Short answer:

Because you’re trusting three different clocks to tell the same time — Shopify, your ERP, and your warehouse — and none of them sync perfectly.

What’s happening:

Each system updates on a different trigger. Shopify adjusts on order creation, your 3PL updates on carton scan, and your ERP posts after fulfillment. Lag those by a few hours, toss in returns, bundles, and partial receipts, and drift becomes permanent.

Fix it:

• Choose one “system of record” for on-hand inventory.
• Use webhooks or event-based syncs, not timed CSV exports.
• Reconcile delta-based changes daily.
• Stop bundling SKUs without mirrored component logic.
• If you can, move to an OMS that unifies orders, inventory, and fulfillment in one ledger.

Short answer:

Because you’re treating a living system like a static one. Demand isn’t linear — it spikes, stalls, and decays faster than your Excel model can blink.

What’s happening:

You’re over-fitting to recent data, ignoring seasonality, or missing your true lag time from PO to delivery. And most small brands don’t separate sell-in (wholesale) from sell-through (actual customer orders), so your forecast is already off by one layer of optimism.

Fix it:

• Base forecasts on velocity per SKU per channel, not just top-line growth.
• Bake in a “confidence band” — ±20% buffer for unknowns.
• Use demand signals: restock alerts, cart data, waitlists, wholesale POs.
• Review weekly; lock monthly. Forecasting isn’t set-and-forget.
• Automate it when you can. Modern demand-planning engines adjust as orders flow in.

Short answer:

Follow the timestamps. Truth lives in the event log.

What’s happening:

Shopify timestamps order creation. Your 3PL timestamps when it receives that order — often minutes or hours later via API delay. If the 3PL clock starts late, every SLA looks “on time” to them and late to you.

Fix it:

• Pull raw order-creation vs. fulfillment timestamps.
• Identify delay points: ingestion, pick/pack, label, ship-confirm.
• Make sure your SLA starts at order creation, not order receipt.
• Automate exception reports daily — late orders, missed scans, etc.
• If they still argue, share the log. Data ends debates.

Short answer:

You’re buying with your heart, not your data.

What’s happening:

Ops and marketing aren’t aligned. You forecasted based on hope ("this one’s gonna blow up") instead of velocity. Suppliers love that; cash flow doesn’t.

Fix it:

• Rank SKUs by turns and margin, not emotion.
• Set reorder points off sell-through rate, not PO quantity.
• Bundle or liquidate dead stock fast — it’s not getting prettier.
• Shift forecasting to channel level — DTC ≠ wholesale ≠ Amazon.
• Build feedback loops between sales and ops. If you can’t meet weekly, automate the signal.

Short answer:

Treat it like a data migration, not a warehouse move.

What’s happening:

SKU IDs, pack sizes, and case units didn’t map 1:1. Your new 3PL imported names instead of IDs, or they used a different UOM (unit of measure).

Fix it:

• Export master SKU list from your system of record.
• Standardize fields: SKU, UPC, dimensions, case pack, UOM.
• Audit one category at a time.
• Push a clean import via CSV or API, then lock naming conventions.
• Going forward, version your product data like code — no ad-hoc edits.

Short answer:

Because your contract rewards them for volume, not efficiency.

What’s happening:

Intro pricing hides surcharges — storage, labeling, pick fees, or dimensional minimums. Once your order volume stabilizes, your rate card shifts to "real" cost.

Fix it:

• Audit invoices line-by-line quarterly.
• Ask for pick/pack time studies and error rates.
• Negotiate “blended” or “per-unit” rates based on actual throughput.
• Use your data: if accuracy and SLA rates are >99%, request a discount.
• Never renew without a full cost-per-order breakdown.

Short answer:

You shouldn’t be typing anything for Target in 2025.

What’s happening:

You’re still building EDI documents by hand or using PDFs instead of data feeds. That’s error-prone, slow, and expensive.

Fix it:

• Use an EDI-enabled OMS or integration layer.
• Map each retailer’s requirements once, then auto-populate from POs.
• Validate against Target’s 856/850 specs before sending.
• If you can’t justify full EDI, use a lightweight translator that syncs via API.
• You’ll pay for automation once — or pay chargebacks forever.

Short answer:

That’s because PO data isn’t standardized. Everyone pretends it is.

What’s happening:

Each system structures POs differently: some treat them like transactions, others like documents. Field names, line-item IDs, and tax logic rarely match.

Fix it:

• Define your PO schema first — don’t let vendors decide.
• Translate once at the API layer, not in every app.
• Store POs as structured data, not PDFs or attachments.
• Log every update event; version control them.
• Systems that use a unified data model (Endless does) avoid this by design.

Short answer:

Follow your order lifecycle — margin leaks hide between systems.

What’s happening:

Fulfillment, freight, and returns eat into margin invisibly because they’re booked post-revenue. You think you’re profitable until invoices arrive.

Fix it:

• Map gross-to-net for every SKU.
• Pull true landed cost: product + freight + fulfillment + returns.
• Tag each order with those costs automatically.
• Visualize margin by channel — wholesale may look big but bleed cash.
• If you can’t see it in one dashboard, you can’t control it.