Industrial Resilience: Risks and Mitigation Strategies in the Automotive Industry

This interdisciplinary research project concerns several research areas of the EPSRC. The focus is on "Operational Research" in order to enable improved decision-making. By analysing operational practices and structural patterns in production and supply networks, the aim of the dissertation project is to identify vulnerabilities in these networks which compromise product safety. Large parts of the operational patterns leading to high profile recalls in many industries in recent years have not been investigated. Recent research has shed some light on possible reasons, for example increasingly complex products have been identified as a source of increasing recall rates. Moreover, the application of new technologies in products with critical safety requirements, such as consumer goods, brings new challenges for management practices and engineering. For example, recalls related to autonomous vehicle technologies increased exponentially within the past 10 years and are expected to increase further. First approaches for mitigation measures to deal with high profile recalls have already been formulated by multiple research scholars and for various industries. These include measures on a policy level, such as the implementation of new standards and regulations as well as an increase in traceability, for example by applying RFID technology. However, these approaches remain fragmented and more importantly, the problem of increasing recall rates in large industry sectors such as automobile production, remain critical . The analysis of recall patterns during my master's thesis indicated that design flaws in products as well as production flaws are root causes of product malfunctions. Yet the question remains what are effective countermeasures on a root cause level? How are they addressed by current management practices? Therefore, the dissertation aims to discuss the causes of product recalls and to develop strategies for increasing product safety.
Furthermore, this research will rely on a mixed method approach and the research area "Artificial Intelligence Technologies" will be of relevance for this dissertation. Much of the potential of AI analytics in manufacturing is not realized yet. A recent study finds that for automotive and assembly alone, annual efficiency gains from AI analytics are estimated to 300 $bn. In this context, analytics applications for risks related to product malfunctions are various. For example, firms sometimes seek reactive recall strategies rather than preventive strategies, meaning that recalls are announced only after a safety hazard has caused injuries or deaths. With new technologies such as predictive analytics, predicting product malfunctions could prevent deaths and could at the same time increase operational efficiency and product conformity, using information in an early stage to correct flaws in design or production. In addition, managers today are often confronted with the question whether a recall should be issued if there are indications of a product malfunction, but no specific evidence. A predictive algorithm therefore could also be a decision support tool for practitioners, in case potentially malfunctioning products entered the market. Whereas well-established quality management techniques, such as "failure modes and effect analysis" are nowadays a prerequisite for component approvals in multiple industrial sectors, new approaches that include data analytics are likely to become more relevant.