A comparative literature analysis of definitions for green and sustainable supply chain management

Quantitative models for sustainable supply chain management: Developments and directions

The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two is...

The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach

Over the last decade, the concept of the circular economy has regained attention, especially related to efforts to achieve a more sustainable society. The ‘revival’ of the circular economy has been accompanied by controversies and confusions across different actors in science and practice. With this article we attempt at contributing to advanced clarity in the field and providing a heuristic that is useful in practice. Initially, we take a focus on the historical development of the concept of circular economy and value retention options (ROs) for products and materials aiming for increased circularity. We propose to distinguish three phases in the evolution of the circular economy and argue that the concept – in its dominant framing – is not as new as frequently claimed. Having established this background knowledge, we give insights into ‘how far we are’ globally, with respect to the implementation of circularity, arguing that high levels of circularity have already been reached in different parts of the globe with regard to longer loop value retention options, such as energy recovery and recycling. Subsequently, we show that the confusion surrounding the circular economy is more far reaching. We summarize the divergent perspectives on retention options and unite the most common views a 10R typology. From our analyses, we conclude that policymakers and businesses should focus their efforts on realization of the more desirable, shorter loop retention options, like remanufacturing, refurbishing and repurposing – yet with a view on feasibility and overall system effects. Scholars, on the other hand, should assist the parties contributing to an increased circular economy in practice by taking up a more active role in attaining consensus in conceptualizing the circular economy.

The circular economy: New or Refurbished as CE 3.0? — Exploring Controversies in the Conceptualization of the Circular Economy through a Focus on History and Resource Value Retention Options

The frequency of Climate-induced Disasters (CID) has tripled in the last three decades, driving the World Economic Forum to identify them as the most likely and most impactful risks worldwide. With more than 70% of the world population expected to be living in cities by 2050, ensuring the resilience of urban infrastructure systems under CID is crucial. The present work employs data analytics and machine learning techniques to develop a performance prediction framework for infrastructure systems under CID. The framework encompasses four stages related to: extracting meaningful information about the impact of CID on infrastructure systems and identifying the latter's performance; investigating the relationship between different CID attributes and previously identified system performance; employing data imputation using unsupervised machine learning techniques; and developing and testing a supervised machine learning model based on the different influencing CID attributes. To demonstrate its application, the developed framework is applied to disaster data compiled by the National Weather Services between 1996 and 2019 in the state of New York. The analysis results showed that: i) power systems in New York are the most vulnerable infrastructure to CID, and particularly to wind-related hazards; ii) power system performance level depends on hazard-system interactions rather than solely hazard characteristics; and iii) a 4-predictors random forest-based model can effectively predict power system performance with an accuracy of 89%. This work is expected to aid stakeholders in developing spatio-temporal preparedness plans under CID, which can facilitate mitigating the adverse impacts of CID on infrastructure systems and improve their resilience.

Infrastructure performance prediction under Climate-Induced Disasters using data analytics

The increased severity and frequency of Climate-Induced Disasters (CID) including those attributed to hydrological, meteorological, and climatological effects have been testing the resilience of cities worldwide. The World Economic Forum highlighted—in its 2020 Global Risk Report—that from 2018 to 2020, three of the top five risks with respect to likelihood and impact are climate related with extreme weather events being the highest ranked risk in terms of likelihood. To alleviate the adverse impacts of CID on cities, this paper aims at predicting the occurrence of CID by linking different climate change indices to historical disaster records. In this respect, a deep learning model was developed for spatial–temporal disaster occurrence prediction. To demonstrate its application, flood disaster data from the Canadian Disaster Database was linked to climate change indices data in Ontario in order to train, test and validate the developed model. The results of the demonstration application showed that the model was able to predict flood disasters with an accuracy of around 96%. In addition to its association with precipitation indices, the study results affirm that flood disasters are closely linked to temperature-related features including the daily temperature gradient, and the number of days with minimum temperature below zero. This work introduces a new perspective in CID prediction, based on historical disaster data, global climate models, and climate change metrics, in an attempt to enhance urban resilience and mitigate CID risks on cities worldwide.

A deep learning model for predicting climate-induced disasters

Tri-level mixed-binary linear programming: Solution approaches and application in defending critical infrastructure

Carousel Storage Systems

The impact of bundling of products in a supply chain has not been studied in the operations management literature. Furthermore, the existing models on bundling have analysed the impact of bundling on the retailer’s profits and ignored its impact on the supplier’s operations. We consider an analytical model of a two-product supply chain, consisting of a retailer and a supplier selling these products either as components or as a bundle to the consumer. We analyse different bundling scenarios and determine a bundling fee that would allow the supply chain partners to use bundles to coordinate their operations to maximise their joint profits.

Elkafi Hassini

Papers

Infrastructure performance prediction under Climate-Induced Disasters using data analytics

A deep learning model for predicting climate-induced disasters

Tri-level mixed-binary linear programming: Solution approaches and application in defending critical infrastructure

Carousel Storage Systems

Bundling in a supply chain: insights and implications