Christian Murano

2026

Human-Centric Mobile Interface

All images have been reworked with placeholder data

2025

Context

A major customer in the Food & Beverage sector asked our team to optimize their end-of-line production and introduce advanced palletizing automation.
Before our intervention:

Operators manually filled pallets with boxes
Every station required physical intervention
Line throughput was inconsistent
No real-time awareness of performance

After the installation of automatic palletizers and our proprietary fleet management software, the next challenge was clear:

Give operators a single mobile interface to monitor, control, and optimize production line modes without removing human authority.

The result needed to empower operators, not replace them.

Context

A major customer in the Food & Beverage sector asked our team to optimize their end-of-line production and introduce advanced palletizing automation.
Before our intervention:

Operators manually filled pallets with boxes
Every station required physical intervention
Line throughput was inconsistent
No real-time awareness of performance

After the installation of automatic palletizers and our proprietary fleet management software, the next challenge was clear:

Give operators a single mobile interface to monitor, control, and optimize production line modes without removing human authority.

The result needed to empower operators, not replace them.

Problem

Automation alone wasn’t enough.

The introduction of robotic palletizers required operators to:

Understand line states in real time
Adapt production based on throughput
Decide when to run lines in automatic vs manual
Intervene efficiently when the system needed support

Operators had no digital tool for this.
They couldn’t:

See which lines were performing well
Know when a line risked slowing down
Identify empty or overloaded segments
Manage decisions proactively

The risk: automation causing new bottlenecks instead of solving old ones.

Problem

Automation alone wasn’t enough.

The introduction of robotic palletizers required operators to:

Understand line states in real time
Adapt production based on throughput
Decide when to run lines in automatic vs manual
Intervene efficiently when the system needed support

Operators had no digital tool for this.
They couldn’t:

See which lines were performing well
Know when a line risked slowing down
Identify empty or overloaded segments
Manage decisions proactively

The risk: automation causing new bottlenecks instead of solving old ones.

My Role

I led the full product and experience design:

Research with operators, supervisors, and automation engineers
Mapping all line states and throughput dependencies
Designing the mobile HMI from scratch
Defining the logic for mode switching (Automatic · Manual · Off)
Integrating insights from production analysis
Ensuring human-centric control: operators first, automation second

I acted as the bridging point between robotics, digital systems, and people.

My Role

I led the full product and experience design:

Research with operators, supervisors, and automation engineers
Mapping all line states and throughput dependencies
Designing the mobile HMI from scratch
Defining the logic for mode switching (Automatic · Manual · Off)
Integrating insights from production analysis
Ensuring human-centric control: operators first, automation second

I acted as the bridging point between robotics, digital systems, and people.

The Journey

Discovery & Field Analysis

Goals:

Understand operator needs in real environments
Map the decision-making flow
Identify when automation was helpful vs harmful

Activities:

Shadowing shifts at end-of-line stations
Interviewing production managers
Monitoring throughput fluctuations
Identifying triggers that cause switching between modes

Key insight:
Automation must adapt to operators, not the other way around.

System Mapping & Mode Architecture

I structured the three operating modes:

Automatic Mode:

Palletizer manages the full cycle
Ideal when throughput is stable

Manual Mode:

Operator supports the palletizer
Used when throughput is low or inconsistent
Prevents idle time and production gaps

Off Mode:

Used when no material is incoming
Prevents unnecessary robot cycles

Then I mapped:

Alerts and thresholds
Performance indicators
Throughput-based suggestions
Line dependencies
Operator actions and confirmations

This became the backbone of the mobile UX.

Mobile Interface Design

I designed a human-centric control interface, not a robotic one.

Core functionalities:

Real-time overview of all end-of-line stations
Instant mode switching per line
Throughput-based suggestions
Performance insights to prevent drops
Clear alerts when lines require manual assistance

Design Principles:

Elevate the operator, from executor to system controller
High legibility for industrial environments
Flexible autonomy, suggestions, not constraints

Validation & Iteration

I validated the interface directly with users and plant manager:

Early sketches tested on the shop floor
Rapid prototyping in realistic lighting and noise conditions
Multiple iteration cycles based on real use
Reduced steps for mode switching
Improved clarity of line status indicators

Outcome

The full automation intervention led to a +30% in productivity

Performance Impact

More stable line throughput
Reduction in idle palletizer cycles
Faster reaction to low-production conditions
Operators empowered to make high-value decisions

Operational Impact

A unified view of all end-of-line stations
Consistent mode management
Clear guidance reduces decision pressure
Better synchronization between automation and human roles

Human Experience

Operators elevated from “manual labor” to system controllers
Strong adoption thanks to user-centered design
Automation perceived as a support, not a threat

All images have been reworked with fictional data