How a Manufacturer Cut Order Prep to 2 Hrs a Week

The client spent 15 hours a week processing customer orders. Orders were downloaded from SAP Cloud, cleaned and normalized by hand to match their internal format, then uploaded to their own ERP. Because of disparate Excel formats, forecast windows and differing business logic, by the time orders were processed and allocated, forecasted revenue was wildly inaccurate — over- and under-reported by month-end, with no clear view of where the process broke down.

ScrumLaunch designed an AI workflow that ingests customer orders, cleans and normalizes them to the client's internal format, and measures forecast demand against actual realized demand — built with Claude paired with Python.

ScrumLaunch designed an AI workflow to replace a manual, error-prone order process. It ingests customer orders downloaded from SAP Cloud, cleans and normalizes them from a patchwork of Excel formats into the client's internal structure, and reconciles forecast demand against actual realized demand — built with Claude paired with Python scripts and connected to the client's JobBoss ERP. The hardest part was capturing the business logic: the tribal knowledge living around the workflow, like the fact that orders actually leave the facility on a different date than the one listed or requested. Those small, insider-only rules drove much of the fine-tuning needed to reach acceptable accuracy. With that logic encoded, what had taken a person fifteen hours a week collapsed to under two, and monthly revenue forecasting — previously thrown off by disparate formats, forecast windows and business logic — improved by 30%. The build came together in under four weeks.

Any company that has to process orders into an internal dataset or ERP. These collection points are the tip of the spear — misclassified or unclean data has waterfall effects downstream — and they open analytics opportunities most companies don't have time to pursue day to day.