Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

In the literature there is no consensus regarding the meaning of the term digital transformation. Therefore, the purpose of this paper is to explore the differing views of the meaning of digital transformation. A case study has been conducted in collaboration with four Swedish manufacturing companies. The results shows that digital transformation can have different meaning within a company and the main challenge when performing digital transformation is knowledge. This study is the first investigation in a research project focusing on coordination of digital transformation. Therefore, is the underlaying goal to identify how the participating companies in the research project describe digital transformation in comparison with the literature. The research intention is not to define digital transformation rather to explore differing views of digital transformation and highlighting similarities and difference in comparison with the literature reviewed. The findings are practically relevant for manufacturing companies by highlighting differing views of digital transformation and in the creation of a common language within a company. 

In order to support manufacturing companies in their digital transformation, challenges and views of the term 'digital transformation' need to be identified since digital transformation is considered a source of competitive advantages. Therefore, this paper aims to explore the challenges and differing views of digital transformation in the manufacturing industry. A case study was conducted in collaboration with four Swedish manufacturing companies. The results were then mapped into categories of three dimensions (people, process and technology), indicating that digital transformation can have different meanings within a company. We conclude that the term 'digitalisation' is more frequently used in the manufacturing industry than 'digital transformation' and identified challenges relate to lack of best practice for digital transformation, degree of standardisation and therefore affects the workload and limits the possibilities of transferring technical solutions between factories. Our findings are relevant to operations managers and other interested in digital transformation. 

Digital transformation of production systems is a challenging task that demands radical responses from existing organizations. During the digital transformation of productions systems tensions occur that need to be managed and the purpose of this paper is to identify paradoxes in the digital transformation of production systems. Paradox theory has been applied as an analytical framework when identifying digital transformation paradoxes and tensions. A case study has been conducted and two manufacturing companies’ digitalization projects have been studied and analyzed in combination with data from workshops around digital transformation. The results were mapped into four types of paradoxes: organizing, performing, belonging, and learning. We conclude that the identified tensions are intertwined, and a major tension is the degree of standardization of technologies (standardization vs customization) and a more agile way of working (learning by doing vs learning before) doing is a trend within the digital transformation of production system. Our findings are relevant to operations managers and others interested in tensions during the digital transformation of production systems.

This thesis presents a distributed, collaborative simultaneous localization and mapping (C-SLAM) approach for embedded systems aiming to support search and rescue missions. A distributed C-SLAM system provides the robustness and flexibility to explore unpredictable environments reliably, even in the case of an unexpected loss of one or multiple of the robots. The approach is demonstrated on the Turtlebot4 and uses ROS2 as the development platform and assumes known initial positions of the robots for the map merging process. The robots in the C-SLAM system constantly share their progress to guarantee the workload can be distributed across all robots and a task can be completed even if individual robots fail. Our proposed system's efficiency is measured as the time needed to explore a given unknown environment. As a result, the C-SLAM system achieves an average of approximately 40 % improvement compared to a single robot system, even with the loss of robots during exploration. This emphasizes the robustness of the system but also the relevance and need for a better navigation system to take full advantage of the multi-robot nature of a C-SLAM system.

Many test prioritisation techniques have been proposed in order to improve test effectiveness of Continuous Integration (CI) pipelines. Particularly, diversity-based testing (DBT) has shown promising and competitive results to improve test effectiveness. We report on a case study considering the CI pipeline of Axis Communications in Sweden. We compared three different prioritisation approaches (i.e., diversity, failure history and time) in terms of their impact on coverage, failure detection rates and reduction on test execution time. Our results reveal that DBT is the best candidate to provide feature coverage, whereas failure rate prioritisation yields better failure coverage. Time-based prioritisation is not a reliable approach to provide cost-effective testing. Moreover, DBT would allow stakeholders to receive quick feedback on many combinations of integrated features to verify their code changes. Our participants report that developers are mainly interested in: (i) receiving quick feedback on a high combination of integrated features to verify their code changes, and (ii) associate their test suites to confidence scores representing the risk of missing failures given that fewer tests are executed.

Convolutional Neural Networks (CNNs) have become integral in safety-critical applications, thus raising concerns about their fault tolerance. Conventional hardwaredependent fault tolerance methods, such as Triple Modular Redundancy (TMR), are computationally expensive, imposing a remarkable overhead on CNNs. Whereas fault tolerance techniques can be applied either at the hardware level or at the model levels, the latter provides more flexibility without sacrificing generality. This paper introduces a model-level hardening approach for CNNs by integrating error correction directly into the neural networks. The approach is hardwareagnostic and does not require any changes to the underlying accelerator device. Analyzing the vulnerability of parameters enables the duplication of selective filters/neurons so that their output channels are effectively corrected with an efficient and robust correction layer. The proposed method demonstrates fault resilience nearly equivalent to TMR-based correction but with significantly reduced overhead. Nevertheless, there exists an inherent overhead to the baseline CNNs. To tackle this issue, a cost-effective parameter vulnerability based pruning technique is proposed that outperforms the conventional pruning method, yielding smaller networks with a negligible accuracy loss. Remarkably, the hardened pruned CNNs perform up to 24% faster than the hardened un-pruned ones.

The reliability of Artificial Neural Networks (ANNs) has emerged as a prominent research topic due to their increasing utilization in safety-critical applications. Long Short-Term Memory (LSTM) ANNs have demonstrated significant advantages in healthcare applications, primarily attributed to their robust processing of time-series data and memory-facilitated capabilities. This paper, for the first time, presents a comprehensive and fine-grain analysis of the resilience of LSTM-based ANNs in the context of gait analysis using fault injection into weights. Additionally, we improve their resilience by replacing faulty weights with zero, enabling ANNs to withstand environments that are up to 20 times harsher while experiencing up to 7 times fewer critical faults than an unprotected ANN.

Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be utilized in various applications due to their capability to learn how to solve complex problems. Over the past decade, rapid advances in ML have presented Deep Neural Networks (DNNs) consisting of a large number of neurons and layers. DNN Hardware Accelerators (DHAs) are leveraged to deploy DNNs in the target applications. Safety-critical applications, where hardware faults/errors would result in catastrophic consequences, also benefit from DHAs. Therefore, the reliability of DNNs is an essential subject of research. In recent years, several studies have been published accordingly to assess the reliability of DNNs. In this regard, various reliability assessment methods have been proposed on a variety of platforms and applications. Hence, there is a need to summarize the state-of-the-art to identify the gaps in the study of the reliability of DNNs. In this work, we conduct a Systematic Literature Review (SLR) on the reliability assessment methods of DNNs to collect relevant research works as much as possible, present a categorization of them, and address the open challenges. Through this SLR, three kinds of methods for reliability assessment of DNNs are identified, including Fault Injection (FI), Analytical, and Hybrid methods. Since the majority of works assess the DNN reliability by FI, we characterize different approaches and platforms of the FI method comprehensively. Moreover, Analytical and Hybrid methods are propounded. Thus, different reliability assessment methods for DNNs have been elaborated on their conducted DNN platforms and reliability evaluation metrics. Finally, we highlight the advantages and disadvantages of the identified methods and address the open challenges in the research area. We have concluded that Analytical and Hybrid methods are light-weight yet sufficiently accurate and have the potential to be extended in future research and to be utilized in establishing novel DNN reliability assessment frameworks.

The superior performance of Deep Neural Networks (DNNs) has led to their application in various aspects of human life. Safety-critical applications are no exception and impose rigorous reliability requirements on DNNs. Quantized Neural Networks (QNNs) have emerged to tackle the complexity of DNN accelerators, however, they are more prone to reliability issues.In this paper, a recent analytical resilience assessment method is adapted for QNNs to identify critical neurons based on a Neuron Vulnerability Factor (NVF). Thereafter, a novel method for splitting the critical neurons is proposed that enables the design of a Lightweight Correction Unit (LCU) in the accelerator without redesigning its computational part.The method is validated by experiments on different QNNs and datasets. The results demonstrate that the proposed method for correcting the faults has a twice smaller overhead than a selective Triple Modular Redundancy (TMR) while achieving a similar level of fault resiliency. 

Deep Neural Networks (DNNs) and their accelerators are being deployed ever more frequently in safety-critical applications leading to increasing reliability concerns. A traditional and accurate method for assessing DNNs' reliability has been resorting to fault injection, which, however, suffers from prohibitive time complexity. While analytical and hybrid fault injection-/analyticalbased methods have been proposed, they are either inaccurate or specific to particular accelerator architectures. In this work, we propose a novel accurate, fine-grain, metric-oriented, and accelerator-agnostic method called DeepVigor that provides vulnerability value ranges for DNN neurons' outputs. An outcome of DeepVigor is an analytical model representing vulnerable and non-vulnerable ranges for each neuron that can be exploited to develop different techniques for improving DNNs' reliability. Moreover, DeepVigor provides reliability assessment metrics based on vulnerability factors for bits, neurons, and layers using the vulnerability ranges. The proposed method is not only faster than fault injection but also provides extensive and accurate information about the reliability of DNNs, independent from the accelerator. The experimental evaluations in the paper indicate that the proposed vulnerability ranges are 99.9% to 100% accurate even when evaluated on previously unseen test data. Also, it is shown that the obtained vulnerability factors represent the criticality of bits, neurons, and layers proficiently. DeepVigor is implemented in the PyTorch framework and validated on complex DNN benchmarks.

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this context, besides energy efficiency and performance, reliability plays a crucial role since a system failure can jeopardize human life. As with any other device, the reliability of hardware architectures running DNNs has to be evaluated, usually through costly fault injection campaigns. This paper explores approximation and fault resiliency of DNN accelerators. We propose to use approximate (AxC) arithmetic circuits to agilely emulate errors in hardware without performing fault injection on the DNN. To allow fast evaluation of AxC DNN, we developed an efficient GPU-based simulation framework. Further, we propose a fine-grain analysis of fault resiliency by examining fault propagation and masking in networks.

Trigger-Action Platforms (TAPs) empower applications (apps) for connecting otherwise unconnected devices and services. The current TAPs like IFTTT require trigger services to push excessive amounts of sensitive data to the TAP regardless of whether the data will be used in the app, at odds with the principle of data minimization. Furthermore, the rich features of modern TAPs, including IFTTT queries to support multiple trigger services and nondeterminism of apps, have been out of the reach of previous data minimization approaches like minTAP. This paper proposes LazyTAP, a new paradigm for fine-grained on-demand data minimization. LazyTAP breaks away from the traditional push-all approach of coarse-grained data over-approximation. Instead, LazyTAP pulls input data on-demand, once it is accessed by the app execution. Thanks to the fine granularity, LazyTAP enables tight minimization that naturally generalizes to support multiple trigger services via queries and is robust with respect to nondeterministic behavior of the apps. We achieve seamlessness for third-party app developers by leveraging laziness to defer computation and proxy objects to load necessary remote data behind the scenes as it becomes needed. We formally establish the correctness of LazyTAP and its minimization properties with respect to both IFTTT and minTAP. We implement and evaluate LazyTAP on app benchmarks showing that on average LazyTAP improves minimization by 95% over IFTTT and by 38% over minTAP, while incurring a tolerable performance overhead. 

Trigger-Action Platforms (TAPs) enable applications to integrate various devices and services otherwise unconnected. Recent features of TAPs introduce additional sources of data such as queries in IFTTT. The current TAPs, like IFTTT, demand that trigger and query services transmit excessive amounts of user data to the TAP. To limit the data to what is actually necessary for the execution to comply with the principle of data minimization, input services should send no more than the necessary data. LazyTAP proposes a new paradigm of data minimization by construction in TAPs, introducing a novel perspective for data collection from input services. While the existing push-all approach of TAPs entails coarse-grained data over-approximation, LazyTAP pulls input data on-demand at the level of attributes, once accessed by the app execution. Thanks to the fine granularity provided by LazyTAP, multiple trigger and query services can be naturally minimized while the behavior of app executions is preserved. In addition, a great benefit of LazyTAP is being seamless for third-party app developers. By leveraging laziness, LazyTAP defers computation and proxies objects to load necessary remote data behind the scenes. Our evaluation study on app benchmarks shows that on average LazyTAP improves minimization by 95% over IFTTT and by 38% over minTAP, with a tolerable performance overhead. This poster goes into further details about LazyTAP and elaborates on its prototype implementation. 

The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SGi) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.

AI methods referred to as interpretable are often discredited as inaccurate by supporters of the existence of a trade-off between interpretability and accuracy. In many problem contexts however this trade-off does not hold. This paper discusses a regression problem context to predict flight take-off delays where the most accurate data regression model was trained via the XGBoost implementation of gradient boosted decision trees. While building an XGB-CBR Twin and converting the XGBoost feature importance into global weights in the CBR model, the resultant CBR model alone provides the most accurate local prediction, maintains the global importance to provide a global explanation of the model, and offers the most interpretable representation for local explanations. This resultant CBR model becomes a benchmark of accuracy and interpretability for this problem context, and hence it is used to evaluate the two additive feature attribute methods SHAP and LIME to explain the XGBoost regression model. The results with respect to local accuracy and feature attribution lead to potentially valuable future work. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.

This report presents a research study plan on human subjects – the influence of stress and alcohol in simulated traffic situations under an H2020 project named SIMUSAFE. This research study focuses on road-users’, i.e., car drivers, motorcyclists, bicyclists and pedestrians, behaviour in relation to retrospective studies, where interaction between the users are considered. Here, the study includes sample size, inclusion/exclusion criteria, detailed study plan, protocols, potential test scenarios and all related ethical issues. The study plan has been included in a national ethics application and received approval for implementation.

I dagens digitala samhälle är företag beroende av internet för att leverera tjänster till sinakunder. Det medför att de riskerar att bli utsatta för cyberattacker som kan hota derasverksamhet. Hotdetektering med logganalys innebär insamling och analys av loggdata för attidentifiera anomalier som indikerar potentiella hot. I detta examensarbete jämförs två av demest populära logganalysverktygen för att avgöra vilket av dem som är lämpligast förhotdetektering. Verktygen Graylog och ManageEngine Eventlog Analyzer jämförs utifrånvilka hot de kan detektera, hur komplexa de är, hur mycket belastning de utgör påvärdenheten och vilka kostnader verktygen har. Syftet med detta examensarbete är attunderlätta valet av logganalysverktyg som kan användas i hotdetekteringssyfte. Arbetet bestårav en litteraturstudie och en laboration. För att utvärdera verktygen har det i en litteraturstudiesamlats in information om vilka hot verktygen kan detektera, vilka kostnader verktygen haroch vilka resurskrav verktygen har. I en laboration har verktygens komplexitet utvärderats. Ilaborationen utvärderades även verktygens belastning på värdenhetens CPU och RAM-minne.Resultatet visar att ManageEngine Eventlog Analyzer är det lämpligaste logganalysverktygetför hotdetektering därför att den detekterar fler hot i jämförelse med Graylog, samtidigt somdet är mindre komplext att använda på grund av den tillgängliga tekniska supporten.

Aim: This thesis aims to investigate how businesses within traffic safety and temporary traffic management can apply visual artificial intelligence (AI) to achieve safer, more environmentally friendly, and cost-effective operations.

Research questions: “How can the application of visual artificial intelligence be used for identifying temporary protective devices on road construction sites?”; “How can the application of visual artificial intelligence be used for identifying personal protective equipment on road construction sites?”; and “What changes can occur regarding the company's operational safety, costs, and carbon emissions with the application of visual artificial intelligence on road construction sites?”.

Methodology: This study was conducted using an abductive approach and a mixed research design. A theoretical framework based on scientific articles and an empirical data collection consisting of a case study, interviews with employees, and calculations based on data from the case company were analyzed to answer the study’s research questions.

Conclusion: By addressing the challenges and leveraging the opportunities that technology offers, AI cameras can be a valuable tool in promoting a safer and more sustainable work environment on road construction sites, as well as for more favorable operations for companies. By applying visual artificial intelligence to identify correct usage of personal protective equipment on road construction sites and combining it with manual inspections, worker safety can be improved, and proper use of personal protective equipment (PPE) can be ensured. Finally, the study indicates that the application of visual artificial intelligence on road construction sites has the potential to improve the safety of workers, road users, and civilians nearby, reduce costs for companies and decrease environmental impacts by reducing carbon emissions.

This thesis aims to investigate and enhance the accessibility of a healthcare application, with a specific focus on the design of menus and functions. The work encompasses a comprehensive literature review, needs analysis, market analysis, and user tests. Additionally, theories and prior research on accessibility and cognitive load are discussed, and how these aspects can be integrated into the design process to create a more user-friendly and inclusive healthcare application.

The study involves a detailed analysis of the design process, from sketches and wireframes to the final Hi-Fidelity prototype. The results from this process, along with insights from the needs analysis, are used to improve the design and usability of the application. Improvements include clearer menus and functions, as well as the introduction of a "readability" feature to make the content more accessible for older users.

Future research should continue to explore how digital healthcare applications can be designed to reduce cognitive load, especially considering the expected significant increase in the global population over the age of 60 by 2050. With the insights from this work, we can create more effective and inclusive digital healthcare solutions.

This study is a bachelor thesis in the field of Information design with focus on accessibility.

Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

The following thesis report is the result of a degree project at the basic C-level in the innovation and product design engineering program at Mälardalen University for Eskilstuna kommun. The degree project is about a product development process to develop a gutter well cover solution intended to improve the method for lifting gutter well covers.

The reader of this report will learn how scientific product development methods were used to develop a final gutter well cover tool prototype and what moments the processes included. The product development tools that were used to enable a final prototype were contributed by the SolidWorks software to construct CAD models that formed the basis for various test constructions and the final prototype.

The result of the thesis report consists of a newly developed gutter well cover opening tool designed to effectively open spring-loaded based gutter well covers.

This master’s thesis aims to contribute to the design and innovation management literature by exploring to understand an alternative shared social kitchen that is people-driven around shared interests, promoting social well-being. This is in order to explore the potential of systemic, regenerative, speculative sustainable design in addressing challenges faced by shared kitchens, while understanding diverse user needs within enabling contexts. Through a design process of ethnography research, frameworks, imperatives and solution finding, the kitchen was explored from different participants’ perspectives and context. The resulting scenario from the ethnography research highlighted technology, food waste, circularity and food system scenarios. The study contributes to future dialogue and understanding of the different processes and perspectives in imaginative shared kitchen using an explorative approach. It is vital to understand the holistic picture of the shared kitchen and not only focus on the functional, environmental and economic aspect, but also on the social development for a more inclusive design.

This master’s thesis explores the role of the urban lifestyle closely linked to the modern urban environment, but the digital era has brought significant changes. The online world has changed the meaning of physical urban space and led to a decline in the quality of life. To address this issue, this report uses a multidisciplinary approach that draws on urban planning and sociology to verify a framework that illustrates how the primary determinant of urban space vitality has shifted from function and aesthetics to socio-spatial interaction. This interaction refers to social needs and people's engagement in a particular space. 

The report presents different spatial and social synergy aspects and proposes a qualitative paradigm based on this concept. Furthermore, the model is tested through an empirical study at Fristadstorget, a town center in Eskilstuna, Sweden.  

The guideline’s principal framework is expected to provide insights into the revitalization of urban public space and previous evaluations of the corresponding designs, providing a theoretical framework that will benefit planners and decision-makers in future projects.

Organizations gather enormous amounts of data and analyze these data to extract insights that can be useful for them and help them to make better decisions. Predictive data analytics is a crucial subfield within data analytics that make accurate predictions. Predictive data analytics extracts insights from data by using machine learning algorithms. This thesis presents the supervised learning algorithm to perform predicative data analytics in Embedded Test System at the Nordic Engineering Partner company. Predictive Maintenance is a concept that is often used in manufacturing industries which refers to predicting asset failures before they occur. The machine learning algorithms used in this thesis are support vector machines, multi-layer perceptrons, random forests, and gradient boosting. Both binary and multi-class classifier have been provided to fit the models, and cross-validation, sampling techniques, and a confusion matrix have been provided to accurately measure their performance. In addition to accuracy, recall, precision, f1, kappa, mcc, and roc auc measurements are used as well. The prediction models that are fitted achieve high accuracy.

This bachelor thesis aims to find a more sustainable and cost-effective solution for a rotation system used for turning walls. The work was carried out by the client Husmuttern AB, which requires a rotation system for rotating wall fixtures in the company's future construction projects in Scandinavia, as well as in Poland and Ukraine.

The current fixture is powered by an electric motor, which the company considers to be a costly and less sustainable solution. Therefore, this work will present a more cost-effective and sustainable solution and address the development process for the proposed design.

The research question for this thesis is: How can an existing electrically driven rotation system be replaced with a manually operated mechanical rotation system while ensuring that the design meets the internal requirements of Husmuttern AB and the external regulations from the Swedish Work Environment Authority regarding load ergonomics?

The work has been done with consideration for cost-effectiveness, ergonomics, and safety. Relevant regulations from the Swedish Work Environment Authority have been reviewed in the course of this work. The work has also focused on developing a concrete proposal where the use of standard components has been prioritized to save resources and result in a cost-effective solution based on available material dimensions from different suppliers.

Computer Aided Design (CAD) has been used to draw and assemble components that the rotation system's design consists in a virtual environment. CAD has been used with the help of Solidworks software to give a closer picture of what the construction might look like, as well as how the components sit and their position and direction.

The work has been completed within the specified time limit and delivers what was specified in the limitations. The result is a new rotation system - a manually operated mechanical rotation system that enables ergonomic and safe work tasks. The report describes the work process, calculations, drawing documents, and a user description of the rotation system's work tasks, thus constituting the delivery of the work.

Efficient production scheduling is important for both maximizing productivity and minimizing costs in manufacturing environments. This thesis presents an approach to optimizing production scheduling using Artificial Intelligence (AI) and Genetic Algorithms (GAs). The primary goal is to develop a generalized solution that can be modified and adapted to the varying needs that different production- or manufacturing lines may have. This research has two main research questions that address the problem at hand. (1) Can Genetic Algorithms be used to optimize a sequence of products in a production line? and (2) How effectively can Genetic Algorithms optimize the sequencing of production tasks in diverse production lines to minimize total order processing times? Through experimentation with various GA configurations the results achieved suggested that GAs were appropriate for sequence optimization. The study demonstrates that GAs can optimize a production line up to almost 42% , which significantly reduced the total processing time. The thesis also highlights the importance of the representation of data, which is essential for the optimization of the sequence. 

 Produktnedläggningsbeslut kan gynna företag på flera områden, inklusive organisatoriska, ekonomiska och strategiska aspekter. Trots dessa fördelar uppstår utmaningar såsom bedömning av finansiell prestation, påverkan på intressenter och behovet av effektiv resursallokering. Detta gör beslutet mer komplext och krävande, vilket ofta möter motstånd från chefer. Trots ämnets vikt finns det en brist på forskning inom området. Av den anledningen undersöks vilka utmaningar, åtgärder och möjligheter som är förknippade med beslutet att lägga ned en produkt i denna studie. För denna studie användes en mixed-method approach, vilket innebar att både kvantitativa och kvalitativa metoder användes för att samla in empiriska data. Både en enkätundersökning och intervjuer med produktchefer inom industriföretag genomfördes för att möjliggöra en holistisk och omfattande undersökning av det studerade ämnesområdet. Den empiriska datan jämfördes sedan med den teori som sammanställts.  PNB kan medföra olika utmaningar relaterade till kunder, externa partners, strategi, ekonomi och organisation. För att hantera dessa utmaningar finns olika åtgärder som chefer inom industriella företag bör vidta, men den viktigaste av dessa är tydlig kommunikation. Det finns också möjligheter som detta beslut kan medföra. Genom att förstå dessa möjligheter kan företag skapa strategier för att maximera de positiva effekterna av PNB och stärka sin position på marknaden. 

Small-and medium-sized enterprises (SMEs) have inherent characteristics, which require specific solutions for improving the sustainability performance of their operations. The purpose of this paper is to increase the knowledge on barriers and enablers for the adoption of sustainable manufacturing by manufacturing SMEs and to provide insights into what enablers can be used to overcome existing barriers. Taking, as a starting point, a systematic literature review, this paper presents a categorization of barriers and enablers for the adoption of sustainable manufacturing by manufacturing SMEs. In total, seven categories for classifying the barriers and enablers for the adoption of sustainable manufacturing within SMEs were identified: organizational, managerial and attitudinal; informational; governmental; financial; training and skills development; market and business context; and technological. Additionally, this study elaborates on what barriers could be mitigated through the enablers. This study found specific enablers with the potential to mitigate a significantly higher number of barriers and referred to them as ‘critical enablers’. SMEs aiming to adopt sustainable manufacturing practices or improve their sustainability performance are encouraged to focus on the enablers in these categories. This paper synthesizes and facilitates interpretation of the existing body of evidence on barriers and enablers for adopting sustainable manufacturing in SMEs. 

Human hands and fingers have been widely studied in different fields, such as animation, biomechanics, ergonomics, rehabilitation, medicine, and robotics. However, since the hands are a highly complex part of the human body capable of developing precise tasks, replicating human hand mechanisms remains challenging and, thus, continues to be an active area. This study focuses on a bioinspired mechanically equivalent finger model. A parametric model was proposed based on the typical architecture of a human finger, allowing adaptation to different anthropometries. A forward kinematic model assesses each phalanx's range of motion (ROM) during flexion-extension and abduction-adduction. A CAD modeling technique based on fused filament fabrication (FFF) is used for easy fabrication, requiring no assembly. The resulting model achieves the desired ROM, offering a simple solution for hand modeling. This bioinspired model aids in training hand exoskeleton robots, accurately mimicking human finger mechanics for various applications, including rehabilitation and prosthetics. This model helps understand complex hand mechanisms and holds potential for robotics and related fields.

This thesis explores the integration of machine learning and computer vision technologies to automate calorie intake tracking, aiming to revolutionize traditional methods by leveraging object detection techniques. The study focuses on training and evaluating different versions of YOLO object detection model (YOLOv5, YOLOv6, YOLOv7 and YOLOv8) on a custom food dataset. Through experiments and simulated testing, the performance of these models is analyzed in terms of accuracy, speed, and practical usability. Additionally, a FastApi web application is developed to provide a user-friendly interface for capturing and uploading food images, processing them with the object detection model, and storing the daily calorie intake. The results demonstrate the potential of machine learning-driven solutions in addressing the challenges of manual calorie tracking, paving the way for more efficient and accurate dietary management systems. Results indicate that YOLOv5 and YOLOv6 exhibited similar performance, offering a balance between accuracy and speed, with YOLOv6 demonstrating slightly better accuracy. In contrast, YOLOv7-tiny, trained with a smaller image size, showed a significant drop in recall and F1 scores compared to its predecessors. Conversely, YOLOv8 achieved the highest accuracy among the tested models due to architecture improvements and optimizations.

Utilizing edge and cloud computing to empower the profitability of manufacturing is drastically increasing in modern industries. As a result of that, several challenges have raised over the years that essentially require urgent attention. Among these, coping with different faults in edge and cloud computing and recovering from permanent and temporary faults became prominent issues to be solved. In this paper, we focus on the challenges of applying fault tolerance techniques on edge and cloud computing in the context of manufacturing and we investigate the current state of the proposed approaches by categorizing them into several groups. Moreover, we identify critical gaps in the research domain as open research directions. 

Cloud computing is a game-changer model that opens new directions for modern manufacturing. It enables services and solutions that help improve the productivity and efficiency of smart production plants. The main objective of the paper is to provide a summary of the various cloud-based manufacturing services currently being offered to manufacturers or that could be offered in the future. Additionally, the paper aims to discuss the various enabling technologies used to support the integration of cloud manufacturing in the manufacturing industry. Furthermore, the paper categorizes the different services based on their functionalities and maps them to four levels of production such as plant level, production line level, machine level, and process level. The categorization of services and mapping them to appropriate levels in production can enhance efficiency and productivity in the manufacturing industry. The study advances the discussion on cloud-based manufacturing from the types of services and enabling technologies perspective.

In the era of the IoT revolution, applications are becoming ever more sophisticated and accompanied by diverse functional and non-functional requirements, including those related to computing resources and performance levels. Such requirements make the development and implementation of these applications complex and challenging. Computing models, such as cloud computing, can provide applications with on-demand computation and storage resources to meet their needs. Although cloud computing is a great enabler for IoT and endpoint devices, its limitations make it unsuitable to fulfill all design goals of novel applications and use cases. Instead of only relying on cloud computing, leveraging and integrating resources at different layers (like IoT, edge, and cloud) is necessary to form and utilize a computing continuum. The layers’ integration in the computing continuum offers a wide range of innovative services, but it introduces new challenges (e.g., monitoring performance and ensuring security) that need to be investigated. A better grasp and more profound understanding of the computing continuum can guide researchers and developers in tackling and overcoming such challenges. Thus, this paper provides a comprehensive and unified view of the computing continuum. The paper discusses computing models in general with a focus on cloud computing, the computing models that emerged beyond the cloud, and the communication technologies that enable computing in the continuum. In addition, two novel reference architectures are presented in this work: one for edge–cloud computing models and the other for edge–cloud communication technologies. We demonstrate real use cases from different application domains (like industry and science) to validate the proposed reference architectures, and we show how these use cases map onto the reference architectures. Finally, the paper highlights key points that express the authors’ vision about efficiently enabling and utilizing the computing continuum in the future.

The paper presents an approach to solve the software and hardware related failures in edge-cloud environments, more precisely, in cloud manufacturing environments. The proposed approach, called TOLERANCER, is composed of distributed components that continuously interact in a peer to peer fashion. Such interaction aims to detect stress situations or node failures, and accordingly, TOLERANCER makes decisions to avoid or solve any potential system failures. The efficacy of the proposed approach is validated through a set of experiments, and the performance evaluation shows that it responds effectively to different faults scenarios.

The users of cloud services prioritize cost andperformance, but they increasingly demand sustainable practices.Sustainability is no longer a choice for businesses but a strategicimperative that shapes global industries. This paper presents anew system for utilizing the render farms in cloud data centers.The system aims to reduce energy consumption and costs in clouddata centers while maintaining a specific level of performance,particularly when rendering images and videos. The system canbe described as a cloud-based expert system that offers renderingas a service, while considering user preferences for performance,cost, and energy efficiency. The system reads different scenerendering parameters and accordingly chooses the most suitableGPUs that fit the user’s requirements. In other words, thesystem inputs are the scene complexity and user preferences.The output is the optimal GPU for rendering. Scene complexityis determined based on several parameters, such as the numberof frames and polygons, resulting in one scene-related value.The user preferences are also normalized to a preferences-relatedvalue. Then, these values are aggregated to determine the optimalavailable GPU to render the scene at the lowest cost, minimumpossible energy consumption, and highest performance.

Abstract 

Level: Master thesis in Innovation and design , 30 credits

Institution: School of Innovation, Design and Technology, Mälardalen University

Author: Cornelia Alenbring (98/05/24)  

Title: In the search of the student's role in innovation ecosystems

Supervisors: Anders Vikström, Erik Bjurström

MDU examinator:Yvonne Eriksson

Keywords: “ecosystems for innovation”, “eco systems for social innovation”, “social innovation ecosystems”, “quadruple helix” “students- innovation ecosystem” “students role in innovation ecosystems”

Purpose: The purpose of this study is to explore the specific role of students in innovation ecosystems. By gaining a deeper understanding of their role, this research aims to contribute to a more nuanced micro-dynamic perspective on the role of students in innovation ecosystems. This thesis explores conceptualizations and metaphors of innovation ecosystems with the aim of exploring different theorizing in the field, related to the role of students within those. 

Research question: What is the role of students in innovation ecosystems?

Method: The study was based on a qualitative method. The empirical data was collected through semi-structured interviews and observations using focus groups with 5 actors within the public, academia, industry and civil society.

Conclusion: The role of the student is at the heart of the innovation ecosystem metaphor - that of emergence and co-evolution. Rather, the empirical evidence of this thesis concurs that the roles available to students also depend on other actors taking them seriously, and that the reasons for less than optimal exchange between students and other actors deserves further investigation.  It is an interesting observation that students fall between the system perspective’s grand theorizing and pedagogics’ narrower focus on education: - So what is the point of pursuing a research approach where either students or ecosystems are not mentioned? There is an obvious gap in where the abstract level of the system perspective, whilst it can serve as an overview of the concept,  does not specify actors and misses to provide a micro perspective on what is occurring between actors, that could explain how it all happens. Are students rather representatives of the civil society, or of what Powell referred to ‘amphibians’, or representative of nothing but themselves as individuals-in-the-making, as a representative of becoming itself and emergence in itself – through their interaction on a campus which they don’t own, but inhabit? And what do companies really want when they want to be a campus? What is special about a campus and life on a campus? What is the very essence of it? 

Inom byggindustrin uppstår många olika situationer som kan leda till olyckor där en person kan skadas eller avlida. Att utveckla metoder för att förhindra olyckor är därför mycket angeläget. I denna studie undersöker jag möjligheterna att skapa en applikation, anpassad för autonoma displaydatorer, för att öka säkerheten runt arbetsmaskiner genom att med objektdetektering och kameraövervakning skapa situationsmedvetenhet runt maskinen. Objektdetekteringen används för att hitta alla personer och koner som är med på bilden från kameran. Genom att detektera utställda trafikkoner byggs ett virtuellt stängsel upp som avgränsar det farliga området runt maskinen. För att sedan bedöma om personer befinner sig innanför området har jag tagit fram och utvärderat tre olika algoritmer. Jag har utvärderat algoritmerna med avseende på om exekveringstiden påverkar möjligheten att analysera 30 bilder per sekund och att de bedömer personens position korrekt i förhållande till det virtuella stängslet i olika scenarion.  Tester har också genomförts för att verifiera att algoritmerna fungerar i praktiken integrerade i realtidsapplikationen. Resultaten visar att inte någon av algoritmerna har exekveringstid som kommer att påverka analysen av 30 bilder per sekund. Vidare bedömer två av algoritmerna alla testade situationer korrekt förutom i vissa specifika fall när en person befinner sig exakt på en avgränsningslinje eller på en kon. Felbedömningen har dock ingen praktisk betydelse i denna tillämpning. Testerna av realtidsapplikationen visar att algoritmerna hanterar att området ändras dynamiskt och att det larmar när en person går in i det farliga området. Utifrån utvärderingen kan jag konstatera att det är möjligt att skapa en applikation för en autonom displaydator för att skapa situationsmedvetenhet för tunga arbetsmaskiner på byggarbetsplatser.

This thesis focuses on the topic of preventive maintenance for electric aircraft and aims to examine its significance and implementation within the aviation industry. Electric aircraft have become increasingly popular as a sustainable alternative to conventional aircraft, making it crucial to develop effective maintenance strategies to ensure their reliability and performance.

To achieve this objective, a comprehensive literature review of existing research and practices in the field of preventive maintenance for electric aircraft is conducted. The study identifies the key components and systems in an electric aircraft that require regular monitoring and maintenance, such as electric motors, batteries, power converters, and control systems.

By analyzing the benefits of preventive maintenance, such as reduced downtime, increased component lifespan, and decreased maintenance costs, various methods, and techniques for implementing preventive maintenance for electric aircraft are presented. This includes the use of condition monitoring systems, sensors, and data-driven analysis to monitor and predict the health of components and anticipate any faults or deviations in advance.

To explore the challenges of preventive maintenance for electric aircraft, aspects such as data management, cooling, and electromagnetic compatibility are also discussed. Furthermore, the need for training and skill development for maintenance personnel to effectively carry out preventive maintenance activities for electric aircraft is explored.

By compiling and analyzing the available information, this thesis provides a comprehensive overview of preventive maintenance for electric aircraft and its significance in ensuring reliability, performance, and sustainability within the aviation industry. The findings can be valuable to airlines, maintenance organizations, and researchers interested in implementing effective maintenance strategies for electric aircraft and contributing to the continued development of sustainable aviation.

The literature review showed that the use of preventive maintenance strategy for electric aircraft is well-founded and supported by research and experiences from various industries. The implementation of IoT and fault diagnostic techniques can enhance the maintenance process and increase the reliability of electric aircraft by identifying and addressing potential faults at an early stage. These findings provide valuable insights and guidelines for the aviation industry in developing and optimizing their maintenance strategies for electric aircraft.

ABSTRACT

Date: 2nd June 2024 

Level: Master thesis in Product and Process Development, advanced level, 30 credits

Institution: School of Innovation, Design and Engineering, Mälardalen University

Author: Raiman Darman, 25th April 2000, Jasmin Alhaj, 12th Mars 1998

Title: Improving the Material Flow through Just-in-time

Tutor: Ola Wassenius & Oliver Söderlund, ABB Robotics, Mats Ahlskog, Mälardalen University 

Keywords: Material flow,  Just-in-time, Lean production, Warehouse management system

Purpose: The purpose of this master thesis is to explore how Just-in-time principles can be applied to improve the internal material flow and to identify necessary functionalities in the warehouse management system to support Just-in-time.

Research questions: 

Q1: How can the material flow in production systems be improved by implementing Just-In-time?

Q2: What type of functionality is needed in the warehouse management system to support Just-in-time?

Method: This study employs qualitative research and an abductive approach to achieve its objectives. Data collection is categorized into secondary research, involving a review of existing literature, and primary research, which includes a case study conducted through interviews and observations. Both semi-structured and unstructured interviews were employed with the case company and the benchmarking organization. The use of benchmarking allows for the comparison of organizational practices, challenges, and outcomes, aiding in identifying areas for improvement and gaining insights into effective strategies.

Conclusion: Addressing the first research question, the material flow can be improved by transitioning to a pull-based system and implementing concepts such as supermarkets and point-of-use storage. Furthermore, integrating automated systems for tracking cart availability and establishing clear protocols for standardized work procedures are crucial. Real-time communication and coordination between departments, facilitated by advanced solutions like real-time visibility screens, are also ways of improving the material flow.  Addressing the second research question, it's critical to integrate the internal WMS with the SAP ERP software, address communication gaps, provide comprehensive user training and support, streamline task sequencing, and conduct regular system updates and maintenance.

Date: 4th of June 2023

Level: Master thesis within Industrial Engineering and Management, Product and Process development – 30 credits.

Institution: School of Innovation, Design, and Engineering at Mälardalen’s University

Author: Nadia Al-Hakim

Title: Lean in the Swedish Armed Forces

Keywords: Lean Philosophy; Lean Thinking; Military logistics; JIT

Research Question: What are the potential enablers and barriers to adapting the Lean philosophy in military logistics operations?

Purpose: This study aims to investigate the potential challenges of implementing the Lean philosophy in the Swedish Armed Forces’ logistics.

Methodology: The study was conducted through an abductive research approach using qualitative methods. A theoretical framework was developed by reviewing extant research within the research scope. The empirical findings were based on semi-structured interviews held with six respondents from the Swedish Armed Forces with different positions in logistics. Finally, the empirical findings were analyzed using a conceptual framework that helps the understanding of the different organizational aspects of Lean.

Conclusion: This study aimed to explore the challenges of implementing Lean philosophy in the logistics of the Swedish Armed Forces. Identified barriers include the lack of long-term thinking, incoherent definitions of the end-user and quality, high inventory dependence, confidentiality constraints, and procurement regulations. However, some of the identified enablers include the Armed Forces' use of JIT concepts, focus on safety, and strategic partnerships with certain suppliers.

Denna studie är en kandidatuppsats inom området informationsdesign med inriktning mot rumslig gestaltning. Syftet med arbetet har varit att skapa ett respektfullt och neutralt navigationssystem för en kyrkogård, avsett att användas av besökare med olika religiösa bakgrunder. Arbetet genomfördes i samarbete med Kyrkogårdsförvaltningen i Stockholm, som bidrog med värdefull information och konceptvisualiseringar. Studien baseras på teorier och principer relevanta för informationsdesign och rumslig gestaltning. Metoderna som användes inkluderar en omvärldsanalys och kombination av workshops med intervjuerna med användare och projektteamet, för att få ett brett perspektiv på behoven och preferenserna kring navigationssystemet. Resultaten visar ett tydligt behov av ett neutralt navigationssystem som samtidigt är effektivt och användarvänligt. Gestaltningsförslaget fokuserar på utformning av navigationshjälpmedel vid utvalda knutpunkter och platser på kyrkogården. Detta exemplifierar hur ett navigationssystem kan utformas med hänsyn till besökarnas religiösa tro och bidrar till att skapa en stark platskänsla. Förslaget syftar till att visa hur design kan användas för att främja inkludering och underlätta navigationen för alla besökare, oavsett deras bakgrund. 

Software testing is a widely used quality assurance activity and often starts from the early development stages. However, starting early in the development process raises difficulties and challenges practitioners must deal with; most typically, hardware is not available in the required quantities, and there is the risk of damaging the hardware while testing. Emulating the physical hardware into virtual versions is a popular approach to overcome the mentioned obstacles. This master thesis, carried out at Westermo AB, investigates the differences between the physical and virtual hardware used for embedded system software testing and the possible benefits of combining both hardware versions in a hybrid system. Investigating differences between the hardware options helps identify which type of tests are more suitable in physical versus virtual hardware. The selected method for this thesis is a case study, starting with a pre-study phase investigating how other industries tackle the difficulties and challenges mentioned. Further, data were collected from two sources, historical test reports, and a questionnaire. The historical test reports showed a timing difference between virtual and physical hardware. The reset process of physical hardware is often faster than virtual hardware, but the link-up/link-down time is slower in physical hardware. The questionnaire also confirmed the timing differences as a significant challenge often experienced by engineers. Another challenge highlighted by the questionnaire answers is that "false positives" are typically caused by virtual hardware, where issues do not always turn up due to the virtual nature of communication. Another difference proven from this thesis is that virtual hardware is more failure-prone during the early stages of testing than physical hardware. The hybrid system could be advantageous in various ways, such as quantitatively expanding the current test systems and increasing test coverage. Future work could contribute with a proof-of-concept implementation of the hybrid system to confirm the advantages and demonstrate the third option of a test system. 

In this paper, we investigate the problem of modeling time-series as a process generated through (i) switching between several independent sub-models; (ii) where each sub-model has heteroskedastic noise, and (iii) a polynomial bias, describing nonlinear dependency on system input. First, we propose a generic nonlinear and heteroskedastic statistical model for the process. Then, we design Maximum Likelihood (ML) parameters estimation method capable of handling heteroscedasticity and exploiting constraints on model structure. We investigate solving the intractable ML optimization using population-based stochastic numerical methods. We then find possible model change-points that maximize the likelihood without over-fitting measurement noise. Finally, we verify the usefulness of the proposed technique in a practically relevant case study, the execution-time of odometry estimation for a robot operating radar sensor, and evaluate the different proposed procedures using both simulations and field data.

This study is based on a Moving Target Defence (MTD) algorithm designed to introduce uncertainty into the controller and another layer of uncertainty to intrusion detection. This randomness complicates the adversary's attempts to craft stealthy attacks while concurrently minimizing the impact of false-data injection attacks. Leveraging concepts from state observer design, the method establishes an optimization framework to determine the parameters of the random signals. These signals are strategically tuned to increase the detectability of stealthy attacks while reducing the deviation resulting from false data injection attempts. We propose here to use two different state observers and two associated MTD algorithms. The first one optimizes the parameters of the random signals to reduce the deviation resulting from false data injection attempts and maintain the stability of the closed-loop system with the desired level of performance. In contrast, the second one optimizes the parameters of the random signals to increase the detectability of stealthy attacks. Dividing the optimization problem into two separate optimization processes simplifies the search process and makes it possible to have higher values of the detection cost function. To illustrate the effectiveness of our approach, we present a case study involving a generic linear time-invariant system and compare the results with a recently published algorithm.

Date: 2 of June 2024

Level: Master Thesis in Product – and Process Development, 30 ECTSInstitution: School of Innovation, Design, and Engineering (IDT), at Mälardalen University

Authors: Ali Alhussain, Ahmad Obiedallah,

Title: Using Discrete-Event Simulation for Bottleneck Identification and Formulation of Future-State Scenarios: A Case Study about painting shop floor

Supervisor: André Löfqvist, Volvo CE, Stavros Vouros, Mälardalen University

Keywords: Discrete event simulation, Production system development, Bottlenecks identification, Painting shop floor.

Aim: The study aims to develop a DES model to identify and enhance bottlenecks in production systems by providing the current production scenario, enabling effective problem-solving and giving a future image of the possible improvements.

Research questions: RQ1: How can a discrete event simulation model be developed to be used in the improvement of manufacturing processes? RQ2: How can a simulation model be used for bottleneck identification, and what are the potential enhancements?

Methodology: This research utilised a deductive methodology, allowing theoretical assumptions to shape the empirical investigation and maintain a continuous dialogue between data analysis and model validation. Empirical data were obtained through methods such as observation, time studies, and documentation. Academic sources were sourced from Scopus, focusing exclusively on peer-reviewed journal articles and book chapters. The construction of the conceptual model adhered to the guidelines outlined by Banks (2005). Drawing on the model construction and subsequent findings from the conceptual model, actionable insights were derived.

Conclusion: The study showed that discrete event simulation (DES) effectively identifies and reduces bottlenecks in manufacturing, specifically at Volvo's paint shop. It developed a DES model through systematic data collection, allowing for scenario testing and process optimization. The research focused on two main questions: building a tailored DES model and applying it to address production inefficiencies. Key findings indicated that the model successfully enhanced production flow and efficiency by identifying critical bottlenecks and suggesting improvements. This confirms DES as a vital tool for enhancing manufacturing operations and continuous process improvement.

Production automation as a service (PAaaS) is seen as a new trend that enables the possibility to use production automation technologies as a service. The technologies are cloud-based which makes the implementation of production automation more effective and cost-effective. This approach is attractive to the companies that have a limited capital investment. The purpose of this thesis was to analyse and understand the implementation of Production Automation as a Service (PAaaS) in the manufacturing industry. In order to understand its implementation, it is important to know what the drivers and benefits with PAaaS implementation and what challenges there are and how to overcome them for a successful implementation. To provide with a comprehensive answer and conclusion about PAaaS implementation in the manufacturing industry the following research questions was studied:1. What are the drivers and benefits of PAaaS implementation in the manufacturing industry, and what needs does it fulfil?2. What are the challenges of implementing PAaaS in the manufacturing industry, and how can they be overcome for a successful implementation and scale-up?To be able to answer these questions, a qualitative research study based on literature review and interviews was conducted. The combination between the literature and the real-life experience in PAaaS implementation provided a greater understanding of the concept. The aim of the research questions is to provide guidance and recommendation for companies seeking for a successful implementation of PAaaS which leads to improved operational efficiency and the ability to utilise the technological advancement provided through PAaaS. The approach applied in this study has been qualitative research with an abductive research approach. By obtaining data through scientific articles and interviews it was possible to analyse it more in-depth in order to find similarities between them.In conclusion, PAaaS implementation in the manufacturing industry provides with key benefits such as cost-effectiveness, improved flexibility, scalability, productivity, efficiency, and improved product quality. These benefits fulfil several needs of manufacturing companies such as being more flexible and being able to use automated solutions at a lower cost. These needs also act as the drivers for the implementation of PAaaS. The drivers are an important aspect of PAaaS implementation, because without any drive and motivation, there won’t be any implementation of PAaaS that can fulfil a certain need of the company. The drivers and motivation for a PAaaS implementation in the manufacturing industry is the possibility for a business model transformation and the technological advancements that the manufacturer gain with the implementation.In addition, there are key challenges that makes the PAaaS implementation more complex for the manufacturing companies. These challenges are, Integration with legacy systems, internet dependency and lack of expertise and knowledge. To able to achieve a successful implementation, it is important to address the challenges, by addressing these problems it was possible to provide with strategies on how to overcome them. Which lays the foundation for future research about this topic.

The purpose of this bachelor’s degree work was to analyse a packaging station in a global manufacturing company in Sweden, and to provide an answer on why it is important with standardised work in preventing wrong deliveries. The purpose of the analysis was to come up with various improvement proposals in order to reduce and prevent wrong deliveries. The analysis of the packaging station was conducted by observing the station and understanding how the workers work and what kind of work procedure do they follow. The observations made it possible to conduct several conclusions on why the problems occurred, one of these conclusions was that the work procedure is not standardised which in turn increased the errors in the station. Through a clear problem description, it was possible to come up with improvement proposals by answering the following research questions:

 Main RQ: Why is a standardised workplace important in a manufacturing company?

To be able to answer the main research question, the question has been divided into two smaller sub-questions:

1.     How can a packaging area be standardised by using lean process improvement tools?

2.     How can operating standards improve the efficiency in a packaging station?

The approach applied in this study and project has been qualitative research with an inductive approach, by reviewing scientific articles and observing the work in the packaging station it was possible to analyse and compare the collected data in order to come with a conclusion to the problem. The data that was collected in form of interviews has been continuously analysed in order to have as relevant data as possible for the improvements work. 

By comparing the collected data from the literature and the case findings it was possible to lay a foundation for the analysis where the proposed improvements came from and to conclude a final proposed improvements that reduces the wrong deliveries in the packaging station. In conclusion, standardised work is important in order to prevent wrong deliveries because it contributes to a more clear and sustainable structure that the workers follow. By having standardised instructions and methods that is being followed, the amount of errors is reduced in the packaging station. 

Wrong deliveries are reduced and prevented when there is a standardised work procedure that is based on the lean ideology in a packaging station