An automated warehouse can look flawless in a vendor demonstration. Pallets move along a defined route, robots complete carefully sequenced tasks, and the control software displays a continuous flow of work. Then an actual shipment arrives with an uneven pallet, a damaged label, or a load profile that falls outside the assumptions used in the design. The robot may be working exactly as intended. The operation still slows down.
That is the uncomfortable truth in many automation business cases: a machine’s rated capacity is not the same thing as a distribution center’s realized capacity. The difference is created by everything the machine depends on—product presentation, packaging, pallet quality, data accuracy, software integration, human exception management, and the ability of the next process to accept what the machine produces. Those dependencies are neither exciting nor photogenic. They can, however, decide whether a multimillion-dollar investment earns its return.
Examples involving XPO Logistics, Staples, and Del Papa Distributing illustrate the issue from different directions. They are examples of organizations addressing the surrounding operating system, not evidence that their automation investments failed. Their experience deserves attention precisely because the mechanical equipment is only part of the story.
The first bottleneck may be the incoming pallet
At XPO Logistics’ fresh-goods operation in Nieuwegein, Netherlands, incoming products move into an automated crane warehouse. Before they enter the system, pallets undergo inspection. When the criteria for rejection do not match the actual requirements of the equipment, pallets can be diverted, placed on support pallets, or reloaded, consuming labor and slowing an operation designed to minimize manual handling.
A CHEP account of its work with XPO explains how the partners reviewed the inspection process and developed targeted employee training aligned to the warehouse’s pallet-quality needs. CHEP reports that rejected pallets and use of support, or ‘slave,’ pallets fell by approximately 50%, with better product flow and lower handling burden.
The specificity of this example matters. It was not a case of allowing unsafe pallets into automation to generate a better metric. It was a case of better distinguishing real mechanical and handling risks from characteristics that did not require rejection. Excessively broad screening can be just as damaging to throughput as insufficient inspection. An automated operation needs a standard that is demanding, measurable, and relevant to the equipment it serves.
A single rejected pallet is a small event; a steady stream of unnecessary rehandling can become an enduring cost structure. This is why upstream packaging and pallet requirements belong in the automation business case, not in a separate procurement discussion after the system is installed.
Software modernization is part of mechanical readiness
An equally important source of variability is the information controlling the work. Manhattan Associates reported in May 2025 that Staples had implemented Manhattan Active Warehouse Management across nine U.S. business-to-business fulfillment facilities within 18 months. The program was intended to improve picking accuracy, inventory management, process consistency, and integration with modern supply chain applications.
This is not a claim that Staples suffered a robotics ROI failure or that the WMS deployment alone produced a particular financial return. Its relevance is architectural. A modern automated system can move a pallet only when the surrounding organization knows what the pallet contains, where it should go, how the task relates to the order, and which exception rules apply when reality does not match the plan.
Warehouses routinely survive on informal workarounds: a supervisor recognizes an item by sight, a clerk corrects inventory after the shift, or an experienced operator knows that a certain product cannot be handled like the others. Those practices may keep a manual process functioning. They become costly weaknesses when an automated operation expects unambiguous identifiers, inventory locations, task priorities, and response rules.
Integration therefore has to be tested with the inconvenient transactions, not only with the normal ones. Short shipments, damaged units, substitutions, uncertain inventory, and orders arriving after cutoffs deserve as much attention as the average order. Otherwise a technically successful installation can leave the operating team to manage a growing collection of manual exceptions around the edges.
Del Papa closes the final gap in the workflow
Del Papa Distributing, a beverage wholesaler in Texas, provides a particularly useful example because the potential bottleneck came after robotic picking. Block One Automation’s Pallet Factory and Lantech’s wrapping equipment were integrated so that completed mixed-case orders could move from robotic pallet building through stretch wrapping and toward the dock without a person intervening at the wrapping stage.
A mixed beverage pallet is an unforgiving object. Shapes, weights, and heights vary; the load has to remain stable; and the stretch film must be controlled so it does not interfere with autonomous mobile robots or other equipment. Lantech’s September 2026 description emphasizes repeatable cycle times and reliable film-tail handling, details that can determine whether the warehouse execution system can dispatch the next job without allowing for a manual rescue.
This is the difference between automating a task and automating a process. If a robot can build 100 pallets an hour but downstream wrapping can prepare only 70 for shipment, the relevant operating capacity is not 100. A warehouse cannot bank the extra 30 pallets indefinitely without creating congestion, inventory, or additional handling. Throughput is governed by the constraint across the full chain of activities, not by the most impressive piece of equipment.
The Del Papa story is a documented integration accomplishment, not proof that every beverage facility can operate without labor or that a particular ROI was achieved. Its analytical value is showing the hidden design work required to make individually capable systems operate together.
An automation business case needs an exception budget
Financial evaluations usually capture capital expenditure, software fees, maintenance, staffing, and expected throughput. They are less consistent at estimating how often the operating environment will present a condition the equipment cannot process automatically, how long recovery will take, and what other work will be disrupted during that recovery.
A credible model should include the frequency of pallet rejections, unscannable labels, unstable loads, task failures, replenishment shortages, integration outages, and manual interventions. It should also distinguish a brief exception that can be cleared in parallel from one that stops a critical aisle or blocks the next workstation. These are different economic events, even if both are recorded as a single exception.
An illustrative system rated for 1,000 pallet movements a day might yield only 900 during real operation after allowing for unavailable material, planned downtime, process imbalance, and recovery work. The missing 100 movements are not necessarily ‘robot downtime.’ They are the gap between component specifications and operational output. Measuring that gap is the beginning of a realistic ROI calculation, not an admission that automation is a poor investment.
Procurement and engineering should also resist the temptation to solve every exception by adding more machine capacity. Sometimes the cheapest improvement is a better pallet standard, a changed inspection threshold, reliable product master data, or a more effective handoff between systems. Conversely, some physical constraints cannot be removed through software and genuinely require different equipment or facility design.
The real performance test starts before commissioning
Automation readiness is a cross-functional responsibility. Operations understands actual order variability; engineering knows machine tolerances; IT knows the data and integration risks; procurement influences handling-unit quality; and finance decides what constitutes a real, measurable improvement. The people who will clear exceptions should be represented before design decisions become expensive to reverse.
The most important question in a warehouse automation investment is not simply whether the robot will meet its specification. It is whether the entire system, including the least glamorous upstream and downstream activities, will meet the business’s required output. In the end, warehouses do not earn returns on robots. They earn returns on orders shipped accurately, safely, and at the promised cost.
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