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Purpose – The purpose of this master thesis is to gain a better understanding of how value creation can be achieved with digital solutions in the energy industry. More specifically, this research aims to increase the understanding of factors that affect customer value in form of exploring the customers’ digital wants and needs. Method – This master thesis uses a combination of exploratory and descriptive approach which is based on qualitative data gathered from semi-structured interviews with area experts and focus groups with customers. In this study three area experts and four focus groups were included. The gathered data were analyzed through an abductive analysis approach and a thematic coding. Findings – The research found three main findings that energy company’s need to take into consideration. The first is that the energy industry needs to simplify the energy concept since customers’ state that the industry is too complex at the moment. Second is that value creation can be achieved in form of a superior mobile application where customers’ have the ability to follow their consumption and can gain improved customer care. The last main finding from this study show that there may be a lot of changes in the energy industry in the future, which entails many possibilities for energy companies to improve customer value. Recommendations – The recommendations from this study is that energy companies need to address that the driving force to buy in the energy industry is that customers want to like their energy company. The energy industry must change their focus and provide marketing strategies that are appealing to the customers’ emotions. Companies in the energy industry also need to deal with the increasing digitalization that is affecting society everywhere. The development of a great mobile application will improve a lot of things for the customers such as making their consumption controllable and easier to grasp, which will make them more satisfied. It is also recommended that energy companies in Sweden unite and together make a proposition to the EU to drop the regulations on electricity charges in order to unlock energy companies’ ability to make energy subscription customer friendly. Research contribution – This master thesis contributes to the three dimensions that customer value derived from, in the energy industry digital solutions affect functional and emotional value but has low impact on social value. It also occurred that simplicity, control, better information and customers’ buy with their heart are key factors that contributes to customer value in the industry, which contributes to both theoretical and practical. Digitalization are also moving fast in the energy industry which companies need to embrace right away and take action to not fall behind. Paper type – Master thesis

I detta examensarbete granskas, utifrån ett beslutsanalytiskt perspektiv, metodologin för lärosätesrankningen Times Higher Education World University Rankings, som rangordnar universitet med avseende på deras kvalitet för utbildning, forskning, kunskapsöverföring samt internationalisering. Först konstateras att rankningen ifråga skapas utifrån åtminstone fyra olika val, nämligen val av indikatorer, val av en kumulativ fördelningsfunktion via z-skalning som normaliseringsmetod, val av a priori/fasta viktkoefficienter samt val av en additiv aggregeringsmetod. Dessa val har följder som kan ses som problematiska. Ett antal av de indikatorer som valts för att representera kvalitén hos lärosäten skulle kunna ses som mindre adekvata då de i vissa fall skulle kunna sakna ett tydligt samband till lärosäteskvalité. Det föreligger även en risk för mindre adekvata värderingar vid normaliseringsprocessen då nyttofunktionerna för indikatorerna förutsätts vara strängt växande men där denna nyttofunktion för vissa indikatorer kan ifrågasättas. Vidare föreligger en risk för fenomenet inbördes byte av ordning hos oförändrade alternativ inom rankningen vilket pekar på ett grundläggande metodproblem eftersom det innebär att den inbördes rangordningen mellan två alternativ inte bara beror på underliggande deskriptiva data och värderingen av dessa, utan även på vilka andra alternativ som ingår i rankningen. Till sist konstateras även en risk för negativa följder utifrån fenomenet icke-stödda effektiva alternativ som är en konsekvens av den kompensatoriska modell som följer av additiv aggregering. Då användaren eller granskaren av rankningen inte har tillgång till underliggande deskriptiva data kan han/hon inte bedöma om ett alternativ som han/hon i något avseende betraktar som oacceptabelt ändå rangordnas högre än ett alternativ som är acceptabelt i alla avseenden.

This thesis investigates the possibility to measure vital signs, such as heartbeat and respiratory rate, by developing a RF-front end for wireless detection. The RF-front has been developed and manufactured as a continuous wave Doppler radar receiver, which utilizes quadrature demodulation by means of a multi-port correlator together with power detectors for down conversion. This thesis has been part of an ongoing research project at Link¨opings University, to develop a sensor platform for wireless vital signs detection. This sensor platform has been broken down into two major parts, a radar RF front-end system and a back-end digital signal processing system. The back-end system consist of data acquisition- and a processing-part. It was shown that very low-frequency signals emulating vital signs can be detected, when direct frequency conversion and demodulation are performed with the multi-port detector. Due to the limitations of the instruments, 10-Hz signals were demonstrated.

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The global supply and demand of energy is facing different challenges. On one hand an increasing energy demand, foremost in developing countries, and an increasing pressure on reaching climate goals changes the requirements on the design of power supply systems. This may be particularly relevant in terms of decentralized energy solutions and hybrid systems that incorporates renewable energy sources. This study exemplifies the use of multi-criteria decision analysis (MCDA) to evaluate different power supply alternatives for a small-scale cocoa processing plant (SSCPP), placed in Côte d´Ivoire. MCDA is an analytical approach to evaluate decision alternatives according to certain criteria, with the aim to find a preferred alternative. The function of a cocoa processing plant depends highly on its power supply. This study has been performed by first analysing the energy needs of the processing plant, which includes electricity, heating and cooling. Based on those specific energy needs different power supply alternatives have been created. In a following evaluation it has been exemplified how MCDA can be used within the energy sector to evaluate different alternatives. Within this exemplifying evaluation, following power supply solutions have been considered: power grid, power grid with back-up generator, power grid with LPG-heating (Liquified Petroleum Gas), power grid with solar energy, off-grid solar system with back-up generator and an off-grid generator with heat exchanger. The evaluation of alternatives has been made by using the three evaluation attributes: levelized cost of energy (LCOE), loss of load hours (LOLH) and carbon footprint of energy (CFOE).

Machine learning is a field within the broader concept of artificial intelligence and addresses the questions of how to build systems which learn from experience. The field is one of the oldest disciplines in computer science but has had many recent advancements due to the large amounts of data being generated. Today, machine learning together with artificial intelligence is seen as the two most rapidly growing fields within computer science. The purpose of this thesis is to explore and identify the current knowledge base of machine learning across data analytics teams, within the matrix organization Wise Inc.. This study has been performed using an exploratory case study method, based on the embedded units within the matrix organization. In this research, the units represent thirteen different cross-functional teams existing within the Wise Inc. organization. All thirteen teams are data analytics teams and performing a variety of different analytics depending on the team’s individual purpose. The analysis of embedded units has been performed within the units, but also across units. Using data collected through a qualitative questionnaire and interview, the knowledge base of machine learning could be explored and identified. Analysing the collected data, it was showed that the knowledge base across the data analytics teams in Wise Inc. is currently relatively low. Two key teams have been identified to have a very high level of knowledge. The knowledge base was examined based on participants theoretical and practical knowledge when it comes to machine learning. The aspect of machine learning usage and experience was included in the analysis and appeared to show a weak positive correlation to the overall knowledge. However, the statistical significance could not be determined. The empirical study also indicates that across teams, the level of knowledge is slightly higher than the level of experience. As a positive result, most participants appear to have a good theoretical understanding of machine learning in relation to artificial intelligence, which normally is one of the most common miss-interpretations. Even though the overall knowledge base is low, there are a few key people which stand out with a high knowledge base amongst teams. Observing the team as a whole the knowledge base is medium, but when looking at the individuals within the team there are a few key members with high expertise. These people are not working within the two teams identified with a high machine learning knowledge base but are part of other analytics teams. These people are important to identify as they can contribute with great value to the Wise Inc. organization.