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Senast publicerade

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    Integrating Risk into Cost-based Sourcing Decisions A Case Study at Volvo Buses
    (2026) Thor, Fredrik; Torberntsson, Joel
    As competition intensifies in the automotive industry and uncertainties continue to grow, purchasing organizations face increasing pressure to balance cost efficiency with supply chain resilience. At Global Buses Purchasing (GBP) within Volvo buses, these two priorities have been identified as central strategic objectives. However, no practical tool currently exists that integrates supplier-related risks into cost-based sourcing decisions. Cost and risk are treated as parallel but separate dimensions where risk is discussed in governance forums without directly influencing the cost figures used to compare supplier quotations. This thesis investigates how supplier-related risks can be more systematically incorporated into the sourcing process at GBP. The study was conducted as a qualitative case study, drawing primarily on semi-structured interviews with 34 respondents across GBP and Prevost, supported by analysis of internal assessment resources and documentation, and an Analytical Hierarchy Process-based (AHP) online survey distributed to internal stakeholders within GBP. The empirical findings reveal a consistent pattern in which risk assessments serve primarily as screening mechanisms rather than integrated inputs to cost-based supplier comparisons. Building on these findings, the thesis develops a risk-adjusted Total Landed Cost (TLC) model that translates a pre-existing risk assessment tool into a risk index which is applied as a multiplier to the baseline TLC. This will allow sourcing alternatives to be compared on a consolidated economic basis within GBP’s existing governance structure. The thesis thereby contributes a structured empirical account of how risk is currently managed within GBP’s sourcing process and proposes a pragmatic model for integrating risk directly into cost-based supplier comparisons in a way that is transparent, usable and compatible with GBP’s existing resources and governance structures.
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    Kartläggning av utmaningar inom svenskt lantbruk En behovsanalys grundad i nära kontakt med svenska lantbrukare och deras utmaningar inom anställningsprocesser
    (2026) Lindgren, Karin; Hammargren , Lukas; Johansson, Josefine; Wallinder , Klara; Ekström, Oskar; Rostedt, Lucas
    The Swedish farmers of today face many different challenges in their daily work. These challenges affect not only individual farms, but also society at large. This report investigates the current challenges faced by Swedish agriculture by mapping farmers’ needs as a foundation for concept development. The report is based on a detailed study consisting of a review of the literature, interviews, survey data, and field observations. Together, these methods enabled a broad mapping of key problem areas and their interrelationships. The findings reveal several significant challenges, including an ageing workforce, a shortage of skilled labour, a high incidence of work-related injuries, mental health issues, and persistent liquidity constraints. Additionally, deficiencies were identified in communication and supervisory systems among farmers, authorities, and certifying bodies. Overall, the results indicate that administrative challenges represent a relevant area of concern within Swedish agriculture. Through the mapping and prioritisation process, recruitment and hiring processes emerged as a recurring and significant problem area. This area was identified as one of the larger issues, with clear potential for improvement. Several concepts were developed within four main areas: introduction to the labour market, recruitment tools, education, and increased exposure to agriculture. From the concepts developed within these areas, summer jobs in agriculture and increased industry knowledge among recruiters were considered particularly relevant for further investigation.
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    Uncertainty-aware weather-related road surface condition classification
    (2026) Bergman, Jakob; Kulaglic, Erman
    Autonomous vehicles rely on collaborative systems for decision-making during operation, one of which is machine learning algorithms. Using cameras and sensors mounted on the car, these algorithms can analyse captured data to interpret surroundings, enabling it to make informed and safe driving decisions. The algorithms are trained on data gathered in the field. In real-time use, samples that do not belong to the training distribution, referred to as Out-Of-Distribution (OOD) samples, can occur, which the algorithm then confidently uses to make driving decisions. In a worst-case scenario, this can lead to injuring passengers or property. The primary objective of this thesis is to implement and analyse methods for detecting OOD samples in a road surface condition model. The machine learning algorithm was a convolutional neural network, trained to detect dry, wet and snowy road surface conditions. The methods used to detect samples were Maximum Softmax Probability, Energy-Based OOD detection, Outlier Exposure, Virtual Outlier Synthesis, Rectified Activations, Deep Nearest Neighbour, and Virtual Logit Matching. Evaluation of the methods involved three datasets - Cars, Slush and Glare - constructed from the same database as the training data, thus considered as near OOD datasets. Additionally, two publicly available datasets, CIFAR-10 and Texture were used as far OOD datasets. Deep Nearest Neighbour and Virtual Logit Matching performed the best, achieving near-perfect results, when evaluated on the far OOD datasets. When tested on the cars dataset, Virtual Logit Matching exhibited a notable deviation in performance compared to the other methods, although it still performed poorly. On the rest of the near OOD data, the detectors did not outperform the two baseline detectors, Energy-Based OOD detection and Maximum Softmax Probability.
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    Structural Analysis of Support Structures for Industrial Cooking Pans A Finite Element Study of the GS23 Series
    (2026) Vatsöy, Emil
    This study presents structural analyses of the right-side support structures used in the GS23 series of industrial cooking pans developed by Steeltech. The analyses were performed using the finite element method (FEM) to assess stress, deformations, and structural stability under representative static load cases. Two support structure variants were considered, corresponding to pan sizes of 150-300 L and 50- 100 L, respectively. The results show that both structures are conservatively dimensioned, with all stress levels remaining below the material yield strength of 252 MPa. The safety factors were approximately 3.2 for the 150-300 L structure and 3.6 for the 50-100 L structure. The most critical region in both designs was identified at the motor mounting hole. The stress levels always remained within acceptable limits. A linear eigenvalue buckling analysis showed high load multiplier factors, suggesting that both structures are far from their critical buckling loads. The maximum total deformation was small in all load cases and is not expected to affect performance. A thickness sensitivity study showed that the 150-300 L structure is sensitive to thickness reductions of rectangular hollow sections (RHS) beams below 2 mm, where stresses increase significantly. The 50-100 L structure is less sensitive and shows no significant increase in stress when the RHS beam thickness is reduced from 3 mm to 1.5 mm. This suggests potential for material savings. The current designs provide sufficient structural performance under the considered load cases. While optimisation potential exists through thickness reduction, greater improvements may be achieved through design modifications aimed at manufacturing efficiency.