mSights

One of the biggest challenges was understanding the manufacturing environment within JLR itself. 

Unlike traditional digital products, the platform served highly specialised users including:

• Weld timer technicians
• ABB robot technicians 
• SEW drive engineers 
• Supervisors 
• Manufacturing managers 
• Energy teams 
• Control rooms teams

To design effectively, I first needed to understand how these users worked, what decision they were making and what information they required throughout their day. 

I spent time reviewing the existing AS-IS experiences of the user, attending walkthrough sessions, analysing workflows and speaking with users and subject matter experts to build a clear understanding of both the technology and operational context.

Through workshops, stakeholder interviews and platform walkthroughs, several recurring themes emerged. 

Key findings:

Information Overload 

Users were presented with large amounts of machine data but often struggled to identify what was the most important. 

Different users needed different level of detail

Senior stakeholders wanted operational summaries whilst engineers required detailed diagnostic information. 

Existing journeys lacked clear hierarchy 

Users could access the information they needed, but navigation between facility, area, line and machine level views wasn't always intuitive.

Alert visibility needed improvement 

Critical issues could become lost amongst large volumes of information.

Following discovery, I worked with stakeholders to define a dashboard structure that balanced overview monitoring with detailed investigation.

The hierarchy evolved into:

Facility > Area > Zone > Line > Equipment 

This approach allowed users to progressively drill down from a high-level operational overview into specific machine insights without becoming overly complex. 

Facility view focused on:

• Overall health status 
• Equipment availability 
• Alert viability 
• Operational Performance 

This enabled managers and supervisors to quickly understand the state of production. 

Equipment view focused on:

•  Machine health 
• Historical trends 
• Fault investigation 
• Performance analysis 

This supported technicians and engineers carrying out deeper investigations. 

I designed a series of dashboard concepts that focused on:

• Clarity over complexity - rather than displaying every metric, the designs prioritised the most relevant information first. 

• Progressive disclosure - users could move from high-level summaries into increasingly detailed views as required. 

• Visual hierarchy - important information such as critical alerts and equipment health indicators were surfaced prominently to support faster decision-making.

• Scalability - the design approach needed to work across multiple equipment types including weld timers, robots, drives and future assets. 

Wireframes and concepts were reviewed regularly with stakeholders and users. Feedback session helped validate:

• Navigation structures 
• Dashboard layouts
• KPI prioritisation 
• Alert presentation 
• Drill-down journeys 

This iterative approach ensured designs remained aligned with operational needs while balancing technical constraints. 

The resulting experience provided a clearer and more scalable foundation for manufacturing monitoring and predictive maintenance. The platform enabled users to:

• Monitor equipment health more effectively 
• Identify issues faster 
• Navigate complex manufacturing data more efficiently
• Access information at the appropriate level of detail

The work also established a design framework that could be reused across future equipment dashboards and manufacturing use cases. 

This project strengthened my ability to design within highly complex enterprise environments. 

The biggest lesson was that successful dashboard design is rarely about displaying more data. It's about helping users understand what matters when it matters and giving them the confidence to act on it. 

By investing time in discovery and understanding the manufacturing domain, I was able to move beyond designing dashboards and instead design experiences that supported real operational decision-making.