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AI-based workload estimation in architectural design projects
No full textDiplomityön tavoitteena on kehittää tekoälypohjainen työmääräarvioinnin malli arkkitehtisuunnitteluprojektien ennustamiseen. Tutkimuksen kohdeyrityksenä toimii Arco Architecture Company Oy. Tavoitteena on parantaa etenkin kiinteähintaisten suunnitteluprojektien kannattavuutta, parantaa tarjouslaskennan tarkkuutta ja pienentää projektikannattavuuteen liittyvää taloudellista riskiä.
Tutkimus on toteutettu kehittämistutkimuksena. Hyödynnetty teoria koostuu projektiliiketoiminnan ja tekoälyn teoriasta. Tutkimuksen empiirinen osuus perustuu yrityksen toiminnanohjausjärjestelmän historialliseen projektidataan, jota hyödyntäen on rakennettu satunnaismetsäalgoritmiin perustuva koneoppimismalli. Malli kehitettiin pääasiassa Exceliä ja Pythonia hyödyntäen.
Tulokset osoittavat, että koneoppimiseen perustuva malli pystyy tuottamaan luotettavia arvioita suunnitteluprojektien työmäärästä. Malli ennustaa projektin tuntimäärän ja hinnan käyttäjän syöttämän suunnittelukohteen pinta-alan ja maantieteellisen sijainnin perusteella hyödyntäen historiallista projektidataa. Lähtödatan laatu ja kattavuus osoittautui keskeiseksi tekijäksi ennusteen luotettavuuden näkökulmasta. Työmääräarviointimalli tarjoaa helppokäyttöisen ja systemaattisen pohjan tarjouslaskennan tueksi.
Koneoppiminen tuo selkeää hyötyä arkkitehtisuunnitteluprojektien työmäärän arviointiin, etenkin kiinteähintaisissa suunnitteluprojekteissa. Tekoälypohjainen ennustemalli vähentää arvioinnin subjektiivisuutta ja nopeuttaa tarjouslaskentaa. Mallin käyttöönotto luo myös perustaa laajemmalle tekoälyn hyödyntämiselle arkkitehtisuunnittelun liiketoimintaprosesseissa.The objective of this master’s thesis is to develop an AI-based workload estimation model for architectural design projects. The case company is Arco Architecture Company Oy. The goal is to improve the profitability of fixed-price design projects, to improve the accuracy of cost estimation and to reduce the financial risk related to project profitability.
The study follows the design science research method. The theoretical framework consists of the literature of project business and artificial intelligence. The empirical part of the research is based on historical project data from the company’s ERP system. This data has been utilized to build a machine learning model based on a random forest algorithm. The model was developed mainly using Excel and Python.
The results show that the machine learning model can produce reliable estimates of the workload of architectural design projects. The model predicts the number of hours and price of a project based on the entered building area and geographical location, utilizing historical project data. The quality and comprehensiveness of the input data proved to be a key factor in the reliability of the forecast. The quality of the input data proved to be a critical factor or reliable predictions. The model offers a systematic and user-friendly approach to support the tender process.
Machine learning brings clear benefits to the estimation of the workload of architectural design projects, especially in fixed-price design projects. An AI-based forecasting model reduces subjectivity and speeds up the tender calculation process. The implementation of the model also creates a foundation for a broader utilization of artificial intelligence in architectural design business processes
Lessons and successes : preparing for crisis communication in the state administration for cyber incidents
No full textKyberturvallisuuden kriisit ovat yleistyneet ja vakavoituneet niin Suomessa kuin globaalisti. Monet julkisen sektorin toimijat ovat joutuneet viime aikoina kokemaan julkisia kyberturvallisuuskriisejä ja tilanne nähdään pahentuvan myös tulevaisuudessa. Valtionhallinnon organisaatiot ovat keskeisessä roolissa yhteiskunnallisen luottamuksen ja resilienssin rakentumisessa suomalaisessa yhteiskunnassa. Onnistunut kyberturvallisuuskriisien hallinta vaatii jo ennakkoon tehtävää varautumista. Onnistuneeseen kriisinhallintaan liittyy aina osana tehokkaat, ennalta suunnitellut kriisiviestintätoimet.
Tämän pro gradu -tutkielman tarkoituksena on pyrkiä tunnistamaan valtionhallinnon organisaatioiden valmiutta ja kykyä kriisiviestintään erilaisissa kyberturvallisuuskriiseissä. Lisäksi tarkoituksena on tunnistaa näistä organisaatiosta kumpuavia keskeisiä kokemuksia, hyviä käytäntöjä tai ideoita kyberturvallisuuden kriisiviestintään varautumisessa sekä organisaation oman valmiuden että yhteiskunnallisen viranomaisen luottamuksen ja resilienssin näkökulmasta.
Tutkimuksen tuloksena tunnistettiin yhdenmukaisuuksia ja eroavaisuuksia valtionhallinnon organisaatioiden välillä. Tuloksena pystyttiin tunnistamaan myös parhaita käytäntöjä tai kokemuksia, joita organisaatioiden kannattaisi toteuttaa. Lisäksi tunnistettiin varautumisen toimenpiteitä, joita olisi mahdollista tehostaa tai niiden toimivuutta kehittää.Cybersecurity crises are becoming more common and more serious in Finland and globally. Many public sector actors have recently had to experience public cybersecurity crises and the situation is seen to worsen in the future. Government organizations play a key role in building societal trust and resilience in Finnish society. Successful management of cybersecurity crises requires advance preparation. Successful crisis management always involves effective, preplanned crisis communication activities.
The purpose of this Master's thesis is to identify the readiness and capability of government organizations for crisis communication in various cybersecurity crises. In addition, the purpose is to identify key experiences, good practices or ideas emanating from these organizations in preparing for cybersecurity crisis communication, both from the perspective of the organization's own readiness and the trust and resilience of the societal authority.
The research identified similarities and differences between government organizations. It also identified best practices or experiences that organizations should implement. It also identified preparedness measures that could be made more effective or their functionality improved
Identifying differential transaction patterns in coffee commodity tradingn : a fuzzy clustering analysis of differential risk and financial outcomes
Get PDFThis thesis investigated the differential risk and profitability patterns for a coffee trading company using fuzzy k-means clustering. The research was based on a dataset of approximately 17,500 matched sale and purchase transactions. The analysis focuses on the Purchase-, Market- and Sale Differential Results as well as operational variables such as transaction volume, holding period, and net result. These variables reflect the financial performance and risk exposure associated with market dynamics and customer behaviour.
Fuzzy clustering was used to group transactions based on similarities in their risk and profitability characteristics, allowing each transaction to partially belong to multiple clusters. The optimal number of clusters was determined using Modified Partition Coefficient, Fuzzy Silhouette Score and Xie-Beni-Index. The results reveal four distinct transaction clusters, each characterized by different risk-return profiles and trading strategies, ranging from high-margin positioning to low-risk fast execution models. These clusters were further analysed by their association with the different customers and coffee origins. Based on these findings strategy recommendations were formulated which aim to help the company to reduce its risk exposure and increase profitability.
The thesis demonstrates how unsupervised machine learning techniques, specifically fuzzy k-means clustering, can be leveraged for customer analytics and financial risk management in volatile commodity markets
Sisäisen materiaalivirran tehostaminen varaston ja tuotannon välissä lean-periaatteilla
No full textEfficient material flow is crucial in manufacturing, as it directly impacts productivity, cost efficiency, and overall operational performance. Lean principles, such as value stream mapping, provide a systematic approach to identifying and eliminating waste, and optimizing processes to achieve better material flow. By applying lean principles, companies can enhance their processes, reduce inefficiencies, and make achieve improvements in warehouse operations.
The goal of this thesis is to improve internal material flow between the warehouse and the production at Eaton Electric Oy. The theoretical part includes a literature review on lean principles and material flow management, and tools from both that can be utilized to analyse and improve the current state. The empirical part involves analysing the current state of material flow, identifying bottlenecks and processes where efficiency is lost, and proposing improvements. During the thesis, the entire current process is mapped, stakeholders are interviewed, and the processes are examined. Key recommendations include standardising communication methods, implementing a pull signal system, utilizing an autonomous mobile robot (AMR) for material transport, and utilizing ABC-XYZ analysis to make the material handling process more efficient.
The results include improvements implemented during the work on the thesis, and a list of proposed improvements to be implemented in the future. The former includes the implementation of a pull signal, a new andon type, and AMR utilization. The other proposals are given to Eaton Electric Oy for future implementation. These improvements are expected to drive forward the continuous improvement and lean culture of the company and improve the overall performance of the material flow.Tehokas materiaalivirta on ratkaisevan tärkeää tuotannolle, koska sillä on suora vaikutus tuottavuuteen, kustannustehokkuuteen ja koko toiminnan suorituskykyyn. Lean-periaatteet, kuten arvovirtojen analyysi, tarjoavat järjestelmällisen lähestymistavan hukan tunnistamiseen ja poistamiseen sekä prosessien optimointiin paremman materiaalivirran saavuttamiseksi. Lean-periaatteita soveltamalla yritykset voivat tehostaa prosessejaan, vähentää tehottomuutta ja tehdä saavutettavia parannuksia varastotoimintoihin.
Tutkielman tavoitteena on parantaa sisäistä materiaalivirtaa varaston ja tuotannon välillä Eaton Electric Oy:ssä. Teoreettinen osa sisältää kirjallisuuskatsauksen lean-periaatteisiin ja materiaalivirran hallintaan sekä niihin liittyviin työkaluihin, joita voidaan hyödyntää nykytilan analysoinnissa ja kehittämisessä. Empiirinen osa koostuu materiaalivirran nykytilan analysoinnista, pullonkaulojen ja tehottomien prosessien tunnistamisesta sekä parannusehdotusten esittämisestä. Työn aikana kartoitetaan koko nykyinen prosessi, haastatellaan sidosryhmiä ja tutkitaan prosesseja. Analyysin perusteella tärkeitä suosituksia ovat viestintämenetelmien standardointi, materiaalin vetosignaalin käyttöönotto, autonomisen mobiilirobotin hyödyntäminen materiaalikuljetuksessa sekä ABC-XYZ-analyysin käyttö materiaalinkäsittelyprosessin tehostamiseksi.
Työn tulokset ovat työn aikana toteutetut parannukset ja luettelo ehdotetuista parannuksista, jotka on tarkoitus toteuttaa tulevaisuudessa. Tehtyihin parannuksiin sisältyy materiaalin vetosignaali, uusi andon tyyppi ja mobiilirobotin käyttöönotto. Muut ehdotukset annetaan Eaton Electric Oy:lle toteutusta varten. Näiden parannusten odotetaan edistävän jatkuvaa parantamista ja lean-kulttuuria yrityksessä sekä parantavan materiaalivirran suorituskykyä
Kraft pulp mill malodorous gases – handling, destruction and safety
Get PDFGrowing awareness of environmental issues related to increasing pulp production has spurred research aimed at finding eco-friendly, economical and safe practices. These practices have reduced the impact of unpleasant odors on the environment and made the reuse of malodorous compounds within the chemical circuit of pulp mills possible. Selfsufficiency of chemicals is enhanced when sulphuric acid and sodium bisulphite are selfproduced. Future modern pulp mills could be facilities that are completely odorless, fossil fuel-free, and operate with closed chemical circuits, producing renewable biobased byproducts.
This dissertation investigates technologies in modern pulp mills that maintain the benefits of odor-free processes while enhancing chemical circulation within the mill. It describes and discusses both past and current systems for collecting and handling non-condensable gases (NCGs). The mills studied in this thesis have designed and implemented their NCG systems within the past ten to fifteen years. New innovations have emerged, offering promising methods to further reduce occasional sulphurous odor emissions from the kraft process. These methods explore pretreatment and sulphuric gas stream reuse options in process units where these gases are generated, or during the transportation of these side streams to their final destruction site.
Using modern practices, malodorous gases can be collected to the extent that a kraft pulp mill is essentially odor-free, pending operator errors or major equipment malfunctions. One of the difficult aspects is that NCG systems in kraft mills are often designed case by case, as well as by various equipment vendors and with varying numbers of destruction sites. The actual detail design depends on the chosen layout, with the process configuration depending on individual equipment purchased and even on the design practices by individual suppliers.ei tietoa saavutettavuudest
The role of UGC marketing in generation Z's impulse purchasing decisions
Get PDFThis thesis explores the role of user generated content (UGC) in influencing impulsive purchasing behaviour among generation Z consumers. Understanding the effect of peer created content and how it influences the consumer behaviour is crucial for marketeers in the digital age. The thesis examines how emotional and psychological factors affect the impulsive consumer behaviour by analysing the qualitative data collected through semi-structured interviews.
Key findings from the thesis are that authenticity, emotional engagement, and social validation are in the central of the effectiveness of UGC marketing. Findings suggest that emotional triggers can override rational evaluation, which can lead to immediate and unplanned purchases. Additionally, certain product categories are more likely to be purchased impulsively. Usually this is caused by visual appeal of the content and product alignment with trend-based content.
Thesis aims to contribute to the literature on consumer behaviour of generation Z by offering new findings into how this generation interacts with user generated content. Additionally, thesis provides applications for brands on how to utilize UGC in their marketing strategies and enhance engagement among gen Z consumers
Lämpötila-aikasarjojen analysointi Kalman-suotimella
Get PDFTässä työssä tutkitaan lämpötila-aikasarjoja Kalman-suotimen avulla. Kalman-suodin on rekursiivinen malli, jonka avulla voidaan arvioida systeemin tilaa aikaisempien mittaustulosten perusteella. Malli koostuu dynaamisesta mallista ja mittausmallista. Työssä on käytetään myös ydintiheysestimaattia, jonka avulla muodostetaan tiheysfunktioita lämpötila-aikasarjoista. Niiden avulla havainnollistetaan lämpötilojen vaihtelua ja muutoksia.
Lämpötiloja on tutkittu vuosilta 1970-2025 ja niitä tarkastellaan eri vuodenaikoina. Lämpötilamittauksiin on sovitettu Kalman-suodin malli, jonka avulla on tutkittu, kuinka mittausten tarkkuutta voitaisiin parantaa. Lämpötilojen vaihtelua on havainnollistettu tiheysfunktioiden avulla ja lopuksi on tutkittu myös lämpötilojen pitkän aikavälin muutoksia
Applying lean to project management : a conceptual model for reducing waste and lead time in project-based business
Get PDFThis master’s thesis addresses the need to identify and reduce non-value-adding activities (Waste) in project management work, as well as to establish a systematic approach for continuous improvement in project-based work. The study employs a literature review to investigate how Lean principles and methods can be effectively applied in a company operating in turnkey project business. The theoretical framework covers Lean examples and applications from different industries, brief theory behind Lean, the various forms of waste specific to project environments, and practises for continuous improvement in project-based operations. The aim is to reduce lead time and improve productivity. As a result, the thesis presents a step-by-step model for implementing Lean in a project business unit which is universally applicable and not designed to suit only for the company which the study is conducted for. The model provides a foundation for further research, including the development of a more detailed implementation roadmap and empirical validation
Challenges in the Environmentally Sustainable Aviation Ecosystem
No full textAir transport serves as a fundamental driver of global economic growth, playing a pivotal role in generating employment opportunities, fostering international trade, fueling the tourism industry, and underpinning sustainable development on a global scale. It serves as a conduit for global connectivity and a catalyst for both social and economic prosperity. However, amid these manifold benefits, the aviation sector faces substantial challenges, the most significant of which is the carbon dioxide (CO2) emissions produced by fossil jet fuels.
Despite accounting for a relatively modest 2–3% of global carbon emissions, the aviation industry faces a discouraging trajectory, with emissions projected to escalate dramatically, potentially reaching as high as 22–30% by mid-century due to the industry’s growth.
This chapter examines the intricate challenges surrounding the pursuit of environmental sustainability within the aviation ecosystem, with a particular focus on the CO2 emissions originating from flight operations. While the aviation industry often spotlights airlines and their operations, it is essential to recognize the multitude of companies and stakeholders intricately involved in aviation activities. Key stakeholders encompass airports, ground handling companies, air navigation service providers, and regulatory authorities. The interplay among these entities significantly influences each other’s success, impacting sustainability initiatives within the broader aviation ecosystem.
This chapter presents an overview of the distinctive characteristics of the aviation ecosystem, providing insights into its current market dynamics and the evolving landscape of sustainability considerations. We consider the complexities of fostering an environmentally sustainable aviation ecosystem in the context of the challenges that must be overcome. To achieve our collective environmental sustainability objectives, the aviation ecosystem must establish shared goals, aligned action plans, and comprehensive metrics. Regulatory frameworks play a pivotal role in facilitating this alignment, ultimately maximizing the environmental sustainability of the aviation sector as a whole.Post-print / Final draf
Adapting Lean Six Sigma for small-scale production : a case study on cellulose foam optimization
No full textAmidst urgent global efforts to replace microplastic-generating petroleum foams, this study assesses the viability of Lean Six Sigma (LSS) methodology to optimise small-scale cellulose foam production for sustainable footwear insoles. As a collaborative effort to bridge the lab-to-market gap, a tailored DMAIC framework is created to address critical quality issues persistent in lab-scale manufacturing for this thesis work. The baseline analysis of existing processes revealed a 44.5% production yield, incapability of maintaining thickness consistency (Cp = 0.22) and a high amount of defects and imperfections. An approach combining statistical inferences from limited data, along with careful implementation of less-resourceintense and rapid tools like workspace reorganisation, mold redesign, and batch optimization was designed. This approach was successful in achieving a 90% yield, 10% reduction in cycle time and 70% reduction in thickness variation. While final process capability (Cp=0.70) still fell short of industrial standards, user testing showed significant improvements in product comfort and fit.
This thesis showcases the suitability of LSS tools to be used for small-scale environments to effectively mitigate the gap between lab-scale innovation and commercial viability for sustainable materials, though there are still challenges for conversion to full industrial scalability. The findings also provide a practical framework for small-scale biomaterial optimization while showcasing the need for integrated process-material solutions