90 minutes wasted per shift: What Motion-Mining® reveals in order picking

Order‑picking processes are among the most standardized workflows within intralogistics. Pick‑by‑Voice systems, clearly defined routing concepts, and fixed process flows ensure structured execution of customer orders. Nevertheless, Motion‑Mining® analyses consistently reveal that even mature and well‑organized picking zones suffer from hidden time losses that remain unnoticed during daily operations.

These losses include short waiting phases, avoidable walking distances, or short interruptions that sum up to as much as 90 minutes of non‑productive time per shift. Through sensor‑based data acquisition, these inefficiencies can be measured objectively for the first time and reduced in a targeted manner.

A recent project example highlights where these hidden time drivers arise, how significant their actual impact is, and how companies can leverage Motion‑Mining® to identify data‑driven optimization potentials in order picking.

Process in Focus: Pick‑by‑Voice in a Floor Block Storage

The analyzed manual process involves order picking from a floor block storage area. The warehouse operators:

• work with Pick‑by‑Voice technology,
• follow a one‑way traffic layout within a closed picking loop,
• operate with one to four roll cages, partially equipped with divider levels for parallel orders,
• and complete their tasks at stationary label printers located at fixed positions.

At first glance, the process appears clean and structured. However, Motion‑Mining® data reveals that significant inefficiencies are hidden beneath this structure - inefficiencies that remain unnoticed in daily operations yet measurably impact overall productivity.

Deep Dive: 1 Hour 30 Minutes of Waiting Time per Shift

The recorded data indicates that each operator experiences approximately 90 minutes of waiting time per shift:

• 14 minutes for short standing/orientation breaks (less than 5 seconds)
• 1 hour and 15 minutes for genuine, active waiting times (more than 5 seconds).

Micro‑interruptions of under five seconds—caused by natural orientation movements or brief handling adjustments—are process‑intrinsic.

However, waiting times above five seconds represent a significant productivity loss that would likely never have received sufficient attention without a data‑driven analysis.

Where Does the Waiting Occur? Spatial Distribution of Idle Times

The analysis reveals a clear pattern:

• Up to 27 minutes per shift accumulate in the area around the label printers.
• Additional delays occur at rack systems, in the drop‑off zone, and in adjacent areas.

This distribution makes one point unmistakably clear: The primary bottleneck is not the picking activity itself, but the final labeling step.

The analysis thus provides crucial information that would be virtually impossible to identify using conventional observation methods.

Activity Distribution Across the Process

The following time shares per shift were recorded:

• Handling: 03:09 h
• Walking: 02:29 h
• Driving 01:29 h
• Standing: 01:30 h

Since the entire picking operation is performed on foot, the activity categories reflect typical motion patterns

• Handling: All interactions with items or roll cages
• Walking: Movement without a roll cage
• Driving: Pushing or pulling the roll cage
• Standing: No movement, including waiting phases (e.g., at the printer)

With an assumed net shift duration of approx. 8:30 hours, standing time accounts for roughly 18% of the total working time - a considerable share compared with productive activities such as handling and walking.

Actionable Levers Based on the Analysis

Two primary optimization levers arise from the data:

1. Expanding stationary printing infrastructure: Adding additional printers or decentralizing their placement reduces operator congestion at fixed printing locations, thereby minimizing waiting times during the final process step.
2. Deploying mobile printing solutions: Mobile printers enable labeling directly at the roll cage or in immediate process proximity. As a result, both walking distances and idle times related to label printing are significantly reduced.

Conclusion: Measurement Data as the Foundation for Targeted Process Adjustments

The analysis shows that considerable time-saving potential remains hidden even in mature, well-structured picking processes. This example illustrates precisely where traditional observation methods reach their limits. In this case, it is not the picking process itself that causes more than an hour of waiting time per employee every day, but rather a structural bottleneck in the final step. This example is representative of a broader pattern: in many areas of intralogistics, it is not the obvious weak points that conceal the greatest efficiency reserves, but rather short, repetitive downtimes that remain invisible in day-to-day business. Motion-Mining® data reveals these patterns. It is objective, localisable and can be used as a basis for targeted optimisation measures.