Each of these is a ratio or a time, and that matters. A raw count of 400 orders says little on its own. Four hundred orders out of 410 is 97.6%, and that figure tells you whether to worry.<\/p>\n\n
Dock-to-stock time and receiving<\/h3>\n\n
Dock-to-stock time exposes a bottleneck before it reaches the customer. If stock sits in a goods-in area for a day, orders can’t be allocated to it, even though it’s physically in the building. Track the average, but also look at the slowest receipts. The average hides the pallets that wait longest, and those are usually the ones behind a missed despatch.<\/p>\n\n
Stock accuracy and cycle counting<\/h3>\n\n
Stock accuracy is the KPI everything else depends on. If the system says 40 units are in a location and there are 35, the pick fails, the order shorts and the client hears about it. Mat Buttar, Head of Solutions at Clarus, who has spent around 25 years in warehouse operations, sees full stock checks and cycle counting working hand in hand. A full count sets the baseline, then regular counts weighted towards fast-moving stock keep it honest. As he puts it: “if I count 500 pallets a week… and it’s 99.2% accurate, then I can accept the rest of my stock is 99.2% accurate.”<\/p>\n\n
The results can move quickly. JODA Freight<\/a>, a 3PL, reached 99% stock accuracy, and its stock counts went from weeks to one day. Our guide to doing a stocktake<\/a> covers the mechanics, and Clarus supports structured stock checks and cycle counting<\/a>.<\/p>\n\n Picking is where accuracy usually slips, and the cost of an error lands on the customer. Pick accuracy counts the picks that were right, but the real value is in spotting which error types keep recurring. Our guide to reducing picking errors<\/a> covers the usual causes. Where a WMS is in place, the check can happen at the point of scanning. Clarus’s picking<\/a> workflow uses barcode scans to confirm the right item at the point of pick, so the error is caught before the parcel leaves.<\/p>\n\n If you’re asking what the 5 KPIs for a warehouse are, start with pick accuracy, on-time despatch, stock accuracy, dock-to-stock time and cost per order. Together they cover accuracy, service, inventory control, speed and cost, so a good number in one area can’t hide a failing number in another.<\/p>\n\n Three tests help you decide whether a measure belongs on the list:<\/p>\n\n If you still work from paper or spreadsheets, begin with stock accuracy and pick accuracy. They need the least new data and expose the biggest risks first. Add cost per order once your costs sit in one place, because a cost figure built from partial data looks precise and isn’t.<\/p>\n\n Not everyone needs the same dashboard. A practical split is by decision and by frequency.<\/p>\n\n The last point matters. Individual productivity figures help when a supervisor uses them to support someone who is struggling. They work badly as a league table, because they reward whoever happens to get the easy aisles. Clarus records who did each task and when, so supervisors can see activity by person without building that view by hand. Read more about operator activity tracking<\/a>.<\/p>\n\n A 3PL needs the standard list plus measures that show each client is charged correctly and served to contract. Two matter most. Billing accuracy is the share of billable events that were captured and invoiced: (billable events invoiced \u00f7 billable events performed) \u00d7 100. Client service level is the share of a client’s orders that met that client’s contracted despatch time. Track both by client, because an average across twenty clients hides the one who is consistently out of SLA.<\/p>\n\n Billing accuracy depends on capturing charges as the work happens. Clarus’s facturaci\u00f3n de clientes<\/a> turns completed tasks into invoices and applies each client’s tariff automatically, so the invoice reflects the work done rather than a month-end estimate. The change shows most clearly in the back office. Almac\u00e9n de St John\u2019s Hall<\/a> had spent around four days at the end of each month checking invoices that had been re-keyed from the WMS into its accounts package. Invoicing time fell by 90%, and that re-keying is gone.<\/p>\n\n Client-facing measures improve too. The portal del cliente<\/a> lets each client see stock levels and order status without calling the warehouse, so status questions don’t have to pass through the operations team.<\/p>\n\n A KPI is easier to judge when you can compare it with others. The Warehousing Education and Research Council (WERC) runs an annual DC Measures study with DC Velocity. Its 2024 edition covered 38 operational metrics, and its online benchmarking tool holds DC Measures data back to 2013, according to MHI Solutions’ report on the 2024 study<\/a>. El WERC DC Measures survey<\/a> asks participants to rate their own performance on each metric, which shows the kind of measures the study tracks.<\/p>\n\n Use benchmarks as a direction check rather than a target. A benchmark from a high-volume automated site tells you little about a multi-client ambient 3PL, so compare like with like. Your own trend line is the best measure of progress, and it’s the one you control.<\/p>\n\n A warehouse KPI template works best as a single sheet with one row per measure. You can build it in Excel now and move it into your WMS reporting later. Use these columns:<\/p>\n\nPrecisi\u00f3n en la selecci\u00f3n<\/h3>\n\n
The five KPIs to start with<\/h2>\n\n
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<\/figure>\n\n\n\nKPIs for 3PLs: billing accuracy and client service<\/h2>\n\n
Benchmarks and where to find them<\/h2>\n\n
Warehouse KPI template and dashboard<\/h2>\n\n
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