How to forecast product demand on Starch
Demand forecasting is the work of turning what you know — past sales, upcoming promotions, seasonal patterns, retailer commitments — into a defensible number for how much product you need, and when. It sits at the center of nearly every downstream decision: how much to produce, when to reorder, how much cash to reserve, what to tell a buyer. The specific shape of this problem varies. For a CPG brand selling through retail, it means reconciling POS velocity with co-packer lead times before you place a production run. For a direct-to-consumer brand, it means reading into subscription churn and paid acquisition signals. For a service business, it means something else entirely. The common thread is the same: most operators are working from a forecast that's really just last month's number with a gut adjustment on top, and paying for that in stockouts, overproduction, or buyer meetings where you don't know your own numbers. On Starch, the apps built for this workflow — Demand Planner, Inventory Planner, and Retail Analytics (all currently in development; request beta access to get notified when they launch) — replace that process with a live dashboard showing SKU-level velocity, reorder triggers, and promotional lift, updated from your actual sales and POS data. What you end up with is a number you can defend, sourced from data you trust, without spending a weekend in a spreadsheet.
Why it matters
A forecast that's wrong in either direction costs real money. Overproduction ties up cash in inventory, creates expiration risk, and strains co-packer relationships. Underproduction means stockouts, lost velocity on shelf, and the kind of supply gaps that get you delisted. Buyers make ranging and promotional decisions based on your projections — showing up with bad numbers damages those relationships in ways that take months to repair. Getting this right protects margin, preserves working capital, and makes every downstream conversation easier.
Common pitfalls
Using last period's shipments instead of POS sell-through — shipments tell you what left your warehouse, not what consumers actually bought. Ignoring lead time when building the forecast — if your co-packer needs eight weeks and you're forecasting four weeks out, you're already late. Treating all SKUs the same — your hero SKU and your newest launch have completely different velocity profiles and deserve different safety stock logic. And updating the forecast monthly when your retail accounts report weekly, which means you're always reacting to data that's already a month stale.
Starch apps used
See this running on Starch
Connect your tools, describe what you want, and the agent builds it. Closed beta is free.
Choose your operator
A version of this guide tailored to your role — same recipe, different starting context.
Related workflows in Ops & Supply
Inventory shrinkage is the gap between what your records say you have and what's actually on the shelf, in the warehouse, or at your co-packer.
Read guide →Closing out the POS at end of night means verifying that what the register says happened actually matches what the cash drawer holds, what the card processor settled, and what the day's receipts show.
Read guide →Contractor job costing is the practice of tracking what a job actually costs — labor, materials, subcontractors, equipment — against what you estimated, and updating that number as work progresses and change orders land.
Read guide →Retailer deductions and distributor chargebacks are line items that show up on your remittance as money already taken — a short-pay for a late delivery, a promotional allowance you never agreed to, a compliance fee for a label that met spec.
Read guide →