Modelagem da gestão de riscos da cadeia de suprimentos na indústria de petróleo e gás

Abstract

Supply chains operate as silent engines that drive economic globalization. There is a broad consensus in the literature and in professional practice that supply chains are increasingly complex and vulnerable to risks that cause disturbances, disruptions, and critical reactions from society. This thesis it is aimed to analyze the opportunities and limitations of a modeling based on Multicriteria Decision-Making/Aiding/Analysis and Artificial Intelligence (MCDM/A-AI) for Supply Chain Risk Management (SCRM), developed through systematic selection, validation, and system testing of the hybrid Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method applied in the oil and natural gas industry. Specifically, it is sought to: a. carry out the state of the art by a systematic review of the literature network on the SCRM Modeling (SCRMM); b. propose and validate a new computational system for supplier selection considering risks, based on the Fuzzy AHP method [Fuzzy Extended AHP algorithm (FEAHP)]; and, c. propose and systematically test a holistic framework for assessment of typical and sustainable risks [multidimensional risks] of the supply chain with computational support of the Fuzzy AHP method [FEAHP algorithm]. To do so, an applied research is conducted, with exploratory, descriptive, and predictive purposes, of combined approach [qualitative and quantitative], using literature research, theoretical and conceptual development, modeling, and case studies. The prospection of the state of the art in SCRMM, carried out with the use of bibliometric methods and tools, makes it possible to establish a systemic understanding of the flow of research in the field over time, providing future research directions. The analysis and interpretation of research gaps and trends, applied in the field, allow the identification, selection, and systematic implementation of the proposed conceptual, mathematical, and computational modeling. In turn, the proposition and validation of a new computational system for supplier selection based on the FEAHP method constitutes a proof of concept to verify the feasibility of implementing the SCRMM. Based on the case study of an oil and natural gas company with onshore assets, it is found that the FEAHP computational modeling is able to automate the supplier selection process in a rational, flexible and agile way, meeting all the necessary performance requirements, thus promoting the choice of the best suppliers in an environment of risk and uncertainty. After the validation of the developed software, the proposition and system test of a holistic framework for multidimensional risk assessment of the supply chain with computational support of the FEAHP method is performed. Based on a multiple case study of ten oil and natural gas companies with onshore operations, it is found that the results of risk identification and assessment subsidize the creation of risk mitigation and control strategies [predictive action versus proactive action], fostering the development of a Supply Chain Risk Management System (SCRMS). Finally, the results of the system test of the FEAHP tool show that all its elements combine correctly and present an overall performance effectively applicable, promoting in an integral, flexible, failure-free, and/or error-free way the improvement of the Supply Chain Risk Assessment (SCRA). It is concluded that the various opportunities and/or potentialities of using an MCDM/A-AI based SCRMM overtake the main limitations and/or challenges. Despite the restrictions, it is fair to assume that it contributes to the fertile field of research and professional practice of SCRM, SCRMM, and SCRA, promoting the improvement of the design, understanding, reflection, and professional practice of Supply Chain Network and Operations Management.