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How many units did your warehouse handle per labour hour yesterday? Not last month, not “on average” – yesterday, broken down by picking, loading and unloading. If answering means calling the shift supervisor and opening a spreadsheet, we have something to talk about.
An SGGW study of three distribution warehouses found that six months after a WMS was implemented, labour productivity was 40% higher than before implementation, and at one site it stabilised at 50% above its previous level (Wicki, Ekonomika i Organizacja Logistyki 5(3), 2020). That 40% gain did not come from faster forklifts. It came from measuring work where it happened – and responding that same day.
The formula is simple: warehouse productivity = units handled / effective working time. The numerator is easy to find – the number of packages, pallets or order lines. The denominator is where it gets tricky. Scheduled hours are based on staffing levels. Effective hours are scheduled hours minus absences, time spent working in other sections, administrative work and downtime. In a spreadsheet, the denominator is a figure someone enters: the picking team worked 600 hours today. In a WMS, it comes from recorded data: those 600 hours are the total time logged on handheld terminals in the picking zone.
That difference matters. In one example, the same warehouse and the same team recorded daily productivity of 86 to 133 units per labour hour over two weeks. Picking productivity ranged from 100 to 153. In other words: the same team handled roughly 50% more units per labour hour on one day than on another. The monthly spreadsheet shows an average of 115 and everyone is happy. The question is: what was happening on the days when the figure dropped to 86?
After assessing warehouse operations at a manufacturing company over two years, researchers at Politechnika Koszalińska came to a familiar conclusion: improve data recording standards and introduce real-time monitoring with a way to respond quickly (Cieciura, Dyczkowska, Zeszyty Naukowe WNE 29/2025). The problem is rarely that nobody measures productivity. It is that the figures are calculated once a month from data reconstructed from memory at the end of the day instead of recorded as the work happens.
A WMS measures productivity differently. Each operation is recorded with its operator, zone, start time and end time. This reveals details that a spreadsheet will miss:
Research from Politechnika Krakowska shows that travel accounts for the largest share of picking time, and that errors stem from work organisation, time pressure and difficulty locating products (Wyraz, Czynniki wpływające na efektywność zautomatyzowanych magazynów). We have also discussed this on LinkedIn. These are areas a WMS can directly influence: picking routes, ABC classification, replenishment of pick locations and multi-order picking. We covered them in our article on warehouse picking – here is one point worth adding: measuring productivity by zone is how you find out whether your ABC setup is improving picking or simply looks good in a report.
This is the most costly question to get wrong. The ManpowerGroup report “Logistyka w Polsce”, prepared with Łukasiewicz – Instytut Logistyki i Magazynowania, shows that warehouse worker was the hardest role to fill in Polish logistics, more than a third of companies identified staffing shortages as a barrier to growth, and around 90% used flexible forms of employment – mainly temporary work (ManpowerGroup / Łukasiewicz ILiM, 2021). Every temporary worker booked “just in case” adds cost. Too few people on shift can mean missed SLA targets, delayed shipments and dissatisfied customers.
The staffing calculation is simple: planned volume in units / productivity standard = effective labour hours required. Allow for absences to calculate the scheduled hours and the headcount you need. The problem is that the productivity standard in the spreadsheet comes from last month’s average. But you are planning for next Friday, before a long weekend, when the share of small-item picking will jump from 70% to 85% and productivity will fall, as it does every year.
A WMS gives you the data you need. You can look at the same period last year, the same order mix and the productivity of each section – day by day, instead of as a monthly average. You can plan staffing by week and by day, and work out how many agency workers you need without guessing. For management, this means one thing: you can manage labour cost per unit instead of simply reporting it after the fact.
The productivity module is designed so nobody has to copy hours into a spreadsheet:
The SGGW study points to one more thing: after a WMS goes live, it takes at least six months for teams to learn to work with it (Wicki, 2020). Productivity does not rise on go-live day. It rises when the shift supervisor sees for the fiftieth time that zone B has been running at 60% of its target since 13:00 and asks why. That routine builds an advantage competitors cannot replicate in a quarter.
Would you like to see how productive your warehouse really is – day by day instead of as an average? We will analyse your data and show you where you can save labour hours.
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Sources: Wicki L., SGGW, Ekonomika i Organizacja Logistyki 5(3), 2020 · ManpowerGroup, Łukasiewicz ILiM, Logistyka w Polsce, 2021 · Cieciura M., Dyczkowska J.A., Politechnika Koszalińska, ZN WNE 29/2025 · Wyraz E., Politechnika Krakowska · GS1 Polska, Akademia Cyfryzacji: System WMS i inne narzędzia w magazynie
Zbiórki magazynowe często decydują o realnym koszcie obsługi zamówienia. W artykule omawiamy, jak łączenie zleceń, multi-order picking, rotacja ABC/XYZ oraz analiza KPI kompletacji pomagają skracać trasy, ograniczać czas pracy i zwiększać przepustowość magazynu bez rozbudowy infrastruktury.
The implementation of WMS is an operational project that requires conscious preparation of the warehouse. Check out a practical checklist that shows you how to organize processes, data, structure and team before the system starts to reduce the risk of errors, shorten stabilization and achieve real business results faster.
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