Analytics and Usability Metrics Collection for Single Page Applications

https://drive.google.com/file/d/1L9jyqNLkn-WEi1Z3LoqKFQJvBljuVugB/view?usp=drive_link

Achieving excellent usability levels within web applications has been a critical factor for product performance but has also been challenging for various reasons. On some occasions, assessing usability within an application is not easily depictable because there is no direct access to stakeholder feedback, making it difficult to evaluate a software product from a usability point of view. It is standard for UI/UX teams to rely on user feedback and make improvements based on continuous feedback. However, to enable continuous improvement of a software product, it is beneficial to engineer a solution that automatically collects analytics and usability metrics since this approach outputs quantitative data that can shift the focus from the users and qualitative feedback. Based on this quantitative data, the UI/UX team can recommend user interface improvements that enable improved experiences for end-users. This technique is especially promising when the end-users cannot adequately give their feedback, which is the case with Critical Manufacturing’s (CMF) Manufacturing Execution System (MES). MES is a web application used by manufacturing operators on the shop floor, meaning that the feedback pipeline can be very inefficient, i.e., it takes work and time to get information from the operators to the UI/UX team. Finding key underlying bottlenecks in software usability can significantly improve the efficiency of the software on the shop-floor level as recurrent actions on the interface become easier to perform by end-users, facilitating operations and accelerating manufacturing. Consequently, this dissertation aims to assemble a general solution for metric collection within single-page applications and apply it to the specific interests of an existing singlepage application, CMF’s MES. The goal is to develop an approach and a tool to gather metrics related to usability automatically and provide an overview with quantitative data of how the system interaction is occurring in a parallel and independent dashboard application. This project was done in the context of an internship with CMF and involves the company’s product development team. The outcome is a scalable solution optimizing CMF’s operations through usability insights. Future work encompasses tasks like enhancing and automating the testing/validation process and refining user centric data collection and analysis from MES, for a more bespoke, effective solution.