Application of Regression Analysis for Throughput Prediction in the Order Picking Process

Julia Lahovnik

Research output: ThesisMaster's Thesis

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Abstract

The throughput is an important indicator for the performance of logistics systems, and a good estimation of the throughput allows a more precise planning of resources. In this Master’s Thesis, regression analysis is applied to predict the throughput for the order picking process in a warehouse for beverages. At first, an introduction to mathematical modelling is given and different types of regression analysis as well as methods to evaluate and compare different models are presented. Then the initial situation and the available data of the application are presented and regression analysis is used to analyse the influence of the various input parameters on the performance of the picking process. Based on the result of this analysis, individual linear models for the operators are created which are used to predict their future performance. Finally, a procedure is described to estimate the throughput of the picking process by using these linear models. The evaluation of this procedure shows that it is possible to achieve a small reduction of personnel costs and to earlier inform the employees about changes in their working time.
Translated title of the contributionAnwendung von Regressionsanalyse zur Vorhersage des Durchsatzes im Kommissionierprozess
Original languageEnglish
QualificationDipl.-Ing.
Awarding Institution
  • Montanuniversität
Supervisors/Advisors
  • Auer, Peter, Supervisor (internal)
  • Spat, Stephan, Supervisor (external), External person
Award date19 Oct 2018
Publication statusPublished - 2018

Bibliographical note

embargoed until null

Keywords

  • regression analysis
  • order picking
  • throughput
  • prediction
  • picking process

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