IR
I.G.M. Rethans
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2 records found
1
Unpacking the Costs
Predictive Analysis and Key Drivers for a Logistics Company's Cost Per Package
This thesis investigates the drivers of the cost per package of a major Dutch parcel corporation and develops a forecasting model to predict these costs accurately. Despite its efficiency in delivering millions of packages daily, a comprehensive understanding of the cost per package is lacking due to inconsistent definitions, unknown key drivers, and incomplete data. The primary objectives are to identify key cost drivers and develop a reliable forecasting model. Through the use of various statistical techniques and feature importance, volume and forecast realization ratio were identified as the most significant cost drivers. A comparative study of multiple forecasting models determined that the Least Squares model provides the best balance of accuracy and ease of implementation. The analysis highlighted the impact of accurate volume forecasts and the need for more detailed data collection and documentation. The research findings offer actionable insights for the company to optimize its operations and reduce costs. Additionally, this thesis contributes to the broader field of time series analysis and forecasting in logistics, serving as a case study for future research. Recommendations include improving volume forecasting accuracy, optimizing data collection processes, and establishing model monitoring. While the study's limitations include the dataset's size and granularity, future research could focus on creating more detailed datasets and exploring the impact of specific events on cost fluctuations and forecasting accuracy.
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This thesis investigates the drivers of the cost per package of a major Dutch parcel corporation and develops a forecasting model to predict these costs accurately. Despite its efficiency in delivering millions of packages daily, a comprehensive understanding of the cost per package is lacking due to inconsistent definitions, unknown key drivers, and incomplete data. The primary objectives are to identify key cost drivers and develop a reliable forecasting model. Through the use of various statistical techniques and feature importance, volume and forecast realization ratio were identified as the most significant cost drivers. A comparative study of multiple forecasting models determined that the Least Squares model provides the best balance of accuracy and ease of implementation. The analysis highlighted the impact of accurate volume forecasts and the need for more detailed data collection and documentation. The research findings offer actionable insights for the company to optimize its operations and reduce costs. Additionally, this thesis contributes to the broader field of time series analysis and forecasting in logistics, serving as a case study for future research. Recommendations include improving volume forecasting accuracy, optimizing data collection processes, and establishing model monitoring. While the study's limitations include the dataset's size and granularity, future research could focus on creating more detailed datasets and exploring the impact of specific events on cost fluctuations and forecasting accuracy.
Language similarity is very useful for enrichment data in both Natural Lanuguage Processing (NLP) and Automatic Speech Recognition (ASR). A clustering algorithm could provide an efficient means to define language similarity in a data-driven way. This research investigates the relation between linguistic classification by origin and data driven classification based on the pronunciation of languages using k-means clustering where the focus is placed
on the Indo-European languages. The results show large variation in cluster results and consequently large variation in correspondence with linguistic
classification. This is caused by a relatively even spread of the data over the feature space. Still, the results indicate significance in the relation between
the two classification methods. Furthermore, this research functions as a foundation and a source of inspiration for a lot of possible future research.
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on the Indo-European languages. The results show large variation in cluster results and consequently large variation in correspondence with linguistic
classification. This is caused by a relatively even spread of the data over the feature space. Still, the results indicate significance in the relation between
the two classification methods. Furthermore, this research functions as a foundation and a source of inspiration for a lot of possible future research.
...
Language similarity is very useful for enrichment data in both Natural Lanuguage Processing (NLP) and Automatic Speech Recognition (ASR). A clustering algorithm could provide an efficient means to define language similarity in a data-driven way. This research investigates the relation between linguistic classification by origin and data driven classification based on the pronunciation of languages using k-means clustering where the focus is placed
on the Indo-European languages. The results show large variation in cluster results and consequently large variation in correspondence with linguistic
classification. This is caused by a relatively even spread of the data over the feature space. Still, the results indicate significance in the relation between
the two classification methods. Furthermore, this research functions as a foundation and a source of inspiration for a lot of possible future research.
on the Indo-European languages. The results show large variation in cluster results and consequently large variation in correspondence with linguistic
classification. This is caused by a relatively even spread of the data over the feature space. Still, the results indicate significance in the relation between
the two classification methods. Furthermore, this research functions as a foundation and a source of inspiration for a lot of possible future research.