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Item type:Publication, Access status: without files , Anticipating customer substitution: a data-driven, distance-based approach for out-of-stock product configurationsPurpose: When facing capacity bottlenecks, manufacturers of configurable, multi-variant products may adjust the product mix to uphold the scheduled output. However, maintaining market attractiveness by choosing the right product configurations as substitutes is a non-trivial task as it involves anticipating the substitution behaviour of customers. Substitution behaviour models currently used in quantitative production planning models for configurable products are either based on domain knowledge of experts, which makes them bias-prone, or they require extensive market research. The purpose of this study is to present a data-driven approach. Design/methodology/approach: Based on data science concepts, distance measures are applied to derive distances between different product configurations from historical order data. Different design options for such a distance measure are discussed regarding configurable products and tested with automotive industry data. Furthermore, the study shows ways to validate the distance results. Findings: The experiments show that the presented distance measure represents the expected customer substitution behaviour quite well. A context-sensitive distance measure including rank information of ordinal product features is most suitable for the automotive data sets. Originality/value: This study presents a new approach to model the substitution behaviour of customers. The attractiveness of a potential substitute is represented by a distance from the customer’s first-choice configuration. The presented distance measure provides an inexpensive tool using existing data instead of expensive market research. Thus, it supports the integration of substitution into quantitative production planning models that deal with a large variety of configurable products.Publicationtype: Journal Article28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Evacuation based extension of the system optimal dynamic traffic assignment problemThe system optimal dynamic traffic assignment (SODTA) is a well-known problem for evacuation planning. While the SODTA provides an easy-to-use framework for traffic assignment, due to its linearity and natural integer variables, in its original form the SODTA also has several shortcomings with respect to its usefulness for realistic evacuation cases. These shortcomings include a) a lack of consideration of non-controllable evacuees and induced background traffic, b) different destination categories as well as evacuees with individual destination preferences, which lead to the well-known FIFO-problem, are not taken into account, c) a lack of consideration of the impact of individual evacuee behaviour. In this presentation a comprehensive extension of the SODTA is introduced which considers a) the occurrence of non-controllable background traffic, b) the appearance of differing evacuee characteristics regarding destination preferences while prohibiting the occurrence of the well-known holding-back effect adjusted to multiple destinations and c) providing simulation results in order to assess the impacts of evacuee behaviour and the FIFO problem. An illustrative case study as well as recommended algorithms for the parametrisation of background traffic and destination preferences are presented. The extension of the SODTA is evaluated regarding its usefulness and also its limitations for evacuation planning purposes. Further, the induced error resulting from the FIFO-problem is evaluated and discussed regarding its implications on the real life application of the newly developed model extensions.Publicationtype: Conference Presentation90