Warehouse Automation Must Prove It Can Protect Dispatch Deadlines
Adding automated packing capacity changes more than the speed at which a parcel can be sealed. It changes how work reaches packing, how orders compete for processing time and whether completed shipments make their dispatch deadlines. A faster process can increase local output while leaving the wider operation constrained elsewhere.
Responses from DigiKey and KION identify different parts of that acceptance problem. DigiKey describes incorporating additional packing throughput into production planning. KION outlines the physical information needed to test automation in an existing warehouse and argues that its value must be measured against operating conditions before deployment.
Put the new capacity into the dispatch plan
Chris Swenson, Vice President, Warehousing and Distribution, DigiKey, said the company uses a structured design and implementation process based on safety, compliance and efficiency, together with a ramp-up plan intended to protect customer service commitments.
His explanation of production planning provides the more specific operational insight. Additional automated packing capacity was incorporated as another process route, rather than treated as an isolated machine installation. “This was simply an additional process path added into our planning systems to account for the additional throughput at a more efficient rate than the manual packing cells,” Swenson said. He added that the planning approach preserves visibility of critical pull times so work can be prioritized to meet service commitments.
The distinction matters because available machine capacity and usable dispatch capacity are different measures. If picking cannot supply the new line at the required pace, packing capacity may remain unused. If completed parcels reach the next stage after its departure deadline, a higher packing rate does not establish an improvement in delivery service. Those are operational implications of the process relationship, rather than failures reported by DigiKey.
Swenson’s response describes how the company accounts for the new route, but does not disclose the readiness thresholds, trial volumes or service results used to authorize live orders.
A simulation needs the warehouse that actually exists
For Johannes Hinckeldeyn, Director Research Engineering, KION Group, useful virtual testing begins with an accurate representation of the operating environment. “Physical AI relies heavily on understanding physical geometry, obstacles, and constraints,” he said.
His requirements extend beyond a floor plan. An existing warehouse needs machine-readable spatial information capturing floor irregularities, column positions, ceiling clearances, rack tolerances, lighting and dynamic bottlenecks. He identifies point clouds and three-dimensional LiDAR or BIM scans as ways to capture that environment.
The information also needs a functional layer: identifying picking stations, staging areas, pedestrian routes, busy intersections and charging zones. Geometry describes where a vehicle can fit; those annotations help describe how the space is used.
The practical limitation follows from that distinction. A virtual route may be physically possible yet operationally unsuitable if its model omits the activity sharing that space. Simulation results therefore depend on which conditions have been represented and which remain outside the test.
Hinckeldeyn also identifies similarity to previously learned environments as an important factor. That qualification makes site assessment consequential: experience gained elsewhere does not, by itself, establish repeatable gains in a different facility.
Define improvement beyond commissioning speed
Hinckeldeyn recommends measuring sustained operational performance, autonomy, system robustness and safety against clear baselines from before AI deployment. Those categories broaden the evaluation beyond commissioning and ramp-up time, although his response does not specify numerical targets or a single preferred performance indicator.
For an operator protecting dispatch commitments, the resulting acceptance question is whether the changed process produces usable output under the conditions that matter. Depending on the operation, that could mean examining orders completed before departure deadlines alongside errors, interruptions and manual interventions. Those are possible measures derived from the operating problem, not metrics disclosed by either company.
Earlier coverage by The Supply Chainer addressed a related testing requirement. Youngjin Kim, President and CEO, Tesollo, said: “The first step should always be to verify whether the gripper can handle the customer's actual products and tasks.”
Kim was discussing robotic grippers, rather than DigiKey’s packing equipment or KION’s systems. His point adds a relevant boundary: an acceptance test must represent the work the customer needs completed. Demonstrating capability on selected tasks cannot establish performance across the operating mix.
Make live-order release an accountable decision
The two responses support complementary requirements, without establishing a shared deployment or a measured performance gain. DigiKey explains the planning connection between additional capacity and service deadlines. KION explains why virtual testing requires detailed site information and comparison with an operating baseline.
What remains for the warehouse team is to turn those requirements into an agreed release decision. The conditions tested, acceptable performance and responsibility for intervention need to be understood before more live work depends on the new process. Otherwise, commissioning can finish while the question that matters to customers remains unanswered: whether their orders will leave on time.
