FROM DATA DIVERSITY TO NORMS: STANDARDIZATION STRATEGIES FOR CONSTRUCTION LOGISTICS

Christoph Mönicks; Sabrina Puslat; Bert Leerkamp

Abstract:

The construction industry is one of the least digitized industries. According to this, there are a large number of different digital solutions for controlling and managing construction logistics. For this reason, it is currently not possible to carry out an overarching data analysis to identify optimization measures in construction logistics. As part of this study, datasets from five construction logistics companies were analysed and compared. The datasets contain information on registered supply and disposal transports, including e.g. arrival times, material volumes and transport vehicles. Aim of the study is to standardize the data structures and collection processes. Structural analysis is used to identify similarities and differences between the datasets. The focus is on mandatory fields, input types (e.g. free text, numerical values, drop-down menus), and attribute categorization (e.g., material type, size, quantity, packaging). The study also examined restrictions underlying the recorded attributes. The evaluation of data quality showed that while formal accuracy is high, the content quality varies significantly. For example, the 'material type' field is consistently filled, but implausible entries restricts the usability. In addition, process analysis were conducted to assess data collection methods. The results show that the data entry processes differ depending on the provider, which is partly due to business models, but also to a lack of standardization. Variations exist in the order of data collection, stored attributes, and validation measures. Standardizing these elements through a DIN guideline could significantly improve the data quality. Finally, recommendations are given for standardized data structures and the collection process. A standardized approach would not only enhance scientific data evaluation but also optimize logistics processes in the construction sector and enable accurate traffic forecasting for urban and transport planning.