AutoMark

Improving factory workflow for a German automotive parts manufacturer

Context

AutoMark Automotive is a German automotive parts manufacturer that has factories opened in almost every major country in the world.

They are looking to reduce factory costs by automating some tasks, reduce risks (like broken machinery that results in delaying orders) and overall increase factory efficiency.

They needed a digital solution to help improve their factory workflow and decrease malfunction repair time

The main challenge was the delivery timeline. We had to ship the solution in almost 5 months. This meant we had to do a solid MVP validation and tight project prioritisation.

The solution also had in the future to map to other locations, not just Romania, as AutoMark plans to deploy the ticketing system in all its locations across the globe.

01. Discover

Qualitative interviews
Survey
Demographic data analysis

02. Define

Define project goal

03. Ideate

Mindmap
Sketch

04. Prototype

Wireframes
Low Res mockup

05. Test

Usability tests

My Process

01. Discover

In tackling this project I needed 2 main pieces of information:

What is the current process in regards to assembly line malfunction

Who are the people that interact with it

Ideally I’d have been present on the factory floor to conduct user interviews and see the assembly lines live. However due to time limitations we had constant video calls with onsite managers that would show us how the factory works.

I also reached out to the onsite HR department for statistics on the employees and I conducted a survey that employees could fill out.

02. Define

Correlating the survey data, the HR department data and the job de scriptions resulted in these 3 personas:

Paul
The Technician

"I need to fix this as fast as I can"

Problem Statement:
Paul needs to a way to know when a station is broken and if parts are available for it in order to do his job faster and get bonuses.

Goals

In line with the business goals, I created a series of goals that would then map onto how the app would be analysed after implementation.

Reduce time between actual malfunction and time of repair.
Improve technician response time
Reduce station downtime by 30% in 3 months (targeted to main parts replacement)

03. Ideate

Due to our upcoming deadline, we had to move through the design phases quite fast. This meant that some steps which would otherwise be clearly defined, now become meddled with the next.
In this case the How Might We exercise went straight into feature prioritisation.

04. Prototype

Based on the How Might We statements we decided to create a mobile app for the factory floor workers . This app will feed data into a Manager desktop app centered around analytics and data reporting.

While the dev team worked on the backend scaffolding for the functionality I went on and tested the created prototype starting first with mapping the ticketing flow happy path.

05. Test

After testing the prototype we had a couple of findings which were then incorporated into the final screens design:

Color coding was not immediately understood, this led to confusion on how critical the malfunction is
We had to make the buttons way bigger than anticipated
People didn’t understand some of the wording we used
The worker shift check in/check out was hidden