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“On time.” That is what the system shows. The delivery has been booked, the order has been completed, and replenishment has been scheduled. Yet the product is still missing from the shelf. For many retail organizations, this is not an exception but an everyday reality that exposes a system gap which cannot be closed through better planning alone. The root cause of the issue lies not in planning, but in execution.
ERP and warehouse management systems are essential. They structure material flows, control inventory, and provide the foundation for planning and reporting. At that level, they deliver exactly what they are designed for.
However, they have a structural limitation: they capture transaction data, not operational reality.
In day to day operations, this means the following. A system records goods receipt as soon as the scan is completed. It does not recognize that the pallet remained in the aisle for twenty minutes because the target area was occupied. It does not capture that an employee walked the same route three times because the putaway logic did not match the current warehouse utilization. And it does not identify that time is consistently lost at the interface between inbound logistics and order picking.
Delays rarely stem from a single clearly identifiable error. Instead, they result from many small deviations that accumulate over the course of a day. These deviations remain invisible in reports until they appear as missed service levels or empty shelves. As long as systems only show what happened but not why, targeted optimization remains based on assumptions.
According to a study by BOSTONtec, the majority of operational costs in modern warehouses is still driven by manual labor. At the same time, this area remains the least transparent and often goes unnoticed in daily operations until reflected in performance metrics. Throughput times fluctuate, service levels are missed, but the underlying causes remain unclear because they cannot be traced back to a single KPI. They are hidden within the gaps of the process.
Examples from practice are easy to find. Travel distances increase because a layout change implemented months earlier was never evaluated for its operational impact. Waiting times occur at the interface between two zones not because employees are working inefficiently, but because process synchronization between areas was never properly aligned. Interruptions caused by system queries, coordination efforts, or missing materials become routine and cost valuable minutes every single day.
When multiplied across all employees and shifts, these effects result in significant cost disadvantages. At the same time, high performing employees often remain unidentified, and organizations miss the opportunity to replicate their best practices. These effects are not reflected in standard KPIs, yet they determine service quality, cost efficiency, and operational capacity on a daily basis.
Traditional systems stop where actual physical work begins. Motion-Mining® addresses precisely this gap.
Using wearables and Bluetooth beacons, the technology creates a digital representation of manual processes, fully anonymized, compliant with data protection regulations, and without requiring changes to existing IT systems. Instead of transaction data, it captures real motion data directly from operations.
The system does not only record what employees are doing, but also where activities take place, how long they take, and where time is lost. If operational data is available, the motion data can be enriched with booking data from systems such as a warehouse management system, providing an even deeper level of process insight.
From this combination of data, highly actionable insights emerge. An order picker may walk twelve kilometers per day, of which three could be avoided through optimized item placement. At the interface between inbound logistics and order picking, an eight minute waiting time may occur every morning due to misaligned shift schedules. In a specific zone, the same process may take up to forty percent longer for different employees not because of individual performance differences, but due to structurally inefficient routing.
These insights are not based on samples or observations. They are derived from complete motion data collected over extended periods of time. This enables a new level of process optimization based on facts instead of assumptions.
Agent based systems will fundamentally transform logistics. They will prioritize, manage, and trigger actions autonomously, not based on historical reports, but on real time operational conditions.
However, for this to work, they require a data foundation that goes far beyond transaction data.
A system that is intended to learn how a warehouse truly operates needs to understand how work is actually performed. Which routes are taken? Where do waiting times occur? How does capacity change depending on shifts, zones, and order volumes? These are the types of data that are largely missing in most organizations today, simply because there has been no scalable method to capture them.
Motion-Mining® closes this gap. The motion data generated from real operations provides a reliable foundation for training agent based systems and physical AI. In doing so, it lays the groundwork for a new level of automation that is capable of responding to operational reality rather than abstract system states.
Our free factsheet provides you with a comprehensive overview of our hardware and software solution.
Overview of the functions of the MPI Dashboard
Details about anonymization and data security
Technical specifications of the hardware and software
