01 — Inventory forecasting Stop running out. Stop overstocking.
We pull your shipment history at the SKU level — usually 12 to 24 months — combine it with vendor lead times and any seasonality you can name, and produce a daily reorder report. For each SKU it shows: days of cover left, expected stockout date at current velocity, and recommended PO size based on lead time and target service level.
Where teams typically land after a few months of tuning: 10 to 15 percent reduction in stockouts, 15 to 25 percent reduction in held inventory dollars on slow movers. Bigger lifts on long-tail SKUs where human attention runs out before the math does.
02 — Pick path optimization Pickers walk less. Wave time drops.
Two parts. Real-time: for each pick wave we compute the shortest route across the locations and reorder the pick list. Most WMS systems do a naive sort by aisle — fine when slotting is fresh, useless after 18 months of demand drift. Strategic: every quarter we re-run slotting math. Top movers near the dock, slow tail pushed to mezzanine, ABC zones rebalanced based on actual recent velocity, not the assumptions someone made when the building opened.
Typical operator outcome: 10 to 15 percent reduction in picker walk time per shift. On a 12-picker floor that's roughly one full FTE recovered without changing headcount.
03 — Anomaly monitoring Catch the weird thing before it becomes the expensive thing.
Every SKU, every location, every shift has a normal pattern. When something drifts outside that pattern — a location that suddenly has triple the misspicks, an SKU whose return rate doubles in a week, a dock door whose throughput collapses on Tuesdays — we alert the right person. Email, Slack, or pushed to whatever you already use.
The win isn't catching every anomaly. It's catching the ones a human would have noticed three weeks later, by which point the damaged pallet has shipped to four customers and the chargebacks are already in the pipeline.
04 — Auto reporting The report writes itself, on schedule, in your client's format.
Most warehouses have someone who spends three to six hours a week pulling data into Excel for weekly client recaps. Inbound vs outbound, accuracy rates, top-moving SKUs, anomalies, exceptions. We automate it: connected to your WMS, written in plain English with the numbers your client actually cares about, delivered as PDF or a shared link.
Per-client templates, per-client cadence. If a client wants daily, they get daily. If a client wants Monday morning with the previous week's exceptions called out, that's what shows up in their inbox at 7am.
05 — SOP digitization Your warehouse SOPs, answerable in plain English.
Most warehouses have a binder. Or a Google Drive folder. Or fifteen years of tribal knowledge living in three people's heads. We take what you have — written, photographed, recorded — and put it behind a chat interface. New hire on day three asks "how do I handle a damaged inbound from carrier X" and gets the answer your senior lead would have given, with the doc reference attached.
Onboarding time drops. The senior team gets fewer interruptions. SOPs that were stale because nobody enforced reading them now get used because the answer shows up where the question is asked.
06 — Returns optimization Resell, refurbish, vendor-return, scrap. Decided by data.
Returns flow into a queue. For each item we look at: condition signal from the inspector, the SKU's current sell-through rate, the cost basis, the carrier's vendor-return policy, and the cost of scrapping. The output is a routing decision per item: back to active inventory, refurbish channel, vendor RMA, or scrap. Plus a weekly report on where the returns dollars actually went.
Most warehouses hold returns longer than they should and resell them at lower margin than they could. Both of those have a data answer.