Background
Singuli is a SAAS product that uses AI to help clients predict product demand, ensuring they produce the right amount—avoiding excess inventory or stockouts that could result in lost revenue.
The Challenge
Clients using Singuli’s AI-powered demand generation software are typically merchandisers or supply chain managers tasked with ensuring they produce the right amount of product.
They rely on accurate forecasts to prevent overproduction or stockouts, both of which can impact revenue.
However, despite the powerful forecasting algorithm, clients struggled to independently set the necessary parameters for each product forecast.
This led to frustration, as no client could complete the setup without assistance from Singuli’s staff.
Time is a coveted resource, and both clients and staff were wasting it on unnecessary guidance.
The flow needed to be redesigned to empower clients to confidently set up demand forecasts for one or more products, enabling them to work autonomously and efficiently.
Research
I conducted a competitive analysis and interviewed subject matter experts to identify client types and uncover common pain points in the product forecasting setup process.
Define User Story
Using the research findings, I reframed the data as How Might We Questions, to facilitate brainstorming and feature prioritization with stakeholders and developers.
Data requirements
To ensure accurate predictions, the algorithm required specific data inputs.
Working with subject matter experts, I mapped out various scenarios to define mandatory information and designed fallback logic for cases where clients lacked complete dat
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User Flow
The products page would host information and the user would need to open tools to set threshold alert levels and go to any store affected by low inventory.
