Courses Offered by SPL Faculty

This research-oriented course explores advanced topics in management systems engineering, focusing on complex industrial, service, governmental, and healthcare systems. Students will review state-of-the-art literature and develop research skills using a systems thinking paradigm to define and analyze efficiency performance measurement. Key topics include economic production theory, data-driven modeling techniques such as data envelopment analysis (DEA), statistical frontier analysis (SFA), and agent-based modeling. Through a translational research project, students will apply theoretical frameworks to real-world socio-technical systems, examining the broader impacts and intellectual merit of efficiency performance. This course equips students with the analytical tools necessary for designing and improving system performance across multiple dimensions, including productivity, quality, and innovation.

Learning Objectives

• Develop an understanding of performance and efficiency measurement system design.
• Define system-level problems, identify their root causes, and model system structures influencing efficiency performance.
• Apply systems thinking and functional modeling to represent production systems, defining key inputs, outputs, and performance metrics.
• Analyze the impact of structural, organizational, and socio-economic variables on system efficiency.
• Utilize data envelopment analysis (DEA) and other quantitative frameworks to assess and improve system performance.
• Interpret model results, integrate decision support systems, and facilitate group modeling initiatives for organizational improvement.

This course provides a foundational understanding of socio-technical systems, integrating principles from systems theory, complexity science, safety science, macro-ergonomics, and policy design. Students explore the conceptualization, design, and management of socio-technical systems, with a focus on human-automation interactions in critical infrastructure, healthcare, and information systems. Through discussions, individual research, and team projects, students apply theoretical frameworks to real-world systems. Designed for graduate-level study, the course is ideal for students in industrial and systems engineering, as well as other engineering disciplines, preparing them to address challenges in automation-driven socio-technical environments.

Learning Objectives

• Identify and analyze key challenges in socio-technical systems.
• Critically evaluate different conceptual representations of socio-technical systems.
• Develop functional models defining inputs, outputs, outcomes, and contextual factors.
• Construct structural representations of socio-technical systems.
• Compare and assess various conceptualization frameworks, including DSRP, functional, structural, hierarchical, and complex adaptive system approaches.
• Apply theoretical concepts to real-world socio-technical systems through individual and team research projects.

Effective quality management is essential for the long-term success of technology-centric enterprises. This course explores the principles and strategies for developing, enhancing, and managing quality across all enterprise systems. Students learn to define quality, assess it using industry standards, compute quality measures, and design improvement strategies that align with broader performance metrics. Through case studies and a translational research project, the course provides practical insights into quality management systems, process optimization, and the impact of quality on cost, efficiency, and profitability. By the end, students are equipped to drive quality improvements in production, service, and managerial processes.

Learning Objectives

• Define quality management problems (knowledge and application outcomes).
• Specify quality dimensions and characteristics for technology-centric enterprises where inputs, outputs, outcomes, and contextual factors are defined for production and service processes based on quality management theory (comprehension outcome).
• Formulate models for quality management assessment (survey, qualitative, other) using quality standards with the translational research project (knowledge outcome).
• Compute quality management capability measures (analysis outcome).
• Design quality improvement plans with the translational research project (synthesis outcome).
• Assess the quality management performance of a production/service system with the translational research project (evaluation outcome).
• Relate quality performance to cost, efficiency, and profitability performance of technology-centric enterprises (analysis outcome).