This dissertation brings together the areas of safety science and operations management through a mixed-methods approach to investigate the complex relationships between two, often conflicting, organizational goals - efficiency and safety, in sociotechnical systems (STSs). This research mainly focuses on production pressure (PrP) which is considered as one of the main negative outcomes of overprioritizing the efficiency aspect of STSs. This work seeks to introduce novel methodologies for assessing PrP in real time for the purpose of mitigating its risks and unwanted consequences, particularly in safety critical environments such as traffic control centers (TCCs).
Essay 1 concentrates on the theoretical underpinnings of PrP by systematically reviewing the existing literature to clarify and unify the concept under the context of safety science. It identifies key factors contributing to PrP, its negative effects on safety performance in various industries, and potential mitigation strategies. By doing so, this essay contributes to the field through laying the groundwork for more effective management strategies to improve workplace safety.
Essay 2 addresses a significant gap identified in Essay 1 by developing a methodology based on Data Envelopment Analysis (DEA) for the ongoing measurement and monitoring of PrP. This innovative approach introduces a quantitative mechanism that juxtaposes efficiency and safety related outcomes of hourly performance in safety critical environments. This proposed method allows for a detailed analysis of performance dynamics within STSs. The practical application of this model is demonstrated through its implementation in the infrastructure management system of INFRABEL, the Belgian National Railroad Company.
Essay 3 advances the conversation by tackling the predictive limitations of the DEA model established in Essay 2. It integrates Machine Learning (ML) techniques with DEA to develop an innovative method for forecasting near-future PrP levels for proactive management of safety risks. The major contribution of Essay 3 is the novel interface between ML and DEA that can improve decision-making capabilities of managers in safety-critical STSs through real-time monitoring and predictive analytics.
Together, these studies contribute to the theoretical discussions around PrP and present practical solutions to longstanding challenges in safety science and operational management. / Doctor of Philosophy / In today's increasingly complex world, the systems that run our industries, from traffic control to healthcare, face a dilemmatic balance between pushing for higher productivity and ensuring safety. This dissertation explores the trade-offs between efficiency and safety which has become more pronounced with the advancement of technology. Traditional safety approaches which used to be effective in simpler systems, struggle in modern STSs where causes and effects are not linear but tangled in a web of unpredictable interactions.
Production pressure (PrP), at the core of the mentioned balance, is the drive to maximize output and efficiency, often at the expense of safety. This pressure can lead to unintended and sometimes catastrophic outcomes in the long term, especially in environments where safety is critical, such as rail traffic control centers. Despite its vital impact, there has been a noticeable gap in understanding and managing PrP. In fact, existing safety frameworks are struggling to capture the dynamic nature of PrP, consequently, its real-time measurement and control remain difficult to achieve.
This work, therefore, tries to broaden our understanding of PrP and to develop methods to monitor, measure, and predict it, to equip managers and policymakers with the tools to navigate the efficiency-safety dichotomy more effectively. Through a series of essays, this dissertation reviews the current state of knowledge on PrP to identify its sources and impacts and also innovates a novel approach to quantify PrP in real-time and predict its future trends.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119467 |
| Date | 17 June 2024 |
| Creators | Hashemian, Seyed Mohammad |
| Contributors | Industrial and Systems Engineering, Triantis, Konstantinos P., Hosseinichimeh, Niyousha, Hoopes, Barbara J., Van Aken, Eileen Morton |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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