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

Topological robustness of the global automotive industry

The manufacturing industry is characterized by large-scale interdependent networks as companies buy goods from one another, but do not control or design the overall flow of materials. The result is a complex emergent structure with which companies connect to each other. The topology of this structure impacts the industry’s robustness to disruptions in companies, countries, and regions. In this work, we propose an analysis framework for examining robustness in the manufacturing industry and validate it using an empirical dataset. Focusing on two key angles, suppliers and products, we highlight macroscopic and microscopic characteristics of the network and shed light on vulnerabilities of the system. It is shown that large-scale data on structural interdependencies can be examined with measures based on network science

It's a trap! The development of a versatile drain biofilm model and its susceptibility to disinfection

Background Pathogens in drain biofilms pose a significant risk for hospital-acquired infection. However, the evidence of product effectiveness in controlling drain biofilm and pathogen dissemination are scarce. A novel in-vitro biofilm model was developed to address the need for a robust, reproduceable and simple testing methodology for disinfection efficacy against a complex drain biofilm. Methods Identical complex drain biofilms were established simultaneously over 8 days, mimicking a sink trap. Reproducibility of their composition was confirmed by next-generation sequencing. The efficacy of sodium hypochlorite 1000 ppm (NaOCl), sodium dichloroisocyanurate 1000 ppm (NaDCC), non-ionic surfactant (NIS) and peracetic acid 4000 ppm (PAA) was explored, simulating normal sink usage conditions. Bacterial viability and recovery following a series of 15-min treatments were measured in three distinct parts of the drain. Results The drain biofilm consisted of 119 mixed species of Gram-positive and -negative bacteria. NaOCl produced a >4 log10 reduction in viability in the drain front section alone, while PAA achieved a >4 log10 reduction in viability in all of the drain sections following three 15-min doses and prevented biofilm regrowth for >4 days. NIS and NaDCC failed to control the biofilm in any drain sections. Conclusions Drains are one source of microbial pathogens in healthcare settings. Microbial biofilms are notoriously difficult to eradicate with conventional chemical biocidal products. The development of this reproducible in-vitro drain biofilm model enabled understanding of the impact of biocidal products on biofilm spatial composition and viability in different parts of the drain. Keyword

Systemic Risk Assessment in Complex Supply Networks

The growth in size and complexity of supply chains has led to compounded risk exposure, which is hard to measure with existing risk management approaches. In this study, we apply the concept of systemic risk to show that centrality metrics can be used for complex supply network risk assessment. We review and select metrics, and set up an exemplary case applied to the material flow and contractual networks of Honda Acura. In the exemplary case study, geographical risk information is incorporated to selected systemic risk assessment metrics and results are compared to assessment without risk indicators in order to draw conclusions on how additional information can enhance systemic risk assessment in supply networks. Katz centrality is used to measure the node’s risk spread using the World Risk Index. Authority and hub centralities are applied to measure the link risk spread using distances between geographical locations. Closeness is used to measure speed of disruption spread. Betweenness centrality is used to identify high-risk middlemen. Our results indicate that these metrics are successful in identifying vulnerabilities in network structure even in simplified cases, which risk practitioners can use to extend with historical data to gain more accurate insights into systemic risk exposure

A generative neural network model for the quality prediction of work in progress products

© 2019 Elsevier B.V. One of the key challenges in manufacturing processes is improving the accuracy of quality monitoring and prediction. This paper proposes a generative neural network model for automatically predicting work-in-progress product quality. Our approach combines an unsupervised feature-extraction step with a supervised learning method. An autoencoding neural network is trained using raw manufacturing process data to extract rich information from production line recordings. Then, the extracted features are reformed as time-series and are fed into a multi-layer perceptron for predicting product quality. Finally, the outputs are decoded into a forecast quality measure. We evaluate the performance of the generative model on a case study from a powder metallurgy process. Our experimental results suggest that our method can precisely capture the defective products

How dirty is your QWERTY? The risk of healthcare pathogen transmission from computer keyboards

Introduction Healthcare environmental surfaces may be contaminated with micro-organisms that cause healthcare-associated infections (HCAIs). Special attention is paid to near-patient surfaces but sites outside the patient zone receive less attention. This paper presents data on keyboard contamination and the risk of pathogen transmission from keyboards. Methods Keyboards from nursing stations in three hospitals and a dental practice were analysed for bacterial contamination. Surfaces were pre-treated to remove planktonic bacteria so that any remaining bacteria were presumed to be associated with biofilm. Bacterial transfer from keyboard keys was studied following wiping with sterile water or sodium hypochlorite. The presence of multi-drug-resistant organisms (MDROs) was sought using selective culture. Results Moist swabbing did not detect bacteria from any keyboard samples. Use of enrichment broth, however, demonstrated MDROs from most samples. Gram-negative bacteria were recovered from almost half (45%) of the samples, with meticillin-resistant Staphylococcus aureus, vancomycin-resistant enterococcus and MDR Acinetobacter spp. recovered from 72%, 31% and 17% of samples, respectively. Isolates were transferred from 69% of samples after wiping with sterile water, and from 54% of samples after wiping with 1000 ppm sodium hypochlorite. Discussion While moist swabbing failed to detect bacteria from keyboards, pathogens were recovered using enrichment culture. Use of water- or NaOCl-soaked wipes transferred bacteria from most samples tested. This study implies that hospital keyboards situated outside the patient zone commonly harbour dry surface biofilms (DSBs) that offer a potential reservoir for transferable pathogens. While the role of keyboards in transmission is uncertain, there is a need to pursue effective solutions for eliminating DSBs from keyboards

Supply network science: Emergence of a new perspective on a classical field

Supply networks emerge as companies procure goods from one another to produce their own products. Due to a chronic lack of data, studies on these emergent structures have long focussed on local neighbourhoods, assuming simple, chain-like structures. However, studies conducted since 2001 have shown that supply chains are indeed complex networks that exhibit similar organisational patterns to other network types. In this paper, we present a critical review of theoretical and model based studies which conceptualise supply chains from a network science perspective, showing that empirical data do not always support theoretical models that were developed, and argue that different industrial settings may present different characteristics. Consequently, a need that arises is the development and reconciliation of interpretation across different supply network layers such as contractual relations, material flow, financial links, and co-patenting, as these different projections tend to remain in disciplinary siloes. Other gaps include a lack of null models that show whether the observed properties are meaningful, a lack of dynamical models that can inform how layers evolve and adopt to changes, and a lack of studies that investigate how local decisions enable emergent outcomes. We conclude by asking the network science community to help bridge these gaps by engaging with this important area of research

Topological robustness of the global automotive industry

The manufacturing industry is characterized by large-scale interdependent networks as companies buy goods from one another, but do not control or design the overall flow of materials. The result is a complex emergent structure with which companies connect to each other. The topology of this structure impacts the industry’s robustness to disruptions in companies, countries, and regions. In this work, we propose an analysis framework for examining robustness in the manufacturing industry and validate it using an empirical dataset. Focusing on two key angles, suppliers and products, we highlight macroscopic and microscopic characteristics of the network and shed light on vulnerabilities of the system. It is shown that large-scale data on structural interdependencies can be examined with measures based on network science

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