An ontology supported risk assessment approach for the intelligent configuration of supply networks

As progress towards globalisation continues, organisations seek ever better ways with which to configure and reconfigure their global production networks so as to better understand and be able to deal with risk. Such networks are complex arrangements of different organisations from potentially diverse and divergent domains and geographical locations. Moreover, greater focus is being put upon global production network systems and how these can be better coordinated, controlled and assessed for risk, so that they are flexible and competitive advantage can be gained from them within the market place. This paper puts forward a reference ontology to support risk assessment for product-service systems applied to the domain of global production networks. The aim behind this is to help accelerate the development of information systems by way of developing a common foundation to improve interoperability and the seamless exchange of information between systems and organisations. A formal common logic based approach has been used to develop the reference ontology, utilising end user information and knowledge from three separate industrial domains. Results are presented which illustrate the ability of the approach, together with areas for further work

A fuzzy dynamic inoperability input-output model for strategic risk management in global production networks

Strategic decision making in Global Production Networks (GPNs) is quite challenging, especially due to the unavailability of precise quantitative knowledge, variety of relevant risk factors that need to be considered and the interdependencies that can exist between multiple partners across the globe. In this paper, a risk evaluation method for GPNs based on a novel Fuzzy Dynamic Inoperability Input Output Model (Fuzzy DIIM) is proposed. A fuzzy multi-criteria approach is developed to determine interdependencies between nodes in a GPN using experts’ knowledge. An efficient and accurate method based on fuzzy interval calculus in the Fuzzy DIIM is proposed. The risk evaluation method takes into account various risk scenarios relevant to the GPN and likelihoods of their occurrences. A case of beverage production from food industry is used to showcase the application of the proposed risk evaluation method. It is demonstrated how it can be used for GPN strategic decision making. The impact of risk on inoperability of alternative GPN configurations considering different risk scenarios is analysed

A Federated Database for Obesity Research:An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.</p