Pharmaceutical product production lines rely on complex measurement systems to ensure quality. Siemens’ SIPAT software provides customized data collection and measurement tools to support these processes through data mining, monitoring and reporting.
The complexity and diversity of the data models posed several challenges to the usability of the software. The workflow, for example, was not reflected in the interface, which made the software hard to use and the complex data models hard to understand. Namahn was therefore asked to improve both the software’s attractiveness and intuitiveness.
The first step was to ensure the team had an in-depth understanding of the existing process analysis workflow. Field studies and remote interviews with different pharmaceutical producers worldwide identified needs and clarified the gaps between the users’ mental models and the actual structure of the software. Based on the insights gathered, the designers drafted a first list of improvements covering technical, functional and User Interface (UI) issues. This list of requirements was prioritized with Siemens. Usage scenarios, together with first sketches of a new conceptual design, were then created during a co-creation workshop.
These sketches became the starting point for an extended set of wireframes, which were presented to users during a series of interviews. Their feedback was incorporated into a static prototype, which was used to further refine the interface, resulting in a new graphic design for the tool. The designers were involved from the earliest stages of the agile development process, contributing to prioritization choices and supporting the developers with design rationales.
Implementation guidelines were formally compiled into a document containing specifications for the different elements of the interface.
The redesigned software now reflects the existing workflow much better, allowing users to easily configure the data analysis process. The improved UI is also easier to use, and provides analysts with a clearer view of SIPAT’s complex data mining processes and results.