14 kilometers per shift & no one noticed

There are familiar logistics challenges that are difficult to quantify. For example, long walking distances between storage locations. Operators searching aisles instead of picking. There are also overhead picking motions that strain the back and slow down the shift.

You can see it. You can feel it. But you cannot accurately measure it – which makes it difficult to assess the true pain points and implement targeted process improvements.

Sulky GmbH, a specialized subsidiary of the GOLDNER Group, wanted to take a closer look at its order picking processes for hanging and flat goods and put an end to guesswork.
The result: savings potentials of up to 26%, identified through an automated efficiency analysis using wearables over a period of ten measurement days – without disrupting ongoing operations in any way.  

The initial situation: process transparency as the foundation of any efficiency analysis

Sulky operates a five-level picking area for hanging and flat-packed goods. A total of 25 employees work in this process every day.

Management deliberately chose a data-driven approach: before defining improvement measures, the process needed to be fully understood. The objective was to create clear process transparency as the basis for sound decisions regarding efficiency and ergonomics improvements.

The Motion-Mining® principle: employee movements are captured automatically, fully anonymized, visualized, and analyzed – without cameras, without manual observations, and without interfering with operations.

The measurement: what ten days of efficiency analysis revealed

Movement data was recorded across the entire picking area over ten measurement days. The results spoke for themselves:

On average, each employee walked 14 kilometers per shift. A significant portion of this distance was non-value-added, resulting from inefficient walking routes and lengthy search times within the aisles.

In addition, an ergonomic issue surfaced that had previously gone unnoticed: 19% of all handling operations were classified as critical, with a high proportion of overhead picking activities. Over time, these lead to physical strain and reduced process speed.

The process analysis identified three key levers for further process development:

• Inefficient walking routes due to a lack of digital control in put-away processes
• High search times caused by missing or unclear aisle and bin labeling
• Ergonomic risks resulting from unsuitable equipment and suboptimal storage layouts

The measures: process optimization and ergonomics analysis in practice

Based on the collected data, the MotionMiners team developed practical, tailored recommendations. Each measure was monetarily evaluated and directly linked to the identified root causes.

No generic best practices but actions precisely aligned with Sulky’s picking processes.

Three core measures were defined:

1. Glove scanners and picking trolleys with step platform
   ‍The introduction of glove scanners speeds up the picking process and reduces system interactions and interruptions. Picking trolleys with integrated step platforms enable ergonomic work at different rack heights and directly reduce the share of critical overhead handling activities.

2. Clear labeling of aisles and storage locations
   Systematic warehouse labeling may seem simple, but it delivers measurable impact. Search times, identified as a major time consumer during the efficiency analysis, can be significantly reduced through clear visual orientation.

3. Digital guidelines for put-away processes
   Digital control of put-away processes ensures goods are stored where they can be accessed most efficiently during picking. This shortens travel distances, reduces search times, and noticeably relieves employees – a central lever of sustainable process optimization in retail logistics.

The result: 26% efficiency potential including immediate improvements

Using Motion-Mining® data, Sulky was able to develop a clear and prioritized action plan.
The efficiency potential identified by the analysis amounted to up to 26%, based on picking process times.

Even more important from an operational perspective: the first improvements became visible shortly after project completion. No lengthy implementation phases. No months of uncertainty.

Key takeaways for logistics managers

The Sulky project is not an isolated case. It reflects a recurring pattern in retail logistics: processes are running, yet no one truly understands them.

Optimization potential remains hidden due to the lack of objective data.

The lesson is clear: process optimization starts with measurement. Not with assumptions. Not with gut feeling. Rather, it should be based on validated process data and clearly defined KPIs captured through a structured assessment of the current state.

Motion-Mining® provides exactly this foundation. Automated. Anonymized. Without interfering with live operations.

Frequently asked questions about process optimization with Motion-Mining®

What is Motion-Mining® and how does it enable process optimization?

Motion-Mining® is a technology for the automated and anonymized capture of movement patterns in logistics and production processes. Visualizing and analyzing this data makes it possible to identify inefficiencies, unnecessary travel distances, and ergonomic risks – forming the basis for targeted process optimization initiatives.

How does an efficiency analysis with MotionMiners CONSULTING work?

A consulting project typically includes a process walkthrough, target definition, on-site data collection, data analysis and interpretation, and the development of concrete improvement measures.Optionally, WMS, ERP, or MES data can be integrated for an even deeper efficiency analysis. Vehicle movements within processes can also be included.

What level of savings can realistically be achieved through ergonomics analysis and picking process optimization?

The Motion Mining® project created a solid data foundation for targeted efficiency and ergonomics improvements, identifying a potential of up to 26%.

The measured average walking distance of 14 kilometers per shift clearly showed where processes can be simplified and value-adding activities strengthened.

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