5,871 research outputs found

    An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains

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    This research aimed to develop an empirical understanding of the relationships between integration, dynamic capabilities and performance in the supply chain domain, based on which, two conceptual frameworks were constructed to advance the field. The core motivation for the research was that, at the stage of writing the thesis, the combined relationship between the three concepts had not yet been examined, although their interrelationships have been studied individually. To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative study, which was undertaken via multiple case studies to investigate lines of enquiry that would address the research questions formulated. This is consistent with the author’s philosophical adoption of the ontology of relativism and the epistemology of constructionism, which was considered appropriate to address the research questions. Empirical data and evidence were collected, and various triangulation techniques were employed to ensure their credibility. Some key features of grounded theory coding techniques were drawn upon for data coding and analysis, generating two levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in improving performance, the performance also informed the former. This reflects a cyclical and iterative approach rather than one purely based on linearity. Adopting a holistic approach towards the relationship was key in producing complementary strategies that can deliver sustainable supply chain performance. The research makes theoretical, methodological and practical contributions to the field of supply chain management. The theoretical contribution includes the development of two emerging conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed insight into their correlations. The latter gives a holistic view of their relationships and how they are connected, reflecting a middle-range theory that bridges theory and practice. The methodological contribution lies in presenting models that address gaps associated with the inconsistent use of terminologies in philosophical assumptions, and lack of rigor in deploying case study research methods. In terms of its practical contribution, this research offers insights that practitioners could adopt to enhance their performance. They can do so without necessarily having to forgo certain desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities

    Machine learning and mixed reality for smart aviation: applications and challenges

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    The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Application of Track Geometry Deterioration Modelling and Data Mining in Railway Asset Management

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    Modernin rautatiejärjestelmän hallinnassa rahankäyttö kohdistuu valtaosin nykyisen rataverkon korjauksiin ja parannuksiin ennemmin kuin uusien ratojen rakentamiseen. Nykyisen rataverkon kunnossapitotyöt aiheuttavat suurten kustannusten lisäksi myös usein liikennerajoitteita tai yhteyksien väliaikaisia sulkemisia, jotka heikentävät rataverkon käytettävyyttä Siispä oikea-aikainen ja pitkäaikaisia parannuksia aikaansaava kunnossapito ovat edellytyksiä kilpailukykyisille ja täsmällisille rautatiekuljetuksille. Tällainen kunnossapito vaatii vankan tietopohjan radan nykyisestä kunnosta päätöksenteon tueksi. Ratainfran omistajat teettävät päätöksenteon tueksi useita erilaisia radan kuntoa kuvaavia mittauksia ja ylläpitävät kattavia omaisuustietorekistereitä. Kenties tärkein näistä datalähteistä on koneellisen radantarkastuksen tuottamat mittaustulokset, jotka kuvastavat radan geometrian kuntoa. Nämä mittaustulokset ovat tärkeitä, koska ne tuottavat luotettavaa kuntotietoa: mittaukset tehdään toistuvasti, 2–6 kertaa vuodessa Suomessa rataosasta riippuen, mittausvaunu pysyy useita vuosia samana, tulokset ovat hyvin toistettavia ja ne antavat hyvän yleiskuvan radan kunnosta. Vaikka laadukasta dataa on paljon saatavilla, käytännön omaisuudenhallinnassa on merkittäviä haasteita datan analysoinnissa, sillä vakiintuneita menetelmiä siihen on vähän. Käytännössä seurataan usein vain mittaustulosten raja-arvojen ylittymistä ja pyritään subjektiivisesti arvioimaan rakenteiden kunnon kehittymistä ja korjaustarpeita. Kehittyneen analytiikan puutteet estävät kuntotietojen laajamittaisen hyödyntämisen kunnossapidon suunnittelussa, mikä vaikeuttaa päätöksentekoa. Tämän väitöskirjatutkimuksen päätavoitteita olivat kehittää ratageometrian heikkenemiseen mallintamismenetelmiä, soveltaa tiedonlouhintaa saatavilla olevan omaisuusdatan analysointiin sekä jalkauttaa kyseiset tutkimustulokset käytännön rataomaisuudenhallintaan. Ratageometrian heikkenemisen mallintamismenetelmien kehittämisessä keskityttiin tuottamaan nykyisin saatavilla olevasta datasta uutta tietoa radan kunnon kehityksestä, tehdyn kunnossapidon tehokkuudesta sekä tulevaisuuden kunnossapitotarpeista. Tiedonlouhintaa sovellettiin ratageometrian heikkenemisen juurisyiden selvittämiseen rataomaisuusdatan perusteella. Lopuksi hyödynnettiin kypsyysmalleja perustana ratageometrian heikkenemisen mallinnuksen ja rataomaisuusdatan analytiikan käytäntöön viennille. Tutkimustulosten perusteella suomalainen radantarkastus- ja rataomaisuusdata olivat riittäviä tavoiteltuihin analyyseihin. Tulokset osoittivat, että robusti lineaarinen optimointi soveltuu hyvin suomalaisen rataverkon ratageometrian heikkenemisen mallinnukseen. Mallinnuksen avulla voidaan tuottaa tunnuslukuja, jotka kuvaavat rakenteen kuntoa, kunnossapidon tehokkuutta ja tulevaa kunnossapitotarvetta, sekä muodostaa havainnollistavia visualisointeja datasta. Rataomaisuusdatan eksploratiiviseen tiedonlouhintaan käytetyn GUHA-menetelmän avulla voitiin selvittää mielenkiintoisia ja vaikeasti havaittavia korrelaatioita datasta. Näiden tulosten avulla saatiin uusia havaintoja ongelmallisista ratarakennetyypeistä. Havaintojen avulla voitiin kohdentaa jatkotutkimuksia näihin rakenteisiin, mikä ei olisi ollut mahdollista, jollei tiedonlouhinnan avulla olisi ensin tunnistettu näitä rakennetyyppejä. Kypsyysmallin soveltamisen avulla luotiin puitteet ratageometrian heikkenemisen mallintamisen ja rataomaisuusdatan analytiikan kehitykselle Suomen rataomaisuuden hallinnassa. Kypsyysmalli tarjosi käytännöllisen tavan lähestyä tarvittavaa kehitystyötä, kun eteneminen voitiin jaotella neljään eri kypsyystasoon, jotka loivat selkeitä välitavoitteita. Kypsyysmallin ja asetettujen välitavoitteiden avulla kehitys on suunniteltua ja edistystä voidaan jaotella, mikä antaa edellytykset tämän laajamittaisen kehityksen onnistuneelle läpiviennille. Tämän väitöskirjatutkimuksen tulokset osoittavat, miten nykyisin saatavilla olevasta datasta saadaan täysin uutta ja merkityksellistä tietoa, kun sitä käsitellään kehittyneen analytiikan avulla. Tämä väitöskirja tarjoaa datankäsittelyratkaisujen luomisen ja soveltamisen lisäksi myös keinoja niiden käytäntöönpanolle, sillä tietopohjaisen päätöksenteon todelliset hyödyt saavutetaan vasta käytännön radanpidossa.In the management of a modern European railway system, spending is predominantly allocated to maintaining and renewing the existing rail network rather than constructing completely new lines. In addition to major costs, the maintenance and renewals of the existing rail network often cause traffic restrictions or line closures, which decrease the usability of the rail network. Therefore, timely maintenance that achieves long-lasting improvements is imperative for achieving competitive and punctual rail traffic. This kind of maintenance requires a strong knowledge base for decision making regarding the current condition of track structures. Track owners commission several different measurements that depict the condition of track structures and have comprehensive asset management data repositories. Perhaps one of the most important data sources is the track recording car measurement history, which depicts the condition of track geometry at different times. These measurement results are important because they offer a reliable condition database; the measurements are done recurrently, two to six times a year in Finland depending on the track section; the same recording car is used for many years; the results are repeatable; and they provide a good overall idea of the condition of track structures. However, although high-quality data is available, there are major challenges in analysing the data in practical asset management because there are few established methods for analytics. Practical asset management typically only monitors whether given threshold values are exceeded and subjectively assesses maintenance needs and development in the condition of track structures. The lack of advanced analytics prevents the full utilisation of the available data in maintenance planning which hinders decision making. The main goals of this dissertation study were to develop track geometry deterioration modelling methods, apply data mining in analysing currently available railway asset data, and implement the results from these studies into practical railway asset management. The development of track geometry deterioration modelling methods focused on utilising currently available data for producing novel information on the development in the condition of track structures, past maintenance effectiveness, and future maintenance needs. Data mining was applied in investigating the root causes of track geometry deterioration based on asset data. Finally, maturity models were applied as the basis for implementing track geometry deterioration modelling and track asset data analytics into practice. Based on the research findings, currently available Finnish measurement and asset data was sufficient for the desired analyses. For the Finnish track inspection data, robust linear optimisation was developed for track geometry deterioration modelling. The modelling provided key figures, which depict the condition of structures, maintenance effectiveness, and future maintenance needs. Moreover, visualisations were created from the modelling to enable the practical use of the modelling results. The applied exploratory data mining method, General Unary Hypotheses Automaton (GUHA), could find interesting and hard-to-detect correlations within asset data. With these correlations, novel observations on problematic track structure types were made. The observations could be utilised for allocating further research for problematic track structures, which would not have been possible without using data mining to identify these structures. The implementation of track geometry deterioration and asset data analytics into practice was approached by applying maturity models. The use of maturity models offered a practical way of approaching future development, as the development could be divided into four maturity levels, which created clear incremental goals for development. The maturity model and the incremental goals enabled wide-scale development planning, in which the progress can be segmented and monitored, which enhances successful project completion. The results from these studies demonstrate how currently available data can be used to provide completely new and meaningful information, when advanced analytics are used. In addition to novel solutions for data analytics, this dissertation research also provided methods for implementing the solutions, as the true benefits of knowledge-based decision making are obtained in only practical railway asset management

    Knowledge-based Modelling of Additive Manufacturing for Sustainability Performance Analysis and Decision Making

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    Additiivista valmistusta on pidetty käyttökelpoisena monimutkaisissa geometrioissa, topologisesti optimoiduissa kappaleissa ja kappaleissa joita on muuten vaikea valmistaa perinteisillä valmistusprosesseilla. Eduista huolimatta, yksi additiivisen valmistuksen vallitsevista haasteista on ollut heikko kyky tuottaa toimivia osia kilpailukykyisillä tuotantomäärillä perinteisen valmistuksen kanssa. Mallintaminen ja simulointi ovat tehokkaita työkaluja, jotka voivat auttaa lyhentämään suunnittelun, rakentamisen ja testauksen sykliä mahdollistamalla erilaisten tuotesuunnitelmien ja prosessiskenaarioiden nopean analyysin. Perinteisten ja edistyneiden valmistusteknologioiden mahdollisuudet ja rajoitukset määrittelevät kuitenkin rajat uusille tuotekehityksille. Siksi on tärkeää, että suunnittelijoilla on käytettävissään menetelmät ja työkalut, joiden avulla he voivat mallintaa ja simuloida tuotteen suorituskykyä ja siihen liittyvän valmistusprosessin suorituskykyä, toimivien korkea arvoisten tuotteiden toteuttamiseksi. Motivaation tämän väitöstutkimuksen tekemiselle on, meneillään oleva kehitystyö uudenlaisen korkean lämpötilan suprajohtavan (high temperature superconducting (HTS)) magneettikokoonpanon kehittämisessä, joka toimii kryogeenisissä lämpötiloissa. Sen monimutkaisuus edellyttää monitieteisen asiantuntemuksen lähentymistä suunnittelun ja prototyyppien valmistuksen aikana. Tutkimus hyödyntää tietopohjaista mallinnusta valmistusprosessin analysoinnin ja päätöksenteon apuna HTS-magneettien mekaanisten komponenttien suunnittelussa. Tämän lisäksi, tutkimus etsii mahdollisuuksia additiivisen valmistuksen toteutettavuuteen HTS-magneettikokoonpanon tuotannossa. Kehitetty lähestymistapa käyttää fysikaalisiin kokeisiin perustuvaa tuote-prosessi-integroitua mallinnusta tuottamaan kvantitatiivista ja laadullista tietoa, joka määrittelee prosessi-rakenne-ominaisuus-suorituskyky-vuorovaikutuksia tietyille materiaali-prosessi-yhdistelmille. Tuloksina saadut vuorovaikutukset integroidaan kaaviopohjaiseen malliin, joka voi auttaa suunnittelutilan tutkimisessa ja täten auttaa varhaisessa suunnittelu- ja valmistuspäätöksenteossa. Tätä varten testikomponentit valmistetaan käyttämällä kahta metallin additiivista valmistus prosessia: lankakaarihitsaus additiivista valmistusta (wire arc additive manufacturing) ja selektiivistä lasersulatusta (selective laser melting). Rakenteellisissa sovelluksissa yleisesti käytetyistä metalliseoksista (ruostumaton teräs, pehmeä teräs, luja niukkaseosteinen teräs, alumiini ja kupariseokset) testataan niiden mekaaniset, lämpö- ja sähköiset ominaisuudet. Lisäksi tehdään metalliseosten mikrorakenteen karakterisointi, jotta voidaan ymmärtää paremmin valmistusprosessin parametrien vaikutusta materiaalin ominaisuuksiin. Integroitu mallinnustapa yhdistää kerätyn kokeellisen tiedon, olemassa olevat analyyttiset ja empiiriset vuorovaikutus suhteet, sekä muut tietopohjaiset mallit (esim. elementtimallit, koneoppimismallit) päätöksenteon tukijärjestelmän muodossa, joka mahdollistaa optimaalisen materiaalin, valmistustekniikan, prosessiparametrien ja muitten ohjausmuuttujien valinnan, lopullisen 3d-tulosteun komponentin halutun rakenteen, ominaisuuksien ja suorituskyvyn saavuttamiseksi. Valmistuspäätöksenteko tapahtuu todennäköisyysmallin, eli Bayesin verkkomallin toteuttamisen kautta, joka on vankka, modulaarinen ja sovellettavissa muihin valmistusjärjestelmiin ja tuotesuunnitelmiin. Väitöstyössä esitetyn mallin kyky parantaa additiivisien valmistusprosessien suorituskykyä ja laatua, täten edistää kestävän tuotannon tavoitteita.Additive manufacturing (AM) has been considered viable for complex geometries, topology optimized parts, and parts that are otherwise difficult to produce using conventional manufacturing processes. Despite the advantages, one of the prevalent challenges in AM has been the poor capability of producing functional parts at production volumes that are competitive with traditional manufacturing. Modelling and simulation are powerful tools that can help shorten the design-build-test cycle by enabling rapid analysis of various product designs and process scenarios. Nevertheless, the capabilities and limitations of traditional and advanced manufacturing technologies do define the bounds for new product development. Thus, it is important that the designers have access to methods and tools that enable them to model and simulate product performance and associated manufacturing process performance to realize functional high value products. The motivation for this dissertation research stems from ongoing development of a novel high temperature superconducting (HTS) magnet assembly, which operates in cryogenic environment. Its complexity requires the convergence of multidisciplinary expertise during design and prototyping. The research applies knowledge-based modelling to aid manufacturing process analysis and decision making in the design of mechanical components of the HTS magnet. Further, it explores the feasibility of using AM in the production of the HTS magnet assembly. The developed approach uses product-process integrated modelling based on physical experiments to generate quantitative and qualitative information that define process-structure-property-performance interactions for given material-process combinations. The resulting interactions are then integrated into a graph-based model that can aid in design space exploration to assist early design and manufacturing decision-making. To do so, test components are fabricated using two metal AM processes: wire and arc additive manufacturing and selective laser melting. Metal alloys (stainless steel, mild steel, high-strength low-alloyed steel, aluminium, and copper alloys) commonly used in structural applications are tested for their mechanical-, thermal-, and electrical properties. In addition, microstructural characterization of the alloys is performed to further understand the impact of manufacturing process parameters on material properties. The integrated modelling approach combines the collected experimental data, existing analytical and empirical relationships, and other data-driven models (e.g., finite element models, machine learning models) in the form of a decision support system that enables optimal selection of material, manufacturing technology, process parameters, and other control variables for attaining desired structure, property, and performance characteristics of the final printed component. The manufacturing decision making is performed through implementation of a probabilistic model i.e., a Bayesian network model, which is robust, modular, and can be adapted for other manufacturing systems and product designs. The ability of the model to improve throughput and quality of additive manufacturing processes will boost sustainable manufacturing goals

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Inclusive Intelligent Learning Management System Framework - Application of Data Science in Inclusive Education

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBeing a disabled student the author faced higher education with a handicap which as experience studying during COVID 19 confinement periods matched the findings in recent research about the importance of digital accessibility through more e-learning intensive academic experiences. Narrative and systematic literature reviews enabled providing context in World Health Organization’s International Classification of Functioning, Disability and Health, legal and standards framework and information technology and communication state-of-the art. Assessing Portuguese higher education institutions’ web sites alerted to the fact that only outlying institutions implemented near perfect, accessibility-wise, websites. Therefore a gap was identified in how accessible the Portuguese higher education websites are, the needs of all students, including those with disabilities, and even the accessibility minimum legal requirements for digital products and the services provided by public or publicly funded organizations. Having identified a problem in society and exploring the scientific base of knowledge for context and state of the art was a first stage in the Design Science Research methodology, to which followed development and validation cycles of an Inclusive Intelligent Learning Management System Framework. The framework blends various Data Science study fields contributions with accessibility guidelines compliant interface design and content upload accessibility compliance assessment. Validation was provided by a focus group whose inputs were considered for the version presented in this dissertation. Not being the purpose of the research to deliver a complete implementation of the framework and lacking consistent data to put all the modules interacting with each other, the most relevant modules were tested with open data as proof of concept. The rigor cycle of DSR started with the inclusion of the previous thesis on Atlântica University Institute Scientific Repository and is to be completed with the publication of this thesis and the already started PhD’s findings in relevant journals and conferences

    Mars delivery service - development of the electro-mechanical systems of the Sample Fetch Rover for the Mars Sample Return Campaign

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    This thesis describes the development of the Sample Fetch Rover (SFR), studied for Mars Sample Return (MSR), an international campaign carried out in cooperation between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The focus of this document is the design of the electro-mechanical systems of the rover. After placing this work into the general context of robotic planetary exploration and summarising the state of the art for what concerns Mars rovers, the architecture of the Mars Sample Return Campaign is presented. A complete overview of the current SFR architecture is provided, touching upon all the main subsystems of the spacecraft. For each area, it is discussed what are the design drivers, the chosen solutions and whether they use heritage technology (in particular from the ExoMars Rover) or new developments. This research focuses on two topics of particular interest, due to their relevance for the mission and the novelty of their design: locomotion and sample acquisition, which are discussed in depth. The early SFR locomotion concepts are summarised, covering the initial trade-offs and discarded designs for higher traverse performance. Once a consolidated architecture was reached, the locomotion subsystem was developed further, defining the details of the suspension, actuators, deployment mechanisms and wheels. This technology is presented here in detail, including some key analysis and test results that support the design and demonstrate how it responds to the mission requirements. Another major electro-mechanical system developed as part of this work is the one dedicated to sample tube acquisition. The concept of operations of this machinery was defined to be robust against the unknown conditions that characterise the mission. The design process led to a highly automated robotic system which is described here in its main components: vision system, robotic arm and tube storage

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
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