Real-world warehouse stocktaking comic showing the relatable challenges of counting inventory and cycle counts.

Humor in Inventory

Counting stock is genuinely funny sometimes, in a way that only people who've done it understand. We've been adding comics to the Yuneva site — real scenarios, not clip art of smiling warehouse workers. If you've ever sworn at a cycle count, some of these will hit close to home.

A warehouse returns area holds unprocessed products while the WMS shows them as available inventory, highlighting the need to classify returns as restock, refurbish, scrap, or vendor return at receiving.

How should businesses manage returned inventory efficiently?

Every warehouse has a returns corner. Nobody planned it. It just... formed. The pain isn't the returns themselves — it's that they sit in a grey zone between resellable, refurbishable, and "ask Kevin." Meanwhile the WMS insists the stock is on the shelf. Pickers know better. A returns process only works if the decision — restock, refurb, scrap, vendor return — happens at receiving, not three weeks later in a spreadsheet.

Ghost inventory: the stock the system swears is there

Stock in the system. Cobwebs on the shelf.

Every warehouse has a slot that shows stock in the WMS and cobwebs in real life. Everyone knows. Nobody fixes it. It quietly funds backorders and awkward customer emails for years. The ghost isn't the missing stock. It's the adjustment somebody almost made in March and then got pulled to the dock.

A warehouse team increases cycle count frequency without improving inventory accuracy because variances are adjusted without addressing root causes, causing the same discrepancies to repeat.

Why do frequent cycle counts fail to improve inventory accuracy?

More counts, same accuracy. It's one of the strangest patterns in warehousing: teams triple their cycle count frequency and the number barely moves. The count isn't the problem. It's what happens after — or doesn't. Variances get adjusted away, root causes never get logged, and the same SKU shows up on next week's recount. You can't audit your way out of a process gap.

Warehouse teams compare AI logistics forecasts with manual overrides on a whiteboard, showing that real savings come from connecting predictions, decisions, and the reasons behind them.

How Predictive Planning Reduces Logistics Costs

Every predictive planning deck promises lower logistics costs. Every warehouse I've walked has a whiteboard of manual overrides next to the printer. The model isn't wrong. It's just not the thing making the call at 6am when a container's late and someone has to decide whether to expedite. The savings show up when the forecast, the override, and the reason are all in the same place — and someone can actually see the pattern.

Warehouse teams face conflicting inventory numbers between the WMS, storage locations, receiving, night shifts, and purchasing, creating a normalised cycle of mistrust and inaccurate reordering.

Your Inventory Number May Be Lying to You

Every warehouse has a number that everyone quotes and nobody trusts. The WMS says one thing, the slot says another, receiving disagrees with nights, and purchasing reorders off whichever one sounds most confident. The scary part isn't the wrong number. It's how normal it feels to work around it.

A warehouse inventory count reveals variances created by unscanned pallets, workarounds, a

Why your inventory results are made in the month, not on count day

Count day doesn't create your variance. It just shows you the month you already had. Every unscanned pallet, every quiet workaround, every "we'll fix it later" — those are the entries. The count is the receipt. If you want a better number in 30 days, the work is on day 4, day 11, day 19. Not the morning of.

A procurement savings claim of 18% is shown separately from hidden costs such as relabeling, short-ships, returns, and dock overtime, which are spread across different teams and overlooked.

The Cheapest Supplier Can Be the Most Expensive Decision

The new supplier came in 18% cheaper, and procurement got a slide. The warehouse got everything else. Pallets labelled 480 that hold 431. Wet cartons. Barcodes with the wrong prefix, so the whole lot gets relabelled by hand. Boxes that fold if you stack them two high. None of it touches the unit price, because it’s spread across receiving, quality checks and the overtime budget. The saving is real. It’s just being paid for in another department.

Retail shrinkage benchmark of about 1.6% and e-commerce shrinkage of 1.8–2%, showing how theft, damage, receiving errors, miscounts, and unknown losses contribute to the overall figure.

What is the average inventory shrinkage rate for retail and ecommerce businesses?

The retail shrinkage benchmark hovers around 1.6%. Ecommerce trends a bit higher, closer to 1.8-2%. That's the number that ends up in board decks. What it actually contains, once you pull the thread, is theft, damage, receiving errors, miscounts, and a category most teams quietly label 'unknown.' The benchmark isn't wrong. It's just doing a lot of work to feel like one number. Worth asking what your 1.6% is actually made of.

Finance and warehouse operations have conflicting inventory figures because they use different counting methods, leading to last-minute month-end reconciliation.

Finance has one number. Ops has another.

Every warehouse has this email thread. Finance has one number. Ops has another. Neither is wrong, exactly. They're just counting different things and nobody wrote down which was which. The reconciliation usually happens on the last day of the month, over coffee, by someone who wasn't there when the returns came in.

AI inventory planning illustration showing demand forecasting aimed at reducing stockouts and excess inventory while emphasizing the need for accurate data, demand shifts, promotions, and supplier lead-time changes.

How does AI inventory optimization reduce both stockouts and excess inventory?

Every AI inventory pitch promises the same thing: fewer stockouts AND less excess. Both. Simultaneously. Forever. In practice, the model is only as good as the data you fed it and the humans checking its outputs. A forecast that hasn't seen a demand shift, a promotion calendar, or a supplier lead-time change is just an expensive average. The wins are real. They're just quieter than the demo.