Rogue seasonality detection in supply chains

Supply chains face disturbances in the provision of goods and services to customers. A key disturbance which is endogenously generated from the nature of the ordering process used is rogue seasonality, which is characterised by orders and other supply chain variables showing cyclicality in their profiles and this cyclicality not present in exogenous demand. It is observed in many supply chains and is a cause of significant economic loss for entities in these chains. A useful way to manage rogue seasonality could be by detecting its presence and intensity in a system and then taking appropriate and timely action for its mitigation. Called "sense and respond", this approach has been used in various domains extensively, but its application in supply chain management has been limited. This thesis explores the application of this approach for managing rogue seasonality, with the findings from it particularly relevant for a context where many multiple echelon supply chains are being managed by a focal company. Multiple methods are used to analyse each of the rogue seasonality generation and detection components of the thesis. For understanding rogue seasonality generation, system dynamics simulations of single and three echelon linear and four echelon non linear (Beer game) systems are used. The impact of different demand processes and parameters, delays, order of delays, ordering processes, backlogs and batching on rogue seasonality is assessed. The simulation analysis is supported with empirical contexts from the steel and grocery sectors. The understanding gained on rogue seasonality together with the related contextual data is used in the sense or detection part of the thesis. The signature based approach, with the signature derived from clustering of time series data of variables is explored for detection, with the data represented in alternative domains such as amplitudes of Fourier transforms, autocorrelation function, coefficients of autoregressive model, cross correlation function and coefficients of discrete wavelet transform. The thesis determined the signature and index for detecting rogue seasonality. While the signature, which is based on the cluster profiles of the system variables indicates the presence of rogue seasonality, the intensity of rogue seasonality is indicated by the index. In a multi supply chain context, the index could be used to identify problematic supply chains from a rogue seasonality perspective and initiate appropriate management action. At present there is no measure for rogue seasonality and defining an index for the same constitutes a major contribution of this thesis. Among alternative time series representations, the frequency domain representation based on Fourier transform was found to be the most appropriate for deriving the signature and index. This is also a major contribution of the diesis, as the comprehensive assessment of time series representations carried out in this study has not been done in many studies across domains, and those that have done so, have not used any supply chain related data in the assessment. Finally, the framework for exploiting the index for managing rogue seasonality is proposed

Supply chain network design models for a circular economy: a review and a case study assessment

Global supply chains are getting increasingly dispersed, and hence, more complex. This has also made them more vulnerable to disruptions and risks. As a result, there is a constant need to reconfigure/redesign them to ensure competitiveness. However, the relevant aspects/facets for doing so are fragmented and scattered across the literature. This study reviews the literature to develop a holistic understanding of the key considerations (environment, cost, efficiency, and risks) in designing/redesigning global supply chains. This understanding is then applied to assess the global supply chain network of a leading multinational tire manufacturing firm; also to provide recommendations on redesigning it. The study has significant practical and research implications for global supply chain management

Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic

The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on the topic has been limited and fragmented, leading to an incomplete understanding of the ‘what’, ‘where’ and ‘how’ of its application, as well as its associated benefits and challenges. This study proposes a comprehensive AI framework for healthcare and assesses its effectiveness within the UAE's healthcare sector. It provides valuable insights into AI applications for healthcare stakeholders that range from the molecular to the population level. The study covers the different computational techniques employed, from machine learning to computer vision, and the various types of data inputs fed into these techniques, including clinical, epidemiological, locational, behavioural and genomic data. Additionally, the research highlights AI's capacity to enhance healthcare's operational, quality-related and social outcomes, and recognises regulatory policies, technological infrastructure, stakeholder cooperation and innovation readiness as key facilitators of AI adoption. Lastly, we stress the importance of addressing challenges such as data privacy, security, generalisability and algorithmic bias. Our findings are relevant beyond the pandemic in facilitating the development of AI-related policy interventions and support mechanisms for building resilient healthcare sector that can withstand future challenges

Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic

The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and highlighted the need for innovative, technology-driven solutions like Artificial Intelligence (AI). However, previous research on the topic has been limited and fragmented, leading to an incomplete understanding of the ‘what’, ‘where’ and ‘how’ of its application, as well as its associated benefits and challenges. This study proposes a comprehensive AI framework for healthcare and assesses its effectiveness within the UAE’s healthcare sector. It provides valuable insights into AI applications for healthcare stakeholders that range from the molecular to the population level. The study covers the different computational techniques employed, from machine learning to computer vision, and the various types of data inputs fed into these techniques, including clinical, epidemiological, locational, behavioural and genomic data. Additionally, the research highlights AI’s capacity to enhance healthcare’s operational, quality-related and social outcomes, and recognises regulatory policies, technological infrastructure, stakeholder cooperation and innovation readiness as key facilitators of AI adoption. Lastly, we stress the importance of addressing challenges such as data privacy, security, generalisability and algorithmic bias. Our findings are relevant beyond the pandemic in facilitating the development of AI-related policy interventions and support mechanisms for building resilient healthcare sector that can withstand future challenges