Multidisciplinary Risk and Systems Engineering

Multidisciplinary Risk and Systems Engineering by TONEX

Multidisciplinary Risk and Systems Engineering Training is a 2-day training program covers both theoretical and practical aspects of multidisciplinary risk and systems engineering principles. This innovative training course provides a comprehensive, multidisciplinary look at all aspects of systems engineering and risk management providing a straightforward look and the entire risk and systems engineering process, providing realistic case studies, examples, workshops and design problems that will enable attendees to gain a firm grasp on the fundamentals of modern multidisciplinary systems engineering.
The Multidisciplinary Risk and Systems Engineering training course will give you a step-by-step guidance and sufficient knowledge (a combination of information, skills, and tools) about multidisciplinary risk assessment, project management, and systems engineering processes.

Learn about  risks and systems engineering from a modern, multidisciplinary engineering approach, providing the understanding that all aspects of systems analysis and design,, software, V&V, test, reliability, safety, availability, security, operations and maintenance and the full life-cycle  factored in to any large-scale system design.

Multidisciplinary risk and system engineering is a key requirement for designers of modern and complex systems involved in systems engineering, particularly engineers that understand a host of disciplines required to design, implement, risk analysis and assessment, and manage complex systems. Systems Engineering along with a host of other skills like business intelligence, human Factors, technology Integration, operations and maintenance, safety and reliability analysis, along with a working knowledge of Science, Technology, Engineering, and Math (STEM) skills.

During the Multidisciplinary Risk and Systems Engineering training course, you will learn how to apply multidisciplinary risk and uncertainty analysis into decision making, systems engineering and project management. The uncertainty assessment also impacts the decision in system analysis and design, reliability and safety assessment, implementation, operation, maintenance, quality control, and research and development.
The Multidisciplinary Risk and Systems Engineering training course will teach you the methods required to assess and perform risk and systems engineering uncertainties and then apply these methods and statistical understanding to interpret the results. The course consists of lectures, short exercises, videos and hands-on applications.

Who Should Attend

The Multidisciplinary Risk and Systems Engineering training is a 2-day course designed for:

• Technicians and engineers
• Technical staff
• Project and program managers
• Auditors
• Operations and maintenance
• Risk analysts
• Quality assurance
• Safety, reliability and availability technicians, engineers and managers

Learning Objectives

Upon the completion of the Multidisciplinary Risk and Systems Engineering training course, the attendees will able to:

• Discuss and understand the principles of multidisciplinary risk and systems engineering
• Discuss support activities for product development process of complex, multidisciplinary engineered systems
• Explain the concept of design for value and be familiar with ways to quantitatively assess the expected life cycle cost of a new system or product
• Sharpen their presentation skills, acquire critical reasoning with respect to the validity and fidelity of their models
• Experience the advantages and challenges of teamwork
• Discuss how to rationalize and quantify a system architecture or product design problem by selecting appropriate objective functions, design variables, parameters and constraints
• Understand how to subdivide a complex system into smaller disciplinary models, manage their interfaces and reintegrate them into an overall system model
• List various optimization techniques such as sequential quadratic programming, simulated annealing or genetic algorithms and select the ones most suitable to the problem at hand
• Perform a critical evaluation and interpretation of simulation and optimization results, including sensitivity analysis and exploration of performance, cost and risk trade-offs
• Discuss with the basic concepts of multi-objective optimization
• Discuss principles behind uncertainty analysis
• Measure uncertainty and risk in different decision-making situations
• Understand the terminology associated with uncertainty analysis and measurement

Course Agenda

Systems Engineering Life Cycles

• Research, Development, Test, and Evaluation
• Acquisition Planning and Systems Engineering
• Classification of Organizational Processes
• Research, Development, Test, and Evaluation Life Cycles
• System Acquisition or Production Life Cycles
• The Planning Life Cycle
• Software Acquisition Life-Cycle Models
• Trends in Systems Engineering Life Cycles

An Introduction to Systems Engineering and Systems Management

• The Multidisciplinary Discipline
• Defining Systems Engineering Management
• Activities and Roles of the Systems Engineering Manager
• Toward a Comprehensive Framework for the Implementation of Systems Engineering Management
• Different Systems Engineering Management Roles for Various Project Types
• The Skills, Tools, and Disciplines Involved in Systems Engineering Management
• Developing Educational and Training Programs in Systems Engineering Management
• Product Development Process
• Importance of Technical Direction and Systems Management
• Additional Definitions of Systems Engineering
• Life-Cycle Methodologies, or Processes, for Systems Engineering
• The Rest of the Handbook of Systems Engineering and Management
• Knowledge Map of the Systems Engineering and Management Handbook
• The Many Dimensions of Systems Engineering
• People, Organizations, Technology, and Architectures and System Families

Risk Analysis, Assessment and Management

• The Process of Risk Assessment and Management
• The Holistic Approach to Risk Analysis
• Risk of Extreme Events
• The Partitioned Multiobjective Risk Method
• The Characteristics of Risk in Human-Engineered Systems
• Selected Cases of Risk-Based Engineering Problems

Introduction to Multidisciplinary Risk and Systems Engineering

• Multidimensional Systems Engineering Processes
• System technology
• Current vs. future technologies
• STEM (Science, technology, engineering and math)
• Human factor
• Presentation and interface needs
• Integration of technology and business needs
• Integration of technology and operations needs
• Human Engineering
• Mission/Business system viability and sustainability
• R&D
• System complexity and usability
• Effects of the Multidisciplinary Risk Assessment and Management Program
• Multidisciplinary Engineering for Modern Systems
• Multidisciplinary systems engineering as a convergence of multiple skills
• Multidisciplinary Engineering as on Overriding Guide for the Design
• Project Management
• Chief Multidimensional Systems Engineer
• Design and Development Team
• Overall design, V&V, operation and the end product (system)
• Uncertainty definition
• Introduction to Cost Risk and Uncertainty
• Cost of risk and uncertainty
• Schedule risk and uncertainty
• Program risk management
• Systems engineering risk management

Impact of Risk and Uncertainty

• Importance of cost risk and uncertainty
• Cost Uncertainty
• Cost Risk
• Cost Uncertainty Analysis
• Cost Risk Analysis
• Monte Carlo simulation
• Point estimate
• Correlation and its importance in simulation
• Analysis of simulated estimate (PDF, CDF)
• Schedule Risk Analysis
• Network-based schedules
• PBS and WBS-based schedules
• Schedule uncertainty
• Modeling uncertainty in schedules

Managing Multidisciplinary Systems Engineering Processes

• Configuration Management within the System Life Cycle
• Cost Management
• Functional Economic Analysis
• Activity-Based Costing
• System Evaluation and Cost Control
• Total Quality Management
• Discovering System Requirements
• System Architectures
• Architecture Evaluation
• Architecture Framework
• Systems Design
• Functional Analysis
• Systems Integration
• Systems Integration in Large, Complex Engineered Systems and a Systems Integration Life Cycle
• Quality Assurance in Systems Integration
• Subsystem Integration and Delivery
• Safety, Reliability, Maintainability, and Availability
• Design for Reliability
• System Reliability Assessment Modeling
• Design for Maintainability
• Concurrent Engineering
• Concurrent Engineering and the Product Life Cycle
• Engineering the Enterprise as a System

Risk Management

• Organizational Needs for Systematic Measurement
• Human Supervisory Control
• Designing for Cognitive Task Performance
• Modeling Organizational and Individual Decision Making
• Organizational Simulation
• Organizational Change: The Role of Culture and Leadership
• Model-Based Design of Human Interaction with Complex Systems
• Evaluation of Systems
• Evaluation Field
• Evaluation Framework
• Evaluation Design Elements
• Issue Formulation
• Methods for the Modeling and Analysis of Alternatives
• Decision Analysis
• Value Analysis
• Decisions With Uncertainty
• Project Planning: Planning for Action
• Risk and Cost Control
• Principles behind Human Systems Integration (HSI)
• Model-Based Systems Engineering (MBSE)
• Design Structure Matrix to Design Program Organizations
• A Framework for Organizational Integration
• Risk-based decision making and risk-based approaches in decision
• Balance between systems engineering knowledge depth and a multidisciplinary knowledge breadth
• The multidisciplinary success process dynamics
• Behavior of the development team

Multidisciplinary System of Systems Engineering (SoSE)

• The Directed System of Systems
• The Collaborative System of Systems
• The Acknowledged System of Systems
• The Virtual System of Systems
• Establishing an Effective Frame of Reference: The Heart of Multidisciplinary Engineering
• Logical Analysis
• Risk Analysis
• Decision Analysis
• Data/Information Analysis
• Requirements Decomposition and Allocation
• Planning Management
• Configuration and Change Management
• Interface Management
• Computational Thought and Application