Equipt: An app for heavy machine monitoring
Equipt helps construction dealers and customers rent, monitor, and manage heavy machinery health in real time, reducing equipment downtime and improving fleet visibility.
Role
UX/UI Designer
Timeline
2-3 weeks
Team
2-3 people
Platform
Mobile application
Equipt redesign
Overview
What is Equipt?
Equipt is a mobile application that enables construction dealers and customers to monitor the health of heavy equipment — excavators, backhoe loaders, skid steers — in real time through IoT sensors. It also surfaces utilisation data to improve machine productivity and fleet management.
The platform serves two distinct user types: customers who rent equipment on-site and need live health alerts, and dealers who manage fleets and need availability, booking and payment oversight.
2 user flows
Customer and dealer journeys designed end-to-end
6 IoT sensors
Pressure, temperature, vibration, ultrasonic, electricity, water
16 screens
Lo-fi through hi-fi, covering all critical tasks
Problem
Why we worked on this
Heavy Machinery Corp. is a leader in manufacturing heavy equipment used for drilling, mining and construction across India. Their machines are sold and rented through a dealer network — but that network had no single, connected tool for managing fleet health or rental logistics.
01
No real-time health visibility
Dealers had no way to remotely track equipment condition. Issues like oil overheating or coil damage were only discovered after the machine had already failed on-site.
02
Fragmented rental workflow
Booking, availability checks, and payments happened across phone calls, spreadsheets and manual ledgers — creating friction for both customers and dealers.
03
Underutilised machinery
Without usage data, dealers couldn't identify idle machines or optimise fleet allocation. Customers had no insight into how intensively they were using rented equipment.
"JCB oil heats up and slows down — by the time we know, we've already lost half a working day."
— Senthil Kumar, 44, site supervisor — field interview
Research
Understanding the field
EXPERT INTERVIEW
IoT sensor types, machine health parameters, industry benchmarks
Competitive Analysis
Existing fleet apps, dealer platforms, rental booking tools
SURVEYS & INTERVIEWS
5 dealers, 8 customers, 2 site engineers — across Pune and Chennai
EXPERT INTERVIEW — IoT Sensors
We researched the parameters that cause machine damage and the IoT sensors used to detect them, which was gathered from an expert interview.
COMPETITIVE ANALYSIS
Caterpillar
Values
01
Integrity
02
Excellence
03
Teamwork
04
Commitment
05
Sustainability
Features
01
Construction Industries
02
Resource Industries
03
Energy & Transportation
04
Services, Distribution & Digital
05
Merchandise center
Technology
01
Solar insight system
02
Additive manufacturing
03
Autonomous trucks
Differentiator
More than 100 years of expertise
JCB
Values
01
Sustainability
02
Trustworthy
03
Teamwork
04
Excellence
Features
01
Construction Industries
02
Mining Industries
03
Power generation
04
Innovative and sustainable solutions
05
Transportation and material handling
Technology
01
JCB operator app
02
Live link fleet portal for immediate machine updates
03
Telescopic handles
Differentiator
Leading manufacturer of earthmoving and construction equipment in India.
SURVEY | INTERVIEW | ETHNOGRAPHIC RESEARCH
"JCB oil heats up and slows down — by the time we know, we've already lost half a working day."
— Senthil Kumar, 44, site supervisor — field interview
USER SEGMENTATION
"We understood the numbers — we just didn't know which machine needed attention first."
— Dealer respondent, survey
Dealers
Businessmen who lend heavy machines on rent and lease. Primary need: fleet visibility, health monitoring, and payment tracking.
Customers
Professionals who hire heavy machines for active construction projects. Primary need: real-time health alerts and booking management.
Business goals
–
Create a smooth rental flow between customers and dealers
–
Ensure proper utilisation of machines
–
Build a loyal, growing customer base
–
Increase equipment sales and lease revenue
User Goals
–
Search and discover available heavy machines easily
–
Track machine location remotely
–
Check machine condition without visiting site
–
Receive alerts for maintenance or repair
Ideation
Shaping the design
We followed a double-diamond process: Discover → Define → Dream → Design → Develop → Deliver. Ideation began with storyboards to test the customer journey narrative, then moved to MoSCoW prioritisation to scope the MVP, and finally information architecture to define the navigation model.
Storyboard — Customer Journey
MOSCOW PRIORITIZATION
Designs & DESIGN SYSTEM
From wireframes to final screens
Low Fidelity WIREFRAMES
Home
Health
Usage
Bookings
Dealer
Payments
HIGH FIDELITY SCREENS (CUSTOMER FLOW ONLY)
UI style guidelines
07
User Testing
Testing with real users
We ran moderated usability sessions to understand whether first-time users could complete the core rental journey without guidance. Each participant worked through the same four tasks, then reflected on what felt clear, confusing, or missing.
02
Participants
04
Core tasks
1:1
Moderated
Qual.
Study type
Test objective
Can users rent with confidence?
01
Login to the application
02
Check the machine health status
03
Find the machine to lend
04
Make payment for rented machinery
Session notes
Synthesis
What the sessions changed
The overall journey felt approachable, but confidence dropped at two decision points: interpreting machine health and locating payment options. Those moments became the priority for refinement.
01
The health icon was not self-explanatory
One participant could not understand it and suggested a heart or diagnostic symbol.
Design response
Pair status icons with plain-language labels and a more recognisable diagnostic cue.
02
Payment needed stronger visibility
The payment option was missed on first view, while cash was actively searched for.
Design response
Surface payment methods earlier and make available options explicit in the rental flow.
03
The core journey already felt easy
Login was described as good, and the overall application as very easy to use.
Design response
Preserve the familiar flow while refining the two local points of friction.
Lessons
What we learned
Dual-persona products require deliberate navigation seams
Designing for both customers and dealers with a shared navigation pattern was a conscious constraint. Shared affordances reduced onboarding friction for both groups — but required rigorous task-flow testing to ensure neither persona hit dead ends.
IoT complexity is best hidden, not surfaced
Early iterations exposed raw sensor values. User testing revealed that numbers without context created anxiety rather than confidence. The final design translates values into plain-language statuses (Good / Needs attention / Critical) with only progressive disclosure of raw data.
Field research changed everything
Desk research pointed us toward feature completeness. Ethnographic work with Senthil Kumar and other supervisors redirected us toward simplicity — the value wasn't in dashboards, it was in the alert that arrived before the machine broke down.
"Financial confidence is shaped more by interface clarity than data complexity — and machine confidence is no different."
— Reflection, post-project