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Senast inlagda
Elektrifiering av regionala lastbilstransporter Drivkrafter, hinder och möjligheter
(2026) Hauge, Thias
Klimatförändringar beskrivs ofta som en av vår tids största globala utmaningar. För att skapa
en grönare framtid behöver vi anpassa och förändra de industrier som skadar vår planet. För
att bekämpa klimatförändringarna har Europeiska unionen introducerat paketet ”Fit for 55”,
med målet att minska nettoutsläppen med minst 55% till år 2030. Transportsektorn är en av de
största bovarna bakom den globala uppvärmningen och står för en stor andel av
växthusgasutsläppen. Detta innebär att sättet vi transporterar varor över världen på måste
förändras på flera sätt. Genom intressentanalys och kvalitativa intervjuer syftar denna studie
till att ge en bild av hur olika intressenter är beroende av varandra. Studien fokuserar på
problemen, för- och nackdelarna med elektrifiering av regionala tunga lastbilstransporter.
Höga investeringskostnader, brist på laddinfrastruktur och osäkerhet kring framtiden leder till
en ovilja att investera i ellastbilar. Resultaten visar att även om intresset för elektrifiering av
lastbilar är stort och tekniken har utvecklats långt, saknas i dagsläget incitament för de
enskilda aktörerna att investera.
Optimized Internal Logistics for Non-Standard Parts
(2026) Arkavazi, Mohamed; Kitevski, Filip
This report focuses on optimizing internal logistics for non-standard parts in Volvo Trucks’ production plant in Tuve. More specifically, the customer adapted (CA) materials are emphasized. The thesis aims to identify the issues in the current logistics flow and propose solutions to improve material availability, reduce lead times, and support production goals. Furthermore, the research covers the challenges induced by having a logistics flow with low-volume and customer specific components.
By analyzing the root causes of delays, errors, and poor coordination, the study identifies areas for improvement, such as better information flow, system support, and quality control measures. Key findings suggest that addressing underlying issues like unclear material specifications, manual handling, and weak communication between departments can significantly improve the robustness of the CA material flow, leading to improved performance and better customer satisfaction. The proposed solutions include implementing real-time scanning systems, enhancing buffer management, and improving coordination across departments. This report shows the importance of adapting logistics systems to handle a high variability and ensure
on-time deliveries of non-standard parts in a complex manufacturing environment.
Analyzing Order-to-Cash Using Process Mining A Case Study in Collaboration with Paulig
(2026) Adolfsson, Jens; Saleh, Dilan
Organizations increasingly rely on digital data to understand and improve their
business processes. Process Mining is a data-driven approach that uses event logs
from information systems to visualize actual process behavior and identify inefficiencies. This thesis investigates how Process Mining can be applied in practice to analyze the Order-to-Cash process, with a particular focus on the use of pre-defined
reference process models and backward-looking analytical techniques.
The study is conducted as a case study in collaboration with Paulig, using Infor’s
Process Mining solution integrated with the ERP system M3. Through a combination of Process Mining analysis, interviews, workshops and shadowing sessions, the thesis evaluates how well a pre-defined industry-specific process model reflects an
organization’s actual Order-to-Cash process and how inefficiencies and bottlenecks
can be identified. The reference process model proved to be a strong baseline for
understanding the overall process structure, while the analysis revealed bottlenecks
related to master data issues that cause unnecessary manual interventions and longer
cycle times.
The results demonstrate that Process Mining can support improvements in both
administrative processes and physical logistics flows by revealing systematic issues
that are difficult to detect through traditional qualitative methods alone. The study
also highlights the importance of combining Process Mining insights with domain
knowledge and stakeholder involvement to correctly interpret results.
A WGAN Based Method for Stochastic Filtering
(2026) Axelsson, Joel
The problem of extracting information about a state from incomplete noisy measurement
is knowns as a ”filtration problem” in the field of stochastic processes.
In this thesis the information extracted corresponds to an estimate of the posterior
conditional distribution of a stochastic process. Recent development in generative
adversarial networks allows for such filtering problems to be solved using a network
class called the WGAN. In this thesis a method of implementing the WGAN for
filtration is investigated. The method is tested for a linear SDE scaled in dimensions
and on a non-linear SDE of singular dimension. both examples were observed
linearly with additive Gaussian noise.
The method was benchmarked against filtering methods not based on machine learning.
In summary it can be stated the the results indicated that some merit to the
method could be deducted. It was however the result that the method was outperformed
by most of its peers. An investigation into where the method could be
improved was conducted.
Updating Paulings rules using a machine learning approach
(2026) Gustavsson, Pontus
Oxides are an important family of materials that have an extremely wide range of
applications in for example semiconductors, pigments and catalysis. It is therefore
important to have a solid understanding of these ubiquitous materials. In 1929
Linus Pauling proposed five rules for oxide stability that are widely used. These
rules are however not good enough to describe oxide stability as only a fraction of
stable oxides fulfil them. In this project a machine learning approach was used to
attempt to find better rules based on the composition of oxides. This was done
by training a set of autoencoders and analysing the latent spaces of these models
by sampling new compositions from the models. Three different autoencoders were
trained and based on the results, three new rules of thumb are proposed; Oxides
containing only reactive non-metals are in general unstable, metals favour stability
and heavier cations favour stability.