While unprecedented amounts of building damage data are now produced after earthquakes, stakeholders do not have a systematic method to synthesize and evaluate damage information, thus leaving many datasets unused. We propose a Geospatial Data Integration Framework (G-DIF) that employs regression kriging to combine a sparse sample of accurate field surveys with spatially exhaustive, though uncertain, damage data from forecasts or remote sensing. The framework can be implemented after an earthquake to produce a spatially distributed estimate of damage and, importantly, its uncertainty. An example application with real data collected after the 2015 Nepal earthquake illustrates how regression kriging can combine a diversity of datasets—and downweight uninformative sources—reflecting its ability to accommodate context-specific variations in data type and quality. Through a sensitivity analysis on the number of field surveys, we demonstrate that with only a few surveys, this method can provide more accurate results than a standard engineering forecast.

The ever-increasing levels of pollution and waste creation have subjected industries around the world to incorporate the concept of circular economy (CE) in their supply chains. The amalgamation of the CE approach along with supply chain management is called circular supply chain management (CSCM). Among other industries, the pharmaceutical industry is also involved in damaging the ecosystem. Hence, an effective framework for the adoption of CSCM in a particular industry is very essential. Therefore, this paper aims to devise a model that will help the pharmaceutical industries to adopt CSCM in their organizations. For this purpose, the study in the first phase identifies ten barriers that are working as an impediment in the adoption of the CSCM approach. To counter those barriers, the study in the second phase identifies a set of twelve enablers. To analyse the barriers and enablers, the study uses a new hybrid methodology. For allocating weights and prioritizing the barriers, the fuzzy multi-criteria decision-making (MCDM) technique, i.e. fuzzy full consistency method (F-FUCOM) is used, whereas the total quality management tool, i.e. fuzzy quality function deployment (FQFD) is used to rank the enablers. The results from F-FUCOM suggest “lack of financial resources and funding”, “market challenges”, and “lack of coordination and collaboration among the entire supply chain network” to be the top-most barriers, respectively, whereas the results achieved from the FQFD suggest “industrial symbiosis”, “Reverse Logistic (RL) infrastructure”, and “block chain technology” to be the top-ranked enablers, respectively. The provision of a facilitating framework for the adoption of CSCM in the pharmaceutical industry and the newly developed hybrid methodology are both novelties of this study.

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