The digital revolution is challenging both individuals and organizations to be more comfortable using various digital technologies. Digital technologies enable and generate high amounts of data, but people are not very good at interpreting or making sense of it. This study aimed to explore the role of digital competencies and data literacy in digital transformations and identify the consequences the lack of digital competencies and data literacy can cause within digital transformation projects. The authors studied technology consultants' perspectives with experience in digital transformation projects using an exploratory qualitative research design building on the empirical data gathered from semi-structured interviews. The authors were able to identify that the technology consultants perceived digital competencies as crucial skills for individuals to possess in digital transformations. At the same time, data literacy was not considered a crucial skill in the context of digital transformations. Regarding the consequences of a digital skills gap, the technology consultants saw issues within the implementation of the project, delays, or indirect waste of resources like monetary assets.

The Digital Twin concept is a novel approach based on Industry 4.0, whichrepresents the fourth industrial revolution. This research aimed to shed lighton why the construction industry lags behind other industries in terms of digitalisationand innovation, and whether the Digital Twin concept could foster innovationand usher the industry into the Industry 4.0 era. Specifically, this study soughtto research the potential value of the Digital Twin concept in enabling theconstruction industry to improve and transform through digitalisation. Toachieve this goal, an abductive research approach was employed, whichencompassed three key stages. First, a comprehensive literature review wasconducted, exploring digitalisation in the construction industry, the Digital Twinconcept, and its application in related industries. Second, qualitative interviewswere conducted in a semi-structured format to collect empirical data. Finally,thematic analysis was performed on the interview data, and themes wereanalysed and discussed in relation to the theoretical material to identifyconnections. The results of this study suggest that the Digital Twin concept holds greatpromise in driving digitalisation in the construction industry and enabling it totransform. Notably, the data-driven nature of the Digital Twin conceptemerged as the primary driver behind its value potential. Additionally,improvements in construction sites through scanning procedures for progresstracking and decision-making were found to be promising. Moreover, buildingbusiness models on the Digital Twin platform was identified as a potential valuecreator for Digital Twins. Furthermore, this study analysed the results againsttheoretical transformation frameworks that evaluate transformation potential.The Digital Twin concept was found to hold potential, although certainchallenges still exist, given the structure and dynamics of the constructionindustry. Overcoming these challenges may require improving collaborationand willingness to share valuable data.

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Hydropower is an important part of renewable power production in Sweden. The voltage stability of the already existing hydropower needs to be improved. One way to do this is by improving the control system that damp terminal voltage oscillations. If the oscillations in the power system are not damped it could lead to lower power outputs or in the worst case a blackout. This thesis focuses on the automatic voltage regulator (AVR) system with a proportional, integral, derivative (PID) controller. The PID controller’s parameters are optimized to dampen the terminal voltage instability in a generator. The aim is to develop a machine learning model that predicts the optimal gain parameters for a PID controller. The model is using the tuned gains from the Ziegler-Nichols (Z-N) method and the amplifier gain as inputs and gives the optimal gains as output. A linearized model of an AVR system, based on transfer functions was developed in a MATLAB script. This model was used to simulate the behaviours of an AVR system when a change in load occurs. The Z-N method and the genetic algorithm (GA) with different settings and fitness functions were used to tune a PID controller. The best performing method is GA with the fitness function developed by Zwe-Lee Gaing (ZL).  The best performing settings are: roulette selection, adapt feasible mutation, and arithmetic crossover. The GA (ZL) was used in the development of a machine learning model. Two different models were developed and tested: the support vector regression (SVR) and the gaussian process regression (GPR). The data that was used to train the models were generated by changing the transfer functions’ time constants 4096 times. At each step, the Z-N, and the GA (ZL) were run. The GPR model is shown to be the superior model with a lower root mean square error (RMSE) and a higher ratio of variation (R^2). The RMSE for GPR is 0.1091, 0.0815, 0.0717 and the R^2 is 87 %, 59 %, and 86%. The result shows that the developed model has capabilities to optimize the PID controller gains of any AVR-system without knowing the characteristics of the components.

he business climate is undergoing a transformation and managers are faced with several challenges, notthe least of which is related to pricing strategy. With an increased transparency in the market as well as anincreased competitive pressure, and with more sophisticated and well-informed consumers, retailbusinesses find it hard to navigate the pricing jungle. At the same time, the conventional wisdom in thefield of pricing and the theoretical models on the topic, originate from a time long before thedigitalization. Old models are not a problem in itself, but when there are new forces in the pricingecosystem, driven by technological advancement, an assessment of the incumbent models is in the bestinterest of both businesses and academia. The reason for this is that, the use of old models that rely oninaccurate assumptions may impact businesses’ prioritizing of resources or their overall business strategy.In addition, researchers might be distracted and the research field disrupted. Thus, the purpose of thisstudy is to discuss whether or not there are additional dimensions in pricing strategy that are not coveredby the incumbent pricing models. Here, dimensions refer to the key components of businesses’ strategicdecision making in regards to pricing.This thesis examines pricing models in today’s business context in order to answer the research question:“Are there additional dimensions of the empirical reality of pricing strategy that are not covered by theincumbent pricing models?” The research question has been studied qualitatively through a literaturereview, a pilot study and twelve case studies, where the pilot study had the purpose of exploring thedepth, whereas the multiple case studies focused on the breadth, of pricing strategies. The case studiescover businesses in different retail industries and of different sizes, namely the industries of Clothing &Accessories, Daily Goods, Furniture and Toys & Tools, and of the following sizes: micro, small, mediumand large. The empirical data has mainly been gathered by conducting interviews with production, salesand management personnel at the case businesses. The data has been structured, reduced and analysedwith the help of a framework of analysis that has been developed throughout the pilot study.The results of this study lean on previous research and a main divider in pricing strategies has beenidentified as businesses use either a data-driven or an intuition-driven approach in their strategic workwith pricing. As such, it is proposed that the division of pricing strategies need to be acknowledged, sincethe separate methodological approaches may lead to different results, while implying different costs,resources and required knowledge. Furthermore, the division may form a basis for competitive advantage,be extended to other areas of strategic management and become clearer, since the adoption of technologyand its impact will increase in the future. As a result, in the future of pricing, they key is going to be toaccount for both the strategic perspectives and the methodological approaches in the strategic decisionmaking process of pricing.

Modern Heavy Duty Vehicles consist of a multitude of components and operate in various conditions. As there is value in goods transported, there is an incentive to avoid unplanned breakdowns. For this, condition based maintenance can be applied.\newline This thesis presents a study comparing the applicability of the data-driven Consensus SelfOrganizing Models (COSMO) method and the model-driven patent series introduced by Fogelstrom, applied on the air processing system for leakage detection on Scania Heavy Duty Vehicles. The comparison of the two methods is done using the Area Under Curve value given by the Receiver Operating Characteristics curves for features in order to reach a verdict.\newline For this purpose, three criteria were investigated. First, the effects of the hyper-parameters were explored to conclude a necessary vehicle fleet size and time period required for COSMO to function. The second experiment regarded whether environmental factors impact the predictability of the method, and finally the effect on the predictability for the case of nonidentical vehicles was determined.\newline The results indicate that the number of representations ought to be at least 60, rather with a larger set of vehicles in the fleet than with a larger window size, and that the vehicles should be close to identical on a component level and be in use in comparable ambient conditions.\newline In cases where the vehicle fleet is heterogeneous, a physical model of each system is preferable as this produces more stable results compared to the COSMO method. / Moderna tunga fordon består av ett stort antal komponenter och används i många olika miljöer. Då värdet för tunga fordon ofta består i hur mycket gods som transporteras uppstår ett incitament till att förebygga oplanerade stopp. Detta görs med fördel med hjälp av tillståndsbaserat underhåll. Denna avhandling undersöker användbarheten av den data-drivna metoden Consensus SelfOrganizing Models (COSMO) kontra en modellbaserad patentserie för att upptäcka läckage på luftsystem i tunga fordon. Metoderna ställs mot varandra med hjälp av Area Under Curve-värdet som kommer från Receiver Operating Characteristics-kurvor från beskrivande signaler. Detta gjordes genom att utvärdera tre kriterier. Dels hur hyperparametrar influerar COSMOmetoden för att avgöra en rimlig storlek på fordonsflottan, dels huruvida omgivningsförhållanden påverkar resultatet och slutligen till vilken grad metoden påverkas av att fordonsflottan inte är identisk. Slutsatsen är att COSMO-metoden med fördel kan användas sålänge antalet representationer överstiger 60 och att fordonen inom flottan är likvärdiga och har använts inom liknande omgivningsförhållanden. Om fordonsflottan är heterogen så föredras en fysisk modell av systemet då detta ger ett mer stabilt resultat jämfört med COSMO-metoden.

The use of big data in urban transport planning is unstoppably gaining momentum and with the help of strategic business partnerships and technological advancements (e.g. transport apps, mobile device location tracking, data processing) the new mobility models are evolving towards an integrated and multimodal urban mobility: Mobility as a Service (MaaS). From the generation of data by Telecom companies to transport end users, a broad range of stakeholders are involved in the data market. This tighter with the call for sustainable alternatives in passenger traffic highlights that business relations are complex, and that businesses in this data market also have long-range transport objectives. This Master Thesis develops a stakeholder analysis of the network of actors related to mobile data and users. It explores the city of Stockholm as case study to identify who are the market players (i.e. companies) and what are their respective roles and business models. Based on sectoral expertise interviews and literature and website review, a three-cluster organization of data suppliers, data facilitators and data end users set the structure to evaluate stakeholder relationships. Data trading opens a debate on which Telecoms not only address raw data processing methods but also reach less accurate mobility outcomes (e.g. trips per person, OD matrices, travel distance, average speed), or, on the other hand, which delegate the added-value service to third parties. The analyzed actor network outstands frictions between the public and private sector and, certainly, when processed data steps on the transport industry (e.g. PT operators, infrastructure managers, private service operators (Uber), passengers). This is an institutional barrier that prevents a full MaaS implementation in the Stockholm region. The challenge resides on revising actor network gaps (i.e. new roles of MaaS Operators or Collecting Agents) and easy flow data transactions to encourage integrated modal choice in transport apps offerings. Despite exiting MaaS initiatives (e.g. UbiGo) in Stockholm and little research in data-based stakeholders, this is a first approximation of a stakeholder map to an immature and innovative research area with great potential in the future. / Införandet av Big Data i stadsplanering har oundvikligen börjat ta fart. Med hjälp av strategiska affärspartnerskap och tekniska framsteg (t.ex. transportappar, spårning av mobila enheter, databehandling) har nya mobilitetsmodeller utvecklats i strävan efter en integrerad och multimodal mobilitet i städerna: Mobilitet som tjänst (MaaS). Från ny datagenerering till att transportera slutanvändare deltar ett brett spektrum av intressenter på en marknad som styrs av tillgång till data. Uppmaningen till hållbara alternativ i passagerartrafik uppmärksammar också komplexa aktörsrelationer som relaterar datahantering till långsiktiga transportmål. Denna uppsats består av en intressentanalys av aktörsnätverket inom mobilitetsdata och undersöker Stockholms stad som en fallstudie för att identifiera vilka som är marknadsaktörer (företag) och respektive roller och affärsmodeller. Baserat på intervjuer av experter inom branschen, litteratur- och webbplatssökning skapas tre kluster av organisationer, för att utvärdera intressentrelationer. Dessa är datalämnare, datatillämpare och slutanvändare. Datahandel öppnar upp en debatt om hur telekomföretag använder nya databehandlingsmetoder men når mindre exakta mobilitetsresultat (t.ex. resor per person, OD-matriser, reseavstånd, genomsnittlig hastighet) eller, å andra sidan, som delegerar mervärdestjänsten till tredje part. Det analyserade aktörsnätverket utestänger friktion mellan den offentliga och privata sektorn, och denna barriär förhindrar en fullständig MaaS-implementering när det gäller bearbetade datasteg inom transportbranschen (t.ex. PT-operatörer, infrastrukturförvaltare, privata serviceoperatörer) i Stockholmsregionen. Utmaningen ligger i att omarbeta luckor mellan aktörsnätverk (MaaS Operator eller Collecting Agent) och förenkla dataflödestransaktioner för att uppmuntra integrerat modalval i transportapps-erbjudanden. Trots existerande MaaS-initiativ (t.ex. UbiGo) och en mindre databaserad intressentforskning är detta en första approximation till ett omoget och innovativt forskningsområde med stor potential inför framtiden. / El uso de big data en la planificación del transporte urbano está ganando un impulso imparable, y de la mano de asociaciones empresariales estratégicas y avances tecnológicos (aplicaciones de transporte, seguimiento de ubicación en dispositivos móviles, procesamiento de datos), los nuevos modelos de movilidad están evolucionando hacia una movilidad urbana integrada y multimodal: Mobility as a Service (MaaS). Desde la generación de datos (empresas de telefonía) hasta un sector transporte como usuario, muchas son las partes interesadas que participan en el mercado de datos. Esto, unido a la llamada de nuevas alternativas sostenibles en el tráfico de pasajeros, hace destacar que las relaciones empresariales son complejas y que los negocios en este mercado de datos también tienen objetivos en un transporte de largo alcance. Esta tesis desarrolla un stakeholder analysis de la red de actores relacionados con los datos móviles y utiliza Estocolmo como caso de estudio para identificar a estos agentes (empresas) y sus respectivos roles y modelos de negocios. Basado ​​en entrevistas a expertos y trabajos de investigación, el análisis organiza los actores en tres grupos proveedores de datos, facilitadores de datos y usuarios finales de datos, siendo esta la estructura base para estudiar sus relaciones. Así mismo, este intercambio de datos abre un debate alrededor de si las empresas de telefonía desarrollan métodos para procesar los datos, aunque los resultados de movilidad sean menos precisos (viajes por persona, matrices OD, distancia de viaje, velocidad promedio) o, por otro lado, si el servicio de dar valor añadido se delega a terceros. Ciertamente, el análisis de la red de actores destaca fricciones entre el sector público y el privado y, en el momento que la industria del transporte ya maneja estos datos procesados (operadores de transporte, gestores de la infraestructura, empresas tipo Uber, etc), esta barrera institucional es la que mayormente impide una implementación total de MaaS en Estocolmo. El desafío está revisar posibles “huecos” en la red de actores (Operador MaaS o un Agente Cobrador) así como un fácil flujo en las transacciones de datos para alentar una elección modal integrada en la oferta de las aplicaciones de transporte. Así, a pesar de la escasa investigación en quienes son los actores que hacen negocio con datos telefónicos, y las de pocas iniciativas (efectivas) en MaaS ( UbiGo) hacen de este proyecto una primera aproximación a un área de investigación que aún es inmadura e innovadora, pero con un gran potencial en el futuro.

Network downtime is costly for providers of information technology services. One cause of network downtime is link failures. The control plane of the network is the entity responsible for ensuring connectivity upon link failures. The data plane of the network forwards packets at line speed and it is controlled by the control plane. One disadvantage of ensuring connectivity at the control plane level is the time needed to react to a failure. The control plane is several orders of magnitude slower than the data plane. Moving the connectivity responsibility to the quicker data plane has therefore the potential to reduce network downtime. This work explored what level of connectivity robustness can be achieved when implementing data plane connectivity algorithms in today’s high-speed speed programmable switches. A literature study of several data plane connectivity algorithms was conducted. A critical aspect considered in this study was the simplicity of the data plane connectivity mechanism as high-speed programmable switches cannot support arbitrarily complex forwarding function. Data-Driven Connectivity (DDC) was selected as a suitable algorithm due to its high guaranteed connectivity and algorithmic simplicity. DDC was implemented in a virtual network environment using P4 programmable software switches. Our solution automates the generation of the virtual network based on a topology description. It also initializes the switches and generates the specific DDC P4 code for each switch. All the functions of DDC P4 have been tested to verify that each function behaved as expected. The path optimality of DDC P4 after several link failures were evaluated on the emulated Google’s wide area network topology, called B4 (2011). The path optimality evaluation shows that the path stretch of DDC P4, i.e., the gap from the shortest path in the number of hops, is not optimal for about 30% of the possible source/destination node pairs in the topology. The throughput of the DDC P4 was also evaluated along different number of link failures. The throughput results show a linear decrease in steps of 0.4 Mbps depending on which outbound link was utilized, starting from a throughput of 6.3 Mbps in the absence of failures. The current DDC P4 implementation does not scale well due to duplicate code for each destination in the topology. Both improving the scalability of the current implementation and an implementation on a hardware programmable switch remain as future work. / Avbrott i nätverket är kostsamt för leverantörer av informationsteknologitjänster. En orsak till avbrott är länkfel. Nätverkets textit kontrollplan är den entitet som ansvarar för att säkerställa anslutning vid länkfel. Nätverkets textit dataplan vidarebefordrar paket så snabbt som nätverkslänken klarar av, och det styrs av kontrollplanet. En nackdel med att säkerställa anslutning i kontrollplanet är den tid som krävs för att reagera på ett fel. Kontrollplanet är många gånger långsammare än dataplanet. Att flytta anslutningsansvaret till det snabbare dataplanet kan därför korta ner avbrotten i nätverket. Detta arbete undersökte vilken nivå av robusthet i anslutningsbarheten som kanuppnås vid implementering av algoritmer för anslutningsbarhet i dataplanet i dagens programmerbara höghastighetsswitchar. En litteraturstudie av flera dataplananslutningsalgoritmergenomfördes. En kritisk aspekt som beaktades i denna studie var enkelheten i dataplananslutningsmekanismen eftersom programmerbara höghastighetsswitchar inte kan stödja godtyckligt komplex vidarebefordringsfunktion. Datadriven anslutningsbarhet (DDC) valdes som en lämpligalgoritm på grund av dess höga garanterade anslutningsbarhet och algoritmiska enkelhet. DDC implementerades i en virtuell nätverksmiljö med P4-programmerbara mjukvaruswitchar. Vår lösning automatiserar genereringen av det virtuella nätverket baserat på en topologibeskrivning.Den initialiserar också switcharna och genererar den specifika DDC P4-koden för varje switch. Alla funktioner i DDC P4 har testats för att verifiera att varje funktion uppträdde som förväntat. Sökvägsoptimaliteten för DDC P4 efter flera länkfel utvärderades på Googles emulerade Wide Area Network (WAN), kallad B4 (2011). Bedömningen av sökvägsoptimaliteten visar att vägsträckningen för DDC P4, textit d.v.s., avståndet från den kortaste vägen i antalet hopp, inte är optimal för cirka 30 % av de möjliga ursprungs-/ destinationsnodparna i topologin. Genomströmningen av DDC P4 utvärderades också utifrån olika antal länkfel. Genomströmningsresultaten visar en linjär minskning i steg på 0,4 Mbps beroende på vilken utgående länk som användes, med utgångspunkt från en genomströmning på 6,3 Mbps vid frånvaro av fel. Den nuvarande DDC P4-implementeringen skalas inte bra på grund av duplicerad kod för varje destination i topologin. Både förbättring av skalbarheten för den nuvarande implementeringen och en implementering av en hårdvaruprogrammerbar switch kvarstår som framtida arbete.

In thiswork, we develop a learning model predictive controller (LMPC) for energy-optimaltracking of periodic trajectories for a quadcopter. The main advantage of this controller isthat it is “reference-free”. Moreover, the controller is able to improve its performance overiterations by incorporating learning from the previous iterations. The proposed learningmodel predictive controller aims to learn the “best” energy-optimal trajectory over timeby learning a terminal constraint set and a terminal cost from the history data of previousiterations. We have shown howto recursively construct terminal constraint set and terminalcost as a convex hull and a convex piece-wise linear approximation of state and inputtrajectories of previous iterations, respectively. These steps allow us to formulate theonline planning problem for the controller as a convex optimization program, therebyavoiding the complex combinatorial optimization problems that alternative formulationsin the literature need to solve. The data-driven terminal constraint set and terminal costnot only ensure recursive feasibility and stability of LMPC but also guarantee convergenceto the neighbourhood of the optimal performance at steady state. Our LMPC formulationincludes linear time-varying system dynamics which is also learnt from stored state andinput trajectories of previous iterations.To show the performance of LMPC, a quadcopter trajectory learning problem in thevertical plane is simulated in MATLAB/SIMULINK. This particular trajectory learningproblem involves non-convex state constraints, which makes the resulting optimal controlproblem difficult to solve. A tangent cut method is implemented to approximate the nonconvexconstraints by convex ones, which allows the optimal control problem to be solvedby efficient convex optimization solvers. Simulation results illustrate the effectiveness ofthe proposed control strategy. / Vi utvecklar en lärande modell-prediktiv regulator för energi-optimalt följande av periodiskatrajektorier för en quadkopter. Den huvudsakliga fördelen med denna regulator äratt den är “referensfri”. Dessutom så klarar regulatorn att förbättra sin prestanda medtiden genom att inkorporera inlärning från föregående iterationer. Syftet med den föreslagnalärande modell-prediktiva regulatorn är att över en viss tid lära sig den “bästa”energioptimala trajektorian genom att lära sig den terminala bivillkorsmängden och denterminala kostnaden från historiskt data från tidigare iterationer. Vi har visat hur man kanrekursivt konstruera terminala bivillkorsmängder och terminala kostnader som konvexahöljen respektive konvexa styckvis linjära approximationer av tillstånds- och insignalstrajektoriernafrån tidigare iterationer. Dessa steg gör det möjligt att formulera onlineplaneringsproblemet för regulatorn som ett konvext optimeringsproblem och på så visundvika de komplexa kombinatoriska optimeringsproblemen som ofta krävs för alternativametoder som kan hittas andra publikationer. Den datadrivna terminala bivillkorsmängdenoch terminala kostnaden garanterar inte bara rekursiv tillåtenhet och stabilitet av LMPC,utan även konvergens till en omgivning av den optimala prestandan efter att ha uppnåttjämvikt. Vår LMPC-formulering innehåller linjär och tidsvarierande systemdynamik, somockså lärs från lagrade tillstånds- och insignalstrajektorier från tidigare iterationer.För att visa prestandan av LMPC så simuleras iMATLAB/SIMULINK ett problem ominlärning av quadkopter-trajektorier i det vertikala planet. Just det trajektorieinlärningsproblemetinnehåller icke-konvexa tillståndsbivillkor, vilket gör det resulterande optimeringsproblemetsvårt att lösa. En tangentsnitt-metod är implementerad för att approximera deicke-konvexa bivillkoren med hjälp av konvexa bivillkor, vilket möjliggör lösningen avdet optimala regleringsproblemet med effektiva lösare för konvexa optimeringsproblem.Simuleringsresultaten visar effektivitet av den föreslagna regleringsmetoden.

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality.