and case studies with similar issues and goals as the case company. Data collection was conducted through interviews and observing current processes within the case company. The data was analyzed through compiling all the interview data to understand the current issues and determine the goal of the case company and then determine the best solution based on data and research. The empirical section explored how AI and ML can be implemented, managed, and evaluated for optimization in an industrial manufacturing context. Literature insights were compared with the results from my observations in the discussion section. The answer to the research questions, the limitations of the study, and future research questions are covered in the conclusion.
...
Asiasanat
Metadata
Näytä kaikki kuvailutiedotKokoelmat
- Pro gradu -tutkielmat [29559]
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Strategic cyber threat intelligence : Building the situational picture with emerging technologies
Voutilainen, Janne; Kari, Martti (Academic Conferences International, 2020)In 2019, e-criminals adopted new tactics to demand enormous ransoms from large organizations by using ransomware, a phenomenon known as “big game hunting.” Big game hunting is an excellent example of a sophisticated and ... -
On Attacking Future 5G Networks with Adversarial Examples : Survey
Zolotukhin, Mikhail; Zhang, Di; Hämäläinen, Timo; Miraghaei, Parsa (MDPI AG, 2023)The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to ... -
Practices and Infrastructures for Machine Learning Systems : An Interview Study in Finnish Organizations
Muiruri, Dennis; Lwakatare, Lucy Ellen; Nurminen, Jukka K.; Mikkonen, Tommi (Institute of Electrical and Electronics Engineers (IEEE), 2022)Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled systems from 16 organizations across various domains in Finland. We observed some well-established artificial intelligence ... -
On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples
Zolotukhin, Mikhail; Miraghaie, Parsa; Zhang, Di; Hämäläinen, Timo (Institute of Electrical and Electronics Engineers (IEEE), 2022)The use of artificial intelligence and machine learning is recognized as the key enabler for 5G mobile networks which would allow service providers to tackle the network complexity and ensure security, reliability and ... -
Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems
Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti (Peregrine Technical Solutions, 2020)Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.