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.