A Framework for Online Detection and Reaction to Disturbances on the Shop Floor Using Process Mining and Machine Learning

The shop floor is a dynamic environment, where deviations to the production plan frequently occur. While there are many tools to support production planning, production control is left unsupported in handling disruptions. The production controller evaluates the deviations and selects the most suitable countermeasures based on his experience. The transparency should be increased in order to improve the decision quality of the production controller by providing meaningful information during his decision process. In this paper, we propose a framework in which an interactive production control system supports the controller in the identification of and reaction to disturbances on the shop floor. At the same time, the system is being improved and updated by the domain knowledge of the controller. The reference architecture consists of three main parts. The first part is the process mining platform, the second part is the machine learning subsystem that consists of a part for the classification of the disturbances and one part for recommending countermeasures to identified disturbances. The third part is the interactive user interface. Integrating the user’s feedback will enable an adaptation to the constantly changing constraints of production control. As an outlook for a technical realization, the design of the user interface and the way of interaction is presented. For the evaluation of our framework, we will use simulated event data of a sample production line. The implementation and test should result in higher production performance by reducing the downtime of the production and increase in its productivity

Utilizing AI in Buyer-supplier Relationships Through Procurement : Case UPM-Kymmene Oyj

The technological revolution continues its course in both civilian and working life. Individuals and organizations around the world are constantly looking for and encountering more versatile ways to utilize technology in their operating environments. Being on the cutting edge of technology can therefore be seen as an element of competitive advantage for companies. As a consequence, organizations are increasingly facing various developmental projects and this adds to the requirements for employees concerning the adaptation of new tools in a more rapid cycle. The need for development is also supported by the increased amount of regulation faced by organizations that employ individuals, along with the investments made towards responsibility and responding to the related regulation. Individuals and organizations alike are seeking assistance for these challenges from artificial intelligence. This study examines how artificial intelligence can be utilized from the point of view of the company's procurement organization. Procurement and, above all, management of supply chains have faced challenges on a global scale in recent years, which in turn increases the role of procurement in maintaining the company’s competitive position and advantage during unstable markets. Current research offers a limited amount of material when it comes to concrete use of artificial intelligence and the benefits of implementing the technology. This is partly explained by the rapid cycle of technological development. To complement the current research offering, this study studies artificial intelligence and its impact on supplier-customer relationships. Research suggests that the procurement organization can utilize artificial intelligence in its role. Supplier-customer relations are primarily the interface where the company's procurement organization adds value to the company by managing the outward cash flow as efficiently and effectively as possible. The research looks at the aforementioned themes from the perspective of the procurement organization of a stock listed company UPM-Kymmene Oyj. The company has its headquarters in Finland, and the company's versatile product portfolio presents its own challenges for the company's procurement organization. The results of the research are based on the material collected by a round of interviews, and the interviewees include a diverse group of suppliers, buyers as well as persons who approach and examine supplier-customer relationships from management’s perspective. The research shows that artificial intelligence is seen as a very potential influencing factor in the supplier-customer relationships of the future. The framework that emerges from the research results utilizes Krajlic's supplier portfolio matrix and it offers the procurement organization an approach to utilizing artificial intelligence in various supplier categories. Through the implementation of artificial intelligence, the procurement organization gain benefits from strengthening its positions in the value chain by bringing artificial intelligence into its own internal processes as well as utilizing it in its own supplier relationships.Teknologinen vallankumous jatkaa kulkuaan niin siviili- kuin työelämässäkin. Yksilöt ja organisaatiot ympäri maailman etsivät ja kohtaavat alati monipuolisempia keinoja teknologian hyödyntämiseen omissa toimintaympäristöissään. Teknologian aallonharjalla oleminen voidaan tätä myötä nähdä myös yhtenä yrityksen kilpailuetuna. Kehityshuuman myötä työntekijöiltä edellytetään uusien työkalujen omaksumista entistä nopeammalla syklillä. Uudistumisen tarvetta kasvattaa myös omalta osaltaan yksilöitä työllistävien organisaatioiden kohtaama kasvanut säätelyn määrä, ja esimerkiksi vastuullisuuteen panostaminen ja siihen liittyvään regulaatioon vastaaminen. Tähän tarpeeseen vastaamisessa niin yksilöt kuin organisaatiotkin toivovat apua muun muassa tekoälyn saralta. Tämän tutkimus tarkastelee, miten tekoälyä voidaan hyödyntää yrityksen osto-organisaation näkökulmasta. Hankintatoimi ja ennen kaikkia toimitusketjujen hallinta ovat kohdanneet viimeisten vuosien aikana globaalin mittakaavan haasteita, mikä omalta osaltaan lisää hankintatoimen roolin merkitystä yritysten pyrkiessä säilyttämään kilpailuasemansa ja -etunsa sangen epävakaassa markkinatilanteessa. Nykytutkimus tarjoaa rajallisen määrän aineistoa mitä tulee tekoälyn konkreettisista käyttökohteisiin sekä kyseisen teknologian implementoinnin hyötyihin. Tämä selittyy omalta osaltaan teknologisen kehittymisen nopean syklin myötä. Täydentääkseen nykyiseen tutkimustarjontaan, tämä tutkimus käsittelee tekoälyä ja sen mahdollista vaikutusta toimittaja-asiakassuhteisiin. Tutkimus tuo esiin keinoja, joiden avulla yrityksen osto-organisaatio voi hyödyntää tekoälyä omassa roolissaan. Toimittaja-asiakassuhteet ovat ensisijaisesti rajapinta, jossa yrityksen osto-organisaation odotetaan tuottavan lisäarvoa yritykselle varmistamalla mahdollisimman tehokkaan ja laadukkaan ulospäin suuntautuvan kassavirran hallinnan. Tutkimus tarkastelee edellä mainittuja teemoja Suomessa pääkonttoriaan pitävän pörssiyhtiö UPM-Kymmene Oyj:n osto-organisaation näkökulmasta, ja yrityksen monipuolinen tuoteportfolio esittää omat haasteensa myös yrityksen osto-organisaatiolle. Tutkimuksen tulokset pohjautuvat haastattelukierroksen tuottamaan aineistoon, ja haastateltavien joukkoon kuuluu monipuolinen joukko niin toimittajia, ostajia kuin toimittaja-asiakassuhteita johtavasta asemasta lähestyviä ja tarkastelevia henkilöitä. Tutkimus osoittaa, että tekoäly nähdään hyvin potentiaalisena vaikuttavana osatekijänä tule-vaisuuden toimittaja-asiakassuhteissa. Tutkimustuloksista ilmi käyvä viitekehys hyödyntää Krajlicin toimittajakategoriamallia ja se tarjoaa osto-organisaatiolle lähestymistavan tekoälyn hyödyntämiselle eri toimittajakategorioissa. Osto-organisaatio voi implementoida tekoälyn tarjoamia mahdollisuuksia ja pyrkiä suoraviivaistamaan ja vahvistamaan asemiaan arvoketjussa tuomalla tekoälyn omaksi omia sisäisä prosesseja kuin hyödyntämällä sitä myös omissa toimittajasuhteissaan

Energy Harvesting Strategies and Upcycling in Manufacturing

This thesis examines the interconnected domain of energy harvesting, modular component build, and upcycling strategies in manufacturing with the goal of achieving net-zero emissions. The research is grounded in the Design for X (DFX) paradigm, which integrates various design considerations to enhance product quality while minimising environmental impact. The first part of the thesis investigates the application of radio frequency (RF) and thermoelectric generator (TEG) energy harvesting technologies in manufacturing settings. These technologies capture waste heat and electromagnetic radiation from industrial equipment and convert them into usable electricity, reducing the overall energy consumption of the manufacturing process. The second part of the thesis explores modular component build, which enables easy replacement, upgrading, and servicing of components, thus reducing waste and prolonging product lifespan. This approach contributes to sustainable manufacturing and complements the energy harvesting aspect by minimising emissions. The third part of the thesis examines upcycling, which involves repurposing waste materials into new products or components. This concept supports the circular economy and synergises with the energy harvesting and modular component build strategies to further reduce waste and emissions in manufacturing. The results reveal that upcycling can substantially enhance manufacturing sustainability. Overall, this thesis emphasises the importance of integrating energy harvesting, modular component build, and upcycling strategies in manufacturing to achieve net-zero emissions. The findings contribute to the growing body of knowledge on sustainable manufacturing practices, offering valuable insights for manufacturers, policymakers, and researchers in the pursuit of net-zero emissions