Three months after a smooth SAP EWM go-live, a distribution center I visited was picking 90 order lines per hour. Before the implementation, the same team consistently hit 120. The IT team insisted the system was configured correctly. They were right. The warehouse manager showed me dashboards I would have been proud of six months earlier. But his pickers were walking an extra four miles per shift, and nobody had noticed because the old system never measured travel time at all. The software did its job. It put the problem on a screen for the first time. Nobody acted on what they saw.<\/p>\n
In one line: SAP EWM won’t raise productivity by itself. It shows you where the operation loses time \u2014 the gains come when slotting, pick paths, and labor planning actually respond to what it measures.<\/strong><\/p>\n SAP EWM<\/a> exposes the problems that were always there: bad slotting, outdated pick paths, undisciplined processes. And now it measures them in real time. Before go-live, a picker walking 200 meters to retrieve a fast-moving SKU was just how things worked. Nobody tracked it. EWM tracks everything, and suddenly the data tells an uncomfortable story. Too many companies confuse “system live” with “operations improved”<\/a>, then spend months blaming the software for reporting what their warehouse was actually doing all along.<\/p>\n Here are the five reasons productivity falls after go-live, and what each one actually costs.<\/p>\n A note on the examples: everything described here comes from real projects. Client names are withheld at their request \u2014 only the industries are mentioned.<\/em><\/p>\n In one FMCG warehouse, the top 20 SKUs made up 60% of pick volume and sat in the three aisles farthest from the dispatch area. This configuration predated EWM by four years. Nobody questioned it because the old WMS only reported order completion, not travel distance. Once EWM started showing the walking patterns, the math was simple: pickers were spending 55% of their shift just moving between locations. The cost showed up as an extra 22 labor hours per day that the pre-EWM staffing model never budgeted for. Quarterly slotting reviews would have surfaced this in week one of post-go-live analysis, but the operations team was still celebrating a clean cutover.<\/p>\n I keep seeing the same pattern: a warehouse spends months mapping its physical processes into EWM exactly as they existed in the legacy system. The 2015 pick path gets digitized, not rethought. EWM executes it faster, but the route itself is still inefficient. The business case for EWM assumed a 25% pick-rate improvement. When the actual number landed at 7%, the steering committee blamed the software. The software ran the route it was given. The fix is unglamorous: walk the physical path with a stopwatch before configuring the system path. A supervisor can map a better route in two hours. That two hours is often the difference between hitting the business case and quietly abandoning it.<\/p>\n After EWM, the work moves around. Tasks consolidate, priorities shift, and some zones suddenly carry more volume than they used to. One distribution center I reviewed was still staffing its picking and packing zones based on a 2019 spreadsheet. EWM data showed packing was overstaffed by three people after lunch while picking was short by four. The result: overtime in one zone, idle time thirty meters away, and a supervisor manually reallocating people by shouting across the floor. Fixing this doesn’t need a project. EWM already shows workload by work center in real time. A monthly labor rebalance using actual task volumes closes this gap without any system change.<\/p>\n Operators bypass the system for “exceptions,” and slowly the exceptions become the norm. I watched a picker skip a system-directed putaway because the bin was full, write the location on a sticky note, and keep going. Three other pickers had done the same thing that morning. Nobody was tracking how often this happened. Within eight weeks of go-live, inventory accuracy had drifted to 94%, triggering cycle counts that pulled experienced pickers off the floor for half a shift each week. Track exception frequency weekly. Every recurring exception is a broken process wearing a “one-time workaround” disguise. When the same exception appears three weeks running, it’s not an exception anymore.<\/p>\n Most post-go-live steering committee decks I see celebrate orders shipped and lines completed. Those are output metrics. Productivity metrics tell a different story: travel time per order, touches per order line, lines picked per labor hour. EWM reports them all. Measure the wrong thing and leadership sees green dashboards while operating cost per order rises 12% in the background. One operation I reviewed hit every daily order target for six straight months while unit picking cost quietly increased because travel time never made it into the monthly review. Shifting KPI reviews from output to productivity metrics takes one meeting to decide and about four weeks of data to make permanent.<\/p>\n What high-performing warehouses do differently isn’t complicated, and none of it requires another IT project:<\/p>\n The warehouses that sustain their go-live gains treat EWM as an operational tool, not an IT asset. The ones that don’t keep wondering why the expensive new system “didn’t deliver.”<\/p>\n A consumer goods company we worked with went live with EWM across three DCs in 2022. The implementation was technically clean. Six months in, pick rates were flat and overtime was 18% above plan. When our team walked the floor, we found slotting hadn’t been touched since cutover. Seasonal volume shifts had pushed fast movers into upper racking while slow movers sat in gold-zone pick faces. Pick paths were the same routes the legacy system used. Operators had developed five different workarounds for an exception the system flagged correctly but nobody had fixed. Over four months we rebuilt the slotting logic against actual velocity data, redesigned pick paths based on physical walk measurement, and eliminated four of the five workarounds through process changes rather than system changes. Pick productivity improved roughly 18\u201320%. The interesting part: we made exactly zero EWM<\/strong><\/a> configuration changes. The system was working properly from day one. The operation just wasn’t aligned with what the system was now measuring.<\/p>\n Go-live is the starting line, not the finish line. Automation makes bad processes faster, not better. Productivity lives in slotting, paths, labor, and discipline, not in configuration. If KPIs are green but costs are rising, you’re measuring the wrong things.<\/p>\n In most cases the software isn’t the problem \u2014 the operation hasn’t caught up with what the system now measures. The usual suspects: slotting nobody has touched since go-live, old pick paths copied straight into the new system, labor plans built on pre-EWM assumptions, workarounds that quietly became the real process, and KPI reviews that only look at orders shipped.<\/p>\n No. SAP EWM measures and directs work \u2014 it doesn’t fix slotting, routes, or discipline on its own. The productivity gains come when the operation acts on the data: quarterly slotting reviews against velocity data, pick paths redesigned from physical walk measurements, and monthly labor rebalancing. In one engagement, operational fixes alone improved pick productivity by 18\u201320% without touching the system configuration.<\/p>\n Watch the metrics that show cost, not just volume: travel time per order, touches per order line, lines picked per labor hour, weekly exception counts, and inventory accuracy. Orders shipped and lines completed tell you the warehouse is busy; they don’t tell you what each order costs. EWM reports all of these \u2014 most operations just never put them in the monthly review.<\/p>\n Faster than most teams expect, because the fixes are operational, not technical. Individual fixes are quick \u2014 a supervisor can map a better pick path in two hours, and a KPI reset takes one meeting plus about four weeks of data. A full recovery program across slotting, paths, labor, and exceptions typically runs three to four months, based on our project experience.<\/p>\n1. Fast Movers Sitting in the Back Aisles<\/h2>\n
2. Digitizing the Old Workflow Instead of Redesigning It<\/h2>\n
3. Labor Planning Frozen in Time<\/h2>\n
4. Exceptions That Become the Real Process<\/h2>\n
5. Measuring the Wrong KPIs<\/h2>\n
What High-Performing Warehouses Do Differently<\/h2>\n
\n
A Real Example: Three DCs, Zero Configuration Changes<\/h2>\n
Key Takeaways<\/h2>\n
Frequently Asked Questions<\/h2>\n
Why did productivity drop after our SAP EWM go-live?<\/h3>\n
Does SAP EWM improve picking productivity by itself?<\/h3>\n
Which KPIs should you track after an SAP EWM go-live?<\/h3>\n
How long does it take to recover warehouse productivity after go-live?<\/h3>\n
About SCM Champs<\/h2>\n