



EfficienTrack
Analyzing and Reducing Energy Consumption in Manufacturing Plants


Meet the Cleint
Energy Insights aims to create meaningful, actionable short-term Energy Insights for Indiana’s small-medium manufacturers that lead to continued investment and growth into smart manufacturing tools.
I worked as a 0-1 Product Designer with a team of 3 other Designers collaborating with the client’s cross-function team for 2.5 months.
Design Process
From identifying business needs to defining product features and creating a high-fidelity prototype, we followed an iterative approach—define, implement, validate, and repeat—until we built a user-friendly MVP!
Define
Design
Test
Repeat
01
Research & Discovery
03
Testing & Validating
02
Design Solution
04
Defining the Business
Did you know?
Industrial plants spend nearly $125 billion on energy consumption every year.

"What?! that's a lot!"
Hence, as a manufacturer, energy management is a top priority in the success and sustainability of your business.

"But wait.. Why are we spending so much on energy?"
There could be multiple reasons like poor machine maintenance or machines running idle and consuming excess energy.

We need a tool to analyze our machine performance & optimize energy consumption
Research & Discovery
5
Interviews Conducted with Client, manufacturing engineers & domain experts.
3
Personas Identified as potential users
8
Competitors analysed to understand client's market position
16
Categories analysed in environmental analysis
Environmental Analysis
From identifying business needs to defining product features and creating a high-fidelity prototype, we followed an iterative approach—define, implement, validate, and repeat—until we built a user-friendly MVP!

Inverviews
From identifying business needs to defining product features and creating a high-fidelity prototype, we followed an iterative approach—define, implement, validate, and repeat—until we built a user-friendly MVP!
Personas
From identifying business needs to defining product features and creating a high-fidelity prototype, we followed an iterative approach—define, implement, validate, and repeat—until we built a user-friendly MVP!