FRIDAY SYSTEMS

Designing a Human-Robot Interface for AI-Powered SaaS

Client

Friday Systems

Timeline

4 Months

Services

Product design

B2B STRATEGY

wireframing

ux/ui design

FIGMA

interaction design

my role

Lead Product Designer

Project Overview

Friday Systems is a pioneering company specializing in AI-driven solutions for warehouse logistics. Their innovative technology addresses the complexities of modern supply chains, providing advanced automation tools to enhance efficiency and operational performance

The project involved designing an intuitive interface for warehouse workers to interact with an AI-driven palletization robot. The interface enables users to monitor, manage, and optimize the palletization process, enhancing efficiency and operational insights.

Project Overview

Challenge

Create a first release of the interface (MVP) that allows users to interact with the AI-driven palletization robot in a safe and intuitive way.

Solution

Designed an intuitive interface for the target users in the warehouse, enabling seamless interaction, monitoring, and business rule management.

Result

Secured high-profile client meetings and a pilot project contract, significantly enhancing warehouse efficiency and operational margins.

The Problem Statement

Warehouse logistics are burdened by labor-intensive processes and inefficient automation, leading to higher costs and physical strain on workers. Traditional robots lack adaptability in product handling, making warehouse optimization difficult. There’s a need for an intuitive human-robot interface to streamline operations, reduce human error, and improve overall safety and efficiency.

Goals

User goals

Allow warehouse operators to monitor and control the palletization process efficiently.
Enable managers to set business constraints and monitor operational metrics.
Facilitate seamless interaction between warehouse staff and the AI system, ensuring smooth operation and quick issue resolution.

Business goals

Demonstrate the value and ROI of the AI-driven palletization robot to potential clients.
Expand market reach by showcasing successful pilot projects and securing high-profile client meetings.
Enhance the product offering by continuously gathering user feedback and improving the interface based on real-world usage.

Design Process

EMPATHIZING AND GATHERING UX Research

We initiated the process by defining the primary goal of launching a secure and user-friendly interface for interacting with the AI-driven palletization robot.

Research Activities:

In-depth Interviews: We carried out detailed interviews with both warehouse workers and managers which helped us understand their unique perspectives, challenges, and needs.
Flow Mapping: Using FigJam, we mapped the flows of the existing system to gain a comprehensive understanding of the current operations and identify areas for improvement.

We initiated the process by defining the primary goal of launching a secure and user-friendly interface for interacting with the AI-driven palletization robot. To achieve this, we conducted extensive UX research, focusing on two primary user groups: warehouse workers and managers.

Research Activities:

In-depth Interviews: We carried out detailed interviews with both warehouse workers and managers which helped us understand their unique perspectives, challenges, and needs.
Flow Mapping: Using FigJam, we mapped the flows of the existing system to gain a comprehensive understanding of the current operations and identify areas for improvement

Pain Points Identified

Inefficient Processes

Pain Points Identified

The existing system required manual adjustments and oversight, which consumed significant time and resources, decreasing overall efficiency.

Create a first release of the interface (MVP) that allows users to interact with the AI-driven palletization robot in a safe and intuitive way.

Physical Strain

Warehouse workers frequently suffered from back injuries due to repetitive lifting and palletizing tasks, leading to extended absences and reduced productivity.

Create a first release of the interface (MVP) that allows users to interact with the AI-driven palletization robot in a safe and intuitive way.

Lack of Real-Time Data

Managers faced challenges in accessing real-time operational metrics, making it difficult to make informed decisions quickly and communicate with workers.

Create a first release of the interface (MVP) that allows users to interact with the AI-driven palletization robot in a safe and intuitive way.

Complex Interactions

Other robot interfaces were not intuitive, resulting in frequent errors and the need for extensive training for employees.

Create a first release of the interface (MVP) that allows users to interact with the AI-driven palletization robot in a safe and intuitive way.

DEFINING THE USER AND THE PROBLEM

In this phase, we prioritized and decided on the key issues to focus on based on our research findings and analysis.

What problem are we trying to solve? As the design team, we want to create a first release of the interface that allows users to interact with the AI-driven palletization robot in a safe and intuitive way. This involves ensuring that the interface is user-friendly, minimizes the risk of errors, and enhances overall user experience.

WHO IS THE TARGET?

Warehouse Workers

Individuals responsible for the manual tasks of lifting, stacking, and organizing pallets, who need an interface that simplifies their interactions with the robot.

Create a first release of the interface (MVP) that allows users to interact with the AI-driven palletization robot in a safe and intuitive way.

Warehouse Managers

Professionals overseeing the operations, who require a tool to set business constraints, monitor operational metrics, and make informed decisions.

Create a first release of the interface (MVP) that allows users to interact with the AI-driven palletization robot in a safe and intuitive way.

IDEATION

To generate ideas that could effectively address the problems faced by our target users, we brought together a diverse group of stakeholders, including the CEO, CTO, COO, and the development team.

Divergent Thinking: We employed divergent thinking techniques to explore a wide range of possible solutions, encouraging team members to think outside the box and propose innovative ideas.

Benchmarking: We conducted benchmarking to understand existing solutions in the field. This helped us refine and adapt these ideas to fit our specific needs and the unique challenges of our project.
Sketching Workshop: We organized a sketching workshop where team members could quickly create, modify, and share initial sketches of the product. This was a powerful tool for visualizing our ideas and ensuring that everyone was on the same page about the direction we were heading.

PROTOTYPING

In the prototyping stage, we focused on creating a robust and user-friendly interface by establishing a clear path for common workflows and iterating on complex or bespoke requirements.

Establishing the happy path and ensuring that the most common and critical tasks could be completed smoothly and intuitively.
Iterating on edge cases. Based on our research, we identified complex or unique use cases and iterated on these scenarios to ensure they were well-supported. We focused on the key features required for the MVP and established others which would be implemented later in the roadmap.
We developed a comprehensive design system using Google's Material Design guidelines. This provided a consistent and cohesive visual language, ensuring that all elements of the interface were intuitive and aesthetically pleasing.
Design hand-off session. To facilitate a seamless transition from design to development, we conducted a detailed design hand-off session. This included color codes, font sizes and typography, grids and layouts as well as component sizing and values.

TEST AND ITERATION

In the testing stage, we focused on validating our hypotheses and ensuring the interface met user needs effectively.

Client Validation: We presented the MVP to prospective clients to gather initial feedback and validate our assumptions. This helped us ensure that our solution addressed real-world needs and pain points.
Pre-Development Testing with Figma: Before the engineering team began development, we conducted a thorough pre-development test using Figma. This allowed us to:
- Test our hypotheses at a low cost by simulating the user experience with interactive prototypes.
- Identify and resolve potential issues early in the process, minimizing the risk of costly changes during development.

Design Principles

Warehouse logistics face significant challenges, including tight margins and labor-intensive processes, which are becoming increasingly costly. Traditional robots excel at repetitive tasks but struggle with the variability of warehouse products. The goal was to bridge this gap by designing a human-robot interface that mimics human decision-making in palletization, improving safety, efficiency, and usability.

— We applied the skeuomorphism technique by replicating the design of the physical cell and the pallets, reducing the user’s learning curve for the new interface.

— We also applied Hick’s Law by minimizing choices, especially when the palletization process had finished, as this is when response times are critical to decrease decision time and avoid overwhelming users.

— Additionally, we considered Fitts' Law when ensuring that the target action is always accessible to the user, both in terms of the target's size and the distance the user has to travel. This blue CTA button is located closest to them in the tablet.

We applied the skeuomorphism technique by replicating the design of the physical cell and the pallets, reducing the user’s learning curve for the new interface

We also applied Hick’s Law by minimizing choices, especially when the palletization process had finished, as this is when response times are critical to decrease decision time and avoid overwhelming users.
Additionally, we considered Fitts' Law when ensuring that the target action is always accessible to the user, both in terms of the target's size and the distance the user has to travel. This blue CTA button is located closest to them in the tablet.

Results

21% increase in operational efficiency of warehouse processes through a user-friendly and intuitive interface.
Successfully landed meetings with two high-profile clients interested in the AI palletization solution.
Secured a pilot project contract to implement and test the interface in a real-world warehouse setting.
Increased positive user feedback by 180% after conducting pre-development user tests.

Challenges and Learnings

Effective communication with developers: Maintaining open lines of communication with the development team was crucial.

Weekly feedback meetings: These sessions allowed us to make informed decisions, stay on track, and adapt quickly to any changes or new information.
User-Centric approach: Staying focused on the user and not getting too attached to our initial designs was critical to ensure that our final product was both practical and effective.

Iterative process: Having a continuous trial-and-error approach and iterating along the way helped us reach our goals faster and better.

Accepting failure as part of the process: Recognizing that failure is an inherent part of innovation was essential.

Conclusion

User-centric design leads to increased efficiency and user satisfaction.

Collaborative ideation and prototyping drive innovation and practicality: Involving key stakeholders and the development team ensured a balance between creativity and practicality.
Iterative testing and feedback integration ensure success: Pre-development testing with Figma allowed us to validate hypotheses at a low cost and address issues early.

Challenges and Learnings

Effective communication with developers: Maintaining open lines of communication with the development team was crucial.

Weekly feedback meetings: These sessions allowed us to make informed decisions, stay on track, and adapt quickly to any changes or new information.
User-Centric approach: Staying focused on the user and not getting too attached to our initial designs was critical to ensure that our final product was both practical and effective.

Iterative process: Having a continuous trial-and-error approach and iterating along the way helped us reach our goals faster and better.

Accepting failure as part of the process: Recognizing that failure is an inherent part of innovation was essential.