Production Pressure in Complex Socio-Technical Systems: Analysis, Measurement, and Prediction

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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Hashemian, Seyed Mohammad

Chair dc:contributor.committeechair

• Triantis, Konstantinos P.

Committee members dc:contributor.committeemember

• Hosseinichimeh, Niyousha
• Hoopes, Barbara J.
• Van Aken, Eileen Morton

• Production pressure
• sociotechnical systems
• infrastructure systems engineering
• efficiency performance measurement
• data envelopment analysis (DEA)
• machine learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:40384

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119467