MSc in Production Systems and Operations Engineering

Modern industrial and mining operations generate vast amounts of data yet translating that data into sound decisions remains one of the field's most persistent challenges. This course builds the analytical competencies and decision-making frameworks needed to bridge that gap, covering statistical analysis, predictive analytics, machine learning applications, optimization-based decision models, data visualization, and quantitative techniques for improving operational performance. Mining- and technology-focused case studies are integrated throughout to illustrate the practical application of these concepts. Students develop competencies in interpreting and analyzing large datasets to support strategic and operational decision-making.

This course examines advanced modeling and optimization methodologies applied to complex engineering, industrial, and operational systems. Examples of industry-specific software tools are incorporated to strengthen the practical application of the methodologies presented. Students develop analytical and quantitative competencies to evaluate, design, and optimize complex systems in dynamic industrial and mining environments.

How do engineering and industrial organizations balance operational performance with environmental responsibility, ethical conduct, and regulatory accountability? This course addresses those questions directly, examining sustainability principles, ethical frameworks, and resource governance strategies as integrated dimensions of engineering practice. Discussions on contemporary ethical cases related to mining and resource management are encouraged throughout. Students analyze the interaction between technological development, operational performance, environmental management, and social impact.

The objective of this course is to prepare students to conduct rigorous applied research in engineering and industrial contexts. It covers qualitative and quantitative research methods, experimental design, statistical analysis, systematic literature review, research ethics, and the interpretation of technical and scientific information. Students develop competencies in engineering problem formulation, research design, data collection and analysis, and evidence-based evaluation of operational challenges. A central output of this course is a formal Capstone Research Proposal, which constitutes the foundational document for the applied research project completed in CAP 690 and CAP 695. The proposal must clearly articulate the engineering or operational problems, research questions, methodological approach, data sources, and expected contributions to professional practice or knowledge in production systems and operations engineering. This deliverable is reviewed and approved by the faculty supervisor before the student advances to the design phase.

This course examines advanced engineering principles and methodologies for the design, analysis, optimization, and management of production systems in industrial and mining environments. Case studies on successful production system implementations in industry are integrated to illustrate the concepts discussed. Students develop analytical and technical competencies to evaluate and improve production efficiency, operational performance, and system reliability through quantitative and engineering-based approaches.

Before a mining system can be optimized, it must first be understood, and simulation is one of the most powerful tools for building that understanding. Students develop advanced competencies in systems modeling and simulation applied to industrial, operational, and mining environments, covering discrete and continuous systems modeling, discrete-event simulation, operational performance analysis, model validation and verification, and scenario analysis. Industry case studies and simulation tools are integrated to strengthen the practical application of the concepts presented. Students develop the ability to analyze, model, and optimize dynamic systems to support decision-making and operational improvement.

This course examines advanced operations research methodologies and optimization techniques for complex industrial, operational, and mining systems. Industry-oriented case studies and analytical software tools are incorporated to strengthen practical application of optimization methodologies. Students develop the ability to analyze, model, and optimize engineering and operational systems using computational and evidence-based approaches.

Digital twins are redefining how engineers monitor, analyze, and optimize industrial operations and this course equips students with the technical competencies to apply them. Industry-based case studies and specialized software tools are incorporated to strengthen practical application of digital twin technologies in operational environments. Students develop the ability to design, analyze, and optimize intelligent operational systems using evidence-based and simulation-based approaches.

Students completing this course will be equipped to evaluate, analyze, and systematically improve mining production systems in complex operational environments. The course covers production planning and control, operational performance analysis, resource allocation, productivity optimization, process integration, equipment utilization, and evidence-based operational decision-making. Industry case studies and specialized mining software tools are incorporated to strengthen practical application of production optimization strategies. Students develop the ability to evaluate, analyze, and improve mining production systems using quantitative, analytical, and engineering-based approaches.

This course examines advanced methodologies for cost analysis and financial evaluation in mining operations and industrial production systems. Industry case studies and analytical software tools are incorporated to strengthen practical application of cost analysis methodologies. Students develop the ability to evaluate operational efficiency, optimize resource allocation, and support strategic planning through quantitative and financial analysis.

Haulage and material transport consistently rank among the largest cost drivers in any mining operation. This course addresses the engineering principles and operational methodologies that underpin efficient, safe, and productive transportation systems in surface and underground mining environments, covering network design, haulage system optimization, equipment selection, fleet management, material handling, and traffic analysis. Industry case studies and specialized mining software tools are incorporated throughout. Students develop the ability to evaluate, design, and optimize mining transportation systems using quantitative and engineering-based approaches.

Autonomy and artificial intelligence are no longer emerging concepts in mining they are operational realities transforming how extraction, haulage, and monitoring are conducted. This course develops the competencies needed to analyze, implement, and optimize AI-based and autonomous systems in industrial and mining environments, covering autonomous equipment, machine learning applications, predictive analytics, computer vision, real-time monitoring, and AI-driven decision-making. Industry case studies and specialized software tools are incorporated throughout. Students develop the ability to analyze, implement, and optimize AI-based operational solutions using evidence-based and engineering-oriented approaches.

This course examines advanced methodologies for mine planning and production scheduling in surface and underground mining operations. Industry case studies and specialized mining software tools are incorporated throughout. Students develop the ability to analyze, design, and optimize production plans using quantitative, analytical, and engineering-based approaches.

Every mining operation today produces more data than ever before, the competitive advantage lies in knowing what to do with it. Students develop advanced competencies in mining data analytics and intelligent operational systems, covering data analysis, predictive analytics, operational performance monitoring, machine learning applications, real-time data integration, and intelligent decision-support systems. Industry case studies and specialized analytical software tools are incorporated throughout. Students develop the ability to analyze, interpret, and optimize mining operations using quantitative, computational, and engineering-based approaches.

This course constitutes the design and development phase of the applied research capstone project in the MSc in Production Systems and Operations Engineering. Building directly on the research proposal approved in ESE 515, students develop a comprehensive Project Design Document that serves as the formal blueprint for the implementation phase conducted in CAP 695. Required deliverables include: (1) a refined engineering or operational problem statement and research question; (2) a complete literature review situating the project within current knowledge in production systems and operations engineering; (3) a detailed methodology specifying data collection approaches, modeling or simulation tools, optimization techniques, and validation criteria; (4) a systems analysis of the industrial or mining operational context; and (5) a project execution plan with milestones, technical feasibility assessment, and risk evaluation. Each student is assigned a faculty supervisor and, where applicable, an industry co-supervisor. The Project Design Document is formally evaluated by the supervisory committee before the student proceeds to CAP 695.

This course constitutes the implementation, evaluation, and final presentation phase of the applied research capstone project in the MSc in Production Systems and Operations Engineering, completing the three-stage research sequence initiated in ESE 515 and designed in CAP 690. Students execute the approved engineering methodology, implementing data collection and analysis, applying modeling, simulation, or optimization solutions, and evaluating technical outcomes against the stated research objectives. Required deliverables include: (1) a Final Applied Research Report structured as a formal engineering document containing introduction, problem statement, theoretical and technical framework, methodology, results, discussion, conclusions, and recommendations for operational implementation; (2) an oral defense presented before a supervisory committee composed of at least two faculty members and, where applicable, one senior industry engineering representative; and (3) an executive technical summary suitable for professional dissemination. The oral defense constitutes the formal act of sustentation required for degree completion. The Final Applied Research Report and the record of the oral defense are the primary documents submitted in the event of a degree recognition or validation process.