Data analytics in supply chain planning

  • This dissertation investigates different applications of data analytics in supply chain planning. In the last years, data analytics became more important, because of the increase of computational power and the larger availability of data. Data analytics is used in various domains to improve operations performance, increase customer satisfaction and revenues. However, both the research and the application of data analytics in supply chain management is still lacking behind other industries. We1 analyze the potential of data analytics in the field of supply chain planning in three exemplary fields: demand forecasting, partial defection prediction and price discrimination. In addition, we demonstrate how to deal with three common challenges in the field of data analytics: the manual effort for method selection and hyperparameter tuning, the difficult interpretability of machine learning methods and the risks associated with data collection through randomized experiments. In the first paper, we develop a method selection approach in the field of intermittent demand prediction. Our model combines high predictive performance with automation and calculation efficiency. Unlike common practice, the prediction method gets automatically chosen for each data set without any manual selection. Our results are stable across three different data sets that come from different sources but all contain intermittent demand time series. We showcase the impact of the proposed forecasting approach with a warehouse operation simulation. We thereby prove the financial benefit with empirical data. In the second paper, we deal with partial defection prediction in a business-tobusiness environment in the logistics industry. The predictions must combine predictive performance with interpretability and profit maximization. Our model uses a large variety of customer-based and time-series-based features to predict the probability of partial defection for each customer. We use a data permutation approach to make the best performing, black-box models interpretable. Furthermore, we use a profit assessment to identify the method that leads to the highest revenue through successful retention actions. In the third paper, we study price sensitivity prediction. We do not use any randomized experiments, because the risk of loosing customers through such experiments is too high. Thereby, we address the challenge of data availability 1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Faber and Spinler (2019a,b,c).

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Author:Andreas D. Faber
Subtitle (English):applications in intermittent demand forecasting, partial defection prediction and price discrimination
Referee:Stefan Spinler, Arnd Huchzermeier
Document Type:Doctoral Thesis
Date of Publication (online):2021/08/11
Date of first Publication:2021/08/11
Publishing Institution:WHU - Otto Beisheim School of Management
Granting Institution:WHU - Otto Beisheim School of Management
Date of final exam:2020/10/02
Release Date:2021/08/11
Tag:Datenanalyse; Supply Chain Management
Data analysis; Supply chain management
Page Number:140
Institutes:WHU Supply Chain Management Group / Kühne Foundation Endowed Chair of Logistics Management
Licence (German):Copyright this PhD thesis