Denna rapport innehåller en fallstudie av företaget HKScan och fokuserar på deras interna hantering av transporter ut till kunderna. Eftersom HKScan verkar i en konkurrensutsatt bransch med små marginaler så är det viktigt att varenda krona används på ett bra sätt. Transporter är en aktivitet som sällan undersöks eftersom det är en operativ aktivitet som är fristående från övrig verksamhet. Det har dock visat sig att även om olika funktioner i ett företag traditionellt styrs separat så finns det starka kopplingar mellan transporter och funktioner som sälj, marknad, inköp, ekonomi, personaladministration och produktion. Denna studies syfte var därför att undersöka vilka faktorer som påverkar transportkostnaderna och ge förslag på hur transportkostnaderna på sikt kan minskas. Undersökningen av vilka faktorer som påverkar transportkostnaderna genomfördes i flera steg. Först sammanställdes de faktorer som nämns i teorin vilket kompletterades med de faktorer som Supply Chain-funktionen på HKScan trodde påverkade transportkostnaderna. Detta resulterade i en modell med 18 möjliga påverkande faktorer som sedan avgränsades beroende på vad HKScan mäter och vad som ansågs relevant att undersöka. Denna avgränsade lista bestod av sju drivare som sedan undersöktes med hjälp av en multipel regressionsanalys. I denna analys kunde det matematiskt bevisas att faktorerna vikt per transportkolli, transporttemperatur, kvantitet, avstånd samt vilken typ av kund som transporten gick till påverkar transportkostnaden både på sändningsnivå och per transportkolli. Efter att dessa drivare identifierats så hölls intervjuer för att undersöka vilka funktioner som har störst möjlighet att påverka dem och därför i förlängningen även kan påverka transportkostnaden direkt. Dessa intervjuer visade att det främst är funktionerna för sälj och Supply Chain som direkt kan påverka transportkostnaden. Även marknad och produktion har möjlighet att påverka transportkostnaden på ett indirekt sätt. Slutligen genomfördes intervjuer med personal från de berörda funktionerna för att undersöka på vilket sätt medvetenheten och styrningen av transportkostnaderna kan förbättras. Detta resulterade i ett stort antal förslag där det mest frekvent förekommande var en kontinuerlig uppföljning av transportkostnaderna. Denna uppföljning ska inte bara vara på nivån att transportkostnaderna varit dyra utan gå djupare och peka på vilka mönster i beställningar och transporter som gett upphov till den högre kostnaden. Om denna uppföljning ska ske manuellt eller med hjälp av ett automatiserat verktyg finns det olika åsikter om men personalen känner genomgående de behöver öka medvetenheten om vad som påverkar transportkostnaderna för att kunna sänka dem. / This report contains a case study of the company HKScan and focuses on their internal handling of transports to customers. As HKScan operates in a competitive industry with small margins, it is important that all money is used in the best way. An activity that costs money but rarely is investigated is the transportation since it is seen as a very operative activity. It has, however, been found that although different functions of a company are traditionally controlled separately, there are strong links between transports and functions such as sales, marketing, purchasing, finance, human resource management and production. The purpose of this study was therefore to investigate which factors that affect transport costs and provide suggestions on how transport costs can be reduced. The study of which factors that affect transport costs was carried out in several stages. First, factors mentioned in the theory were compiled and complemented by the factors that the Supply Chain department at HKScan believed could affect the cost. This resulted in a list of 18 possible influencing factors that were then demarcated depending on what HKScan measures and which was considered relevant to investigate. This demarcated list consisted of seven factors, which were then investigated using a multiple regression analysis. From this analysis, it could be mathematically proven that the parameters quantity, distance, weight per transport package, temperature, and the type of customer to which the transport went, affects the transport cost both at the shipping level and per transport package. After these drivers had been identified, interviews were held to investigate which departments have the greatest potential to influence them and, in the long run, affect transport costs. These interviews showed that it was primarily the sales and Supply Chain departments that affect the shipping costs, even though market and production have a more indirect impact. Finally, interviews were conducted with staff from the relevant departments to investigate how awareness and management of transport costs can be improved. This resulted in a large number of proposals where the most frequent one mentioned was a continuous follow-up on transport costs. This follow-up should point out which patterns in orders and shipments that gave rise to the higher cost. If this follow-up is to be done manually or by means of an automated tool, there are different views, but the staff feels that in order to reduce transport costs, they need increased awareness of what affects them. / <p>Observera att alla siffror och resultat i rapporten är modifierade och speglar inte det verkliga resultatet.</p>

In recent years the amount of newly registered electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles has increased rapidly in the Swedish market. These vehicles could be classified as battery-powered electric vehicles, and a majority carry a lithium-ion battery. The demand for lithium is expected to increase considerably, as a result of such a swift growth in battery-powered electric vehicles. Thus, if the recycling rate of lithium stays at a low level, demand could reach a scarcity-level by 2050. While neither any infrastructure nor an established process for recycling lithium-ion batteries currently exists in Sweden, this study aims to provide necessary input and verified tools for the design of a future reverse supply chain for discarded lithium-ion batteries in Sweden. The literature review of this study covers the subjects of reverse logistics, supply chain network design, and operations research. A thorough situation analysis of the Swedish market for battery-powered electric vehicles is conducted, and the composition, function, and characteristics of lithium-ion batteries are studied. The study finds that estimations of future demand of recyclable lithium-ion batteries in Sweden could be between 206 711 and 726 974 tons accumulated, based on actual and predicted sales numbers until 2030. Even if it is obvious that there are going to be large quantities of such batteries requiring recycling in the future, and even if some established processes exist, there is no defined supply chain for the collection of those batteries. Finally, a mixed-integer programming model for the design and development of a future reverse supply chain is presented. The model, characterized as a discrete multi-period facility location/allocation model, can with minor modifications be used for problems with fluctuating demand or when the demand is assumed to slowly progress until it has reached a steady state.

Pesquisadores de diversas áreas estão estudando o desenvolvimento do que chamamos de Cidades Inteligentes: a integração de Sistemas de Informação e Comunicação com tecnologias de Internet das Coisas para utilizar os recursos de uma cidade de forma mais inteligente. Um dos principais objetivos das cidades inteligentes é solucionar os problemas relacionados à mobilidade urbana, que afeta significativamente a qualidade de vida da população. Um problema observável nas grandes metrópoles é a qualidade dos seus serviços de transporte público, especialmente quando nos referimos ao modal ônibus. A falta de informações confiáveis, associada à baixa qualidade dos serviços de transporte coletivo disponibilizados, leva o usuário a não optar pela utilização desse recurso, o que agrava problemas urbanos sociais e ambientais. Para reverter esse cenário, as iniciativas em cidades inteligentes propõem o uso de Sistemas de Transportes Inteligentes que podem utilizar diversos sensores e equipamentos para coletar diferente tipos de dados referente aos serviços de transporte público. A captura e processamento desses dados permite, em tese, permite que o cidadão possa utilizar o transporte público com confiabilidade e previsibilidade. Contudo, esses dados podem ser insuficientes ou de baixa qualidade para uso em tempo real. Neste trabalho de mestrado investigamos o uso de dados obtidos via colaboração coletiva (crowdsourcing) como complemento dessas informações. Para mitigar as incertezas introduzidas pelo uso de crowdsourcing, este trabalho propõe a utilização de técnicas de aprendizado de máquina para criação de métodos de análise de confiabilidade dos dados coletados para o sistema de transporte público (por ônibus) do município de São Paulo. Para mitigar as incertezas introduzidas pelo uso de crowdsourcing, este trabalho propõe e compara o uso de diferentes técnicas de aprendizado de máquina para criar um modelo de análise de confiabilidade para os dados coletados, especializado no sistema de transporte coletivo (por ônibus) da cidade de São Paulo. Os resultados demostram, que os algoritmos de Árvore de Decisão e Gaussian Naive Bayes foram mais eficazes e eficientes na realização da atividade de classificação dos dados obtidos com crowdsourcing. O algoritmo de Árvore de Decisão, apresentou os melhores indicadores de desempenho em termos de acurácia (94,34\\%) e F-score (99\\%), e o segundo melhor tempo de execução (0,023074 segundo). Já o algoritmo de Gaussian Naive Bayes foi o mais eficiente, com tempo médio de execução de 0,003182 segundos e foi o quarto melhor resultado em termos de acurácia (98,18\\%) e F-score (97\\%) / Researchers from different areas are studying the development of what we call Smart Cities: integrating Information and Communication Systems with Internet of Things to use city resources more intelligently. A major objective of smart cities is to solve problems related to urban mobility that significantly affects the quality of life of the population. An observable problem in big cities is the quality of their public transport services, specifically when we refer to the bus modal. The lack of reliable information, associated with the poor quality of public transport services, encouraging the user to look for alternatives, which aggravates urban social and environmental problems. To reverse this scenario, smart cities initiatives propose the use Intelligent Transport Systems, that can use various sensors and equipment to collect several types of data on public transport services. The capture and processing of these data allows, in theory, citizens to use the public transport with reliability and predictability. However, this data can be insufficient or of poor quality for usage in real-time. This master\'s work investigates the use of crowdsourcing data as a complement to this information. To mitigate the uncertainties introduced by the use of crowdsourcing, this research proposes and compares the use of different machine learning techniques to create a reliability analysis model for the data collected that is specialized for use on public transport system (bus) in the city of São Paulo. The results show that the Decision Tree and Gaussian Naive Bayes algorithms are more effective and efficient in performing the classification activity of the data obtained with crowdsourcing. The Decision Tree algorithm presented the best performance indicators in terms of accuracy (94.34\\%) and F-score (99\\%), and the second best execution time (0.023074 seconds). The Gaussian Naive Bayes algorithm was the most efficient, with an average execution time of 0.003182 seconds and was the forth best result in terms of accuracy (98.18\\%) and F-score (97\\%)

Pesquisadores de diversas áreas estão estudando o desenvolvimento do que chamamos de Cidades Inteligentes: a integração de Sistemas de Informação e Comunicação com tecnologias de Internet das Coisas para utilizar os recursos de uma cidade de forma mais inteligente. Um dos principais objetivos das cidades inteligentes é solucionar os problemas relacionados à mobilidade urbana, que afeta significativamente a qualidade de vida da população. Um problema observável nas grandes metrópoles é a qualidade dos seus serviços de transporte público, especialmente quando nos referimos ao modal ônibus. A falta de informações confiáveis, associada à baixa qualidade dos serviços de transporte coletivo disponibilizados, leva o usuário a não optar pela utilização desse recurso, o que agrava problemas urbanos sociais e ambientais. Para reverter esse cenário, as iniciativas em cidades inteligentes propõem o uso de Sistemas de Transportes Inteligentes que podem utilizar diversos sensores e equipamentos para coletar diferente tipos de dados referente aos serviços de transporte público. A captura e processamento desses dados permite, em tese, permite que o cidadão possa utilizar o transporte público com confiabilidade e previsibilidade. Contudo, esses dados podem ser insuficientes ou de baixa qualidade para uso em tempo real. Neste trabalho de mestrado investigamos o uso de dados obtidos via colaboração coletiva (crowdsourcing) como complemento dessas informações. Para mitigar as incertezas introduzidas pelo uso de crowdsourcing, este trabalho propõe a utilização de técnicas de aprendizado de máquina para criação de métodos de análise de confiabilidade dos dados coletados para o sistema de transporte público (por ônibus) do município de São Paulo. Para mitigar as incertezas introduzidas pelo uso de crowdsourcing, este trabalho propõe e compara o uso de diferentes técnicas de aprendizado de máquina para criar um modelo de análise de confiabilidade para os dados coletados, especializado no sistema de transporte coletivo (por ônibus) da cidade de São Paulo. Os resultados demostram, que os algoritmos de Árvore de Decisão e Gaussian Naive Bayes foram mais eficazes e eficientes na realização da atividade de classificação dos dados obtidos com crowdsourcing. O algoritmo de Árvore de Decisão, apresentou os melhores indicadores de desempenho em termos de acurácia (94,34\\%) e F-score (99\\%), e o segundo melhor tempo de execução (0,023074 segundo). Já o algoritmo de Gaussian Naive Bayes foi o mais eficiente, com tempo médio de execução de 0,003182 segundos e foi o quarto melhor resultado em termos de acurácia (98,18\\%) e F-score (97\\%) / Researchers from different areas are studying the development of what we call Smart Cities: integrating Information and Communication Systems with Internet of Things to use city resources more intelligently. A major objective of smart cities is to solve problems related to urban mobility that significantly affects the quality of life of the population. An observable problem in big cities is the quality of their public transport services, specifically when we refer to the bus modal. The lack of reliable information, associated with the poor quality of public transport services, encouraging the user to look for alternatives, which aggravates urban social and environmental problems. To reverse this scenario, smart cities initiatives propose the use Intelligent Transport Systems, that can use various sensors and equipment to collect several types of data on public transport services. The capture and processing of these data allows, in theory, citizens to use the public transport with reliability and predictability. However, this data can be insufficient or of poor quality for usage in real-time. This master\'s work investigates the use of crowdsourcing data as a complement to this information. To mitigate the uncertainties introduced by the use of crowdsourcing, this research proposes and compares the use of different machine learning techniques to create a reliability analysis model for the data collected that is specialized for use on public transport system (bus) in the city of São Paulo. The results show that the Decision Tree and Gaussian Naive Bayes algorithms are more effective and efficient in performing the classification activity of the data obtained with crowdsourcing. The Decision Tree algorithm presented the best performance indicators in terms of accuracy (94.34\\%) and F-score (99\\%), and the second best execution time (0.023074 seconds). The Gaussian Naive Bayes algorithm was the most efficient, with an average execution time of 0.003182 seconds and was the forth best result in terms of accuracy (98.18\\%) and F-score (97\\%)

Driverless vehicles might fundamentally change the transport system in multiple ways. Reducing driver costs in mobility services could create opportunities for new mobility concepts. Research on driverless vehicles have previously concentrated on urban areas, though driverless vehicles in rural areas could have greater positive effects. Hence, the aim of the study is to see how driverless vehicles can be used in rural areas to contribute to a more sustainable transport system. Three rural mobility concepts for driverless vehicles are developed and by applying these to different case locations, the feasibility of the concepts is discussed. Interviews with local actors in Sweden were conducted to learn about general and local challenges with specific case locations. What rural mobility concept for driverless vehicles to use depends on access to public transport, distance to main roads and spatial density of travel demand. A modelling approach of a first and last mile feeder service is used to evaluate the feasibility of this mobility concept further. Model results show that driverless shuttles can feed travel demands of 100-150 passengers daily and still perform alternative tasks. Even though rural areas have general challenges, local issues also need consideration to optimize the benefits of the services. Public transport authorities are experts on local challenges and could take more responsibility in questions regarding driverless vehicles. For instance, flexibility, accessibility and equality could be improved by merging routes and shorten travel times for entire bus routes. Furthermore, other societal functions can be developed by reinvesting capital in other areas.

<p>This master thesis is to highlight the importance of what needs to be identified in the CVIS system, how this could be done, how different techniques affect privacy and security and how the privacy and security mechanisms can be improved for the whole system. The report starts with a background of ERTICO – ITS Europe, followed by a description of how the CVIS project is organized, how the CVIS system will work, and a presentation of privacy, security and identification, both in general and in CVIS. After this follows the analysis and the report is finally wrapped up with conclusions and recommendations.</p><p>Why this is an important topic to highlight and discuss and the reason being for this master thesis, is because there is a clear need within the CVIS consortium to harmonise these topics. As it is today, different persons and different sub-projects have different views and opinions on what needs to be identified for example. This needs to be harmonised in order for everyone to know what is being developed, but also, and much more importantly, to in the end get acceptance for the CVIS system. If people do not feel they can trust the system, if they feel it is not secure or that it violates their privacy, they will not use it, even if it has been proved the technique works.</p><p>The key question discussed in the report is what needs to be identified. This is the most important question to solve. There must be very good reasons and consensus why a certain entity is to be identified, otherwise identification of that entity will always be questioned. This also links very tightly with privacy.</p><p>The objective of this master thesis is to bring forward this critical question about identification, to highlight different reasons for identifying or not identifying different entities and to get the discussion started.</p><p>Finally, the main conclusions and recommendations on what to actually identify is the vehicle and the different parts in the central sub-system. The best technique would be by using single sign on with a very strong encryption, for example random numbers, that will be handle by a new node Identification Management Centre or that it will be a part of the Host Management Centre. To ensure privacy in the system, the single sign on mechanism should be combined with the approach of using pseudonyms when communicating in the CVIS system.</p>

This study assesses how to measure and evaluate performance impacts of IntelligentTransport Systems (ITS) in the transport chain.The importance of transportation in global trade has increased significantly in the lastdecades. Cost pressure, rising customer demand for sophisticated logistics services, sustainabilityand security as well as safety issues have boosted the need for more efficient,effective and differentiated transport operations. Intelligent Transport Systems werefound to have the potential to address these challenges in the transport chain. However,due to the novelty of the technology both ITS developers and users face huge uncertaintyabout the performance impacts of ITS. Evaluating ITS in the transport chain beforethe rollout based on concrete measures is likely to reduce the uncertainty involvedin ITS developments and enhance the adoption rate of the new technology. The increasingnumber of ITS projects, like the Secure Intermodal Transport Systems at VolvoTechnology, create a need for a structured approach to measure and evaluate ITS.A literature review concerning the characteristics of the transport industry, technologyadoption, ITS and performance measurements served as a basis for the empirical studyin which 8 semi-structured interviews with different stakeholders in the transport industrywere conducted in order to find out how the performance impacts of ITS are perceivedin the industry and how they could possibly be assessed. The focus groupmethod was used to validate and apply the findings from the interview study to a GeofencingITS-service.The study has confirmed that the concept of ITS is still an emerging phenomenon in thetransportation industry. There is no common understanding of ITS among researchersand practitioners in the transport industry and still a lack of knowledge regarding theperformance impacts of ITS. Even though it could be found that ITS leverages mainlythe service level that can be offered to the customer and that they increase the efficiencyin the back office, the great variety of ITS-services calls for an individual assessment.Structuring the assessment into the phases of measurement design, implementation anduse of the measures facilitates this process. For the different phases a set of activitiescritical for a successful assessment of ITS have been identified. Despite its usefulnessfor mitigating the uncertainty related to the new technology, the focus group validationuncovered that a comprehensive measurement for ITS is not appropriate from the outset,but should be assessed based on the cost of the measurement, the ITS project priority,the customer relations as well as the hierarchical structure in the provider firm.

This master thesis is to highlight the importance of what needs to be identified in the CVIS system, how this could be done, how different techniques affect privacy and security and how the privacy and security mechanisms can be improved for the whole system. The report starts with a background of ERTICO – ITS Europe, followed by a description of how the CVIS project is organized, how the CVIS system will work, and a presentation of privacy, security and identification, both in general and in CVIS. After this follows the analysis and the report is finally wrapped up with conclusions and recommendations. Why this is an important topic to highlight and discuss and the reason being for this master thesis, is because there is a clear need within the CVIS consortium to harmonise these topics. As it is today, different persons and different sub-projects have different views and opinions on what needs to be identified for example. This needs to be harmonised in order for everyone to know what is being developed, but also, and much more importantly, to in the end get acceptance for the CVIS system. If people do not feel they can trust the system, if they feel it is not secure or that it violates their privacy, they will not use it, even if it has been proved the technique works. The key question discussed in the report is what needs to be identified. This is the most important question to solve. There must be very good reasons and consensus why a certain entity is to be identified, otherwise identification of that entity will always be questioned. This also links very tightly with privacy. The objective of this master thesis is to bring forward this critical question about identification, to highlight different reasons for identifying or not identifying different entities and to get the discussion started. Finally, the main conclusions and recommendations on what to actually identify is the vehicle and the different parts in the central sub-system. The best technique would be by using single sign on with a very strong encryption, for example random numbers, that will be handle by a new node Identification Management Centre or that it will be a part of the Host Management Centre. To ensure privacy in the system, the single sign on mechanism should be combined with the approach of using pseudonyms when communicating in the CVIS system.

With perpetual strains on resources, road agencies need to develop network-level decision-making frameworks to ensure optimum resource allocation. This is especially true for incident management services and in particular variable message signs (VMS), which are relatively immature disciplines compared to traditional road engineering. The objective of incident management and VMS is to minimise the safety, efficiency, reliability and environmental impacts of incidents on the operations of the transport system. This may be achieved by informing travellers of the incidents so they can adapt their behaviour in a manner that reduces community impacts, such as lateness and the associated vehicle emissions, unreliability of travel times, as well as secondary accidents due to incidents. Generally, road authorities do carry out needs assessments, but qualitatively in many cases. Therefore, this masters research presents a framework that is systematic, quantitative and relatively easy to implement. In order to prioritise VMS infrastructure deployment, a risk management approach was taken that focuses on minimising the impacts on, and costs to the community. In the framework and case study conducted, safety, efficiency and reliability, and environmental impacts are quantified using an economic risk management approach to determine an overall risk score. This score can be used to rank road sections within the network, indicating the roads with the highest risk of incident network impacts and therefore the roads with the highest need for intervention. A cost-effectiveness based risk-reduction ranking can then be determined for each incident management treatment type, comparing the net risk with treatment to that without treatment, and dividing by the net present value of deployment. The two types of ranking, pure risk and cost-effectiveness based risk reduction, will help to minimise the network impacts on the community and optimise resource allocation.

Background: Due to the rapid development of globalization, increased competition has led to significant market changes. Enterprise resource planning (ERP) has been a crucial part of gaining competitive advantage, but implementing an ERP system have shown to be complicated and expensive. Hence, understanding the critical success factors (CSF) when implementing an ERP system is vital as it can provide a successful implementation. Previous research has mainly focused on the general perspective, whereas the user perspective towards the CSFs has been lacking. This thesis complements the research by concentrating on the operational user and their perception towards the importance of each CSF as well as in which implementation phase each CSF is essential.  Purpose: The purpose of this study is to investigate the CSFs of an ERP implementation from a user perspective and why these are important in different implementation phases.  Method: As this study adopted a qualitative approach, a multiple case study of three case companies was conducted with an inductive research approach. The empirical data have been gathered through semi-structured interviews. A theoretical framework has been used for the literature in this study. The empirical data was then analyzed and compared to the literature. By analyzing the data in a within-case analysis before combining the findings in a cross-case analysis, it enabled the possibility to compare and derive a conclusion covering all case companies.  Conclusion: The results of this thesis shows that users from the case companies believed that more users should be involved in the ERP implementation. Furthermore, two case companies believed each 13 CSFs to be critical in an ERP implementation, whereas one case company believed 12 CSFs to be important. Where the CSFs ‘minimum customization', ‘education and training,' ‘technical possibilities' and ‘performance measurement' were perceived to be essential in this study compared to previous research when users did not perceive these CSFs as important. Lastly, a first attempt at grouping the CSF with the phases of an ERP implementation was made. The findings from the cross-case analysis revealed that there is a shared perception across some of the companies of why the CSFs are essential concerning the specific phases.