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Applying machine learning on the data of a controltower in a retail distribution landscape

Kolner, Thomas (2019) Applying machine learning on the data of a controltower in a retail distribution landscape.

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Abstract:Retail distribution is the activity of getting goods into stores where they are sold to thepublic. A new concept in the retail distribution in the Netherlands is the transport con-trol tower. In this context, a transport control tower is defined as an integrated platformwhere transportation companies and their stakeholders share information or data andconnect different services. This thesis aims to build a predictive model to predict theon-time arrival rate of trucks at the stores and help to explain the variance in on-timearrivals of trucks by using the data from a transport control tower. Building a predictivemodel and explaining the on-time arrivals, this thesis asks: How can a control tower pro-vide insights and be valuable within the chain distribution of a retailer using on plannedand actual truck arrivals? And how can a control tower be used to explain the variancein the on-time arrival rate of trucks? Based on a review of the literature on integration platforms in the transportation, ithas been argued, at a conceptual level, that there is a huge potential for the use of acontrol tower in the field of retail distribution. To validate the use of the control tower,this thesis conducted a case study to apply machine learning on the control data in col-laboration with Albert Heijn. This show a successful application of machine learning onthe data of a control tower in a retail distribution landscape. It describes a method toextend the control tower data with open data on weather and traffic, and apply machinelearning on the extended control tower data. The results show that the Random Forestmodel is most suited for the detection of on-time arrivals. The Random Forest classifierachieves an f1 score of 0.86. Analysis of the outcomes showed that the on-time arrivalrate is caused by several variables. The most important variables in this case studyare ranked by using the feature importance from the proposed Random Forest model.Human factors, could influence the time of arrival, and it is concluded that such factorsshould be considered in future research.
Item Type:Essay (Master)
Clients:
Albert Heijn, Geldermalsen, Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Business Information Technology MSc (60025)
Link to this item:http://purl.utwente.nl/essays/79530
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