ENLYZE

0 → 800+ users · seven-figure ARR

Built from zero

I helped build the products from zero. By the end more than 800 people used them and annual revenue ran to seven figures.

UX as a buying factor

Customers saw the UI and UX as what set ENLYZE apart. For several, it decided the purchase.

weeks → days

Data onboarding

Getting usable data from a newly connected machine used to take weeks. With my workflow for data contextualisation it takes a few days.

No measurement without context

A measurement alone says nothing. Only the order, product and shift make it useful. So I brought process data and bookings together in one view.

Objects before screens

I described production as an object model of machine, order, run, downtime, reason, operator and shift. Product, design and engineering build on it.

0 min manual entry · first-pass print 70 → 78 %

Automatic Downtime Detection

At a maker of flexible packaging, nobody enters downtime by hand anymore. The review happens daily instead of once a month.

Deciding what not to build

The plants lacked usable data. So instead of generic dashboards we built ENLYZE Edge. Plants now fix data connection faults themselves, which frees up support.

ENLYZE Edge

I co-owned the concept and delivery of ENLYZE Edge.

Part of the front end

I wrote part of the front end for the interface myself.

Process Data Analysis

We built it for ourselves first, then gave it to customers. We saw the need in our own effort, not in a market assumption.

Production overview

With engineering I set up a decision tree before any screen existed. It decides which machine stands out first in the overview.

Drill-down to the reason

OEE is just a number. It splits into availability, performance and quality. In my design a drill-down leads from each part to its reasons.

Designed for the meeting

Reports are for decisions, not for the archive. I designed them for the plants' weekly and monthly meetings.

Learning from the best run

A plant's best knowledge sits in its own best run. With the Golden Run I made that run the best practice for every run after it.

Real data, not theory

Only real data should be the basis for production, not theoretical documentation. So in the digital setup sheet the settings come from the plant's own production history.

Where the work happens

Targets only help where the work happens. I designed the Co-Pilot for the machine terminal. The operator sees at once which parameter is off.

Proof without extra work

Documentation should be a by-product of the work, not an extra task. I designed the batch protocol to build itself from the machine data after every order.

Design system

I built the design system in Figma and developed it further for the whole company.

Voice of the customer

In the team I spoke for the users. To do that I combined interviews with numbers from product analytics.

Research on site

I did research on site in the plants, from machine operators to management.

Product trio

I made product decisions in a trio with the CTO and the CPO, close to sales and customer success.

Handoff to engineering

I reorganised the design process and the handoff to engineering. Product, design and engineering worked together better afterwards.

Hired a designer

I hired and onboarded a designer.

Design principles

I wrote five design principles for ENLYZE and anchored them in the team.