The moderating impact of supply network topology on the effectiveness of risk management

While supply chain risk management offers a rich toolset for dealing with risk at the dyadic level, less attention has been given to the effectiveness of risk management in complex supply networks. We bridge this gap by building an agent based model to explore the relationship between topological characteristics of complex supply networks and their ability to recover through inventory mitigation and contingent rerouting. We simulate upstream supply networks, where each agent represents a supplier. Suppliers’ connectivity patterns are generated through random and preferential attachment models. Each supplier manages its inventory using an anchor-and-adjust ordering policy. We then randomly disrupt suppliers and observe how different topologies recover when risk management strategies are applied. Our results show that topology has a moderating effect on the effectiveness of risk management strategies. Scale-free supply networks generate lower costs, have higher fill-rates, and need less inventory to recover when exposed to random disruptions than random networks. Random networks need significantly more inventory distributed across the network to achieve the same fill rates as scale-free networks. Inventory mitigation improves fill-rate more than contingent rerouting regardless of network topology. Contingent rerouting is not effective for scale-free networks due to the low number of alternative suppliers, particularly for short-lasting disruptions. We also find that applying inventory mitigation to the most disrupted suppliers is only effective when the network is exposed to frequent disruptions; and not cost effective otherwise. Our work contributes to the emerging field of research on the relationship between complex supply network topology and resilience

A cognitive prosthesis for complex decision-making

While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies – using simplified as opposed to analytic decision rules – have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called “IMAGE” to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed

A Mathematical Model of the Beer Game

The beer production-distribution game, in short "The Beer Game", is a multiplayer board game, where each individual player acts as an independent agent. The game is widely used in management education aiming to give an experience to the participants about the potential dynamic problems that can be encountered in supply chain management, such as oscillations and amplification of oscillations as one moves from downstream towards upstream echelons. The game is also used in numerous scientific studies. In this paper, we construct a mathematical model that is an exact one-to-one replica of the original board version of The Beer Game. We apply model replication principles and discuss the difficulties we faced in the process of constructing the mathematical model. Accordingly, the model is presented in full precision including necessary assumptions, explanations, and units for all parameters and variables. In addition, the adjustable parameters are stated, the equations governing the artificial agents' decision making processes are mentioned, and an R code of the model is provided. We also shortly discuss how the R code can be used in experimentation and how it can also be used to create a single-player or multiplayer beer game on a computer. Our code can produce the exact same benchmark cost values reported by Sterman (1989) verifying that it is correctly implemented. The mathematical model and the R code presented in this paper aims to facilitate potential future studies based on The Beer Game

The moderating impact of supply network topology on the effectiveness of risk management

While supply chain risk management offers a rich toolset for dealing with risk at the dyadic level, less attention has been given to the effectiveness of risk management in complex supply networks. We bridge this gap by building an agent based model to explore the relationship between topological characteristics of complex supply networks and their ability to recover through inventory mitigation and contingent rerouting. We simulate upstream supply networks, where each agent represents a supplier. Suppliers’ connectivity patterns are generated through random and preferential attachment models. Each supplier manages its inventory using an anchor-and-adjust ordering policy. We then randomly disrupt suppliers and observe how different topologies recover when risk management strategies are applied. Our results show that topology has a moderating effect on the effectiveness of risk management strategies. Scale-free supply networks generate lower costs, have higher fill-rates, and need less inventory to recover when exposed to random disruptions than random networks. Random networks need significantly more inventory distributed across the network to achieve the same fill rates as scale-free networks. Inventory mitigation improves fill-rate more than contingent rerouting regardless of network topology. Contingent rerouting is not effective for scale-free networks due to the low number of alternative suppliers, particularly for short-lasting disruptions. We also find that applying inventory mitigation to the most disrupted suppliers is only effective when the network is exposed to frequent disruptions; and not cost effective otherwise. Our work contributes to the emerging field of research on the relationship between complex supply network topology and resilience