AcuForecast
Overview
Our goal is to design an app that helps users move from manual labor planning to a more accurate automated process, which will result in increased labor savings. Since they trust their own process and are cautious about new tools (10% adoption), the challenge is figuring out how to increase adoption of a tool they might initially distrust.
Hypothesis
If we understand the user’s manual workflow, the data they rely on to make decisions, and important data that they don’t currently have, we can design an experience users can be confident in, which will lead to higher adoption rates. As more users adopt the tool, they will plan labor more efficiently, resulting in significant savings due to more effective labor planning.
Results
- $2 Million in annual labor planning savings
- Schedule efficiency increased by 700 bps
- Associate satisfaction score increased by 4.15 points
Persona Development
Understanding User Needs
I led user interviews to understand how they plan labor day-to-day. By uncovering their manual process, their key pain points, and their reasons for not using the auto-scheduler feature, I shaped the Information Architecture around their real workflow, creating a strong foundation for the application.
Findings
Through user interviews, I identified two distinct user groups: those focused on division-level performance and those responsible for editing demand forecasts. I recommended separating these workflows to create a more focused experience, but due to resource constraints, leadership opted for a single end-to-end solution instead. I pushed back, as designing for four users with different motivations conflicted with our goal of mirroring their manual process for easier adoption. Ultimately, the business prioritized short-term adoption to hit KPIs, with the plan to refine the interface to better align with user needs once more resources became available.
Design Process
Iterative Design
I mapped out the necessary components based on user workflows, quickly iterating from wireframes to high-fidelity designs. This was a true zero-to-one project, made even more interesting by the fact that I was working alongside our design system lead on an early-stage design system. Since many components didn’t yet exist, I sourced potential solutions, which had to be reviewed, approved, and then stress-tested in my designs. It was a unique challenge—building both the experience and the design system simultaneously.
Design Process
Iterative Design
Working on this project, I quickly learned the importance of documenting design decisions. When debates over operational logic put the project on hold, having clear records made it easier to pick up where we left off. In some cases, we even revisited scrapped designs, reinforcing the need for strong version control. This experience highlighted how essential it is to track decisions to maintain momentum and ensure a smooth design process.
Iteration #1
An early design allowed bulk editing of any value in the demand forecast, but users found it overwhelming. It also wasn’t ideal from a design and development standpoint—making all fields editable added unnecessary complexity to the logic and overall experience.
Iteration #2
Another iteration limited edits to total fields instead of day-by-day adjustments. This approach was considered because operations hadn’t finalized how the backend calculations would work.
Iteration #3
After months of iteration and stakeholder discussions, we aligned on the ideal user and editing process. We revisited the row edit best practice I had proposed earlier, which users responded well to due to its clear, focused approach.
