Publication
Supply chains (SC) are complex systems in which human beings play a key role. Whereas human impact is often taken into account in simulation models in terms of physical flows (material handling, fabrication), it is far less the case in terms of information and decision flows. This can be explained by the intrinsic characteristics of human decisions: variability across people, bounded rationality, intuition, variability in data consultation and interpretation. This complexity explains why human decision making is either eluded in simulation models or replaced by algorithms that systematically optimize the decisions to be made. The goal of this research is to propose a methodology that allows modeling human decision making in a procurement context in a descriptive way. By descriptive modeling it is meant modeling of the decisions as they are rather than as they should be. To do so a participatory simulation platform is developed. This platform's concept is to create a virtual decision environment within which a human agent can consult several information about the system state (inventory levels, supplier reliability, MRP, etc.) and can make decisions that he/she feels relevant (order anticipation, postponement, validation, order grouping). The participatory simulation platform is then used within an academic application case involving one student, and an industrial application case involving 14 procurement agents from the Swiss industry field. The modeling phase then consists in selecting and testing the suitability of several modeling paradigms such as decision trees, connectionist networks, discrete choice and clustering analysis. The obtained results lead to interesting conclusions concerning the advantages and limitations of each of these three modeling approaches. It is in particular shown that at an individual level and for simplified decision making situations, decision trees and ARAM (Adaptive Resonance Associative Map) networks perform in a similar way in terms of classification performance, while ARAM networks outperform decision trees in terms of cognitive relevance as they generate rules that better fit the human decision making pattern as outlined by post-experimentation interviews. At a sample-based modeling level, (the whole set of experiments being considered), discrete choice analysis provides interesting results for the understanding of the impact of consulted data over the decisions made by procurement agents as well as the identification of counterintuitive relations that deserve further refinement and investigation. Finally, a clustering of human decision making behaviors based on a set of proposed metrics allows to identify several behavioral profiles that represent the behavior of sub-sets of people who share similar decision making behavior characteristics. Finally a global hierarchical modeling methodology taking benefits from the above mentioned modeling methods is proposed to build human decision making models with minimu