Using Supervised Learning to Predict Process Steps for Process Planning of Third-Party Logistics

Veigt, Marius; Steinbacher, Lennart M.; Freitag, Michael

doi:10.1007/978-3-031-05359-7_34

Lecture Notes in Logistics

2022

DOI: 10.1007/978-3-031-05359-7_34

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Using Supervised Learning to Predict Process Steps for Process Planning of Third-Party Logistics

Marius Veigt

Lennart M. Steinbacher

Michael Freitag

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2023

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Cited by 2 publications

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Automatic model generation for material flow simulations of Third-Party Logistics

Steinbacher,

Düe,

Veigt

et al. 2023

J Intell Manuf

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The use of Third-Party Logistics (TPL) is a common practice among manufacturing companies seeking to increase profitability. However, the tender process in selecting a TPL service provider can be challenging, requiring significant effort from both the tendering company and the service provider. The latter must meticulously plan processes and calculate pricing positions while running the risk of losing the bid. This risk impedes verifying logistical feasibility and comparing different logistic concepts extensively, such as layouts, which are often work-intensive. With the ongoing progress of research toward automatic simulation model generation for material flow, it is left to answer whether such approaches can improve the planning processes of TPL service providers by using planning data to generate simulation models. Therefore, this work presents a system with an underlying ontology to generate material flow simulations by developing a model transformation methodology. The system’s functions are tested to determine whether they can support the planning process using defined case studies that cover everyday planning decisions. As a result, the system is capable of verifying the performance of planned logistic systems with minimal manual modelling efforts. This encompasses the evaluation of alternative logistical concepts for configuring the planned systems.

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Automatic model generation for material flow simulations of Third-Party Logistics

Steinbacher,

Düe,

Veigt

et al. 2023

J Intell Manuf

View full text Add to dashboard Cite

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Supervised learning to support the process planning of contract logistics projects

Veigt,

Freitag

2023

Prod. Eng. Res. Devel.

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Due to the outsourcing trend, contract logistics is a constantly growing industry. Especially for the essential and time-consuming planning of logistics processes in a contract logistics project, experienced planners are required. However, the growing shortage of skilled workers makes recruiting these planners increasingly difficult. Hence, a supervised learning approach will be investigated to support especially inexperienced planners in process planning. This article explores how supervised learning can extract the process knowledge contained in legacy contract logistics project documentation to suggest process steps during a new project process planning. The investigation results in boosted decision trees predicting the next process step correctly in 81% of the cases. In addition, the article guides what data should be collected today for even better results in future applications.

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Using Supervised Learning to Predict Process Steps for Process Planning of Third-Party Logistics

Cited by 2 publications

References 7 publications

Automatic model generation for material flow simulations of Third-Party Logistics

Automatic model generation for material flow simulations of Third-Party Logistics

Supervised learning to support the process planning of contract logistics projects

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