Designing an Industrial IoT Ecosystem to revolutionize operations in remote, high-reliability environments.

Product Design

Team Leadership

Research

New Product Introduction

Objective

Design a mobile application that ties together data from IoT devices, sensors, and other data sources to provide real-time, relevent insights and alerts to operators and managers of heavy equipment in remote locations.

Approach

The project began with a deep dive into the existing product, user needs, and the competitive landscape. We conducted user interviews, stakeholder interviews, and competitive analysis to identify opportunities for improvement and innovation.

Journey Mapping

Using Design Thinking and ethnograpgic research, we explored the daily experiences of operators, supervisors, and engineers in the field. We explored how they communicate and manage site operations across shifts, stages of production, and maintenance periods. Additionally, we recognized the significance of logs as a means to document site activities and the role of informal knowledge in shaping standard operating procedures and asset maintenance practices.

Feature Prioritization

Features prioritized according to the value for the users by each feature.

Maintenance Logging
Product Identification
Asset Status History
Notifications
Condition Monitoring

Scenario Based Design

We used scenarios to build realistic stories for key actors in the field operations. The scenarios describe how actors would use the mobile app as a tool to complete tasks. This lead us to:

Create and verify realistic use cases
Seek domain specific knowledge
Design features as screens for each task

Experience Principles

Streamline data entry and access

Easy identification of products and access to info
Easy data entry (easier than current methods)

Centralize Information to improve efficiency

Useful centralized views of information (for inventory management, and maintenance)
Contextual prompts and triggers
Easy information/knowledge transfer

Make it intelligent

Predictive suggestions and prompts
Aggregated data and trend views
Invisible eﬃciency