Evaluation of supply chain resilience index: a graph theory based approach

A graph-based modeling framework for tracing hydrological pollutant transport in surface waters

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework - which we call HydroGraphs - for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides a flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

Supply chain resilience and business responses to disruptions of the COVID-19 pandemic

Purpose

At its peak, the COVID-19 pandemic has created disruption to food supply chains in the UK and for the entire world. Although societal changes created some resilience within the supply chains, high volatility in demand creates supply, logistics and distribution issues. This is reflected in the economic instability of businesses and small and medium enterprises (SMEs). In this paper, the authors explore factors behind this initial disruption in the supply chains and offer suggestions to businesses based on the established practices and theories.

Design/methodology/approach

The authors use mixed methods research. First, the authors conducted an exploratory study by collecting data from published online sources. Then, the authors analysed possible scenarios from the available information using regression. The authors then conducted two interviews with UK retail sector representatives. These scenarios have been compared and contrasted to provide decision-making points to businesses and supply chain players to tackle current and any future potential disruptions.

Findings

The findings from the current exploratory study inform the volatility of supply chains. The authors suggested some possible responses from businesses, during and after the pandemic.

Originality/value

The regression model provides a decision-making approach to help supply chain businesses during the pandemic outbreak. Once a complete data set of COVID-19 is available, the authors can create a resilience model that can help businesses and supply chains.