Hall: 4 Stand-Nr.: C25
New Level for Process Analysis in Intralogistics through the Combination of Movement and Activity Data / Integration of WMS, ERP, TMS, and MES Data / Regular Operation Started / Targeted Identification of Process Bottlenecks and Deviations with Higher Detail Level
The intralogistics optimizer MotionMiners has successfully transitioned its new "Operational Data Matching" feature into regular operation. The first user and former pilot partner is Zufall Logistics Group, which can now break down its piece goods handling processes to the relation, packaging, and customer levels for the first time. This is achieved by combining anonymized movement data (collected using Motion-Mining technology) with its own TMS data on the analysis platform, MotionMiners PROCESS INTELLIGENCE. MotionMiners will present the innovative "Operational Data Matching" at the LogiMAT logistics trade fair in Stuttgart (March 11-13, 2025, Hall 4, Booth C25).
Sensor Data as a solid Basis
The foundation for Operational Data Matching is the anonymized movement data of employees or vehicles, recorded by the user with Motion-Mining sensor sets. "In our 11,000 m² piece goods hall in Göttingen, we collected a total of 600 data hours in the area of international piece goods traffic over three weeks," outlines Daniel Kaiser, Senior Expert in Business Process Optimization at Zufall Logistics, and explains, "these are data that were previously unavailable and now bring transparency to the handling processes during presorting, at the ramp, and during loading."
Deeper Analyses through Combination with TMS, WMS, ERP, or Machine Data
With Operational Data Matching, process engineers gain an additional dimension. Users are enabled to correlate the analysis results from MotionMiners with their own operational data from existing systems (TMS, WMS, ERP, machine data) on the MotionMiners platform. This elevates process analysis in intralogistics to a new level. User Kaiser confirms: "Operational Data Matching helps us to detail evaluations and more precisely identify potential process bottlenecks or deviations."
Optimization Potentials through Machine Learning
Data generation and determination of process metrics are carried out using the MotionMiners PROCESS INTELLIGENCE analysis dashboard. "We use machine learning to identify inefficiencies and uncover optimization potentials," explains Sascha Kaczmarek, co-founder of MotionMiners, highlighting the newly created added value, "through integrated Operational Data Matching, companies receive metrics in a new depth and granularity within a short time. For example, efforts per order, customer, or item can now be determined to uncover optimization potentials more precisely. The application spectrum is versatile: the matching tool supports benchmarking, creates transparency, and serves to audit existing process models."
Moving away from Averages with Order Data
The import of operational data to the platform is possible in CSV format, ensuring quick and flexible processing without the need for an IT interface to existing systems. Using an example, Kaiser demonstrates the gain in information for piece goods handling: "The MotionMiners measurements showed an average piece goods loading movement of 1:45 minutes for our 30 to 40 international relations. Through operational data matching, we know that country-specific times deviate by up to 30 percent from this average." Order data such as relations, packaging types, and recipients/senders were included in the analysis.
This is what informed Decisions look like
By clearly assigning movement data to order data, the correlations become clear, and reliable evaluation bases for decisions are created. As possible measures at Zufall Logistics, Kaiser derives the adjustment of traffic control and the flexibilization of personnel planning.
Comprehensive Collection of Process Data pays off
Economically, process analysis with Motion-Mining also pays off for the logistics company. "Through the sensor technology used, we get transparent data on all loadings over two to three weeks. With manual recording, we could only focus on three to four loadings with significantly fewer measurements," compares Kaiser.
Goal: Process Metrics on a Daily Basis
In the next step, the logistics company aims to develop operational data matching from a project-based application to a continuous process. The goal is to have relevant process metrics available on a daily basis in the future. Zufall Logistics has been using the hardware and software for collecting and evaluating process data since 2022. Various projects have been carried out in the transshipment warehouses in Göttingen, Haiger, Nohra, and Fulda, as well as in the logistics center in Kandel, each with diverse measurement series.
New Level for Process Analysis in Intralogistics through the Combination of Movement and Activity Data / Integration of WMS, ERP, TMS, and MES Data / Regular Operation Started / Targeted Identification of Process Bottlenecks and Deviations with Higher Detail Level
The intralogistics optimizer MotionMiners has successfully transitioned its new "Operational Data Matching" feature into regular operation. The first user and former pilot partner is Zufall Logistics Group, which can now break down its piece goods handling processes to the relation, packaging, and customer levels for the first time. This is achieved by combining anonymized movement data (collected using Motion-Mining technology) with its own TMS data on the analysis platform, MotionMiners PROCESS INTELLIGENCE. MotionMiners will present the innovative "Operational Data Matching" at the LogiMAT logistics trade fair in Stuttgart (March 11-13, 2025, Hall 4, Booth C25).
Sensor Data as a solid Basis
The foundation for Operational Data Matching is the anonymized movement data of employees or vehicles, recorded by the user with Motion-Mining sensor sets. "In our 11,000 m² piece goods hall in Göttingen, we collected a total of 600 data hours in the area of international piece goods traffic over three weeks," outlines Daniel Kaiser, Senior Expert in Business Process Optimization at Zufall Logistics, and explains, "these are data that were previously unavailable and now bring transparency to the handling processes during presorting, at the ramp, and during loading."
Deeper Analyses through Combination with TMS, WMS, ERP, or Machine Data
With Operational Data Matching, process engineers gain an additional dimension. Users are enabled to correlate the analysis results from MotionMiners with their own operational data from existing systems (TMS, WMS, ERP, machine data) on the MotionMiners platform. This elevates process analysis in intralogistics to a new level. User Kaiser confirms: "Operational Data Matching helps us to detail evaluations and more precisely identify potential process bottlenecks or deviations."
Optimization Potentials through Machine Learning
Data generation and determination of process metrics are carried out using the MotionMiners PROCESS INTELLIGENCE analysis dashboard. "We use machine learning to identify inefficiencies and uncover optimization potentials," explains Sascha Kaczmarek, co-founder of MotionMiners, highlighting the newly created added value, "through integrated Operational Data Matching, companies receive metrics in a new depth and granularity within a short time. For example, efforts per order, customer, or item can now be determined to uncover optimization potentials more precisely. The application spectrum is versatile: the matching tool supports benchmarking, creates transparency, and serves to audit existing process models."
Moving away from Averages with Order Data
The import of operational data to the platform is possible in CSV format, ensuring quick and flexible processing without the need for an IT interface to existing systems. Using an example, Kaiser demonstrates the gain in information for piece goods handling: "The MotionMiners measurements showed an average piece goods loading movement of 1:45 minutes for our 30 to 40 international relations. Through operational data matching, we know that country-specific times deviate by up to 30 percent from this average." Order data such as relations, packaging types, and recipients/senders were included in the analysis.
This is what informed Decisions look like
By clearly assigning movement data to order data, the correlations become clear, and reliable evaluation bases for decisions are created. As possible measures at Zufall Logistics, Kaiser derives the adjustment of traffic control and the flexibilization of personnel planning.
Comprehensive Collection of Process Data pays off
Economically, process analysis with Motion-Mining also pays off for the logistics company. "Through the sensor technology used, we get transparent data on all loadings over two to three weeks. With manual recording, we could only focus on three to four loadings with significantly fewer measurements," compares Kaiser.
Goal: Process Metrics on a Daily Basis
In the next step, the logistics company aims to develop operational data matching from a project-based application to a continuous process. The goal is to have relevant process metrics available on a daily basis in the future. Zufall Logistics has been using the hardware and software for collecting and evaluating process data since 2022. Various projects have been carried out in the transshipment warehouses in Göttingen, Haiger, Nohra, and Fulda, as well as in the logistics center in Kandel, each with diverse measurement series.
