The use of novel data and optimization algorithms in warehouse management

  • In 2020, approximately 151,000 warehouses were operating worldwide, with a total annual expenditure of e300 billion, representing roughly half of total supply chain costs. Optimized warehouse management may provide competitive advantages from both cost and customer service perspectives. One way to achieve these is to leverage the abundance of data collected in supply chains in combination with powerful algorithms. This dissertation investigates how novel data sources and optimization algorithms, such as machine learning, can be applied in the context of warehouse advancement. In this research, we1 analyze the warehouse environment from two perspectives. On the one hand, we examine two available resources in warehouses—equipment and employees—and explore how predicting breakdowns and productivity, respectively, may improve warehouse performance. On the other hand, we investigate whether the warehouse concept of crossdocking can be applied virtually to allow dynamic transfers between delivery vehicles. In our first paper, we partner with one of the largest logistics service providers to examine how master, usage and sensor data on material handling equipment can be incorporated into a predictive maintenance model. Existing literature focuses on either time- or condition-based variables, whereas we show, in a comprehensive study of statistical learning methods, that both variable types can be included simultaneously. Our predictive maintenance model is able to capture the majority of breakdowns (> 85%), while maintaining a low false-positive ratio (< 7%). In our second paper, we work with the same logistics service provider and apply Extreme Gradient Boosting to predict the productivity of new hires. We include operator, shift and product data to show that productivity can be predicted on an individual employee basis while substantially decreasing the forecasting error (50%), which translates into cost savings. In our third paper, we look at dynamic and synchronized transshipments during direct deliveries. This concept uses transfers between vehicles, as carried out in cross-docking, but without the need for a physical warehouse. This reduces the proportion of empty return trips by increasing the proximity of vehicles to their location of origin. Our easy-to-implement multi-algorithm reduces the total distance by 15% on average compared with simple direct deliveries, and solves large problem instances within reasonable computational times. This dissertation with its individual research contributions highlights how novel data sources and optimization algorithms can contribute to advancing warehouse management, and highlights the managerial implications of all three topics. 1The term “we” refers to the authors of the respective chapters, as denoted at the beginning of each chapter.

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Author:Sven F. Falkenberg
Place of publication:Vallendar
Referee:Stefan Spinler, Arne Strauss
Document Type:Doctoral Thesis
Date of Publication (online):2023/03/03
Date of first Publication:2023/03/03
Publishing Institution:WHU - Otto Beisheim School of Management
Granting Institution:WHU - Otto Beisheim School of Management
Date of final exam:2022/10/28
Release Date:2023/03/03
Tag:Arbeitskräfte; Lager; Lagermanagement; Produktivität
Employees; Productivity; Supply chain management; Warehouse; Warehouse management
Page Number:90
Institutes:WHU Supply Chain Management Group / Kühne Foundation Endowed Chair of Logistics Management
Licence (German):Copyright this PhD thesis