We built Automcore because demand planning shouldn’t require a data science team.

Origin

The spreadsheet that kept showing up everywhere

Before starting Automcore, Angela spent seven years as a supply chain consultant at Verdanta Group, a boutique operations practice based in Los Angeles. Her clients ranged from $40M apparel brands in the Arts District to industrial parts distributors in the Inland Empire. Every engagement started the same way: open the demand planner’s laptop, find the Excel file, discover it was last updated three weeks ago.

The data was already there. The POS system had hourly transaction records. The ERP had supplier lead-time history. Weather APIs were free. Nobody had connected them — not because planners were lazy, but because reconciling three systems with different formats and update cadences is a half-day job that doesn’t survive contact with a buying cycle. Planners hedged with buffer stock and still got blindsided by stockouts, because the buffer was in the wrong SKUs.

In 2019, Angela started evaluating what was on the market for mid-market teams — 10–200 SKU categories, a 3-person planning department, no data science headcount. The enterprise platforms (Kinaxis, o9, Anaplan) required a 6-month implementation and a permanent data engineering team to maintain. Simple reorder tools couldn’t handle seasonality or lead-time variance. Nothing fit.

So she built it. Marcus joined as co-founder and Head of Data Science in early 2021, bringing eight years of applied ML from logistics software. Automcore shipped its first production forecast in Q3 2021. The goal has never changed: give a 3-person planning team the forecast accuracy that used to require a 10-person analytics department.