In recent years, the number of digital services and tools available has increased rapidly. When companies want to digitalize their business, they have the opportunity to browse a large number of existing platforms and applications available on the market to find a good match for their specific needs. However, when a company wishes to digitalize a work task that already has a well-established workflow, problems may arise. Due to this, a tailored digital solution may in some cases be the better suited option, rather than the ones available on the market.  The intention of this work was to investigate the challenges that companies face in relation to digitalization of the workplace in general, and the challenges of a company’s expense management process in particular. As an example of how a workplace digitalization can take place, a collaboration with a forest industry company was conducted. An evaluation of their analog and internal expense management process was done, where the found challenges were assessed with respect to chosen quality factors. The evaluation and the found challenges regarding digitalization constituted the basis for a process mapping and a digital solution aiming to improve the company’s expense management process. The resulting work emphasizes how a digital solution can be tailored with simple means within a limited time frame, taking specific needs and existing challenges into account in order to digitalize the workplace. In addition, the work presents what challenges that exists within the concept of digitalizing the workplace and regarding expense management, and how quality factors can be used in combination with a process improvement in order to relieve and eliminate them.

In our progressively digitized world, the escalating demand for software solutions intensifies the need for proficient developers. Low-Code Development Platforms (LCDPs) present a promising approach to address this necessity, empowering individuals without traditional programming skills to create software applications. However, despite their potential, these platforms are often not accessible or intuitive for non-professional developers. This research examines the design of LCDPs, with an emphasis on enhancing the user experience for non-programmers. By investigating the usability of LCDPs and designing a prototype based on the findings, my aim is to contribute to the ongoing discussion about the democratization of software development and to propose enhancements that could make LCDPs more user-friendly, inclusive, and usable across devices. The research involves a combination of literature review, interviews, prototype development, and user testing, providing a multifaceted perspective on the topic. Moreover, it discusses potential implications for the design of LCDPs, as well as for the broader field of interaction design.

Purpose: This study aims to identify the operational and financial outcomes of companies implementing the no-code manufacturing execution system (MES). Methods: This study has been conducted using inductive reasoning to develop new theories in this unexplored subject. The thesis has conducted a multiple case study to collect qualitative, empirical data. Qualitative data has been collected through conducting three interviews from two separate companies. The Research Background and Findings section was then cross analyzed to find commonalities to form conclusions.  Conclusion: Implementing a no-code MES offers operational and financial benefits for manufacturing companies. It improves productivity, reduces lead times, increases flexibility, and enhances quality efforts. Cost savings are achieved through paper reduction and lower implementation costs compared to traditional MES solutions, which do not have no-code features. Overall, a no-code MES delivers advantageous outcomes efficiently and eliminates the need for significant capital investments and technical skills. Theoretical Contribution: This thesis contributes to the field of science by unifying Manufacturing Execution System (MES) with the existent subject of low-code/no-code. This study creates a deeper knowledge by merging science with empirics.  Practical Contribution: The thesis contributes to practitioners in the manufacturing industry by indicating the relevance and importance of the beneficial operational and financial outcomes of adopting a no-code MES. Firstly, there are strong indications that a no-code MES could be a possible solution for companies that could be impacted by the labor shortage in software developer jobs. Secondly, as the no-code MES mitigates the barriers with traditional MES solutions, having the MES built on no-code makes the solution more cost-effective and easy-to-maintain. Limitations & Future Research: This study is limited to two case companies and three interviews. The study’s findings are limited by the short duration of a no-code MES implementation in the case companies, preventing a full investigation of financial outcomes. Further research is necessary to fully examine the financial outcomes of implementing a no-code MES.

In the data-driven landscape of modern business, predictive analytics plays a pivotal role inanticipating and mitigating customer churn—a critical challenge for organizations. However, thetraditional complexities of machine learning hinder accessibility for decision-makers. EnterMachine Learning as a Service (MLaaS), offering a gateway to predictive modeling without theneed for extensive coding or infrastructure.This thesis presents a comprehensive evaluation of cloud-based and cloud-agonostic AutoML(Automated Machine Learning) platforms for customer churn prediction. The study focuses onfour prominent platforms: Azure ML, AWS SageMaker, GCP Vertex AI, and Databricks. Theevaluation encompasses various performance metrics including accuracy, AUC-ROC, precision,recall to assess the predictive capabilities of each platform. Furthermore, the ease of use andlearning curve for model development are compared, considering factors such as data preparation,training steps, and coding requirements. Additionally, model training times are analyzed toidentify platform efficiencies. Preliminary results indicate that AWS SageMaker exhibits thehighest accuracy, suggesting strong predictive capabilities. GCP Vertex AI excels in AUC,indicating robust discriminatory power. Azure ML demonstrates a balanced performance,achieving notable accuracy and AUC scores. Databricks being platform independent is a winnerand has also shown good metrics. Its capability to generate notebook is an added advantage whichcan be modified by experts to fine tune the results more. This research provides valuable insightsfor organizations seeking to implement different AutoML solutions for customer churnprediction.

This study investigates how small and medium sized enterprises (SMEs) and other resource-lacking organisations can utilise automated machine learning (AutoML) to lessen the development hurdles associated with machine learning model development. This is achieved by comparing the performance, cost of usage, as well as usability and documentation for machine learning models developed through two AutoML frameworks: Vertex AI on Google Cloud™ and the open-source library AutoGluon, developed by Amazon Web Services. The study also presents a roadmap and a time plan that can be utilised by resource-lacking enterprises to guide the development of machine learning solutions implemented through AutoML frameworks. The results of the study show that AutoML frameworks are easy to use and capable in generating machine learning models. However, performance is not guaranteed and machine learning projects utilising AutoML frameworks still necessitates substantial development effort. Furthermore, the limiting factor in model performance is often training data quality which AutoML frameworks do not address.

The growing demand for IT professionals to develop and maintain software is creating huge concerns for organizations looking to keep up and build resilience. The trends show that there is a large shortage compared to the demand. Manufacturing companies are no exception to this, and the adoption of Industry 4.0 technologies will most likely increase this need even further. However, developing software solutions often requires specialized knowledge, and organizations are looking far and wide for the competence. If it is present, it is often found in the organization’s centralized IT departments. Low-code development might offer a solution, by lowering the threshold for employees and individuals to develop, deploy and maintained applications with limited technical expertise. Low-code development is method that enables software applications to be built using graphical user interfaces in so called low-code development platforms. The platforms provide templates, drag-and-drop modules and integration with other systems to reduce the complexity of the application development process. These platforms could offer advantages for manufacturing firms, including quicker development that could aid in responding to new requirements. Other benefits include: cost savings and shorter development cycles, allowing for faster releases which can help enterprises stay more competitive and reduce risk. This thesis probes the factors to consider with low-code platforms, such as the role of citizen developers and the balance between accessibility and control in the context of manufacturing industries. It explores how low-code development could enhance efficiency and presents a case study from the engine assembly facility in Scania Södertälje. Best practices for building an organization around low-code developers are discussed. The insights from this study provide an understanding of the complexities and potential of low-code development, laying the groundwork for further exploration and implementation. Combined, the results and insights offer a clear perspective on how low-code development can assist organizations in the digital age.

Even a professional photographer sometimes uses automatic default settings that come up with the camera to take a photo. One can debate the quality of outcome from manual vs automatic mode. Until and unless we have a professional level of competence in taking a photo, updating our skills/knowledge as per the latest market trends and having enough time to try out different settings manually, it is worthwhile to use Auto-mode. As camera manufacturers, after several iterations of testing, comes up with the list of ideal parameter values, which is embedded as a factory default setting when we choose auto-mode. For non-professional photographers or amateurs recommend using the auto-mode that comes with the camera for not missing the moment. Similarly, in the context of developing machine learning models, until and unless we have the required data-engineering and ML development competence, time to train and test different ML models and tune different hyper parameter settings, it is worth to try out to Automatic Machine learning feature provided out-of-shelf by all the Cloud-based and Cloud-agnostic ML platforms. This thesis deep dives into evaluating possibility of generating automatic machine learning models with no-code/low-code experience provided by GCP, AWS, Azure and Databricks. We have made a comparison between different ML platforms on generating automatic ML model and presenting the results. It also covers the lessons learnt by developing automatic ML models from a sample dataset across all four ML platforms. Later, we have outlined machine learning subject matter expert’s viewpoints about using Automatic Machine learning models. From this research, we found automatic machine learning can come handy for many off-the-shelf analytical use-cases, this can be highly beneficial especially for time-constrained projects, when resource competence or staffing is a bottleneck and even when competent data scientists want a second-opinion or compare AutoML results with the custom ML model built.