X Conferencia Internacional de Ingeniería Mecánica "COMEC 2019" -V Simposio de Logística y Gestión de la Calidad

X Conferencia Internacional de Ingeniería Mecánica

COMEC 2019

Monitoring of Operational Logistic Processes in General Cargo Warehouses Using Predictive Analysis

Resumen [ES]

• Statement of the Problem: Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. • Objective(s): It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. • Methodology: Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. • Results and discussion: The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. • Conclusions: After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.

Resumen [EN]

• Statement of the Problem: Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. • Objective(s): It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. • Methodology: Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. • Results and discussion: The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. • Conclusions: After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.

Sobre el ponente

Andreas Neubert

Mr. Andreas Neubert

PKE Deutschland GmbH, OvGU Magdeburg (PhD student) Flag of Alemania
Información Práctica
English (US)
No definido
30 minutos
No definido
Autores
Mr. Andreas Neubert
Palabras clave
data mining
general cargo warehouse
monitoring
predictive analysis